diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml index 2fb23051c11..7423495de42 100644 --- a/.github/ISSUE_TEMPLATE/bug-report.yml +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -25,7 +25,7 @@ body: id: system-info attributes: label: System Info - description: If needed, you can share your lerobot configuration with us by running `python -m lerobot.scripts.display_sys_info` and copy-pasting its outputs below + description: Please share your LeRobot configuration by running `lerobot-info` (if installed) or `python -m lerobot.scripts.display_sys_info` (if not installed) and pasting the output below. render: Shell placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration validations: diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml new file mode 100644 index 00000000000..af91c9f582e --- /dev/null +++ b/.github/workflows/stale.yml @@ -0,0 +1,68 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This workflow handles closing stale issues and PRs. +name: Stale +on: + # Allows running this workflow manually from the Actions tab + workflow_dispatch: + + # Runs at 02:00 + schedule: + - cron: "0 2 * * *" + +env: + CLOSE_ISSUE_MESSAGE: > + This issue was closed because it has been stalled for 14 days with no activity. + Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions. + CLOSE_PR_MESSAGE: > + This PR was closed because it has been stalled for 14 days with no activity. + Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions. + WARN_ISSUE_MESSAGE: > + This issue has been automatically marked as stale because it has not had + recent activity (6 months). It will be closed if no further activity occurs. + Thank you for your contributions. + WARN_PR_MESSAGE: > + This PR has been automatically marked as stale because it has not had + recent activity (6 months). It will be closed if no further activity occurs. + Thank you for your contributions. + +jobs: + # This job runs the actions/stale action to close stale issues and PRs. + stale: + name: Close Stale Issues and PRs + runs-on: ubuntu-latest + permissions: + actions: write + contents: write # only for delete-branch option + issues: write + pull-requests: write + steps: + - uses: actions/stale@v10 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-label: stale + stale-pr-label: stale + exempt-issue-labels: never-stale + exempt-pr-labels: never-stale + days-before-issue-stale: 180 # TODO(Steven): Will modify this to 90 after initial cleanup + days-before-issue-close: 14 + days-before-pr-stale: 180 + days-before-pr-close: 14 + delete-branch: true + close-issue-message: ${{ env.CLOSE_ISSUE_MESSAGE }} + close-pr-message: ${{ env.CLOSE_PR_MESSAGE }} + stale-issue-message: ${{ env.WARN_ISSUE_MESSAGE }} + stale-pr-message: ${{ env.WARN_PR_MESSAGE }} + operations-per-run: 500 diff --git a/.github/workflows/unbound_deps_tests.yml b/.github/workflows/unbound_deps_tests.yml new file mode 100644 index 00000000000..902074a8381 --- /dev/null +++ b/.github/workflows/unbound_deps_tests.yml @@ -0,0 +1,183 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This workflow handles full testing with unboud dependencies versions. +name: Unbound Dependency Tests + +on: + # Allows running this workflow manually from the Actions tab + workflow_dispatch: + + # Run on the 1st and 15th of every month at 09:00 UTC + schedule: + - cron: '0 2 1,15 * *' + +permissions: + contents: read + +# Sets up the environment variables +env: + UV_VERSION: "0.8.0" + PYTHON_VERSION: "3.10" + DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:unbound + +# Ensures that only the latest action is built, canceling older runs. +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +jobs: + + # This job runs the E2E tests + pytest with all unbound extras + full-tests: + name: Full Unbound Tests + runs-on: ubuntu-latest + env: + MUJOCO_GL: egl + steps: + - uses: actions/checkout@v4 + with: + lfs: true + persist-credentials: false + + - name: Install apt dependencies + run: | + sudo apt-get update && sudo apt-get install -y build-essential \ + git curl libglib2.0-0 libegl1-mesa-dev ffmpeg libusb-1.0-0-dev \ + speech-dispatcher libgeos-dev portaudio19-dev + + - name: Setup uv and Python + uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses] + with: + enable-cache: true + version: ${{ env.UV_VERSION }} + python-version: ${{ env.PYTHON_VERSION }} + + - name: Unbound dependencies + run: | + sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml + echo "Dependencies unbound:" && cat pyproject.toml + + - name: Install lerobot with all extras + run: uv sync --all-extras + + - name: Run pytest (all extras) + run: uv run pytest tests -vv + + - name: Run end-to-end tests + run: uv run make test-end-to-end + + # This job builds a GPU enabled image for testing + build-and-push-docker: + name: Build and Push Docker + runs-on: + group: aws-general-8-plus + outputs: + image_tag: ${{ env.DOCKER_IMAGE_NAME }} + env: + GITHUB_REF: ${{ github.ref }} + steps: + - name: Install Git LFS + run: | + sudo apt-get update + sudo apt-get install git-lfs + git lfs install + - uses: actions/checkout@v4 + with: + lfs: true + persist-credentials: false + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses] + with: + cache-binary: false + - name: Login to Docker Hub + uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses] + with: + username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }} + password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }} + - name: Build and push Docker image + uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses] + with: + context: . + file: ./docker/Dockerfile.internal + push: true + tags: ${{ env.DOCKER_IMAGE_NAME }} + build-args: | + UNBOUND_DEPS=true + + # This job runs pytest with all unbound extras in a GPU enabled host + # It runs everytime a test image is created + gpu-tests: + name: GPU Unbound Tests + needs: [build-and-push-docker] + runs-on: + group: aws-g6-4xlarge-plus + env: + HF_HOME: /home/user_lerobot/.cache/huggingface + HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot + TORCH_HOME: /home/user_lerobot/.cache/torch + TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton + container: + image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images] + options: --gpus all --shm-size "16gb" + credentials: + username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }} + password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }} + defaults: + run: + shell: bash + working-directory: /lerobot + steps: + - name: Run pytest on GPU + run: pytest tests -vv + - name: Run end-to-end tests + run: make test-end-to-end + + # This job deletes the test image recently created + # It runs everytime after the gpu-tests have finished + delete-unbound-image: + name: Delete Unbound Image + needs: [gpu-tests, build-and-push-docker] + if: always() && needs.build-and-push-docker.result == 'success' + runs-on: ubuntu-latest + steps: + - name: Get Docker Hub Token and Delete Image + # zizmor: ignore[template-injection] + run: | + IMAGE_NAME=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f1) + IMAGE_TAG=$(echo "${{ needs.build-and-push-docker.outputs.image_tag }}" | cut -d':' -f2) + + echo "Attempting to delete image: $IMAGE_NAME:$IMAGE_TAG" + + TOKEN=$(curl -s -H "Content-Type: application/json" \ + -X POST \ + -d '{"username": "${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}", "password": "${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}"}' \ + https://hub.docker.com/v2/users/login/ | jq -r .token) + + if [ "$TOKEN" == "null" ] || [ -z "$TOKEN" ]; then + echo "::error::Failed to get Docker Hub token." + exit 1 + fi + + HTTP_RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" \ + -H "Authorization: JWT ${TOKEN}" \ + -X DELETE \ + https://hub.docker.com/v2/repositories/${IMAGE_NAME}/tags/${IMAGE_TAG}/) + + if [ "$HTTP_RESPONSE" -eq 204 ]; then + echo "Successfully deleted Docker image tag: $IMAGE_NAME:$IMAGE_TAG" + else + echo "::error::Failed to delete Docker image. HTTP status: $HTTP_RESPONSE" + exit 1 + fi diff --git a/.gitignore b/.gitignore index c4d1f769f18..b47e22cbfad 100644 --- a/.gitignore +++ b/.gitignore @@ -173,3 +173,7 @@ outputs/ # Dev folders .cache/* +*.stl +*.urdf +*.xml +*.part diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f09017991f0..7f5beff801e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -86,11 +86,12 @@ repos: # TODO(Steven): Uncomment when ready to use ##### Static Analysis & Typing ##### - # - repo: https://github.com/pre-commit/mirrors-mypy - # rev: v1.16.0 - # hooks: - # - id: mypy - # args: [--python-version=3.10] + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.16.0 + hooks: + - id: mypy + args: [--config-file=pyproject.toml] + exclude: ^(examples|benchmarks|tests)/ ##### Docstring Checks ##### # - repo: https://github.com/akaihola/darglint2 diff --git a/README.md b/README.md index 9fd45a7b722..357e62cc1e9 100644 --- a/README.md +++ b/README.md @@ -197,12 +197,12 @@ wandb login ### Visualize datasets -Check out [example 1](https://github.com/huggingface/lerobot/blob/main/examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub. +Check out [example 1](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub. You can also locally visualize episodes from a dataset on the hub by executing our script from the command line: ```bash -python -m lerobot.scripts.visualize_dataset \ +lerobot-dataset-viz \ --repo-id lerobot/pusht \ --episode-index 0 ``` @@ -210,7 +210,7 @@ python -m lerobot.scripts.visualize_dataset \ or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`) ```bash -python -m lerobot.scripts.visualize_dataset \ +lerobot-dataset-viz \ --repo-id lerobot/pusht \ --root ./my_local_data_dir \ --local-files-only 1 \ @@ -221,19 +221,19 @@ It will open `rerun.io` and display the camera streams, robot states and actions https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144 -Our script can also visualize datasets stored on a distant server. See `python -m lerobot.scripts.visualize_dataset --help` for more instructions. +Our script can also visualize datasets stored on a distant server. See `lerobot-dataset-viz --help` for more instructions. ### The `LeRobotDataset` format A dataset in `LeRobotDataset` format is very simple to use. It can be loaded from a repository on the Hugging Face hub or a local folder simply with e.g. `dataset = LeRobotDataset("lerobot/aloha_static_coffee")` and can be indexed into like any Hugging Face and PyTorch dataset. For instance `dataset[0]` will retrieve a single temporal frame from the dataset containing observation(s) and an action as PyTorch tensors ready to be fed to a model. -A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](https://github.com/huggingface/lerobot/blob/main/examples/1_load_lerobot_dataset.py) for more details on `delta_timestamps`. +A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) for more details on `delta_timestamps`. Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor. Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects: -```` +``` dataset attributes: ├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example: │ ├ observation.images.cam_high (VideoFrame): @@ -269,7 +269,7 @@ dataset attributes: ├ root (Path): local directory where the dataset is stored ├ image_transforms (Callable): optional image transformations to apply to visual modalities └ delta_timestamps (dict): optional delta timestamps for temporal queries -decoding videos (e.g., 'pyav', 'torchcodec') +``` A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely: @@ -279,42 +279,6 @@ A `LeRobotDataset` is serialised using several widespread file formats for each Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the `root` argument if it's not in the default `~/.cache/huggingface/lerobot` location. -### Evaluate a pretrained policy - -Check out [example 2](https://github.com/huggingface/lerobot/blob/main/examples/2_evaluate_pretrained_policy.py) that illustrates how to download a pretrained policy from Hugging Face hub, and run an evaluation on its corresponding environment. - -We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht): - -```bash -lerobot-eval \ - --policy.path=lerobot/diffusion_pusht \ - --env.type=pusht \ - --eval.batch_size=10 \ - --eval.n_episodes=10 \ - --policy.use_amp=false \ - --policy.device=cuda -```` - -Note: After training your own policy, you can re-evaluate the checkpoints with: - -```bash -lerobot-eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model -``` - -See `lerobot-eval --help` for more instructions. - -### Train your own policy - -Check out [example 3](https://github.com/huggingface/lerobot/blob/main/examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](https://github.com/huggingface/lerobot/blob/main/examples/4_train_policy_with_script.md) that shows how to use our training script from command line. - -To use wandb for logging training and evaluation curves, make sure you've run `wandb login` as a one-time setup step. Then, when running the training command above, enable WandB in the configuration by adding `--wandb.enable=true`. - -A link to the wandb logs for the run will also show up in yellow in your terminal. Here is an example of what they look like in your browser. Please also check [here](https://github.com/huggingface/lerobot/blob/main/examples/4_train_policy_with_script.md#typical-logs-and-metrics) for the explanation of some commonly used metrics in logs. - -\WandB logs example - -Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `lerobot-eval --help` for more instructions. - #### Reproduce state-of-the-art (SOTA) We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances. @@ -373,3 +337,7 @@ If you want, you can cite this work with: ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline) + +``` + +``` diff --git a/src/lerobot/utils/benchmark.py b/benchmarks/video/benchmark.py similarity index 100% rename from src/lerobot/utils/benchmark.py rename to benchmarks/video/benchmark.py diff --git a/benchmarks/video/run_video_benchmark.py b/benchmarks/video/run_video_benchmark.py index 5472551f579..9f34b227369 100644 --- a/benchmarks/video/run_video_benchmark.py +++ b/benchmarks/video/run_video_benchmark.py @@ -35,12 +35,13 @@ from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity from tqdm import tqdm +from benchmarks.video.benchmark import TimeBenchmark from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.datasets.video_utils import ( decode_video_frames_torchvision, encode_video_frames, ) -from lerobot.utils.benchmark import TimeBenchmark +from lerobot.utils.constants import OBS_IMAGE BASE_ENCODING = OrderedDict( [ @@ -117,7 +118,7 @@ def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None: hf_dataset = dataset.hf_dataset.with_format(None) # We only save images from the first camera - img_keys = [key for key in hf_dataset.features if key.startswith("observation.image")] + img_keys = [key for key in hf_dataset.features if key.startswith(OBS_IMAGE)] imgs_dataset = hf_dataset.select_columns(img_keys[0]) for i, item in enumerate( diff --git a/docker/Dockerfile.internal b/docker/Dockerfile.internal index 8c77fe49731..2616cd06c01 100644 --- a/docker/Dockerfile.internal +++ b/docker/Dockerfile.internal @@ -39,6 +39,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ software-properties-common build-essential git curl \ libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \ libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \ + cmake pkg-config ninja-build \ && add-apt-repository -y ppa:deadsnakes/ppa \ && apt-get update \ && apt-get install -y --no-install-recommends \ @@ -74,6 +75,14 @@ RUN uv venv --python python${PYTHON_VERSION} # Install Python dependencies for caching COPY --chown=user_lerobot:user_lerobot pyproject.toml README.md MANIFEST.in ./ COPY --chown=user_lerobot:user_lerobot src/ src/ + +ARG UNBOUND_DEPS=false + +RUN if [ "$UNBOUND_DEPS" = "true" ]; then \ + sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml; \ + echo "Dependencies unbound:" && cat pyproject.toml; \ + fi + RUN uv pip install --no-cache ".[all]" # Copy the rest of the application source code diff --git a/docker/Dockerfile.user b/docker/Dockerfile.user index bcd067637bb..c1b28445351 100644 --- a/docker/Dockerfile.user +++ b/docker/Dockerfile.user @@ -31,6 +31,7 @@ ENV DEBIAN_FRONTEND=noninteractive \ RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential git curl libglib2.0-0 libegl1-mesa-dev ffmpeg \ libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev \ + cmake pkg-config ninja-build \ && curl -LsSf https://astral.sh/uv/install.sh | sh \ && mv /root/.local/bin/uv /usr/local/bin/uv \ && useradd --create-home --shell /bin/bash user_lerobot \ @@ -60,6 +61,14 @@ RUN uv venv # Install Python dependencies for caching COPY --chown=user_lerobot:user_lerobot pyproject.toml README.md MANIFEST.in ./ COPY --chown=user_lerobot:user_lerobot src/ src/ + +ARG UNBOUND_DEPS=false + +RUN if [ "$UNBOUND_DEPS" = "true" ]; then \ + sed -i 's/,[[:space:]]*<[0-9\.]*//g' pyproject.toml; \ + echo "Dependencies unbound:" && cat pyproject.toml; \ + fi + RUN uv pip install --no-cache ".[all]" # Copy the rest of the application code diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index 5f5a509c7bf..3b6cccc9596 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -19,16 +19,36 @@ title: Train RL in Simulation - local: async title: Use Async Inference + title: "Tutorials" +- sections: + - local: lerobot-dataset-v3 + title: Using LeRobotDataset - local: porting_datasets_v3 title: Porting Large Datasets - title: "Tutorials" + title: "Datasets" - sections: + - local: act + title: ACT - local: smolvla - title: Finetune SmolVLA + title: SmolVLA + - local: pi0 + title: π₀ (Pi0) + - local: pi05 + title: π₀.₅ (Pi05) + - local: libero + title: Using Libero title: "Policies" - sections: - - local: hope_jr - title: Hope Jr + - local: introduction_processors + title: Introduction to Robot Processors + - local: debug_processor_pipeline + title: Debug your processor pipeline + - local: implement_your_own_processor + title: Implement your own processor + - local: processors_robots_teleop + title: Processors for Robots and Teleoperators + title: "Robot Processors" +- sections: - local: so101 title: SO-101 - local: so100 @@ -37,9 +57,15 @@ title: Koch v1.1 - local: lekiwi title: LeKiwi + - local: hope_jr + title: Hope Jr - local: reachy2 title: Reachy 2 title: "Robots" +- sections: + - local: phone_teleop + title: Phone + title: "Teleoperators" - sections: - local: notebooks title: Notebooks diff --git a/docs/source/act.mdx b/docs/source/act.mdx new file mode 100644 index 00000000000..e3294ca6945 --- /dev/null +++ b/docs/source/act.mdx @@ -0,0 +1,92 @@ +# ACT (Action Chunking with Transformers) + +ACT is a **lightweight and efficient policy for imitation learning**, especially well-suited for fine-grained manipulation tasks. It's the **first model we recommend when you're starting out** with LeRobot due to its fast training time, low computational requirements, and strong performance. + +
+ +
+ +_Watch this tutorial from the LeRobot team to learn how ACT works: [LeRobot ACT Tutorial](https://www.youtube.com/watch?v=ft73x0LfGpM)_ + +## Model Overview + +Action Chunking with Transformers (ACT) was introduced in the paper [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://arxiv.org/abs/2304.13705) by Zhao et al. The policy was designed to enable precise, contact-rich manipulation tasks using affordable hardware and minimal demonstration data. + +### Why ACT is Great for Beginners + +ACT stands out as an excellent starting point for several reasons: + +- **Fast Training**: Trains in a few hours on a single GPU +- **Lightweight**: Only ~80M parameters, making it efficient and easy to work with +- **Data Efficient**: Often achieves high success rates with just 50 demonstrations + +### Architecture + +ACT uses a transformer-based architecture with three main components: + +1. **Vision Backbone**: ResNet-18 processes images from multiple camera viewpoints +2. **Transformer Encoder**: Synthesizes information from camera features, joint positions, and a learned latent variable +3. **Transformer Decoder**: Generates coherent action sequences using cross-attention + +The policy takes as input: + +- Multiple RGB images (e.g., from wrist cameras, front/top cameras) +- Current robot joint positions +- A latent style variable `z` (learned during training, set to zero during inference) + +And outputs a chunk of `k` future action sequences. + +## Installation Requirements + +1. Install LeRobot by following our [Installation Guide](./installation). +2. ACT is included in the base LeRobot installation, so no additional dependencies are needed! + +## Training ACT + +ACT works seamlessly with the standard LeRobot training pipeline. Here's a complete example for training ACT on your dataset: + +```bash +lerobot-train \ + --dataset.repo_id=${HF_USER}/your_dataset \ + --policy.type=act \ + --output_dir=outputs/train/act_your_dataset \ + --job_name=act_your_dataset \ + --policy.device=cuda \ + --wandb.enable=true \ + --policy.repo_id=${HF_USER}/act_policy +``` + +### Training Tips + +1. **Start with defaults**: ACT's default hyperparameters work well for most tasks +2. **Training duration**: Expect a few hours for 100k training steps on a single GPU +3. **Batch size**: Start with batch size 8 and adjust based on your GPU memory + +### Train using Google Colab + +If your local computer doesn't have a powerful GPU, you can utilize Google Colab to train your model by following the [ACT training notebook](./notebooks#training-act). + +## Evaluating ACT + +Once training is complete, you can evaluate your ACT policy using the `lerobot-record` command with your trained policy. This will run inference and record evaluation episodes: + +```bash +lerobot-record \ + --robot.type=so100_follower \ + --robot.port=/dev/ttyACM0 \ + --robot.id=my_robot \ + --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \ + --display_data=true \ + --dataset.repo_id=${HF_USER}/eval_act_your_dataset \ + --dataset.num_episodes=10 \ + --dataset.single_task="Your task description" \ + --policy.path=${HF_USER}/act_policy +``` diff --git a/docs/source/async.mdx b/docs/source/async.mdx index 397c513cff4..be10f8bafca 100644 --- a/docs/source/async.mdx +++ b/docs/source/async.mdx @@ -31,15 +31,15 @@ Then, spin up a policy server (in one terminal, or in a separate machine) specif You can spin up a policy server running: ```shell -python src/lerobot/scripts/server/policy_server.py \ - --host=127.0.0.1 \ - --port=8080 \ +python -m lerobot.async_inference.policy_server \ + --host=127.0.0.1 \ + --port=8080 ``` This will start a policy server listening on `127.0.0.1:8080` (`localhost`, port 8080). At this stage, the policy server is empty, as all information related to which policy to run and with which parameters are specified during the first handshake with the client. Spin up a client with: ```shell -python src/lerobot/scripts/server/robot_client.py \ +python -m lerobot.async_inference.robot_client \ --server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server --robot.type=so100_follower \ # ROBOT: your robot type --robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port @@ -113,17 +113,17 @@ As such, spinning up a policy server is as easy as specifying the host address a ```bash -python -m lerobot.scripts.server.policy_server \ - --host="localhost" \ - --port=8080 +python -m lerobot.async_inference.policy_server \ + --host=127.0.0.1 \ + --port=8080 ``` ```python -from lerobot.scripts.server.configs import PolicyServerConfig -from lerobot.scripts.server.policy_server import serve +from lerobot.async_inference.configs import PolicyServerConfig +from lerobot.async_inference.policy_server import serve config = PolicyServerConfig( host="localhost", @@ -148,7 +148,7 @@ The `RobotClient` streams observations to the `PolicyServer`, and receives actio ```bash -python src/lerobot/scripts/server/robot_client.py \ +python -m lerobot.async_inference.robot_client \ --server_address=127.0.0.1:8080 \ # SERVER: the host address and port of the policy server --robot.type=so100_follower \ # ROBOT: your robot type --robot.port=/dev/tty.usbmodem585A0076841 \ # ROBOT: your robot port @@ -171,9 +171,9 @@ python src/lerobot/scripts/server/robot_client.py \ import threading from lerobot.robots.so100_follower import SO100FollowerConfig from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig -from lerobot.scripts.server.configs import RobotClientConfig -from lerobot.scripts.server.robot_client import RobotClient -from lerobot.scripts.server.helpers import visualize_action_queue_size +from lerobot.async_inference.configs import RobotClientConfig +from lerobot.async_inference.robot_client import RobotClient +from lerobot.async_inference.helpers import visualize_action_queue_size # 1. Create the robot instance """Check out the cameras available in your setup by running `python lerobot/find_cameras.py`""" diff --git a/docs/source/backwardcomp.mdx b/docs/source/backwardcomp.mdx index 0e1d0163657..3366c8ab916 100644 --- a/docs/source/backwardcomp.mdx +++ b/docs/source/backwardcomp.mdx @@ -1,5 +1,61 @@ # Backward compatibility +## Policy Normalization Migration (PR #1452) + +**Breaking Change**: LeRobot policies no longer have built-in normalization layers embedded in their weights. Normalization is now handled by external `PolicyProcessorPipeline` components. + +### What changed? + +| | Before PR #1452 | After PR #1452 | +| -------------------------- | ------------------------------------------------ | ------------------------------------------------------------ | +| **Normalization Location** | Embedded in model weights (`normalize_inputs.*`) | External `PolicyProcessorPipeline` components | +| **Model State Dict** | Contains normalization statistics | **Clean weights only** - no normalization parameters | +| **Usage** | `policy(batch)` handles everything | `preprocessor(batch)` → `policy(...)` → `postprocessor(...)` | + +### Impact on existing models + +- Models trained **before** PR #1452 have normalization embedded in their weights +- These models need migration to work with the new `PolicyProcessorPipeline` system +- The migration extracts normalization statistics and creates separate processor pipelines + +### Migrating old models + +Use the migration script to convert models with embedded normalization: + +```shell +python src/lerobot/processor/migrate_policy_normalization.py \ + --pretrained-path lerobot/act_aloha_sim_transfer_cube_human \ + --push-to-hub \ + --branch migrated +``` + +The script: + +1. **Extracts** normalization statistics from model weights +2. **Creates** external preprocessor and postprocessor pipelines +3. **Removes** normalization layers from model weights +4. **Saves** clean model + processor pipelines +5. **Pushes** to Hub with automatic PR creation + +### Using migrated models + +```python +# New usage pattern (after migration) +from lerobot.policies.factory import make_policy, make_pre_post_processors + +# Load model and processors separately +policy = make_policy(config, ds_meta=dataset.meta) +preprocessor, postprocessor = make_pre_post_processors( + policy_cfg=config, + dataset_stats=dataset.meta.stats +) + +# Process data through pipeline +processed_batch = preprocessor(raw_batch) +action = policy.select_action(processed_batch) +final_action = postprocessor(action) +``` + ## Hardware API redesign PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is a overview of what changed and how you can continue to work with datasets created before this pull request. diff --git a/docs/source/debug_processor_pipeline.mdx b/docs/source/debug_processor_pipeline.mdx new file mode 100644 index 00000000000..4826c947ecc --- /dev/null +++ b/docs/source/debug_processor_pipeline.mdx @@ -0,0 +1,299 @@ +# Debug Your Processor Pipeline + +Processor pipelines can be complex, especially when chaining multiple transformation steps. +Unlike simple function calls, pipelines lack natural observability, you can't easily see what happens +between each step or where things go wrong. +This guide provides debugging tools and techniques specifically designed to address these challenges +and help you understand data flow through your pipelines. + +We'll explore three complementary debugging approaches: **hooks** for runtime monitoring, **step-through debugging** for detailed inspection, and **feature validation** for catching structural mismatches. Each serves a different purpose and together they provide complete visibility into your pipeline's behavior. + +## Understanding Hooks + +Hooks are functions that get called at specific points during pipeline execution. +They provide a way to inspect, monitor, or modify data without changing your pipeline code. +Think of them as "event listeners" for your pipeline. + +### What is a Hook? + +A hook is a callback function that gets automatically invoked at specific moments during pipeline execution. +The concept comes from event-driven programming, imagine you could "hook into" the pipeline's execution flow to observe or react to what's happening. + +Think of hooks like inserting checkpoints into your pipeline. Every time the pipeline reaches one of these checkpoints, it pauses briefly to call your hook function, giving you a chance to inspect the current state, log information, and validate data. + +A hook is simply a function that accepts two parameters: + +- `step_idx: int` - The index of the current processing step (0, 1, 2, etc.) +- `transition: EnvTransition` - The data transition at that point in the pipeline + +The beauty of hooks is their non-invasive nature: you can add monitoring, validation, or debugging logic without changing a single line of your pipeline code. The pipeline remains clean and focused on its core logic, while hooks handle the cross-cutting concerns like logging, monitoring, and debugging. + +### Before vs After Hooks + +The pipeline supports two types of hooks: + +- **Before hooks** (`register_before_step_hook`) - Called before each step executes +- **After hooks** (`register_after_step_hook`) - Called after each step completes + +```python +def before_hook(step_idx: int, transition: EnvTransition): + """Called before step processes the transition.""" + print(f"About to execute step {step_idx}") + # Useful for: logging, validation, setup + +def after_hook(step_idx: int, transition: EnvTransition): + """Called after step has processed the transition.""" + print(f"Completed step {step_idx}") + # Useful for: monitoring results, cleanup, debugging + +processor.register_before_step_hook(before_hook) +processor.register_after_step_hook(after_hook) +``` + +### Implementing a NaN Detection Hook + +Here's a practical example of a hook that detects NaN values: + +```python +def check_nans(step_idx: int, transition: EnvTransition): + """Check for NaN values in observations.""" + obs = transition.get(TransitionKey.OBSERVATION) + if obs: + for key, value in obs.items(): + if isinstance(value, torch.Tensor) and torch.isnan(value).any(): + print(f"NaN detected in {key} at step {step_idx}") + +# Register the hook to run after each step +processor.register_after_step_hook(check_nans) + +# Process your data - the hook will be called automatically +output = processor(input_data) + +# Remove the hook when done debugging +processor.unregister_after_step_hook(check_nans) +``` + +### How Hooks Work Internally + +Understanding the internal mechanism helps you use hooks more effectively. The pipeline maintains two separate lists: one for before-step hooks and another for after-step hooks. When you register a hook, it's simply appended to the appropriate list. + +During execution, the pipeline follows a strict sequence: for each processing step, it first calls all before-hooks in registration order, then executes the actual step transformation, and finally calls all after-hooks in registration order. This creates a predictable, sandwich-like structure around each step. + +The key insight is that hooks don't change the core pipeline logic—they're purely additive. The pipeline's `_forward` method orchestrates this dance between hooks and processing steps, ensuring that your debugging or monitoring code runs at exactly the right moments without interfering with the main data flow. + +Here's a simplified view of how the pipeline executes hooks: + +```python +class DataProcessorPipeline: + def __init__(self): + self.steps = [...] + self.before_step_hooks = [] # List of before hooks + self.after_step_hooks = [] # List of after hooks + + def _forward(self, transition): + """Internal method that processes the transition through all steps.""" + for step_idx, processor_step in enumerate(self.steps): + # 1. Call all BEFORE hooks + for hook in self.before_step_hooks: + hook(step_idx, transition) + + # 2. Execute the actual processing step + transition = processor_step(transition) + + # 3. Call all AFTER hooks + for hook in self.after_step_hooks: + hook(step_idx, transition) + + return transition + + def register_before_step_hook(self, hook_fn): + self.before_step_hooks.append(hook_fn) + + def register_after_step_hook(self, hook_fn): + self.after_step_hooks.append(hook_fn) +``` + +### Execution Flow + +The execution flow looks like this: + +``` +Input → Before Hook → Step 0 → After Hook → Before Hook → Step 1 → After Hook → ... → Output +``` + +For example, with 3 steps and both hook types: + +```python +def timing_before(step_idx, transition): + print(f"⏱️ Starting step {step_idx}") + +def validation_after(step_idx, transition): + print(f"✅ Completed step {step_idx}") + +processor.register_before_step_hook(timing_before) +processor.register_after_step_hook(validation_after) + +# This will output: +# ⏱️ Starting step 0 +# ✅ Completed step 0 +# ⏱️ Starting step 1 +# ✅ Completed step 1 +# ⏱️ Starting step 2 +# ✅ Completed step 2 +``` + +### Multiple Hooks + +You can register multiple hooks of the same type - they execute in the order registered: + +```python +def log_shapes(step_idx: int, transition: EnvTransition): + obs = transition.get(TransitionKey.OBSERVATION) + if obs: + print(f"Step {step_idx} observation shapes:") + for key, value in obs.items(): + if isinstance(value, torch.Tensor): + print(f" {key}: {value.shape}") + +processor.register_after_step_hook(check_nans) # Executes first +processor.register_after_step_hook(log_shapes) # Executes second + +# Both hooks will be called after each step in registration order +output = processor(input_data) +``` + +While hooks are excellent for monitoring specific issues (like NaN detection) or gathering metrics during normal pipeline execution, sometimes you need to dive deeper. When you want to understand exactly what happens at each step or debug complex transformation logic, step-through debugging provides the detailed inspection you need. + +## Step-Through Debugging + +Step-through debugging is like having a slow-motion replay for your pipeline. Instead of watching your data get transformed in one quick blur from input to output, you can pause and examine what happens after each individual step. + +This approach is particularly valuable when you're trying to understand a complex pipeline, debug unexpected behavior, or verify that each transformation is working as expected. Unlike hooks, which are great for automated monitoring, step-through debugging gives you manual, interactive control over the inspection process. + +The `step_through()` method is a generator that yields the transition state after each processing step, allowing you to inspect intermediate results. Think of it as creating a series of snapshots of your data as it flows through the pipeline—each snapshot shows you exactly what your data looks like after one more transformation has been applied. + +### How Step-Through Works + +The `step_through()` method fundamentally changes how the pipeline executes. Instead of running all steps in sequence and only returning the final result, it transforms the pipeline into an iterator that yields intermediate results. + +Here's what happens internally: the method starts by converting your input data into the pipeline's internal transition format, then yields this initial state. Next, it applies the first processing step and yields the result. Then it applies the second step to that result and yields again, and so on. Each `yield` gives you a complete snapshot of the transition at that point. + +This generator pattern is powerful because it's lazy—the pipeline only computes the next step when you ask for it. This means you can stop at any point, inspect the current state thoroughly, and decide whether to continue. You're not forced to run the entire pipeline just to debug one problematic step. + +Instead of running the entire pipeline and only seeing the final result, `step_through()` pauses after each step and gives you the intermediate transition: + +```python +# This creates a generator that yields intermediate states +for i, intermediate_result in enumerate(processor.step_through(input_data)): + print(f"=== After step {i} ===") + + # Inspect the observation at this stage + obs = intermediate_result.get(TransitionKey.OBSERVATION) + if obs: + for key, value in obs.items(): + if isinstance(value, torch.Tensor): + print(f"{key}: shape={value.shape}, dtype={value.dtype}") +``` + +### Interactive Debugging with Breakpoints + +You can add breakpoints in the step-through loop to interactively debug: + +```python +# Step through the pipeline with debugging +for i, intermediate in enumerate(processor.step_through(data)): + print(f"Step {i}: {processor.steps[i].__class__.__name__}") + + # Set a breakpoint to inspect the current state + breakpoint() # Debugger will pause here + + # You can now inspect 'intermediate' in the debugger: + # - Check tensor shapes and values + # - Verify expected transformations + # - Look for unexpected changes +``` + +During the debugger session, you can: + +- Examine `intermediate[TransitionKey.OBSERVATION]` to see observation data +- Check `intermediate[TransitionKey.ACTION]` for action transformations +- Inspect any part of the transition to understand what each step does + +Step-through debugging is perfect for understanding the _data_ transformations, but what about the _structure_ of that data? While hooks and step-through help you debug runtime behavior, you also need to ensure your pipeline produces data in the format expected by downstream components. This is where feature contract validation comes in. + +## Validating Feature Contracts + +Feature contracts define what data structure your pipeline expects as input and produces as output. +Validating these contracts helps catch mismatches early. + +### Understanding Feature Contracts + +Each processor step has a `transform_features()` method that describes how it changes the data structure: + +```python +# Get the expected output features from your pipeline +initial_features = { + PipelineFeatureType.OBSERVATION: { + "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(7,)), + "observation.image": PolicyFeature(type=FeatureType.IMAGE, shape=(3, 224, 224)) + }, + PipelineFeatureType.ACTION: { + "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)) + } +} + +# Check what your pipeline will output +output_features = processor.transform_features(initial_features) + +print("Input features:") +for feature_type, features in initial_features.items(): + print(f" {feature_type}:") + for key, feature in features.items(): + print(f" {key}: {feature.type.value}, shape={feature.shape}") + +print("\nOutput features:") +for feature_type, features in output_features.items(): + print(f" {feature_type}:") + for key, feature in features.items(): + print(f" {key}: {feature.type.value}, shape={feature.shape}") +``` + +### Verifying Expected Features + +Check that your pipeline produces the features you expect: + +```python +# Define what features you expect the pipeline to produce +expected_keys = ["observation.state", "observation.image", "action"] + +print("Validating feature contract...") +for expected_key in expected_keys: + found = False + for feature_type, features in output_features.items(): + if expected_key in features: + feature = features[expected_key] + print(f"✅ {expected_key}: {feature.type.value}, shape={feature.shape}") + found = True + break + + if not found: + print(f"❌ Missing expected feature: {expected_key}") +``` + +This validation helps ensure your pipeline will work correctly with downstream components that expect specific data structures. + +## Summary + +Now that you understand the three debugging approaches, you can tackle any pipeline issue systematically: + +1. **Hooks** - For runtime monitoring and validation without modifying pipeline code +2. **Step-through** - For inspecting intermediate states and understanding transformations +3. **Feature validation** - For ensuring data structure contracts are met + +**When to use each approach:** + +- Start with **step-through debugging** when you need to understand what your pipeline does or when something unexpected happens +- Add **hooks** for continuous monitoring during development and production to catch issues automatically +- Use **feature validation** before deployment to ensure your pipeline works with downstream components + +These three tools work together to give you the complete observability that complex pipelines naturally lack. With hooks watching for issues, step-through helping you understand behavior, and feature validation ensuring compatibility, you'll be able to debug any pipeline confidently and efficiently. diff --git a/docs/source/hilserl.mdx b/docs/source/hilserl.mdx index f8a5c69b2e6..ad1c74f9a79 100644 --- a/docs/source/hilserl.mdx +++ b/docs/source/hilserl.mdx @@ -4,7 +4,13 @@ In this tutorial you will go through the full Human-in-the-Loop Sample-Efficient HIL-SERL is a sample-efficient reinforcement learning algorithm that combines human demonstrations with online learning and human interventions. The approach starts from a small set of human demonstrations, uses them to train a reward classifier, and then employs an actor-learner architecture where humans can intervene during policy execution to guide exploration and correct unsafe behaviors. In this tutorial, you'll use a gamepad to provide interventions and control the robot during the learning process. -It combines three key ingredients: 1. **Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point. 2. **On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour. 3. **Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe. +It combines three key ingredients: + +1. **Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point. + +2. **On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour. + +3. **Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe. Together these elements let HIL-SERL reach near-perfect task success and faster cycle times than imitation-only baselines. @@ -56,49 +62,258 @@ pip install -e ".[hilserl]" ### Understanding Configuration -The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/envs/configs.py`. Which is defined as: +The training process begins with proper configuration for the HILSerl environment. The main configuration class is `GymManipulatorConfig` in `lerobot/rl/gym_manipulator.py`, which contains nested `HILSerlRobotEnvConfig` and `DatasetConfig`. The configuration is organized into focused, nested sub-configs: ```python +class GymManipulatorConfig: + env: HILSerlRobotEnvConfig # Environment configuration (nested) + dataset: DatasetConfig # Dataset recording/replay configuration (nested) + mode: str | None = None # "record", "replay", or None (for training) + device: str = "cpu" # Compute device + class HILSerlRobotEnvConfig(EnvConfig): robot: RobotConfig | None = None # Main robot agent (defined in `lerobot/robots`) - teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`) - wrapper: EnvTransformConfig | None = None # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py` - fps: int = 10 # Control frequency + teleop: TeleoperatorConfig | None = None # Teleoperator agent, e.g., gamepad or leader arm + processor: HILSerlProcessorConfig # Processing pipeline configuration (nested) name: str = "real_robot" # Environment name - mode: str = None # "record", "replay", or None (for training) - repo_id: str | None = None # LeRobot dataset repository ID - dataset_root: str | None = None # Local dataset root (optional) - task: str = "" # Task identifier - num_episodes: int = 10 # Number of episodes for recording - episode: int = 0 # episode index for replay - device: str = "cuda" # Compute device - push_to_hub: bool = True # Whether to push the recorded datasets to Hub - pretrained_policy_name_or_path: str | None = None # For policy loading - reward_classifier_pretrained_path: str | None = None # For reward model - number_of_steps_after_success: int = 0 # For reward classifier, collect more positive examples after a success to train a classifier + task: str | None = None # Task identifier + fps: int = 10 # Control frequency + +# Nested processor configuration +class HILSerlProcessorConfig: + control_mode: str = "gamepad" # Control mode + observation: ObservationConfig | None = None # Observation processing settings + image_preprocessing: ImagePreprocessingConfig | None = None # Image crop/resize settings + gripper: GripperConfig | None = None # Gripper control and penalty settings + reset: ResetConfig | None = None # Environment reset and timing settings + inverse_kinematics: InverseKinematicsConfig | None = None # IK processing settings + reward_classifier: RewardClassifierConfig | None = None # Reward classifier settings + max_gripper_pos: float | None = 100.0 # Maximum gripper position + +# Sub-configuration classes +class ObservationConfig: + add_joint_velocity_to_observation: bool = False # Add joint velocities to state + add_current_to_observation: bool = False # Add motor currents to state + display_cameras: bool = False # Display camera feeds during execution + +class ImagePreprocessingConfig: + crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None # Image cropping parameters + resize_size: tuple[int, int] | None = None # Target image size + +class GripperConfig: + use_gripper: bool = True # Enable gripper control + gripper_penalty: float = 0.0 # Penalty for inappropriate gripper usage + +class ResetConfig: + fixed_reset_joint_positions: Any | None = None # Joint positions for reset + reset_time_s: float = 5.0 # Time to wait during reset + control_time_s: float = 20.0 # Maximum episode duration + terminate_on_success: bool = True # Whether to terminate episodes on success detection + +class InverseKinematicsConfig: + urdf_path: str | None = None # Path to robot URDF file + target_frame_name: str | None = None # End-effector frame name + end_effector_bounds: dict[str, list[float]] | None = None # EE workspace bounds + end_effector_step_sizes: dict[str, float] | None = None # EE step sizes per axis + +class RewardClassifierConfig: + pretrained_path: str | None = None # Path to pretrained reward classifier + success_threshold: float = 0.5 # Success detection threshold + success_reward: float = 1.0 # Reward value for successful episodes + +# Dataset configuration +class DatasetConfig: + repo_id: str # LeRobot dataset repository ID + task: str # Task identifier + root: str | None = None # Local dataset root directory + num_episodes_to_record: int = 5 # Number of episodes for recording + replay_episode: int | None = None # Episode index for replay + push_to_hub: bool = False # Whether to push datasets to Hub ``` +### Processor Pipeline Architecture + +HIL-SERL uses a modular processor pipeline architecture that processes robot observations and actions through a series of composable steps. The pipeline is divided into two main components: + +#### Environment Processor Pipeline + +The environment processor (`env_processor`) handles incoming observations and environment state: + +1. **VanillaObservationProcessorStep**: Converts raw robot observations into standardized format +2. **JointVelocityProcessorStep** (optional): Adds joint velocity information to observations +3. **MotorCurrentProcessorStep** (optional): Adds motor current readings to observations +4. **ForwardKinematicsJointsToEE** (optional): Computes end-effector pose from joint positions +5. **ImageCropResizeProcessorStep** (optional): Crops and resizes camera images +6. **TimeLimitProcessorStep** (optional): Enforces episode time limits +7. **GripperPenaltyProcessorStep** (optional): Applies penalties for inappropriate gripper usage +8. **RewardClassifierProcessorStep** (optional): Automated reward detection using vision models +9. **AddBatchDimensionProcessorStep**: Converts data to batch format for neural network processing +10. **DeviceProcessorStep**: Moves data to the specified compute device (CPU/GPU) + +#### Action Processor Pipeline + +The action processor (`action_processor`) handles outgoing actions and human interventions: + +1. **AddTeleopActionAsComplimentaryDataStep**: Captures teleoperator actions for logging +2. **AddTeleopEventsAsInfoStep**: Records intervention events and episode control signals +3. **InterventionActionProcessorStep**: Handles human interventions and episode termination +4. **Inverse Kinematics Pipeline** (when enabled): + - **MapDeltaActionToRobotActionStep**: Converts delta actions to robot action format + - **EEReferenceAndDelta**: Computes end-effector reference and delta movements + - **EEBoundsAndSafety**: Enforces workspace safety bounds + - **InverseKinematicsEEToJoints**: Converts end-effector actions to joint targets + - **GripperVelocityToJoint**: Handles gripper control commands + +#### Configuration Examples + +**Basic Observation Processing**: + +```json +{ + "env": { + "processor": { + "observation": { + "add_joint_velocity_to_observation": true, + "add_current_to_observation": false, + "display_cameras": false + } + } + } +} +``` + +**Image Processing**: + +```json +{ + "env": { + "processor": { + "image_preprocessing": { + "crop_params_dict": { + "observation.images.front": [180, 250, 120, 150], + "observation.images.side": [180, 207, 180, 200] + }, + "resize_size": [128, 128] + } + } + } +} +``` + +**Inverse Kinematics Setup**: + +```json +{ + "env": { + "processor": { + "inverse_kinematics": { + "urdf_path": "path/to/robot.urdf", + "target_frame_name": "end_effector", + "end_effector_bounds": { + "min": [0.16, -0.08, 0.03], + "max": [0.24, 0.2, 0.1] + }, + "end_effector_step_sizes": { + "x": 0.02, + "y": 0.02, + "z": 0.02 + } + } + } + } +} +``` + +### Advanced Observation Processing + +The HIL-SERL framework supports additional observation processing features that can improve policy learning: + +#### Joint Velocity Processing + +Enable joint velocity estimation to provide the policy with motion information: + +```json +{ + "env": { + "processor": { + "observation": { + "add_joint_velocity_to_observation": true + } + } + } +} +``` + +This processor: + +- Estimates joint velocities using finite differences between consecutive joint position readings +- Adds velocity information to the observation state vector +- Useful for policies that need motion awareness for dynamic tasks + +#### Motor Current Processing + +Monitor motor currents to detect contact forces and load conditions: + +```json +{ + "env": { + "processor": { + "observation": { + "add_current_to_observation": true + } + } + } +} +``` + +This processor: + +- Reads motor current values from the robot's control system +- Adds current measurements to the observation state vector +- Helps detect contact events, object weights, and mechanical resistance +- Useful for contact-rich manipulation tasks + +#### Combined Observation Processing + +You can enable multiple observation processing features simultaneously: + +```json +{ + "env": { + "processor": { + "observation": { + "add_joint_velocity_to_observation": true, + "add_current_to_observation": true, + "display_cameras": false + } + } + } +} +``` + +**Note**: Enabling additional observation features increases the state space dimensionality, which may require adjusting your policy network architecture and potentially collecting more training data. + ### Finding Robot Workspace Bounds Before collecting demonstrations, you need to determine the appropriate operational bounds for your robot. This helps simplify the problem of learning on the real robot in two ways: 1) by limiting the robot's operational space to a specific region that solves the task and avoids unnecessary or unsafe exploration, and 2) by allowing training in end-effector space rather than joint space. Empirically, learning in joint space for reinforcement learning in manipulation is often a harder problem - some tasks are nearly impossible to learn in joint space but become learnable when the action space is transformed to end-effector coordinates. -**Using find_joint_limits.py** +**Using lerobot-find-joint-limits** This script helps you find the safe operational bounds for your robot's end-effector. Given that you have a follower and leader arm, you can use the script to find the bounds for the follower arm that will be applied during training. Bounding the action space will reduce the redundant exploration of the agent and guarantees safety. ```bash -python -m lerobot.scripts.find_joint_limits \ - --robot.type=so100_follower \ - --robot.port=/dev/tty.usbmodem58760431541 \ - --robot.id=black \ - --teleop.type=so100_leader \ - --teleop.port=/dev/tty.usbmodem58760431551 \ - --teleop.id=blue +lerobot-find-joint-limits \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=black \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=blue ``` **Workflow** @@ -128,24 +343,58 @@ With the bounds defined, you can safely collect demonstrations for training. Tra **Setting Up Record Mode** -Create a configuration file for recording demonstrations (or edit an existing one like [env_config_so100.json](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json)): +Create a configuration file for recording demonstrations (or edit an existing one like [env_config.json](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/env_config.json)): -1. Set `mode` to `"record"` -2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name") -3. Set `num_episodes` to the number of demonstrations you want to collect -4. Set `crop_params_dict` to `null` initially (we'll determine crops later) -5. Configure `robot`, `cameras`, and other hardware settings +1. Set `mode` to `"record"` at the root level +2. Specify a unique `repo_id` for your dataset in the `dataset` section (e.g., "username/task_name") +3. Set `num_episodes_to_record` in the `dataset` section to the number of demonstrations you want to collect +4. Set `env.processor.image_preprocessing.crop_params_dict` to `{}` initially (we'll determine crops later) +5. Configure `env.robot`, `env.teleop`, and other hardware settings in the `env` section Example configuration section: ```json -"mode": "record", -"repo_id": "username/pick_lift_cube", -"dataset_root": null, -"task": "pick_and_lift", -"num_episodes": 15, -"episode": 0, -"push_to_hub": true +{ + "env": { + "type": "gym_manipulator", + "name": "real_robot", + "fps": 10, + "processor": { + "control_mode": "gamepad", + "observation": { + "display_cameras": false + }, + "image_preprocessing": { + "crop_params_dict": {}, + "resize_size": [128, 128] + }, + "gripper": { + "use_gripper": true, + "gripper_penalty": 0.0 + }, + "reset": { + "reset_time_s": 5.0, + "control_time_s": 20.0 + } + }, + "robot": { + // ... robot configuration ... + }, + "teleop": { + // ... teleoperator configuration ... + } + }, + "dataset": { + "repo_id": "username/pick_lift_cube", + "root": null, + "task": "pick_and_lift", + "num_episodes_to_record": 15, + "replay_episode": 0, + "push_to_hub": true + }, + "mode": "record", + "device": "cpu" +} ``` ### Using a Teleoperation Device @@ -191,10 +440,20 @@ The gamepad provides a very convenient way to control the robot and the episode To setup the gamepad, you need to set the `control_mode` to `"gamepad"` and define the `teleop` section in the configuration file. ```json +{ + "env": { "teleop": { - "type": "gamepad", - "use_gripper": true + "type": "gamepad", + "use_gripper": true }, + "processor": { + "control_mode": "gamepad", + "gripper": { + "use_gripper": true + } + } + } +} ```

@@ -216,11 +475,21 @@ The SO101 leader arm has reduced gears that allows it to move and track the foll To setup the SO101 leader, you need to set the `control_mode` to `"leader"` and define the `teleop` section in the configuration file. ```json +{ + "env": { "teleop": { - "type": "so101_leader", - "port": "/dev/tty.usbmodem585A0077921", # check your port number - "use_degrees": true + "type": "so101_leader", + "port": "/dev/tty.usbmodem585A0077921", + "use_degrees": true }, + "processor": { + "control_mode": "leader", + "gripper": { + "use_gripper": true + } + } + } +} ``` In order to annotate the success/failure of the episode, **you will need** to use a keyboard to press `s` for success, `esc` for failure. @@ -246,12 +515,12 @@ During the online training, press `space` to take over the policy and `space` ag Start the recording process, an example of the config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json): ```bash -python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config_so100.json +python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/env_config_so100.json ``` During recording: -1. The robot will reset to the initial position defined in the configuration file `fixed_reset_joint_positions` +1. The robot will reset to the initial position defined in the configuration file `env.processor.reset.fixed_reset_joint_positions` 2. Complete the task successfully 3. The episode ends with a reward of 1 when you press the "success" button 4. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0 @@ -277,7 +546,7 @@ Note: If you already know the crop parameters, you can skip this step and just s Use the `crop_dataset_roi.py` script to interactively select regions of interest in your camera images: ```bash -python -m lerobot.scripts.rl.crop_dataset_roi --repo-id username/pick_lift_cube +python -m lerobot.rl.crop_dataset_roi --repo-id username/pick_lift_cube ``` 1. For each camera view, the script will display the first frame @@ -310,11 +579,19 @@ observation.images.front: [180, 250, 120, 150] Add these crop parameters to your training configuration: ```json -"crop_params_dict": { - "observation.images.side": [180, 207, 180, 200], - "observation.images.front": [180, 250, 120, 150] -}, -"resize_size": [128, 128] +{ + "env": { + "processor": { + "image_preprocessing": { + "crop_params_dict": { + "observation.images.side": [180, 207, 180, 200], + "observation.images.front": [180, 250, 120, 150] + }, + "resize_size": [128, 128] + } + } + } +} ``` **Recommended image resolution** @@ -338,31 +615,57 @@ Before training, you need to collect a dataset with labeled examples. The `recor To collect a dataset, you need to modify some parameters in the environment configuration based on HILSerlRobotEnvConfig. ```bash -python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/reward_classifier_train_config.json +python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/reward_classifier_train_config.json ``` **Key Parameters for Data Collection** -- **mode**: set it to `"record"` to collect a dataset -- **repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub -- **num_episodes**: Number of episodes to record -- **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected -- **fps**: Number of frames per second to record -- **push_to_hub**: Whether to push the dataset to the hub +- **mode**: set it to `"record"` to collect a dataset (at root level) +- **dataset.repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub +- **dataset.num_episodes_to_record**: Number of episodes to record +- **env.processor.reset.terminate_on_success**: Whether to automatically terminate episodes when success is detected (default: `true`) +- **env.fps**: Number of frames per second to record +- **dataset.push_to_hub**: Whether to push the dataset to the hub + +The `env.processor.reset.terminate_on_success` parameter allows you to control episode termination behavior. When set to `false`, episodes will continue even after success is detected, allowing you to collect more positive examples with the reward=1 label. This is crucial for training reward classifiers as it provides more success state examples in your dataset. When set to `true` (default), episodes terminate immediately upon success detection. -The `number_of_steps_after_success` parameter is crucial as it allows you to collect more positive examples. When a success is detected, the system will continue recording for the specified number of steps while maintaining the reward=1 label. Otherwise, there won't be enough states in the dataset labeled to 1 to train a good classifier. +**Important**: For reward classifier training, set `terminate_on_success: false` to collect sufficient positive examples. For regular HIL-SERL training, keep it as `true` to enable automatic episode termination when the task is completed successfully. Example configuration section for data collection: ```json { + "env": { + "type": "gym_manipulator", + "name": "real_robot", + "fps": 10, + "processor": { + "reset": { + "reset_time_s": 5.0, + "control_time_s": 20.0, + "terminate_on_success": false + }, + "gripper": { + "use_gripper": true + } + }, + "robot": { + // ... robot configuration ... + }, + "teleop": { + // ... teleoperator configuration ... + } + }, + "dataset": { + "repo_id": "hf_username/dataset_name", + "dataset_root": "data/your_dataset", + "task": "reward_classifier_task", + "num_episodes_to_record": 20, + "replay_episode": null, + "push_to_hub": true + }, "mode": "record", - "repo_id": "hf_username/dataset_name", - "dataset_root": "data/your_dataset", - "num_episodes": 20, - "push_to_hub": true, - "fps": 10, - "number_of_steps_after_success": 15 + "device": "cpu" } ``` @@ -421,9 +724,17 @@ To use your trained reward classifier, configure the `HILSerlRobotEnvConfig` to ```python -env_config = HILSerlRobotEnvConfig( - reward_classifier_pretrained_path="path_to_your_pretrained_trained_model", - # Other environment parameters +config = GymManipulatorConfig( + env=HILSerlRobotEnvConfig( + processor=HILSerlProcessorConfig( + reward_classifier=RewardClassifierConfig( + pretrained_path="path_to_your_pretrained_trained_model" + ) + ), + # Other environment parameters + ), + dataset=DatasetConfig(...), + mode=None # For training ) ``` @@ -432,14 +743,25 @@ or set the argument in the json config file. ```json { - "reward_classifier_pretrained_path": "path_to_your_pretrained_model" + "env": { + "processor": { + "reward_classifier": { + "pretrained_path": "path_to_your_pretrained_model", + "success_threshold": 0.7, + "success_reward": 1.0 + }, + "reset": { + "terminate_on_success": true + } + } + } } ``` Run `gym_manipulator.py` to test the model. ```bash -python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config.json +python -m lerobot.rl.gym_manipulator --config_path path/to/env_config.json ``` The reward classifier will automatically provide rewards based on the visual input from the robot's cameras. @@ -447,12 +769,12 @@ The reward classifier will automatically provide rewards based on the visual inp **Example Workflow for training the reward classifier** 1. **Create the configuration files**: - Create the necessary json configuration files for the reward classifier and the environment. Check the examples [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/tree/main). + Create the necessary json configuration files for the reward classifier and the environment. Check the examples [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/reward_classifier/config.json). 2. **Collect a dataset**: ```bash - python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json + python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json ``` 3. **Train the classifier**: @@ -463,7 +785,7 @@ The reward classifier will automatically provide rewards based on the visual inp 4. **Test the classifier**: ```bash - python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json + python -m lerobot.rl.gym_manipulator --config_path src/lerobot/configs/env_config.json ``` ### Training with Actor-Learner @@ -472,7 +794,7 @@ The LeRobot system uses a distributed actor-learner architecture for training. T **Configuration Setup** -Create a training configuration file (example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_config_hilserl_so100.json)). The training config is based on the main `TrainRLServerPipelineConfig` class in `lerobot/configs/train.py`. +Create a training configuration file (example available [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/train_config.json)). The training config is based on the main `TrainRLServerPipelineConfig` class in `lerobot/configs/train.py`. 1. Configure the policy settings (`type="sac"`, `device`, etc.) 2. Set `dataset` to your cropped dataset @@ -485,7 +807,7 @@ Create a training configuration file (example available [here](https://huggingfa First, start the learner server process: ```bash -python -m lerobot.scripts.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json +python -m lerobot.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json ``` The learner: @@ -500,7 +822,7 @@ The learner: In a separate terminal, start the actor process with the same configuration: ```bash -python -m lerobot.scripts.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json +python -m lerobot.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json ``` The actor: diff --git a/docs/source/hilserl_sim.mdx b/docs/source/hilserl_sim.mdx index c739be835c2..e2dddd9edb1 100644 --- a/docs/source/hilserl_sim.mdx +++ b/docs/source/hilserl_sim.mdx @@ -26,15 +26,18 @@ pip install -e ".[hilserl]" ## Configuration -To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/gym_hil_env.json). Key configuration sections include: +To use `gym_hil` with LeRobot, you need to create a configuration file. An example is provided [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/gym_hil/env_config.json). Key configuration sections include: ### Environment Type and Task ```json { - "type": "hil", - "name": "franka_sim", - "task": "PandaPickCubeGamepad-v0", + "env": { + "type": "gym_manipulator", + "name": "gym_hil", + "task": "PandaPickCubeGamepad-v0", + "fps": 10 + }, "device": "cuda" } ``` @@ -45,28 +48,40 @@ Available tasks: - `PandaPickCubeGamepad-v0`: With gamepad control - `PandaPickCubeKeyboard-v0`: With keyboard control -### Gym Wrappers Configuration +### Processor Configuration ```json -"wrapper": { - "gripper_penalty": -0.02, - "control_time_s": 15.0, - "use_gripper": true, - "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785], - "end_effector_step_sizes": { - "x": 0.025, - "y": 0.025, - "z": 0.025 - }, - "control_mode": "gamepad" +{ + "env": { + "processor": { + "control_mode": "gamepad", + "gripper": { + "use_gripper": true, + "gripper_penalty": -0.02 + }, + "reset": { + "control_time_s": 15.0, + "fixed_reset_joint_positions": [ + 0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785 + ] + }, + "inverse_kinematics": { + "end_effector_step_sizes": { + "x": 0.025, + "y": 0.025, + "z": 0.025 + } + } } + } +} ``` Important parameters: -- `gripper_penalty`: Penalty for excessive gripper movement -- `use_gripper`: Whether to enable gripper control -- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector +- `gripper.gripper_penalty`: Penalty for excessive gripper movement +- `gripper.use_gripper`: Whether to enable gripper control +- `inverse_kinematics.end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector - `control_mode`: Set to `"gamepad"` to use a gamepad controller ## Running with HIL RL of LeRobot @@ -75,39 +90,50 @@ Important parameters: To run the environment, set mode to null: - -```python -python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json +```bash +python -m lerobot.rl.gym_manipulator --config_path path/to/gym_hil_env.json ``` - ### Recording a Dataset To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record: - -```python -python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json +```json +{ + "env": { + "type": "gym_manipulator", + "name": "gym_hil", + "task": "PandaPickCubeGamepad-v0" + }, + "dataset": { + "repo_id": "username/sim_dataset", + "root": null, + "task": "pick_cube", + "num_episodes_to_record": 10, + "replay_episode": null, + "push_to_hub": true + }, + "mode": "record" +} +``` + +```bash +python -m lerobot.rl.gym_manipulator --config_path path/to/gym_hil_env.json ``` - ### Training a Policy -To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers: +To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/gym_hil/train_config.json) and run the actor and learner servers: - -```python -python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json +```bash +python -m lerobot.rl.actor --config_path path/to/train_gym_hil_env.json ``` - In a different terminal, run the learner server: - -```python -python -m lerobot.scripts.rl.learner --config_path path/to/train_gym_hil_env.json +```bash +python -m lerobot.rl.learner --config_path path/to/train_gym_hil_env.json ``` - The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots. diff --git a/docs/source/il_robots.mdx b/docs/source/il_robots.mdx index 905046bef74..91df14028cd 100644 --- a/docs/source/il_robots.mdx +++ b/docs/source/il_robots.mdx @@ -200,7 +200,7 @@ from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderCo from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader from lerobot.utils.control_utils import init_keyboard_listener from lerobot.utils.utils import log_say -from lerobot.utils.visualization_utils import _init_rerun +from lerobot.utils.visualization_utils import init_rerun from lerobot.record import record_loop NUM_EPISODES = 5 @@ -237,7 +237,7 @@ dataset = LeRobotDataset.create( # Initialize the keyboard listener and rerun visualization _, events = init_keyboard_listener() -_init_rerun(session_name="recording") +init_rerun(session_name="recording") # Connect the robot and teleoperator robot.connect() @@ -517,13 +517,16 @@ from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerCon from lerobot.robots.so100_follower.so100_follower import SO100Follower from lerobot.utils.control_utils import init_keyboard_listener from lerobot.utils.utils import log_say -from lerobot.utils.visualization_utils import _init_rerun +from lerobot.utils.visualization_utils import init_rerun from lerobot.record import record_loop +from lerobot.policies.factory import make_processor NUM_EPISODES = 5 FPS = 30 EPISODE_TIME_SEC = 60 TASK_DESCRIPTION = "My task description" +HF_MODEL_ID = "/" +HF_DATASET_ID = "/" # Create the robot configuration camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)} @@ -535,7 +538,7 @@ robot_config = SO100FollowerConfig( robot = SO100Follower(robot_config) # Initialize the policy -policy = ACTPolicy.from_pretrained("/") +policy = ACTPolicy.from_pretrained(HF_MODEL_ID) # Configure the dataset features action_features = hw_to_dataset_features(robot.action_features, "action") @@ -544,7 +547,7 @@ dataset_features = {**action_features, **obs_features} # Create the dataset dataset = LeRobotDataset.create( - repo_id="/eval_", + repo_id=HF_DATASET_ID, fps=FPS, features=dataset_features, robot_type=robot.name, @@ -554,11 +557,17 @@ dataset = LeRobotDataset.create( # Initialize the keyboard listener and rerun visualization _, events = init_keyboard_listener() -_init_rerun(session_name="recording") +init_rerun(session_name="recording") # Connect the robot robot.connect() +preprocessor, postprocessor = make_processor( + policy_cfg=policy, + pretrained_path=HF_MODEL_ID, + dataset_stats=dataset.meta.stats, +) + for episode_idx in range(NUM_EPISODES): log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}") @@ -568,6 +577,8 @@ for episode_idx in range(NUM_EPISODES): events=events, fps=FPS, policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, dataset=dataset, control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, diff --git a/docs/source/il_sim.mdx b/docs/source/il_sim.mdx index 3dd80dc4b3a..9b7d7c1117e 100644 --- a/docs/source/il_sim.mdx +++ b/docs/source/il_sim.mdx @@ -22,13 +22,38 @@ pip install -e ".[hilserl]" ## Teleoperate and Record a Dataset -To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_gym_hil_il.json). - -To teleoperate and collect a dataset, we need to modify this config file and you should add your `repo_id` here: `"repo_id": "il_gym",` and `"num_episodes": 30,` and make sure you set `mode` to `record`, "mode": "record". +To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/sim_il/env_config.json). + +To teleoperate and collect a dataset, we need to modify this config file. Here's an example configuration for imitation learning data collection: + +```json +{ + "env": { + "type": "gym_manipulator", + "name": "gym_hil", + "task": "PandaPickCubeGamepad-v0", + "fps": 10 + }, + "dataset": { + "repo_id": "your_username/il_gym", + "root": null, + "task": "pick_cube", + "num_episodes_to_record": 30, + "replay_episode": null, + "push_to_hub": true + }, + "mode": "record", + "device": "cuda" +} +``` -If you do not have a Nvidia GPU also change `"device": "cuda"` parameter in the config file (for example to `mps` for MacOS). +Key configuration points: -By default the config file assumes you use a controller. To use your keyboard please change the envoirment specified at `"task"` in the config file and set it to `"PandaPickCubeKeyboard-v0"`. +- Set your `repo_id` in the `dataset` section: `"repo_id": "your_username/il_gym"` +- Set `num_episodes_to_record: 30` to collect 30 demonstration episodes +- Ensure `mode` is set to `"record"` +- If you don't have an NVIDIA GPU, change `"device": "cuda"` to `"mps"` for macOS or `"cpu"` +- To use keyboard instead of gamepad, change `"task"` to `"PandaPickCubeKeyboard-v0"` Then we can run this command to start: @@ -36,14 +61,14 @@ Then we can run this command to start: ```bash -python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json +python -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json ``` ```bash -mjpython -m lerobot.scripts.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json +mjpython -m lerobot.rl.gym_manipulator --config_path path/to/env_config_gym_hil_il.json ``` @@ -140,9 +165,32 @@ huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \ ## Evaluate your policy in Sim -To evaluate your policy we have to use the config file that can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json). +To evaluate your policy we have to use a configuration file. An example can be found [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/sim_il/eval_config.json). + +Here's an example evaluation configuration: + +```json +{ + "env": { + "type": "gym_manipulator", + "name": "gym_hil", + "task": "PandaPickCubeGamepad-v0", + "fps": 10 + }, + "dataset": { + "repo_id": "your_username/il_sim_dataset", + "dataset_root": null, + "task": "pick_cube" + }, + "pretrained_policy_name_or_path": "your_username/il_sim_model", + "device": "cuda" +} +``` + +Make sure to replace: -Make sure to replace the `repo_id` with the dataset you trained on, for example `pepijn223/il_sim_dataset` and replace the `pretrained_policy_name_or_path` with your model id, for example `pepijn223/il_sim_model` +- `repo_id` with the dataset you trained on (e.g., `your_username/il_sim_dataset`) +- `pretrained_policy_name_or_path` with your model ID (e.g., `your_username/il_sim_model`) Then you can run this command to visualize your trained policy @@ -150,14 +198,14 @@ Then you can run this command to visualize your trained policy ```bash -python -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json +python -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json ``` ```bash -mjpython -m lerobot.scripts.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json +mjpython -m lerobot.rl.eval_policy --config_path=path/to/eval_config_gym_hil.json ``` diff --git a/docs/source/implement_your_own_processor.mdx b/docs/source/implement_your_own_processor.mdx new file mode 100644 index 00000000000..5b7d4f78ac1 --- /dev/null +++ b/docs/source/implement_your_own_processor.mdx @@ -0,0 +1,273 @@ +# Implement your own Robot Processor + +In this tutorial, you'll learn how to implement your own Robot Processor. +It begins by exploring the need for a custom processor, then uses the `NormalizerProcessorStep` as the running example to explain how to implement, configure, and serialize a processor. Finally, it lists all helper processors that ship with LeRobot. + +## Why would you need a custom processor? + +In most cases, when reading raw data from sensors or when models output actions, you need to process this data to make it compatible with your target system. For example, a common need is normalizing data ranges to make them suitable for neural networks. + +LeRobot's `NormalizerProcessorStep` handles this crucial task: + +```python +# Input: raw joint positions in [0, 180] degrees +raw_action = torch.tensor([90.0, 45.0, 135.0]) + +# After processing: normalized to [-1, 1] range for model training +normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=dataset_stats) +normalized_result = normalizer(transition) +# ... +``` + +Other common processing needs include: + +- **Device placement**: Moving tensors between CPU/GPU and converting data types +- **Format conversion**: Transforming between different data structures +- **Batching**: Adding/removing batch dimensions for model compatibility +- **Safety constraints**: Applying limits to robot commands + +```python +# Example pipeline combining multiple processors +pipeline = PolicyProcessorPipeline([ + RenameObservationsProcessorStep(rename_map={}), + AddBatchDimensionProcessorStep(), + NormalizerProcessorStep(features=features, stats=stats), + DeviceProcessorStep(device="cuda"), + # ... +]) +``` + +LeRobot provides a pipeline mechanism to implement sequences of processing steps for both input data and output actions, making it easy to compose these transformations in the right order for optimal performance. + +## How to implement your own processor? + +We'll use the `NormalizerProcessorStep` as our main example because it demonstrates essential processor patterns including state management, configuration serialization, and tensor handling that you'll commonly need. + +Prepare the sequence of processing steps necessary for your problem. A processor step is a class that implements the following methods: + +- `__call__`: implements the processing step for the input transition. +- `get_config`: gets the configuration of the processor step. +- `state_dict`: gets the state of the processor step. +- `load_state_dict`: loads the state of the processor step. +- `reset`: resets the state of the processor step. +- `feature_contract`: displays the modification to the feature space during the processor step. + +### Implement the `__call__` method + +The `__call__` method is the core of your processor step. It takes an `EnvTransition` and returns a modified `EnvTransition`. Here's how the `NormalizerProcessorStep` works: + +```python +@dataclass +@ProcessorStepRegistry.register("normalizer_processor") +class NormalizerProcessorStep(ProcessorStep): + """Normalize observations/actions using dataset statistics.""" + + features: dict[str, PolicyFeature] + norm_map: dict[FeatureType, NormalizationMode] + stats: dict[str, dict[str, Any]] | None = None + eps: float = 1e-8 + _tensor_stats: dict = field(default_factory=dict, init=False, repr=False) + + def __post_init__(self): + """Convert stats to tensors for efficient computation.""" + self.stats = self.stats or {} + self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=torch.float32) + + def __call__(self, transition: EnvTransition) -> EnvTransition: + new_transition = transition.copy() + # Normalize observations + # ... + # Normalize action + # ... + return new_transition + +``` + +See the full implementation in `src/lerobot/processor/normalize_processor.py` for complete details. + +**Key principles:** + +- **Always use `transition.copy()`** to avoid side effects +- **Handle both observations and actions** consistently +- **Separate config from state**: `get_config()` returns JSON-serializable params, `state_dict()` returns tensors +- **Convert stats to tensors** in `__post_init__()` for efficient computation + +### Configuration and State Management + +Processors support serialization through three methods that separate configuration from tensor state. The `NormalizerProcessorStep` demonstrates this perfectly - it carries dataset statistics (tensors) in its state, and hyperparameters in its config: + +```python +# Continuing the NormalizerProcessorStep example... + +def get_config(self) -> dict[str, Any]: + """JSON-serializable configuration (no tensors).""" + return { + "eps": self.eps, + "features": {k: {"type": v.type.value, "shape": v.shape} for k, v in self.features.items()}, + "norm_map": {ft.value: nm.value for ft, nm in self.norm_map.items()}, + # ... + } + +def state_dict(self) -> dict[str, torch.Tensor]: + """Tensor state only (e.g., dataset statistics).""" + flat: dict[str, torch.Tensor] = {} + for key, sub in self._tensor_stats.items(): + for stat_name, tensor in sub.items(): + flat[f"{key}.{stat_name}"] = tensor.cpu() # Always save to CPU + return flat + +def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: + """Restore tensor state at runtime.""" + self._tensor_stats.clear() + for flat_key, tensor in state.items(): + key, stat_name = flat_key.rsplit(".", 1) + # Load to processor's configured device + self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to( + dtype=torch.float32, device=self.device + ) + # ... +``` + +**Usage:** + +```python +# Save (e.g., inside a policy) +config = normalizer.get_config() +tensors = normalizer.state_dict() + +# Restore (e.g., loading a pretrained policy) +new_normalizer = NormalizerProcessorStep(**config) +new_normalizer.load_state_dict(tensors) +# Now new_normalizer has the same stats and configuration +``` + +### Transform features + +The `transform_features` method defines how your processor transforms feature names and shapes. This is crucial for policy configuration and debugging. + +For `NormalizerProcessorStep`, features are typically preserved unchanged since normalization doesn't alter keys or shapes: + +```python +def transform_features(self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """Normalization preserves all feature definitions.""" + return features # No changes to feature structure + # ... +``` + +When your processor renames or reshapes data, implement this method to reflect the mapping for downstream components. For example, a simple rename processor: + +```python +def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + # Simple renaming + if "pixels" in features: + features["observation.image"] = features.pop("pixels") + + # Pattern-based renaming + for key in list(features.keys()): + if key.startswith("env_state."): + suffix = key[len("env_state."):] + features[f"observation.{suffix}"] = features.pop(key) + # ... + + return features +``` + +**Key principles:** + +- Use `features.pop(old_key)` to remove and get the old feature +- Use `features[new_key] = old_feature` to add the renamed feature +- Always return the modified features dictionary +- Document transformations clearly in the docstring + +### Using overrides + +You can override step parameters at load-time using `overrides`. This is handy for non-serializable objects or site-specific settings. It works both in policy factories and with `DataProcessorPipeline.from_pretrained(...)`. + +**Foundational model adaptation**: This is particularly useful when working with foundational pretrained policies where you rarely have access to the original training statistics. You can inject your own dataset statistics to adapt the normalizer to your specific robot or environment data. + +Example: during policy evaluation on the robot, override the device and rename map. +Use this to run a policy trained on CUDA on a CPU-only robot, or to remap camera keys when the robot uses different names than the dataset. + +Direct usage with `from_pretrained`: + +```python +from lerobot.processor import RobotProcessorPipeline + +# Load a foundational policy trained on diverse robot data +# but adapt normalization to your specific robot/environment +new_stats = LeRobotDataset(repo_id="username/my-dataset").meta.stats +processor = RobotProcessorPipeline.from_pretrained( + "huggingface/foundational-robot-policy", # Pretrained foundation model + overrides={ + "normalizer_processor": {"stats": new_stats}, # Inject your robot's statistics + "device_processor": {"device": "cuda:0"}, # registry name for registered steps + "rename_processor": {"rename_map": robot_key_map}, # Map your robot's observation keys + # ... + }, +) +``` + +## Best Practices + +Based on analysis of all LeRobot processor implementations, here are the key patterns and practices: + +### 1. **Safe Data Handling** + +Always create copies of input data to avoid unintended side effects. Use `transition.copy()` and `observation.copy()` rather than modifying data in-place. This prevents your processor from accidentally affecting other components in the pipeline. + +Check for required data before processing and handle missing data gracefully. If your processor expects certain keys (like `"pixels"` for image processing), validate their presence first. For optional data, use safe access patterns like `transition.get()` and handle `None` values appropriately. + +When data validation fails, provide clear, actionable error messages that help users understand what went wrong and how to fix it. + +### 2. **Choose Appropriate Base Classes** + +LeRobot provides specialized base classes that reduce boilerplate code and ensure consistency. Use `ObservationProcessorStep` when you only need to modify observations, `ActionProcessorStep` for action-only processing, and `RobotActionProcessorStep` specifically for dictionary-based robot actions. + +Only inherit directly from `ProcessorStep` when you need full control over the entire transition or when processing multiple transition components simultaneously. The specialized base classes handle the transition management for you and provide type safety. + +### 3. **Registration and Naming** + +Register your processors with descriptive, namespaced names using `@ProcessorStepRegistry.register()`. Use organization prefixes like `"robotics_lab/safety_clipper"` or `"acme_corp/vision_enhancer"` to avoid naming conflicts. Avoid generic names like `"processor"` or `"step"` that could clash with other implementations. + +Good registration makes your processors discoverable and enables clean serialization/deserialization when saving and loading pipelines. + +### 4. **State Management Patterns** + +Distinguish between configuration parameters (JSON-serializable values) and internal state (tensors, buffers). Use dataclass fields with `init=False, repr=False` for internal state that shouldn't appear in the constructor or string representation. + +Implement the `reset()` method to clear internal state between episodes. This is crucial for stateful processors that accumulate data over time, like moving averages or temporal filters. + +Remember that `get_config()` should only return JSON-serializable configuration, while `state_dict()` handles tensor state separately. + +### 5. **Input Validation and Error Handling** + +Validate input types and shapes before processing. Check tensor properties like `dtype` and dimensions to ensure compatibility with your algorithms. For robot actions, verify that required pose components or joint values are present and within expected ranges. + +Use early returns for edge cases where no processing is needed. Provide clear, descriptive error messages that include the expected vs. actual data types or shapes. This makes debugging much easier for users. + +### 6. **Device and Dtype Awareness** + +Design your processors to automatically adapt to the device and dtype of input tensors. Internal tensors (like normalization statistics) should match the input tensor's device and dtype to ensure compatibility with multi-GPU training, mixed precision, and distributed setups. + +Implement a `to()` method that moves your processor's internal state to the specified device. Check device/dtype compatibility at runtime and automatically migrate internal state when needed. This pattern enables seamless operation across different hardware configurations without manual intervention. + +## Conclusion + +You now have all the tools to implement custom processors in LeRobot! The key steps are: + +1. **Define your processor** as a dataclass with the required methods (`__call__`, `get_config`, `state_dict`, `load_state_dict`, `reset`, `transform_features`) +2. **Register it** using `@ProcessorStepRegistry.register("name")` for discoverability +3. **Integrate it** into a `DataProcessorPipeline` with other processing steps +4. **Use base classes** like `ObservationProcessorStep` when possible to reduce boilerplate +5. **Implement device/dtype awareness** to support multi-GPU and mixed precision setups + +The processor system is designed to be modular and composable, allowing you to build complex data processing pipelines from simple, focused components. Whether you're preprocessing sensor data for training or post-processing model outputs for robot execution, custom processors give you the flexibility to handle any data transformation your robotics application requires. + +Key principles for robust processors: + +- **Device/dtype adaptation**: Internal tensors should match input tensors +- **Clear error messages**: Help users understand what went wrong +- **Base class usage**: Leverage specialized base classes to reduce boilerplate +- **Feature contracts**: Declare data structure changes with `transform_features()` + +Start simple, test thoroughly, and ensure your processors work seamlessly across different hardware configurations! diff --git a/docs/source/integrate_hardware.mdx b/docs/source/integrate_hardware.mdx index 7b2e3833f08..ed9dc8dd563 100644 --- a/docs/source/integrate_hardware.mdx +++ b/docs/source/integrate_hardware.mdx @@ -8,7 +8,7 @@ To that end, we provide the [`Robot`](https://github.com/huggingface/lerobot/blo - Your own robot which exposes a communication interface (e.g. serial, CAN, TCP) - A way to read sensor data and send motor commands programmatically, e.g. manufacturer's SDK or API, or your own protocol implementation. -- LeRobot installed in your environment. Follow our [Installation Guide](./installation.mdx). +- LeRobot installed in your environment. Follow our [Installation Guide](./installation). ## Choose your motors @@ -65,7 +65,7 @@ class MyCoolRobotConfig(RobotConfig): ``` -[Cameras tutorial](./cameras.mdx) to understand how to detect and add your camera. +[Cameras tutorial](./cameras) to understand how to detect and add your camera. Next, we'll create our actual robot class which inherits from `Robot`. This abstract class defines a contract you must follow for your robot to be usable with the rest of the LeRobot tools. @@ -335,6 +335,134 @@ For implementing teleoperation devices, we also provide a [`Teleoperator`](https The main differences are in the I/O functions: a teleoperator allows you to produce action via `get_action` and can receive feedback actions via `send_feedback`. Feedback could be anything controllable on the teleoperation device that could help the person controlling it understand the consequences of the actions sent. Think motion/force feedback on a leader arm, vibrations on a gamepad controller for example. To implement a teleoperator, you can follow this same tutorial and adapt it for these two methods. +## Using Your Own `LeRobot` Devices 🔌 + +You can easily extend `lerobot` with your own custom hardware—be it a camera, robot, or teleoperation device—by creating a separate, installable Python package. If you follow a few simple conventions, the `lerobot` command-line tools (like `lerobot-teleop` and `lerobot-record`) will **automatically discover and integrate your creations** without requiring any changes to the `lerobot` source code. + +This guide outlines the conventions your plugin must follow. + +### The 4 Core Conventions + +To ensure your custom device is discoverable, you must adhere to the following four rules. + +#### 1\. Create an Installable Package with a Specific Prefix + +Your project must be a standard, installable Python package. Crucially, the name of your package (as defined in `pyproject.toml` or `setup.py`) must begin with one of these prefixes: + +- `lerobot_robot_` for a robot. +- `lerobot_camera_` for a camera. +- `lerobot_teleoperator_` for a teleoperation device. + +This prefix system is how `lerobot` automatically finds your plugin in the Python environment. + +#### 2\. Follow the `SomethingConfig`/`Something` Naming Pattern + +Your device's implementation class must be named after its configuration class, simply by removing the `Config` suffix. + +- **Config Class:** `MyAwesomeTeleopConfig` +- **Device Class:** `MyAwesomeTeleop` + +#### 3\. Place Your Files in a Predictable Structure + +The device class (`MyAwesomeTeleop`) must be located in a predictable module relative to its configuration class (`MyAwesomeTeleopConfig`). `lerobot` will automatically search in these locations: + +- In the **same module** as the config class. +- In a **submodule named after the device** (e.g., `my_awesome_teleop.py`). + +The recommended and simplest structure is to place them in separate, clearly named files within the same directory. + +#### 4\. Expose Classes in `__init__.py` + +Your package's `__init__.py` file should import and expose both the configuration and the device classes, making them easily accessible. + +### Putting It All Together: A Complete Example + +Let's create a new teleoperator called `my_awesome_teleop`. + +#### Directory Structure + +Here is what the project folder should look like. The package name, `lerobot_teleoperator_my_awesome_teleop`, follows **Convention \#1**. + +``` +lerobot_teleoperator_my_awesome_teleop/ +├── pyproject.toml # (or setup.py) lists lerobot as a dependency +└── lerobot_teleoperator_my_awesome_teleop/ + ├── __init__.py + ├── config_my_awesome_teleop.py + └── my_awesome_teleop.py +``` + +#### File Contents + +- **`config_my_awesome_teleop.py`**: Defines the configuration class. Note the `Config` suffix (**Convention \#2**). + + ```python + from dataclasses import dataclass + + from lerobot.teleoperators.config import TeleoperatorConfig + + @TeleoperatorConfig.register_subclass("my_awesome_teleop") + @dataclass + class MyAwesomeTeleopConfig(TeleoperatorConfig): + # Your configuration fields go here + port: str = "192.168.1.1" + ``` + +- **`my_awesome_teleop.py`**: Implements the device. The class name `MyAwesomeTeleop` matches its config class name (**Convention \#2**). This file structure adheres to **Convention \#3**. + + ```python + from lerobot.teleoperators.teleoperator import Teleoperator + + from .config_my_awesome_teleop import MyAwesomeTeleopConfig + + class MyAwesomeTeleop(Teleoperator): + config_class = MyAwesomeTeleopConfig + name = "my_awesome_teleop" + + def __init__(self, config: MyAwesomeTeleopConfig): + super().__init__(config) + self.config = config + + # Your device logic (e.g., connect) goes here + ``` + +- **`__init__.py`**: Exposes the key classes (**Convention \#4**). + + ```python + from .config_my_awesome_teleop import MyAwesomeTeleopConfig + from .my_awesome_teleop import MyAwesomeTeleop + ``` + +### Installation and Usage + +1. **Install your new plugin in your Python environment.** You can install your local plugin package using `pip`'s editable mode or from PyPi. + + ```bash + # Locally + # Navigate to your plugin's root directory and install it + cd lerobot_teleoperator_my_awesome_teleop + pip install -e . + + # From PyPi + pip install lerobot_teleoperator_my_awesome_teleop + ``` + +2. **Use it directly from the command line.** Now, you can use your custom device by referencing its type. + + ```bash + lerobot-teleoperate --teleop.type=my_awesome_teleop \ + # other arguments + ``` + +And that's it\! Your custom device is now fully integrated. + +### Looking for an example ? + +Check out these two packages from the community: + +- https://github.com/SpesRobotics/lerobot-robot-xarm +- https://github.com/SpesRobotics/lerobot-teleoperator-teleop + ## Wrapping Up Once your robot class is complete, you can leverage the LeRobot ecosystem: diff --git a/docs/source/introduction_processors.mdx b/docs/source/introduction_processors.mdx new file mode 100644 index 00000000000..6f376861541 --- /dev/null +++ b/docs/source/introduction_processors.mdx @@ -0,0 +1,314 @@ +# Introduction to Processors + +In robotics, there's a fundamental mismatch between the data that robots and humans produce and what machine learning models expect. +Robots output raw sensor data like camera images and joint positions that need normalization, batching, and device placement before models can process them. +Language instructions from humans must be tokenized into numerical representations, and different robots use different coordinate systems that need standardization. + +The challenge extends to model outputs as well. +Models might output end-effector positions while robots need joint-space commands, or teleoperators produce relative movements while robots expect absolute commands. +Model predictions are often normalized and need conversion back to real-world scales. + +Cross-domain translation adds another layer of complexity. +Training data from one robot setup needs adaptation for deployment on different hardware, models trained with specific camera configurations must work with new arrangements, and datasets with different naming conventions need harmonization. + +**That's where processors come in.** They serve as universal translators that bridge these gaps, ensuring seamless data flow from sensors to models to actuators. +Processors handle all the preprocessing and postprocessing steps needed to convert raw environment data into model-ready inputs and vice versa. + +This means that your favorite policy can be used like this: + +```python +import torch + +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.policies.factory import make_pre_post_processors +from lerobot.policies.your_policy import YourPolicy +from lerobot.processor.pipeline import RobotProcessorPipeline, PolicyProcessorPipeline +dataset = LeRobotDataset("hf_user/dataset", episodes=[0]) +sample = dataset[10] + +model = YourPolicy.from_pretrained( + "hf_user/model", +) +model.eval() +model.to("cuda") +preprocessor, postprocessor = make_pre_post_processors(model.config, pretrained_path="hf_user/model", dataset_stats=dataset.meta.stats) + +preprocessed_sample = preprocessor(sample) +action = model.select_action(preprocessed_sample) +postprocessed_action = postprocessor(action) +``` + +## What are Processors? + +In robotics, data comes in many forms: images from cameras, joint positions from sensors, text instructions from users, and more. Each type of data requires specific transformations before a model can use it effectively. Models need this data to be: + +- **Normalized**: Scaled to appropriate ranges for neural network processing +- **Batched**: Organized with proper dimensions for batch processing +- **Tokenized**: Text converted to numerical representations +- **Device-placed**: Moved to the right hardware (CPU/GPU) +- **Type-converted**: Cast to appropriate data types + +Processors handle these transformations through composable, reusable steps that can be chained together into pipelines. Think of them as a modular assembly line where each station performs a specific transformation on your data. + +## Core Concepts + +### EnvTransition: The Universal Data Container + +The `EnvTransition` is the fundamental data structure that flows through all processors. +It's a typed dictionary that represents a complete robot-environment interaction: + +- **OBSERVATION**: All sensor data (images, states, proprioception) +- **ACTION**: The action to execute or that was executed +- **REWARD**: Reinforcement learning signal +- **DONE/TRUNCATED**: Episode boundary indicators +- **INFO**: Arbitrary metadata +- **COMPLEMENTARY_DATA**: Task descriptions, indices, padding flags, inter-step data + +### ProcessorStep: The Building Block + +A `ProcessorStep` is a single transformation unit that processes transitions. It's an abstract base class with two required methods: + +```python +from lerobot.processor import ProcessorStep, EnvTransition + +class MyProcessorStep(ProcessorStep): + """Example processor step - inherit and implement abstract methods.""" + + def __call__(self, transition: EnvTransition) -> EnvTransition: + """Transform the transition - REQUIRED abstract method.""" + # Your processing logic here + return transition + + def transform_features(self, features): + """Declare how this step transforms feature shapes/types - REQUIRED abstract method.""" + return features # Most processors return features unchanged +``` + +`__call__` is the core of your processor step. It takes an `EnvTransition` and returns a modified `EnvTransition`. + +`transform_features` is used to declare how this step transforms feature shapes/types. + +### DataProcessorPipeline: The Generic Orchestrator + +The `DataProcessorPipeline[TInput, TOutput]` chains multiple `ProcessorStep` instances together: + +```python +from lerobot.processor import RobotProcessorPipeline, PolicyProcessorPipeline + +# For robot hardware (unbatched data) +robot_processor = RobotProcessorPipeline[RobotAction, RobotAction]( + steps=[step1, step2, step3], + name="robot_pipeline" +) + +# For model training/inference (batched data) +policy_processor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=[step1, step2, step3], + name="policy_pipeline" +) +``` + +## RobotProcessorPipeline vs PolicyProcessorPipeline + +The key distinction is in the data structures they handle: + +| Aspect | RobotProcessorPipeline | PolicyProcessorPipeline | +| --------------- | -------------------------------------------- | ---------------------------------------- | +| **Input** | `dict[str, Any]` - Individual robot values | `dict[str, Any]` - Batched tensors | +| **Output** | `dict[str, Any]` - Individual robot commands | `torch.Tensor` - Policy predictions | +| **Use Case** | Real-time robot control | Model training/inference | +| **Data Format** | Unbatched, heterogeneous | Batched, homogeneous | +| **Examples** | `{"joint_1": 0.5}` | `{"observation.state": tensor([[0.5]])}` | + +**Use `RobotProcessorPipeline`** for robot hardware interfaces: + +```python +# Robot data structures: dict[str, Any] for observations and actions +robot_obs: dict[str, Any] = { + "joint_1": 0.5, # Individual joint values + "joint_2": -0.3, + "camera_0": image_array # Raw camera data +} + +robot_action: dict[str, Any] = { + "joint_1": 0.2, # Target joint positions + "joint_2": 0.1, + "gripper": 0.8 +} +``` + +**Use `PolicyProcessorPipeline`** for model training and batch processing: + +```python +# Policy data structures: batch dicts and tensors +policy_batch: dict[str, Any] = { + "observation.state": torch.tensor([[0.5, -0.3]]), # Batched states + "observation.images.camera0": torch.tensor(...), # Batched images + "action": torch.tensor([[0.2, 0.1, 0.8]]) # Batched actions +} + +policy_action: torch.Tensor = torch.tensor([[0.2, 0.1, 0.8]]) # Model output tensor +``` + +## Converter Functions + +LeRobot provides converter functions to bridge different data formats in `lerobot.processor.converters`. These functions handle the crucial translations between robot hardware data structures, policy model formats, and the internal `EnvTransition` representation that flows through processor pipelines. + +| Category | Function | Description | +| ------------------------------ | ----------------------------- | ------------------------------- | +| **Robot Hardware Converters** | `robot_action_to_transition` | Robot dict → EnvTransition | +| | `observation_to_transition` | Robot obs → EnvTransition | +| | `transition_to_robot_action` | EnvTransition → Robot dict | +| **Policy/Training Converters** | `batch_to_transition` | Batch dict → EnvTransition | +| | `transition_to_batch` | EnvTransition → Batch dict | +| | `policy_action_to_transition` | Policy tensor → EnvTransition | +| | `transition_to_policy_action` | EnvTransition → Policy tensor | +| **Utilities** | `create_transition` | Build transitions with defaults | +| | `identity_transition` | Pass-through converter | + +The key insight is that **robot hardware converters** work with individual values and dictionaries, while **policy/training converters** work with batched tensors and model outputs. The converter functions automatically handle the structural differences, so your processor steps can focus on the core transformations without worrying about data format compatibility. + +## Processor Examples + +The following examples demonstrate real-world processor configurations for policy training and inference. + +Here is an example processor for policy training and inference: + +```python +# Training data preprocessing (optimized order for GPU performance) +training_preprocessor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=[ + RenameObservationsProcessorStep(rename_map={}), # Standardize keys + AddBatchDimensionProcessorStep(), # Add batch dims + TokenizerProcessorStep(tokenizer_name="...", ...), # Tokenize language + DeviceProcessorStep(device="cuda"), # Move to GPU first + NormalizerProcessorStep(features=..., stats=...), # Normalize on GPU + ] +) + +# Model output postprocessing +training_postprocessor = PolicyProcessorPipeline[torch.Tensor, torch.Tensor]( + steps=[ + DeviceProcessorStep(device="cpu"), # Move to CPU + UnnormalizerProcessorStep(features=..., stats=...), # Denormalize + ] + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, +) +``` + +### An interaction between a robot and a policy with processors + +The most common real-world scenario combines both pipeline types robot hardware generates observations that need policy processing, and policy outputs need robot-compatible postprocessing: + +```python +# Real deployment: Robot sensors → Model → Robot commands +with torch.no_grad(): + while not done: + raw_obs = robot.get_observation() # dict[str, Any] + + # Add your robot observation to policy observation processor + + policy_input = policy_preprocessor(raw_obs) # Batched dict + + policy_output = policy.select_action(policy_input) # Policy tensor + + policy_action = policy_postprocessor(policy_output) + + # Add your robot action to policy action processor + + robot.send_action(policy_action) +``` + +## Feature Contracts: Shape and Type Transformation + +Processors don't just transform data - they can also **change the data structure itself**. The `transform_features()` method declares these changes, which is crucial for dataset recording and policy creation. + +### Why Feature Contracts Matter + +When building datasets or policies, LeRobot needs to know: + +- **What data fields will exist** after processing +- **What shapes and types** each field will have +- **How to configure models** for the expected data structure + +```python +# Example: A processor that adds velocity to observations +class VelocityProcessor(ObservationProcessorStep): + def observation(self, obs): + new_obs = obs.copy() + if "observation.state" in obs: + # concatenate computed velocity field to the state + new_obs["observation.state"] = self._compute_velocity(obs["observation.state"]) + return new_obs + + def transform_features(self, features): + """Declare the new velocity field we're adding.""" + state_feature = features[PipelineFeatureType.OBSERVATION].get("observation.state") + if state_feature: + double_shape = (state_feature.shape[0] * 2,) if state_feature.shape else (2,) + features[PipelineFeatureType.OBSERVATION]["observation.state"] = PolicyFeature( + type=FeatureType.STATE, shape=double_shape + ) + return features +``` + +### Feature Specification Functions + +`create_initial_features()` and `aggregate_pipeline_dataset_features()` solve a critical dataset creation problem: determining the exact final data structure before any data is processed. +Since processor pipelines can add new features (like velocity fields), change tensor shapes (like cropping images), or rename keys, datasets need to know the complete output specification upfront to allocate proper storage and define schemas. +These functions work together by starting with robot hardware specifications (`create_initial_features()`) then simulating the entire pipeline transformation (`aggregate_pipeline_dataset_features()`) to compute the final feature dictionary that gets passed to `LeRobotDataset.create()`, ensuring perfect alignment between what processors output and what datasets expect to store. + +```python +from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features + +# Start with robot's raw features +initial_features = create_initial_features( + observation=robot.observation_features, # {"joint_1.pos": float, "camera_0": (480,640,3)} + action=robot.action_features # {"joint_1.pos": float, "gripper.pos": float} +) + +# Apply processor pipeline to compute final features +final_features = aggregate_pipeline_dataset_features( + pipeline=my_processor_pipeline, + initial_features=initial_features, + use_videos=True +) + +# Use for dataset creation +dataset = LeRobotDataset.create( + repo_id="my_dataset", + features=final_features, # Knows exactly what data to expect + ... +) +``` + +## Common Processor Steps + +LeRobot provides many registered processor steps. Here are the most commonly used core processors: + +### Essential Processors + +- **`normalizer_processor`**: Normalize observations/actions using dataset statistics (mean/std or min/max) +- **`device_processor`**: Move tensors to CPU/GPU with optional dtype conversion +- **`to_batch_processor`**: Add batch dimensions to transitions for model compatibility +- **`rename_observations_processor`**: Rename observation keys using mapping dictionaries +- **`tokenizer_processor`**: Tokenize natural language task descriptions into tokens and attention masks + +### Next Steps + +- **[Implement Your Own Processor](./implement_your_own_processor)** - Create custom processor steps +- **[Debug Your Pipeline](./debug_processor_pipeline)** - Troubleshoot and optimize pipelines +- **[Processors for Robots and Teleoperators](./processors_robots_teleop)** - Real-world integration patterns + +## Summary + +Processors solve the data translation problem in robotics by providing: + +- **Modular transformations**: Composable, reusable processing steps +- **Type safety**: Generic pipelines with compile-time checking +- **Performance optimization**: GPU-accelerated operations +- **Robot/Policy distinction**: Separate pipelines for different data structures +- **Comprehensive ecosystem**: 30+ registered processors for common tasks + +The key insight: `RobotProcessorPipeline` handles unbatched robot hardware data, while `PolicyProcessorPipeline` handles batched model data. Choose the right tool for your data structure! diff --git a/docs/source/koch.mdx b/docs/source/koch.mdx index 3e94899a8a6..813b9bd67d5 100644 --- a/docs/source/koch.mdx +++ b/docs/source/koch.mdx @@ -277,7 +277,7 @@ leader.disconnect() -Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./il_robots) > [!TIP] > If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/docs/source/lekiwi.mdx b/docs/source/lekiwi.mdx index 14c06e444c0..875394d71d0 100644 --- a/docs/source/lekiwi.mdx +++ b/docs/source/lekiwi.mdx @@ -323,7 +323,7 @@ To replay an episode run the API example below, make sure to change `remote_ip`, python examples/lekiwi/replay.py ``` -Congrats 🎉, your robot is all set to learn a task on its own. Start training it by the training part of this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by the training part of this tutorial: [Getting started with real-world robots](./il_robots) ## Evaluate your policy diff --git a/docs/source/lerobot-dataset-v3.mdx b/docs/source/lerobot-dataset-v3.mdx new file mode 100644 index 00000000000..cf1942fdcfb --- /dev/null +++ b/docs/source/lerobot-dataset-v3.mdx @@ -0,0 +1,281 @@ +# LeRobotDataset v3.0 + +`LeRobotDataset v3.0` is a standardized format for robot learning data. It provides unified access to multi-modal time-series data, sensorimotor signals and multi‑camera video, as well as rich metadata for indexing, search, and visualization on the Hugging Face Hub. + +This docs will guide you to: + +- Understand the v3.0 design and directory layout +- Record a dataset and push it to the Hub +- Load datasets for training with `LeRobotDataset` +- Stream datasets without downloading using `StreamingLeRobotDataset` +- Apply image transforms for data augmentation during training +- Migrate existing `v2.1` datasets to `v3.0` + +## What’s new in `v3` + +- **File-based storage**: Many episodes per Parquet/MP4 file (v2 used one file per episode). +- **Relational metadata**: Episode boundaries and lookups are resolved through metadata, not filenames. +- **Hub-native streaming**: Consume datasets directly from the Hub with `StreamingLeRobotDataset`. +- **Lower file-system pressure**: Fewer, larger files ⇒ faster initialization and fewer issues at scale. +- **Unified organization**: Clean directory layout with consistent path templates across data and videos. + +## Installation + +`LeRobotDataset v3.0` will be included in `lerobot >= 0.4.0`. + +Until that stable release, you can use the main branch by following the [build from source instructions](./installation#from-source). + +## Record a dataset + +Run the command below to record a dataset with the SO-101 and push to the Hub: + +```bash +lerobot-record \ + --robot.type=so101_follower \ + --robot.port=/dev/tty.usbmodem585A0076841 \ + --robot.id=my_awesome_follower_arm \ + --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ + --teleop.type=so101_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=my_awesome_leader_arm \ + --display_data=true \ + --dataset.repo_id=${HF_USER}/record-test \ + --dataset.num_episodes=5 \ + --dataset.single_task="Grab the black cube" +``` + +See the [recording guide](./il_robots#record-a-dataset) for more details. + +## Format design + +A core v3 principle is **decoupling storage from the user API**: data is stored efficiently (few large files), while the public API exposes intuitive episode-level access. + +`v3` has three pillars: + +1. **Tabular data**: Low‑dimensional, high‑frequency signals (states, actions, timestamps) stored in **Apache Parquet**. Access is memory‑mapped or streamed via the `datasets` stack. +2. **Visual data**: Camera frames concatenated and encoded into **MP4**. Frames from the same episode are grouped; videos are sharded per camera for practical sizes. +3. **Metadata**: JSON/Parquet records describing schema (feature names, dtypes, shapes), frame rates, normalization stats, and **episode segmentation** (start/end offsets into shared Parquet/MP4 files). + +> To scale to millions of episodes, tabular rows and video frames from multiple episodes are **concatenated** into larger files. Episode‑specific views are reconstructed **via metadata**, not file boundaries. + +

+
+ LeRobotDataset v3 diagram +
+ From episode‑based to file‑based datasets +
+
+
+ +### Directory layout (simplified) + +- **`meta/info.json`**: canonical schema (features, shapes/dtypes), FPS, codebase version, and **path templates** to locate data/video shards. +- **`meta/stats.json`**: global feature statistics (mean/std/min/max) used for normalization; exposed as `dataset.meta.stats`. +- **`meta/tasks.jsonl`**: natural‑language task descriptions mapped to integer IDs for task‑conditioned policies. +- **`meta/episodes/`**: per‑episode records (lengths, tasks, offsets) stored as **chunked Parquet** for scalability. +- **`data/`**: frame‑by‑frame **Parquet** shards; each file typically contains **many episodes**. +- **`videos/`**: **MP4** shards per camera; each file typically contains **many episodes**. + +## Load a dataset for training + +`LeRobotDataset` returns Python dictionaries of PyTorch tensors and integrates with `torch.utils.data.DataLoader`. Here is a code example showing its use: + +```python +import torch +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +repo_id = "yaak-ai/L2D-v3" + +# 1) Load from the Hub (cached locally) +dataset = LeRobotDataset(repo_id) + +# 2) Random access by index +sample = dataset[100] +print(sample) +# { +# 'observation.state': tensor([...]), +# 'action': tensor([...]), +# 'observation.images.front_left': tensor([C, H, W]), +# 'timestamp': tensor(1.234), +# ... +# } + +# 3) Temporal windows via delta_timestamps (seconds relative to t) +delta_timestamps = { + "observation.images.front_left": [-0.2, -0.1, 0.0] # 0.2s and 0.1s before current frame +} + +dataset = LeRobotDataset(repo_id, delta_timestamps=delta_timestamps) + +# Accessing an index now returns a stack for the specified key(s) +sample = dataset[100] +print(sample["observation.images.front_left"].shape) # [T, C, H, W], where T=3 + +# 4) Wrap with a DataLoader for training +batch_size = 16 +data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size) + +device = "cuda" if torch.cuda.is_available() else "cpu" +for batch in data_loader: + observations = batch["observation.state"].to(device) + actions = batch["action"].to(device) + images = batch["observation.images.front_left"].to(device) + # model.forward(batch) +``` + +## Stream a dataset (no downloads) + +Use `StreamingLeRobotDataset` to iterate directly from the Hub without local copies. This allows to stream large datasets without the need to downloading them onto disk or loading them onto memory, and is a key feature of the new dataset format. + +```python +from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset + +repo_id = "yaak-ai/L2D-v3" +dataset = StreamingLeRobotDataset(repo_id) # streams directly from the Hub +``` + +
+
+ StreamingLeRobotDataset +
+ Stream directly from the Hub for on‑the‑fly training. +
+
+
+ +## Image transforms + +Image transforms are data augmentations applied to camera frames during training to improve model robustness and generalization. LeRobot supports various transforms including brightness, contrast, saturation, hue, and sharpness adjustments. + +### Using transforms during dataset creation/recording + +Currently, transforms are applied during **training time only**, not during recording. When you create or record a dataset, the raw images are stored without transforms. This allows you to experiment with different augmentations later without re-recording data. + +### Adding transforms to existing datasets (API) + +Use the `image_transforms` parameter when loading a dataset for training: + +```python +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig, ImageTransformConfig + +# Option 1: Use default transform configuration (disabled by default) +transforms_config = ImageTransformsConfig( + enable=True, # Enable transforms + max_num_transforms=3, # Apply up to 3 transforms per frame + random_order=False, # Apply in standard order +) +transforms = ImageTransforms(transforms_config) + +dataset = LeRobotDataset( + repo_id="your-username/your-dataset", + image_transforms=transforms +) + +# Option 2: Create custom transform configuration +custom_transforms_config = ImageTransformsConfig( + enable=True, + max_num_transforms=2, + random_order=True, + tfs={ + "brightness": ImageTransformConfig( + weight=1.0, + type="ColorJitter", + kwargs={"brightness": (0.7, 1.3)} # Adjust brightness range + ), + "contrast": ImageTransformConfig( + weight=2.0, # Higher weight = more likely to be selected + type="ColorJitter", + kwargs={"contrast": (0.8, 1.2)} + ), + "sharpness": ImageTransformConfig( + weight=0.5, # Lower weight = less likely to be selected + type="SharpnessJitter", + kwargs={"sharpness": (0.3, 2.0)} + ), + } +) + +dataset = LeRobotDataset( + repo_id="your-username/your-dataset", + image_transforms=ImageTransforms(custom_transforms_config) +) + +# Option 3: Use pure torchvision transforms +from torchvision.transforms import v2 + +torchvision_transforms = v2.Compose([ + v2.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), + v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)), +]) + +dataset = LeRobotDataset( + repo_id="your-username/your-dataset", + image_transforms=torchvision_transforms +) +``` + +### Available transform types + +LeRobot provides several transform types: + +- **`ColorJitter`**: Adjusts brightness, contrast, saturation, and hue +- **`SharpnessJitter`**: Randomly adjusts image sharpness +- **`Identity`**: No transformation (useful for testing) + +You can also use any `torchvision.transforms.v2` transform by passing it directly to the `image_transforms` parameter. + +### Configuration options + +- **`enable`**: Enable/disable transforms (default: `False`) +- **`max_num_transforms`**: Maximum number of transforms applied per frame (default: `3`) +- **`random_order`**: Apply transforms in random order vs. standard order (default: `False`) +- **`weight`**: Sampling probability for each transform (higher = more likely, if sum of weights is not 1, they will be normalized) +- **`kwargs`**: Transform-specific parameters (e.g., brightness range) + +### Visualizing transforms + +Use the visualization script to preview how transforms affect your data: + +```bash +lerobot-imgtransform-viz \ + --repo-id=your-username/your-dataset \ + --output-dir=./transform_examples \ + --n-examples=5 +``` + +This saves example images showing the effect of each transform, helping you tune parameters. + +### Best practices + +- **Start conservative**: Begin with small ranges (e.g., brightness 0.9-1.1) and increase gradually +- **Test first**: Use the visualization script to ensure transforms look reasonable +- **Monitor training**: Strong augmentations can hurt performance if too aggressive +- **Match your domain**: If your robot operates in varying lighting, use brightness/contrast transforms +- **Combine wisely**: Using too many transforms simultaneously can make training unstable + +## Migrate `v2.1` → `v3.0` + +A converter aggregates per‑episode files into larger shards and writes episode offsets/metadata. Convert your dataset using the instructions below. + +```bash +# Pre-release build with v3 support: +pip install "https://github.com/huggingface/lerobot/archive/33cad37054c2b594ceba57463e8f11ee374fa93c.zip" + +# Convert an existing v2.1 dataset hosted on the Hub: +python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id= +``` + +**What it does** + +- Aggregates parquet files: `episode-0000.parquet`, `episode-0001.parquet`, … → **`file-0000.parquet`**, … +- Aggregates mp4 files: `episode-0000.mp4`, `episode-0001.mp4`, … → **`file-0000.mp4`**, … +- Updates `meta/episodes/*` (chunked Parquet) with per‑episode lengths, tasks, and byte/frame offsets. diff --git a/docs/source/libero.mdx b/docs/source/libero.mdx new file mode 100644 index 00000000000..3f2b92406e8 --- /dev/null +++ b/docs/source/libero.mdx @@ -0,0 +1,166 @@ +# LIBERO + +**LIBERO** is a benchmark designed to study **lifelong robot learning**. The idea is that robots won’t just be pretrained once in a factory, they’ll need to keep learning and adapting with their human users over time. This ongoing adaptation is called **lifelong learning in decision making (LLDM)**, and it’s a key step toward building robots that become truly personalized helpers. + +- 📄 [LIBERO paper](https://arxiv.org/abs/2306.03310) +- 💻 [Original LIBERO repo](https://github.com/Lifelong-Robot-Learning/LIBERO) + +To make progress on this challenge, LIBERO provides a set of standardized tasks that focus on **knowledge transfer**: how well a robot can apply what it has already learned to new situations. By evaluating on LIBERO, different algorithms can be compared fairly and researchers can build on each other’s work. + +LIBERO includes **five task suites**: + +- **LIBERO-Spatial (`libero_spatial`)** – tasks that require reasoning about spatial relations. +- **LIBERO-Object (`libero_object`)** – tasks centered on manipulating different objects. +- **LIBERO-Goal (`libero_goal`)** – goal-conditioned tasks where the robot must adapt to changing targets. +- **LIBERO-90 (`libero_90`)** – 90 short-horizon tasks from the LIBERO-100 collection. +- **LIBERO-Long (`libero_10`)** – 10 long-horizon tasks from the LIBERO-100 collection. + +Together, these suites cover **130 tasks**, ranging from simple object manipulations to complex multi-step scenarios. LIBERO is meant to grow over time, and to serve as a shared benchmark where the community can test and improve lifelong learning algorithms. + +![An overview of the LIBERO benchmark](https://libero-project.github.io/assets/img/libero/fig1.png) + +## Evaluating with LIBERO + +At **LeRobot**, we ported [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) into our framework and used it mainly to **evaluate [SmolVLA](https://huggingface.co/docs/lerobot/en/smolvla)**, our lightweight Vision-Language-Action model. + +LIBERO is now part of our **multi-eval supported simulation**, meaning you can benchmark your policies either on a **single suite of tasks** or across **multiple suites at once** with just a flag. + +To Install LIBERO, after following LeRobot official instructions, just do: +`pip install -e ".[libero]"` + +### Single-suite evaluation + +Evaluate a policy on one LIBERO suite: + +```bash +lerobot-eval \ + --policy.path="your-policy-id" \ + --env.type=libero \ + --env.task=libero_object \ + --eval.batch_size=2 \ + --eval.n_episodes=3 +``` + +- `--env.task` picks the suite (`libero_object`, `libero_spatial`, etc.). +- `--eval.batch_size` controls how many environments run in parallel. +- `--eval.n_episodes` sets how many episodes to run in total. + +--- + +### Multi-suite evaluation + +Benchmark a policy across multiple suites at once: + +```bash +lerobot-eval \ + --policy.path="your-policy-id" \ + --env.type=libero \ + --env.task=libero_object,libero_spatial \ + --eval.batch_size=1 \ + --eval.n_episodes=2 +``` + +- Pass a comma-separated list to `--env.task` for multi-suite evaluation. + +### Policy inputs and outputs + +When using LIBERO through LeRobot, policies interact with the environment via **observations** and **actions**: + +- **Observations** + - `observation.state` – proprioceptive features (agent state). + - `observation.images.image` – main camera view (`agentview_image`). + - `observation.images.image2` – wrist camera view (`robot0_eye_in_hand_image`). + + ⚠️ **Note:** LeRobot enforces the `.images.*` prefix for any multi-modal visual features. Always ensure that your policy config `input_features` use the same naming keys, and that your dataset metadata keys follow this convention during evaluation. + If your data contains different keys, you must rename the observations to match what the policy expects, since naming keys are encoded inside the normalization statistics layer. + This will be fixed with the upcoming Pipeline PR. + +- **Actions** + - Continuous control values in a `Box(-1, 1, shape=(7,))` space. + +We also provide a notebook for quick testing: +Training with LIBERO + +## Training with LIBERO + +When training on LIBERO tasks, make sure your dataset parquet and metadata keys follow the LeRobot convention. + +The environment expects: + +- `observation.state` → 8-dim agent state +- `observation.images.image` → main camera (`agentview_image`) +- `observation.images.image2` → wrist camera (`robot0_eye_in_hand_image`) + +⚠️ Cleaning the dataset upfront is **cleaner and more efficient** than remapping keys inside the code. +To avoid potential mismatches and key errors, we provide a **preprocessed LIBERO dataset** that is fully compatible with the current LeRobot codebase and requires no additional manipulation: +👉 [HuggingFaceVLA/libero](https://huggingface.co/datasets/HuggingFaceVLA/libero) + +For reference, here is the **original dataset** published by Physical Intelligence: +👉 [physical-intelligence/libero](https://huggingface.co/datasets/physical-intelligence/libero) + +--- + +### Example training command + +```bash +lerobot-train \ + --policy.type=smolvla \ + --policy.repo_id=${HF_USER}/libero-test \ + --policy.load_vlm_weights=true \ + --dataset.repo_id=HuggingFaceVLA/libero \ + --env.type=libero \ + --env.task=libero_10 \ + --output_dir=./outputs/ \ + --steps=100000 \ + --batch_size=4 \ + --eval.batch_size=1 \ + --eval.n_episodes=1 \ + --eval_freq=1000 \ +``` + +--- + +### Note on rendering + +LeRobot uses MuJoCo for simulation. You need to set the rendering backend before training or evaluation: + +- `export MUJOCO_GL=egl` → for headless servers (e.g. HPC, cloud) + +## Reproducing π₀.₅ results + +We reproduce the results of π₀.₅ on the LIBERO benchmark using the LeRobot implementation. We take the Physical Intelligence LIBERO base model (`pi05_libero`) and finetune for an additional 6k steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero). + +The finetuned model can be found here: + +- **π₀.₅ LIBERO**: [lerobot/pi05_libero_finetuned](https://huggingface.co/lerobot/pi05_libero_finetuned) + +We then evaluate the finetuned model using the LeRobot LIBERO implementation, by running the following command: + +```bash +python src/lerobot/scripts/eval.py \ + --output_dir=/logs/ \ + --env.type=libero \ + --env.task=libero_spatial,libero_object,libero_goal,libero_10 \ + --eval.batch_size=1 \ + --eval.n_episodes=10 \ + --policy.path=pi05_libero_finetuned \ + --policy.n_action_steps=10 \ + --output_dir=./eval_logs/ \ + --env.max_parallel_tasks=1 +``` + +**Note:** We set `n_action_steps=10`, similar to the original OpenPI implementation. + +### Results + +We obtain the following results on the LIBERO benchmark: + +| Model | LIBERO Spatial | LIBERO Object | LIBERO Goal | LIBERO 10 | Average | +| -------- | -------------- | ------------- | ----------- | --------- | -------- | +| **π₀.₅** | 97.0 | 99.0 | 98.0 | 96.0 | **97.5** | + +These results are consistent with the original [results](https://github.com/Physical-Intelligence/openpi/tree/main/examples/libero#results) reported by Physical Intelligence: + +| Model | LIBERO Spatial | LIBERO Object | LIBERO Goal | LIBERO 10 | Average | +| -------- | -------------- | ------------- | ----------- | --------- | --------- | +| **π₀.₅** | 98.8 | 98.2 | 98.0 | 92.4 | **96.85** | diff --git a/docs/source/phone_teleop.mdx b/docs/source/phone_teleop.mdx new file mode 100644 index 00000000000..76e3c367c38 --- /dev/null +++ b/docs/source/phone_teleop.mdx @@ -0,0 +1,191 @@ +# Phone + +Use your phone (iOS or Android) to control your robot. + +**In this guide you'll learn:** + +- How to connect an iOS/Android phone +- How phone pose is mapped to robot end‑effector (EE) targets +- How to tweak safety limits, gripper control, and IK settings + +To use phone to control your robot, install the relevant dependencies with: + +```bash +pip install lerobot[phone] +``` + +## Get started + +### Supported platforms + +- iOS: Uses the HEBI Mobile I/O app (ARKit pose + buttons). Download the app first, open it and the examples will discover it on your network and stream the phone pose and inputs. +- Android: Uses the `teleop` package (WebXR). When you start the Python process, it prints a local URL. Open the link on your phone, tap Start, then use Move to stream pose. + +Links: + +- Android WebXR library: [`teleop` on PyPI](https://pypi.org/project/teleop/) +- iOS app: [HEBI Mobile I/O](https://docs.hebi.us/tools.html#mobile-io) + +### Phone orientation and controls + +- Orientation: hold the phone with the screen facing up and the top edge pointing in the same direction as the robot gripper. This ensures calibration aligns the phone’s frame with the robot frame so motion feels natural, see the image below for reference. +- Enable/disable: + - iOS: Hold `B1` to enable teleoperation, release to stop. The first press captures a reference pose. + - Android: Press and hold the `Move` button, release to stop. The first press captures a reference pose. +- Gripper control: + - iOS: Analog input `A3` controls the gripper as velocity input. + - Android: Buttons `A` and `B` act like increment/decrement (A opens, B closes). You can tune velocity in the `GripperVelocityToJoint` step. + +Phone teleop orientation + +### Step 1: Choose the platform + +Modify the examples to use `PhoneOS.IOS` or `PhoneOS.ANDROID` in `PhoneConfig`. The API is identical across platforms, only the input source differs. All examples are under `examples/` and have `phone_so100_*.py` variants. + +Teleoperation example: + +```36:43:examples/phone_so100_teleop.py +from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS + +teleop_config = PhoneConfig(phone_os=PhoneOS.IOS) # or PhoneOS.ANDROID +teleop_device = Phone(teleop_config) +``` + +### Step 2: Connect and calibrate + +When `Phone(teleop_config)` is created and `connect()` is called, calibration is prompted automatically. Hold the phone in the orientation described above, then: + +- iOS: press and hold `B1` to capture the reference pose. +- Android: press `Move` button on the WebXR page to capture the reference pose. + +Why calibrate? We capture the current pose so subsequent poses are expressed in a robot aligned frame. When you again press the button to enable control, the position is recaptured to avoid drift when your phone is repositioned while it was disabled. + +### Step 3: Run an example + +Run on of the examples scripts to teleoperate, record a dataset, replay a dataset or evaluate a policy. + +All scripts assume you configured your robot (e.g., SO-100 follower) and set the correct serial port. + +Additionally you need to **copy the urdf of the robot to the examples folder**. For the examples in this tutorial (Using SO100/SO101) it is highly recommended to use the urdf in the [SO-ARM100 repo](https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf) + +- Run this example to teleoperate: + + ```bash + python examples/phone_to_so100/teleoperate.py + ``` + +After running the example: + +- Android: after starting the script, open the printed local URL on your phone, tap Start, then press and hold Move. +- iOS: open HEBI Mobile I/O first; B1 enables motion. A3 controls the gripper. + +Additionally you can customize mapping or safety limits by editing the processor steps shown in the examples. You can also remap inputs (e.g., use a different analog input) or adapt the pipeline to other robots (e.g., LeKiwi) by modifying the input and kinematics steps. More about this in the [Processors for Robots and Teleoperators](./processors_robots_teleop) guide. + +- Run this example to record a dataset, which saves absolute end effector observations and actions: + + ```bash + python examples/phone_to_so100/record.py + ``` + +- Run this example to replay recorded episodes: + + ```bash + python examples/phone_to_so100/replay.py + ``` + +- Run this example to evaluate a pretrained policy: + + ```bash + python examples/phone_to_so100/evaluate.py + ``` + +### Important pipeline steps and options + +- Kinematics are used in multiple steps. We use [Placo](https://github.com/Rhoban/placo) which is a wrapper around Pinocchio for handling our kinematics. We construct the kinematics object by passing the robot's URDF and target frame. We set `target_frame_name` to the gripper frame. + + ```examples/phone_to_so100/teleoperate.py + kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(robot.bus.motors.keys()), + ) + + ``` + +- The `MapPhoneActionToRobotAction` step converts the calibrated phone pose and inputs into target deltas and gripper commands, below is shown what the step outputs. + + ```src/lerobot/teleoperators/phone/phone_processor.py + action["enabled"] = enabled + action["target_x"] = -pos[1] if enabled else 0.0 + action["target_y"] = pos[0] if enabled else 0.0 + action["target_z"] = pos[2] if enabled else 0.0 + action["target_wx"] = rotvec[1] if enabled else 0.0 + action["target_wy"] = rotvec[0] if enabled else 0.0 + action["target_wz"] = -rotvec[2] if enabled else 0.0 + action["gripper_vel"] = gripper_vel # Still send gripper action when disabled + ``` + +- The `EEReferenceAndDelta` step converts target deltas to an absolute desired EE pose, storing a reference on enable, the `end_effector_step_sizes` are the step sizes for the EE pose and can be modified to change the motion speed. + + ```examples/phone_to_so100/teleoperate.py + EEReferenceAndDelta( + kinematics=kinematics_solver, + end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5}, + motor_names=list(robot.bus.motors.keys()), + use_latched_reference=True, + ), + ``` + +- The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` are the step limits for the EE pose and can be modified to change the safety limits. + + ```examples/phone_to_so100/teleoperate.py + EEBoundsAndSafety( + end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, + max_ee_step_m=0.10, + ) + ``` + +- The `GripperVelocityToJoint` step turns a velocity‑like gripper input into absolute gripper position using the current measured state. The `speed_factor` is the factor by which the velocity is multiplied. + + ```examples/phone_to_so100/teleoperate.py + GripperVelocityToJoint(speed_factor=20.0) + ``` + +#### Different IK initial guesses + +We use different IK initial guesses in the kinematic steps. As initial guess either the current measured joints or the previous IK solution is used. + +- Closed loop (used in record/eval): sets `initial_guess_current_joints=True` so IK starts from the measured joints each frame. + + ```examples/phone_to_so100/record.py + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=True, # closed loop + ) + ``` + +- Open loop (used in replay): sets `initial_guess_current_joints=False` so IK continues from the previous IK solution rather than the measured state. This preserves action stability when we replay without feedback. + + ```examples/phone_to_so100/replay.py + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=False, # open loop + ) + ``` + +### Pipeline steps explained + +- MapPhoneActionToRobotAction: converts calibrated phone pose and inputs into target deltas and a gripper command. Motion is gated by an enable signal (B1 on iOS, Move on Android). +- EEReferenceAndDelta: latches a reference EE pose on enable and combines it with target deltas to produce an absolute desired EE pose each frame. When disabled, it keeps sending the last commanded pose. +- EEBoundsAndSafety: clamps the EE pose to a workspace and rate‑limits jumps for safety. Also declares `action.ee.*` features. +- InverseKinematicsEEToJoints: turns an EE pose into joint positions with IK. `initial_guess_current_joints=True` is recommended for closed‑loop control; set `False` for open‑loop replay for stability. +- GripperVelocityToJoint: integrates a velocity‑like gripper input into an absolute gripper position using the current measured state. +- ForwardKinematicsJointsToEE: computes `observation.state.ee.*` from observed joints for logging and training on EE state. + +### Troubleshooting + +- iOS not discovered: ensure HEBI Mobile I/O is open and your laptop/phone are on the same network. +- Android URL not reachable: check local you used `https` instead of `http`, use the exact IP printed by the script and allow your browser to enter and ignore the certificate issue. +- Motion feels inverted: adjust the sign flips in `MapPhoneActionToRobotAction` or swap axes to match your setup. diff --git a/docs/source/pi0.mdx b/docs/source/pi0.mdx new file mode 100644 index 00000000000..d36fe0ce4c5 --- /dev/null +++ b/docs/source/pi0.mdx @@ -0,0 +1,79 @@ +# π₀ (Pi0) + +π₀ is a **Vision-Language-Action model for general robot control**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository. + +## Model Overview + +π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi0). Unlike traditional robot programs that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks. + +### The Vision for Physical Intelligence + +As described by Physical Intelligence, while AI has achieved remarkable success in digital domains, from chess-playing to drug discovery, human intelligence still dramatically outpaces AI in the physical world. To paraphrase Moravec's paradox, winning a game of chess represents an "easy" problem for AI, but folding a shirt or cleaning up a table requires solving some of the most difficult engineering problems ever conceived. π₀ represents a first step toward developing artificial physical intelligence that enables users to simply ask robots to perform any task they want, just like they can with large language models. + +### Architecture and Approach + +π₀ combines several key innovations: + +- **Flow Matching**: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models) +- **Cross-Embodiment Training**: Trained on data from 8 distinct robot platforms including UR5e, Bimanual UR5e, Franka, Bimanual Trossen, Bimanual ARX, Mobile Trossen, and Mobile Fibocom +- **Internet-Scale Pre-training**: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model +- **High-Frequency Control**: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation + +## Installation Requirements + +1. Install LeRobot by following our [Installation Guide](./installation). +2. Install Pi0 dependencies by running: + + ```bash + pip install -e ".[pi]" + ``` + +## Training Data and Capabilities + +π₀ is trained on the largest robot interaction dataset to date, combining three key data sources: + +1. **Internet-Scale Pre-training**: Vision-language data from the web for semantic understanding +2. **Open X-Embodiment Dataset**: Open-source robot manipulation datasets +3. **Physical Intelligence Dataset**: Large and diverse dataset of dexterous tasks across 8 distinct robots + +## Usage + +To use π₀ in LeRobot, specify the policy type as: + +```python +policy.type=pi0 +``` + +## Training + +For training π₀, you can use the standard LeRobot training script with the appropriate configuration: + +```bash +python src/lerobot/scripts/lerobot_train.py \ + --dataset.repo_id=your_dataset \ + --policy.type=pi0 \ + --output_dir=./outputs/pi0_training \ + --job_name=pi0_training \ + --policy.pretrained_path=lerobot/pi0_base \ + --policy.repo_id=your_repo_id \ + --policy.compile_model=true \ + --policy.gradient_checkpointing=true \ + --policy.dtype=bfloat16 \ + --steps=3000 \ + --policy.device=cuda \ + --batch_size=32 +``` + +### Key Training Parameters + +- **`--policy.compile_model=true`**: Enables model compilation for faster training +- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training +- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency +- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory +- **`--policy.pretrained_path=lerobot/pi0_base`**: The base π₀ model you want to finetune, options are: + - [lerobot/pi0_base](https://huggingface.co/lerobot/pi0_base) + - [lerobot/pi0_libero](https://huggingface.co/lerobot/pi0_libero) (specifically trained on the Libero dataset) + +## License + +This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi). diff --git a/docs/source/pi05.mdx b/docs/source/pi05.mdx new file mode 100644 index 00000000000..b6267fc5e30 --- /dev/null +++ b/docs/source/pi05.mdx @@ -0,0 +1,107 @@ +# π₀.₅ (Pi05) Policy + +π₀.₅ is a **Vision-Language-Action model with open-world generalization**, from Physical Intelligence. The LeRobot implementation is adapted from their open source [OpenPI](https://github.com/Physical-Intelligence/openpi) repository. + +## Model Overview + +π₀.₅ represents a significant evolution from π₀, developed by [Physical Intelligence](https://www.physicalintelligence.company/blog/pi05) to address a big challenge in robotics: **open-world generalization**. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training. + +### The Generalization Challenge + +As Physical Intelligence explains, the fundamental challenge isn't performing tasks of agility or dexterity, but generalization, the ability to correctly perform tasks in new settings with new objects. Consider a robot cleaning different homes: each home has different objects in different places. Generalization must occur at multiple levels: + +- **Physical Level**: Understanding how to pick up a spoon (by the handle) or plate (by the edge), even with unseen objects in cluttered environments +- **Semantic Level**: Understanding task semantics, where to put clothes and shoes (laundry hamper, not on the bed), and what tools are appropriate for cleaning spills +- **Environmental Level**: Adapting to "messy" real-world environments like homes, grocery stores, offices, and hospitals + +### Co-Training on Heterogeneous Data + +The breakthrough innovation in π₀.₅ is **co-training on heterogeneous data sources**. The model learns from: + +1. **Multimodal Web Data**: Image captioning, visual question answering, object detection +2. **Verbal Instructions**: Humans coaching robots through complex tasks step-by-step +3. **Subtask Commands**: High-level semantic behavior labels (e.g., "pick up the pillow" for an unmade bed) +4. **Cross-Embodiment Robot Data**: Data from various robot platforms with different capabilities +5. **Multi-Environment Data**: Static robots deployed across many different homes +6. **Mobile Manipulation Data**: ~400 hours of mobile robot demonstrations + +This diverse training mixture creates a "curriculum" that enables generalization across physical, visual, and semantic levels simultaneously. + +## Installation Requirements + +1. Install LeRobot by following our [Installation Guide](./installation). +2. Install Pi0.5 dependencies by running: + + ```bash + pip install -e ".[pi]" + ``` + +## Usage + +To use π₀.₅ in your LeRobot configuration, specify the policy type as: + +```python +policy.type=pi05 +``` + +## Training + +### Training Command Example + +Here's a complete training command for finetuning the base π₀.₅ model on your own dataset: + +```bash +python src/lerobot/scripts/lerobot_train.py\ + --dataset.repo_id=your_dataset \ + --policy.type=pi05 \ + --output_dir=./outputs/pi05_training \ + --job_name=pi05_training \ + --policy.repo_id=your_repo_id \ + --policy.pretrained_path=lerobot/pi05_base \ + --policy.compile_model=true \ + --policy.gradient_checkpointing=true \ + --wandb.enable=true \ + --policy.dtype=bfloat16 \ + --steps=3000 \ + --policy.device=cuda \ + --batch_size=32 +``` + +### Key Training Parameters + +- **`--policy.compile_model=true`**: Enables model compilation for faster training +- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training +- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency +- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory +- **`--policy.pretrained_path=lerobot/pi05_base`**: The base π₀.₅ model you want to finetune, options are: + - [lerobot/pi05_base](https://huggingface.co/lerobot/pi05_base) + - [lerobot/pi05_libero](https://huggingface.co/lerobot/pi05_libero) (specifically trained on the Libero dataset) + +If your dataset is not converted with `quantiles`, you can convert it with the following command: + +```bash +python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \ + --repo-id=your_dataset \ +``` + +Or train pi05 with this normalization mapping: `--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'` + +## Performance Results + +### Libero Benchmark Results + +π₀.₅ has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the libero base model for an additional 6k steps on the Libero dataset and compared the results to the OpenPI reference results. + +| Benchmark | LeRobot Implementation | OpenPI Reference | +| ------------------ | ---------------------- | ---------------- | +| **Libero Spatial** | 97.0% | 98.8% | +| **Libero Object** | 99.0% | 98.2% | +| **Libero Goal** | 98.0% | 98.0% | +| **Libero 10** | 96.0% | 92.4% | +| **Average** | 97.5% | 96.85% | + +These results demonstrate π₀.₅'s strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section. + +## License + +This model follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi). diff --git a/docs/source/processors_robots_teleop.mdx b/docs/source/processors_robots_teleop.mdx new file mode 100644 index 00000000000..3d8dcb409f1 --- /dev/null +++ b/docs/source/processors_robots_teleop.mdx @@ -0,0 +1,151 @@ +# Processors for Robots and Teleoperators + +This guide shows how to build and modify processing pipelines that connect teleoperators (e.g., phone) to robots and datasets. Pipelines standardize conversions between different action/observation spaces so you can swap teleops and robots without rewriting glue code. + +We use the Phone to SO‑100 follower examples for concreteness, but the same patterns apply to other robots. + +**What you'll learn** + +- Absolute vs. relative EE control: What each means, trade‑offs, and how to choose for your task. +- Three-pipeline pattern: How to map teleop actions → dataset actions → robot commands, and robot observations → dataset observations. +- Adapters (`to_transition` / `to_output`): How these convert raw dicts to `EnvTransition` and back to reduce boilerplate. +- Dataset feature contracts: How steps declare features via `transform_features(...)`, and how to aggregate/merge them for recording. +- Choosing a representation: When to store joints, absolute EE poses, or relative EE deltas—and how that affects training. +- Pipeline customization guidance: How to swap robots/URDFs safely and tune bounds, step sizes, and options like IK initialization. + +### Absolute vs relative EE control + +The examples in this guide use absolute end effector (EE) poses because they are easy to reason about. In practice, relative EE deltas or joint position are often preferred as learning features. + +With processors, you choose the learning features you want to use for your policy. This could be joints positions/velocities, absolute EE, or relative EE positions. You can also choose to store other features, such as joint torques, motor currents, etc. + +## Three pipelines + +We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match. +Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline. + +1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets) +2. Pipeline 2: Dataset action space → robot command space (EE targets → joints) +3. Pipeline 3: Robot observation space → dataset observation space (joints → EE pose) + +Below is an example of the three pipelines that we use in the phone to SO-100 follower examples: + +```69:90:examples/phone_so100_record.py +phone_to_robot_ee_pose_processor = RobotProcessorPipeline[RobotAction, RobotAction]( # teleop -> dataset action + steps=[ + MapPhoneActionToRobotAction(platform=teleop_config.phone_os), + EEReferenceAndDelta( + kinematics=kinematics_solver, end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5}, motor_names=list(robot.bus.motors.keys()), + ), + EEBoundsAndSafety( + end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, max_ee_step_m=0.20, + ), + GripperVelocityToJoint(), + ], + to_transition=robot_action_to_transition, + to_output=transition_to_robot_action, +) + +robot_ee_to_joints_processor = RobotProcessorPipeline[RobotAction, RobotAction]( # dataset action -> robot + steps=[ + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()), initial_guess_current_joints=True, + ), + ], + to_transition=robot_action_to_transition, + to_output=transition_to_robot_action, +) + +robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation]( # robot obs -> dataset obs + steps=[ + ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())) + ], + to_transition=observation_to_transition, + to_output=transition_to_observation, +) +``` + +## Why to_transition / to_output + +To convert from robot/teleoperator to pipeline and back, we use the `to_transition` and `to_output` pipeline adapters. +They standardize conversions to reduce boilerplate code, and form the bridge between the robot and teleoperators raw dictionaries and the pipeline’s `EnvTransition` format. +In the phone to SO-100 follower examples we use the following adapters: + +- `robot_action_to_transition`: transforms the teleop action dict to a pipeline transition. +- `transition_to_robot_action`: transforms the pipeline transition to a robot action dict. +- `observation_to_transition`: transforms the robot observation dict to a pipeline transition. +- `transition_to_observation`: transforms the pipeline transition to a observation dict. + +Checkout [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details. + +## Dataset feature contracts + +Dataset features are determined by the keys saved in the dataset. Each step can declare what features it modifies in a contract called `transform_features(...)`. Once you build a processor, the processor can then aggregate all of these features with `aggregate_pipeline_dataset_features()` and merge multiple feature dicts with `combine_feature_dicts(...)`. + +Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples: + +```src/lerobot/robots/so100_follower/robot_kinematic_processor.py + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We only use the ee pose in the dataset, so we don't need the joint positions + for n in self.motor_names: + features[PipelineFeatureType.ACTION].pop(f"{n}.pos", None) + # We specify the dataset features of this step that we want to be stored in the dataset + for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]: + features[PipelineFeatureType.ACTION][f"ee.{k}"] = PolicyFeature( + type=FeatureType.STATE, shape=(1,) + ) + return features +``` + +Here we declare what PolicyFeatures we modify in this step, so we know what features we can expect when we run the processor. These features can then be aggregated and used to create the dataset features. + +Below is an example of how we aggregate and merge features in the phone to SO-100 record example: + +```121:145:examples/phone_so100_record.py +features=combine_feature_dicts( + # Run the feature contract of the pipelines + # This tells you how the features would look like after the pipeline steps + aggregate_pipeline_dataset_features( + pipeline=phone_to_robot_ee_pose_processor, + initial_features=create_initial_features(action=phone.action_features), # <- Action features we can expect, these come from our teleop device (phone) and action processor + use_videos=True, + ), + aggregate_pipeline_dataset_features( + pipeline=robot_joints_to_ee_pose, + initial_features=create_initial_features(observation=robot.observation_features), # <- Observation features we can expect, these come from our robot and observation processor + use_videos=True, + patterns=["observation.state.ee"], # <- Here you could optionally filter the features we want to store in the dataset, with a specific pattern + + ), + ), +``` + +How it works: + +- `aggregate_pipeline_dataset_features(...)`: applies `transform_features` across the pipeline and filters by patterns (images included when `use_videos=True`, and state features included when `patterns` is specified). +- `combine_feature_dicts(...)`: combine multiple feature dicts. +- Recording with `record_loop(...)` uses `build_dataset_frame(...)` to build frames consistent with `dataset.features` before we call `add_frame(...)` to add the frame to the dataset. + +## Guidance when customizing robot pipelines + +You can store any of the following features as your action/observation space: + +- Joint positions +- Absolute EE poses +- Relative EE deltas +- Other features: joint velocity, torques, etc. + +Pick what you want to use for your policy action and observation space and configure/modify the pipelines and steps accordingly. + +### Different robots + +- You can easily reuse pipelines, for example to use another robot with phone teleop, modify the examples and swap the robot `RobotKinematics` (URDF) and `motor_names` to use your own robot with Phone teleop. Additionally you should ensure `target_frame_name` points to your gripper/wrist. + +### Safety first + +- When changing pipelines, start with tight bounds, implement safety steps when working with real robots. +- Its advised to start with simulation first and then move to real robots. + +Thats it! We hope this guide helps you get started with customizing your robot pipelines, If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support. diff --git a/docs/source/smolvla.mdx b/docs/source/smolvla.mdx index 89c475a90cb..a56298b5ecb 100644 --- a/docs/source/smolvla.mdx +++ b/docs/source/smolvla.mdx @@ -1,4 +1,4 @@ -# Finetune SmolVLA +# SmolVLA SmolVLA is Hugging Face’s lightweight foundation model for robotics. Designed for easy fine-tuning on LeRobot datasets, it helps accelerate your development! @@ -29,7 +29,7 @@ SmolVLA is Hugging Face’s lightweight foundation model for robotics. Designed ## Collect a dataset SmolVLA is a base model, so fine-tuning on your own data is required for optimal performance in your setup. -We recommend recording ~50 episodes of your task as a starting point. Follow our guide to get started: [Recording a Dataset](https://huggingface.co/docs/lerobot/getting_started_real_world_robot#record-a-dataset) +We recommend recording ~50 episodes of your task as a starting point. Follow our guide to get started: [Recording a Dataset](./il_robots) @@ -93,7 +93,7 @@ lerobot-train --help ## Evaluate the finetuned model and run it in real-time -Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./getting_started_real_world_robot#record-a-dataset). +Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./il_robots). Once you are logged in, you can run inference in your setup by doing: ```bash diff --git a/docs/source/so100.mdx b/docs/source/so100.mdx index 8578e1e8d18..3c73ae801f3 100644 --- a/docs/source/so100.mdx +++ b/docs/source/so100.mdx @@ -634,7 +634,7 @@ leader.disconnect() -Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./il_robots) > [!TIP] > If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/docs/source/so101.mdx b/docs/source/so101.mdx index b9fb9cab4fc..00ec3eb7455 100644 --- a/docs/source/so101.mdx +++ b/docs/source/so101.mdx @@ -430,7 +430,7 @@ leader.disconnect() -Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./getting_started_real_world_robot) +Congrats 🎉, your robot is all set to learn a task on its own. Start training it by following this tutorial: [Getting started with real-world robots](./il_robots) > [!TIP] > If you have any questions or need help, please reach out on [Discord](https://discord.com/invite/s3KuuzsPFb). diff --git a/examples/2_evaluate_pretrained_policy.py b/examples/2_evaluate_pretrained_policy.py deleted file mode 100644 index c0c7845e80c..00000000000 --- a/examples/2_evaluate_pretrained_policy.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -This script demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local -training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first. - -It requires the installation of the 'gym_pusht' simulation environment. Install it by running: -```bash -pip install -e ".[pusht]" -``` -""" - -from pathlib import Path - -import gym_pusht # noqa: F401 -import gymnasium as gym -import imageio -import numpy -import torch - -from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy - -# Create a directory to store the video of the evaluation -output_directory = Path("outputs/eval/example_pusht_diffusion") -output_directory.mkdir(parents=True, exist_ok=True) - -# Select your device -device = "cuda" - -# Provide the [hugging face repo id](https://huggingface.co/lerobot/diffusion_pusht): -pretrained_policy_path = "lerobot/diffusion_pusht" -# OR a path to a local outputs/train folder. -# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion") - -policy = DiffusionPolicy.from_pretrained(pretrained_policy_path) - -# Initialize evaluation environment to render two observation types: -# an image of the scene and state/position of the agent. The environment -# also automatically stops running after 300 interactions/steps. -env = gym.make( - "gym_pusht/PushT-v0", - obs_type="pixels_agent_pos", - max_episode_steps=300, -) - -# We can verify that the shapes of the features expected by the policy match the ones from the observations -# produced by the environment -print(policy.config.input_features) -print(env.observation_space) - -# Similarly, we can check that the actions produced by the policy will match the actions expected by the -# environment -print(policy.config.output_features) -print(env.action_space) - -# Reset the policy and environments to prepare for rollout -policy.reset() -numpy_observation, info = env.reset(seed=42) - -# Prepare to collect every rewards and all the frames of the episode, -# from initial state to final state. -rewards = [] -frames = [] - -# Render frame of the initial state -frames.append(env.render()) - -step = 0 -done = False -while not done: - # Prepare observation for the policy running in Pytorch - state = torch.from_numpy(numpy_observation["agent_pos"]) - image = torch.from_numpy(numpy_observation["pixels"]) - - # Convert to float32 with image from channel first in [0,255] - # to channel last in [0,1] - state = state.to(torch.float32) - image = image.to(torch.float32) / 255 - image = image.permute(2, 0, 1) - - # Send data tensors from CPU to GPU - state = state.to(device, non_blocking=True) - image = image.to(device, non_blocking=True) - - # Add extra (empty) batch dimension, required to forward the policy - state = state.unsqueeze(0) - image = image.unsqueeze(0) - - # Create the policy input dictionary - observation = { - "observation.state": state, - "observation.image": image, - } - - # Predict the next action with respect to the current observation - with torch.inference_mode(): - action = policy.select_action(observation) - - # Prepare the action for the environment - numpy_action = action.squeeze(0).to("cpu").numpy() - - # Step through the environment and receive a new observation - numpy_observation, reward, terminated, truncated, info = env.step(numpy_action) - print(f"{step=} {reward=} {terminated=}") - - # Keep track of all the rewards and frames - rewards.append(reward) - frames.append(env.render()) - - # The rollout is considered done when the success state is reached (i.e. terminated is True), - # or the maximum number of iterations is reached (i.e. truncated is True) - done = terminated | truncated | done - step += 1 - -if terminated: - print("Success!") -else: - print("Failure!") - -# Get the speed of environment (i.e. its number of frames per second). -fps = env.metadata["render_fps"] - -# Encode all frames into a mp4 video. -video_path = output_directory / "rollout.mp4" -imageio.mimsave(str(video_path), numpy.stack(frames), fps=fps) - -print(f"Video of the evaluation is available in '{video_path}'.") diff --git a/examples/4_train_policy_with_script.md b/examples/4_train_policy_with_script.md deleted file mode 100644 index ffa7de66e23..00000000000 --- a/examples/4_train_policy_with_script.md +++ /dev/null @@ -1,311 +0,0 @@ -This tutorial will explain the training script, how to use it, and particularly how to configure everything needed for the training run. - -> **Note:** The following assumes you're running these commands on a machine equipped with a cuda GPU. If you don't have one (or if you're using a Mac), you can add `--policy.device=cpu` (`--policy.device=mps` respectively). However, be advised that the code executes much slower on cpu. - -## The training script - -LeRobot offers a training script at [`lerobot/scripts/train.py`](../src/lerobot/scripts/train.py). At a high level it does the following: - -- Initialize/load a configuration for the following steps using. -- Instantiates a dataset. -- (Optional) Instantiates a simulation environment corresponding to that dataset. -- Instantiates a policy. -- Runs a standard training loop with forward pass, backward pass, optimization step, and occasional logging, evaluation (of the policy on the environment), and checkpointing. - -## Overview of the configuration system - -In the training script, the main function `train` expects a `TrainPipelineConfig` object: - - -```python -# train.py -@parser.wrap() -def train(cfg: TrainPipelineConfig): -``` - - -You can inspect the `TrainPipelineConfig` defined in [`lerobot/configs/train.py`](../src/lerobot/configs/train.py) (which is heavily commented and meant to be a reference to understand any option) - -When running the script, inputs for the command line are parsed thanks to the `@parser.wrap()` decorator and an instance of this class is automatically generated. Under the hood, this is done with [Draccus](https://github.com/dlwh/draccus) which is a tool dedicated to this purpose. If you're familiar with Hydra, Draccus can similarly load configurations from config files (.json, .yaml) and also override their values through command line inputs. Unlike Hydra, these configurations are pre-defined in the code through dataclasses rather than being defined entirely in config files. This allows for more rigorous serialization/deserialization, typing, and to manipulate configuration as objects directly in the code and not as dictionaries or namespaces (which enables nice features in an IDE such as autocomplete, jump-to-def, etc.) - -Let's have a look at a simplified example. Amongst other attributes, the training config has the following attributes: - - -```python -@dataclass -class TrainPipelineConfig: - dataset: DatasetConfig - env: envs.EnvConfig | None = None - policy: PreTrainedConfig | None = None -``` - - -in which `DatasetConfig` for example is defined as such: - - -```python -@dataclass -class DatasetConfig: - repo_id: str - episodes: list[int] | None = None - video_backend: str = "pyav" -``` - - -This creates a hierarchical relationship where, for example assuming we have a `cfg` instance of `TrainPipelineConfig`, we can access the `repo_id` value with `cfg.dataset.repo_id`. -From the command line, we can specify this value by using a very similar syntax `--dataset.repo_id=repo/id`. - -By default, every field takes its default value specified in the dataclass. If a field doesn't have a default value, it needs to be specified either from the command line or from a config file – which path is also given in the command line (more in this below). In the example above, the `dataset` field doesn't have a default value which means it must be specified. - -## Specifying values from the CLI - -Let's say that we want to train [Diffusion Policy](../src/lerobot/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this: - -```bash -lerobot-train \ - --dataset.repo_id=lerobot/pusht \ - --policy.type=diffusion \ - --env.type=pusht -``` - -Let's break this down: - -- To specify the dataset, we just need to specify its `repo_id` on the hub which is the only required argument in the `DatasetConfig`. The rest of the fields have default values and in this case we are fine with those so we can just add the option `--dataset.repo_id=lerobot/pusht`. -- To specify the policy, we can just select diffusion policy using `--policy` appended with `.type`. Here, `.type` is a special argument which allows us to select config classes inheriting from `draccus.ChoiceRegistry` and that have been decorated with the `register_subclass()` method. To have a better explanation of this feature, have a look at this [Draccus demo](https://github.com/dlwh/draccus?tab=readme-ov-file#more-flexible-configuration-with-choice-types). In our code, we use this mechanism mainly to select policies, environments, robots, and some other components like optimizers. The policies available to select are located in [lerobot/policies](../src/lerobot/policies) -- Similarly, we select the environment with `--env.type=pusht`. The different environment configs are available in [`lerobot/envs/configs.py`](../src/lerobot/envs/configs.py) - -Let's see another example. Let's say you've been training [ACT](../src/lerobot/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with: - -```bash -lerobot-train \ - --policy.type=act \ - --dataset.repo_id=lerobot/aloha_sim_insertion_human \ - --env.type=aloha \ - --output_dir=outputs/train/act_aloha_insertion -``` - -> Notice we added `--output_dir` to explicitly tell where to write outputs from this run (checkpoints, training state, configs etc.). This is not mandatory and if you don't specify it, a default directory will be created from the current date and time, env.type and policy.type. This will typically look like `outputs/train/2025-01-24/16-10-05_aloha_act`. - -We now want to train a different policy for aloha on another task. We'll change the dataset and use [lerobot/aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human) instead. Of course, we also need to change the task of the environment as well to match this other task. -Looking at the [`AlohaEnv`](../src/lerobot/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using: - -```bash -lerobot-train \ - --policy.type=act \ - --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \ - --env.type=aloha \ - --env.task=AlohaTransferCube-v0 \ - --output_dir=outputs/train/act_aloha_transfer -``` - -## Loading from a config file - -Now, let's assume that we want to reproduce the run just above. That run has produced a `train_config.json` file in its checkpoints, which serializes the `TrainPipelineConfig` instance it used: - -```json -{ - "dataset": { - "repo_id": "lerobot/aloha_sim_transfer_cube_human", - "episodes": null, - ... - }, - "env": { - "type": "aloha", - "task": "AlohaTransferCube-v0", - "fps": 50, - ... - }, - "policy": { - "type": "act", - "n_obs_steps": 1, - ... - }, - ... -} -``` - -We can then simply load the config values from this file using: - -```bash -lerobot-train \ - --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \ - --output_dir=outputs/train/act_aloha_transfer_2 -``` - -`--config_path` is also a special argument which allows to initialize the config from a local config file. It can point to a directory that contains `train_config.json` or to the config file itself directly. - -Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.: - -```bash -lerobot-train \ - --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \ - --output_dir=outputs/train/act_aloha_transfer_2 - --policy.n_action_steps=80 -``` - -> Note: While `--output_dir` is not required in general, in this case we need to specify it since it will otherwise take the value from the `train_config.json` (which is `outputs/train/act_aloha_transfer`). In order to prevent accidental deletion of previous run checkpoints, we raise an error if you're trying to write in an existing directory. This is not the case when resuming a run, which is what you'll learn next. - -`--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running: - -```bash -lerobot-train --config_path=lerobot/diffusion_pusht -``` - -will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht) - -## Resume training - -Being able to resume a training run is important in case it crashed or aborted for any reason. We'll demonstrate how to do that here. - -Let's reuse the command from the previous run and add a few more options: - -```bash -lerobot-train \ - --policy.type=act \ - --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \ - --env.type=aloha \ - --env.task=AlohaTransferCube-v0 \ - --log_freq=25 \ - --save_freq=100 \ - --output_dir=outputs/train/run_resumption -``` - -Here we've taken care to set up the log frequency and checkpointing frequency to low numbers so we can showcase resumption. You should be able to see some logging and have a first checkpoint within 1 minute (depending on hardware). Wait for the first checkpoint to happen, you should see a line that looks like this in your terminal: - -``` -INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100 -``` - -Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with: - -```bash -lerobot-train \ - --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \ - --resume=true -``` - -You should see from the logging that your training picks up from where it left off. - -Another reason for which you might want to resume a run is simply to extend training and add more training steps. The number of training steps is set by the option `--steps`, which is 100 000 by default. -You could double the number of steps of the previous run with: - -```bash -lerobot-train \ - --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \ - --resume=true \ - --steps=200000 -``` - -## Outputs of a run - -In the output directory, there will be a folder called `checkpoints` with the following structure: - -```bash -outputs/train/run_resumption/checkpoints -├── 000100 # checkpoint_dir for training step 100 -│ ├── pretrained_model/ -│ │ ├── config.json # policy config -│ │ ├── model.safetensors # policy weights -│ │ └── train_config.json # train config -│ └── training_state/ -│ ├── optimizer_param_groups.json # optimizer param groups -│ ├── optimizer_state.safetensors # optimizer state -│ ├── rng_state.safetensors # rng states -│ ├── scheduler_state.json # scheduler state -│ └── training_step.json # training step -├── 000200 -└── last -> 000200 # symlink to the last available checkpoint -``` - -## Fine-tuning a pre-trained policy - -In addition to the features currently in Draccus, we've added a special `.path` argument for the policy, which allows to load a policy as you would with `PreTrainedPolicy.from_pretrained()`. In that case, `path` can be a local directory that contains a checkpoint or a repo_id pointing to a pretrained policy on the hub. - -For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with: - -```bash -lerobot-train \ - --policy.path=lerobot/act_aloha_sim_transfer_cube_human \ - --dataset.repo_id=lerobot/aloha_sim_insertion_human \ - --env.type=aloha \ - --env.task=AlohaInsertion-v0 -``` - -When doing so, keep in mind that the features of the fine-tuning dataset would have to match the input/output features of the pretrained policy. - -## Typical logs and metrics - -When you start the training process, you will first see your full configuration being printed in the terminal. You can check it to make sure that you configured your run correctly. The final configuration will also be saved with the checkpoint. - -After that, you will see training log like this one: - -``` -INFO 2024-08-14 13:35:12 ts/train.py:192 step:0 smpl:64 ep:1 epch:0.00 loss:1.112 grdn:15.387 lr:2.0e-07 updt_s:1.738 data_s:4.774 -``` - -or evaluation log: - -``` -INFO 2024-08-14 13:38:45 ts/train.py:226 step:100 smpl:6K ep:52 epch:0.25 ∑rwrd:20.693 success:0.0% eval_s:120.266 -``` - -These logs will also be saved in wandb if `wandb.enable` is set to `true`. Here are the meaning of some abbreviations: - -- `smpl`: number of samples seen during training. -- `ep`: number of episodes seen during training. An episode contains multiple samples in a complete manipulation task. -- `epch`: number of time all unique samples are seen (epoch). -- `grdn`: gradient norm. -- `∑rwrd`: compute the sum of rewards in every evaluation episode and then take an average of them. -- `success`: average success rate of eval episodes. Reward and success are usually different except for the sparsing reward setting, where reward=1 only when the task is completed successfully. -- `eval_s`: time to evaluate the policy in the environment, in second. -- `updt_s`: time to update the network parameters, in second. -- `data_s`: time to load a batch of data, in second. - -Some metrics are useful for initial performance profiling. For example, if you find the current GPU utilization is low via the `nvidia-smi` command and `data_s` sometimes is too high, you may need to modify batch size or number of dataloading workers to accelerate dataloading. We also recommend [pytorch profiler](https://github.com/huggingface/lerobot?tab=readme-ov-file#improve-your-code-with-profiling) for detailed performance probing. - -## In short - -We'll summarize here the main use cases to remember from this tutorial. - -#### Train a policy from scratch – CLI - -```bash -lerobot-train \ - --policy.type=act \ # <- select 'act' policy - --env.type=pusht \ # <- select 'pusht' environment - --dataset.repo_id=lerobot/pusht # <- train on this dataset -``` - -#### Train a policy from scratch - config file + CLI - -```bash -lerobot-train \ - --config_path=path/to/pretrained_model \ # <- can also be a repo_id - --policy.n_action_steps=80 # <- you may still override values -``` - -#### Resume/continue a training run - -```bash -lerobot-train \ - --config_path=checkpoint/pretrained_model/ \ - --resume=true \ - --steps=200000 # <- you can change some training parameters -``` - -#### Fine-tuning - -```bash -lerobot-train \ - --policy.path=lerobot/act_aloha_sim_transfer_cube_human \ # <- can also be a local path to a checkpoint - --dataset.repo_id=lerobot/aloha_sim_insertion_human \ - --env.type=aloha \ - --env.task=AlohaInsertion-v0 -``` - ---- - -Now that you know the basics of how to train a policy, you might want to know how to apply this knowledge to actual robots, or how to record your own datasets and train policies on your specific task? -If that's the case, head over to the next tutorial [`7_get_started_with_real_robot.md`](./7_get_started_with_real_robot.md). - -Or in the meantime, happy training! 🤗 diff --git a/examples/backward_compatibility/replay.py b/examples/backward_compatibility/replay.py index 6c680f204fe..6bca0570f6d 100644 --- a/examples/backward_compatibility/replay.py +++ b/examples/backward_compatibility/replay.py @@ -44,6 +44,7 @@ so100_follower, so101_follower, ) +from lerobot.utils.constants import ACTION from lerobot.utils.robot_utils import busy_wait from lerobot.utils.utils import ( init_logging, @@ -78,16 +79,16 @@ def replay(cfg: ReplayConfig): robot = make_robot_from_config(cfg.robot) dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode]) - actions = dataset.hf_dataset.select_columns("action") + actions = dataset.hf_dataset.select_columns(ACTION) robot.connect() log_say("Replaying episode", cfg.play_sounds, blocking=True) for idx in range(dataset.num_frames): start_episode_t = time.perf_counter() - action_array = actions[idx]["action"] + action_array = actions[idx][ACTION] action = {} - for i, name in enumerate(dataset.features["action"]["names"]): + for i, name in enumerate(dataset.features[ACTION]["names"]): key = f"{name.removeprefix('main_')}.pos" action[key] = action_array[i].item() diff --git a/examples/1_load_lerobot_dataset.py b/examples/dataset/load_lerobot_dataset.py similarity index 99% rename from examples/1_load_lerobot_dataset.py rename to examples/dataset/load_lerobot_dataset.py index ac4a843c799..a96c170cfa0 100644 --- a/examples/1_load_lerobot_dataset.py +++ b/examples/dataset/load_lerobot_dataset.py @@ -136,7 +136,7 @@ # PyTorch datasets. dataloader = torch.utils.data.DataLoader( dataset, - num_workers=0, + num_workers=4, batch_size=32, shuffle=True, ) diff --git a/examples/dataset/use_dataset_image_transforms.py b/examples/dataset/use_dataset_image_transforms.py new file mode 100644 index 00000000000..c28f2ef0c64 --- /dev/null +++ b/examples/dataset/use_dataset_image_transforms.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This example demonstrates how to use image transforms with LeRobot datasets for data augmentation during training. + +Image transforms are applied to camera frames to improve model robustness and generalization. They are applied +at training time only, not during dataset recording, allowing you to experiment with different augmentations +without re-recording data. +""" + +import torch +from torchvision.transforms import v2 +from torchvision.transforms.functional import to_pil_image + +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.datasets.transforms import ImageTransformConfig, ImageTransforms, ImageTransformsConfig + + +def save_image(tensor, filename): + """Helper function to save a tensor as an image file.""" + if tensor.dim() == 3: # [C, H, W] + if tensor.max() > 1.0: + tensor = tensor / 255.0 + tensor = torch.clamp(tensor, 0.0, 1.0) + pil_image = to_pil_image(tensor) + pil_image.save(filename) + print(f"Saved: {filename}") + else: + print(f"Skipped {filename}: unexpected tensor shape {tensor.shape}") + + +def example_1_default_transforms(): + """Example 1: Use default transform configuration and save original vs transformed images""" + print("\n Example 1: Default Transform Configuration with Image Saving") + + repo_id = "pepijn223/record_main_0" # Example dataset + + try: + # Load dataset without transforms (original) + dataset_original = LeRobotDataset(repo_id=repo_id) + + # Load dataset with transforms enabled + transforms_config = ImageTransformsConfig( + enable=True, # Enable transforms (disabled by default) + max_num_transforms=2, # Apply up to 2 transforms per frame + random_order=False, # Apply in standard order + ) + dataset_with_transforms = LeRobotDataset( + repo_id=repo_id, image_transforms=ImageTransforms(transforms_config) + ) + + # Save original and transformed images for comparison + if len(dataset_original) > 0: + frame_idx = 0 # Use first frame + original_sample = dataset_original[frame_idx] + transformed_sample = dataset_with_transforms[frame_idx] + + print(f"Saving comparison images (frame {frame_idx}):") + + for cam_key in dataset_original.meta.camera_keys: + if cam_key in original_sample and cam_key in transformed_sample: + cam_name = cam_key.replace(".", "_").replace("/", "_") + + # Save original and transformed images + save_image(original_sample[cam_key], f"{cam_name}_original.png") + save_image(transformed_sample[cam_key], f"{cam_name}_transformed.png") + + except Exception as e: + print(f"Could not load dataset '{repo_id}': {e}") + + +def example_2_custom_transforms(): + """Example 2: Create custom transform configuration and save examples""" + print("\n Example 2: Custom Transform Configuration") + + repo_id = "pepijn223/record_main_0" # Example dataset + + try: + # Create custom transform configuration with strong effects + custom_transforms_config = ImageTransformsConfig( + enable=True, + max_num_transforms=2, # Apply up to 2 transforms per frame + random_order=True, # Apply transforms in random order + tfs={ + "brightness": ImageTransformConfig( + weight=1.0, + type="ColorJitter", + kwargs={"brightness": (0.5, 1.5)}, # Strong brightness range + ), + "contrast": ImageTransformConfig( + weight=1.0, # Higher weight = more likely to be selected + type="ColorJitter", + kwargs={"contrast": (0.6, 1.4)}, # Strong contrast + ), + "sharpness": ImageTransformConfig( + weight=0.5, # Lower weight = less likely to be selected + type="SharpnessJitter", + kwargs={"sharpness": (0.2, 2.0)}, # Strong sharpness variation + ), + }, + ) + + dataset_with_custom_transforms = LeRobotDataset( + repo_id=repo_id, image_transforms=ImageTransforms(custom_transforms_config) + ) + + # Save examples with strong transforms + if len(dataset_with_custom_transforms) > 0: + sample = dataset_with_custom_transforms[0] + print("Saving custom transform examples:") + + for cam_key in dataset_with_custom_transforms.meta.camera_keys: + if cam_key in sample: + cam_name = cam_key.replace(".", "_").replace("/", "_") + save_image(sample[cam_key], f"{cam_name}_custom_transforms.png") + + except Exception as e: + print(f"Could not load dataset '{repo_id}': {e}") + + +def example_3_torchvision_transforms(): + """Example 3: Use pure torchvision transforms and save examples""" + print("\n Example 3: Pure Torchvision Transforms") + + repo_id = "pepijn223/record_main_0" # Example dataset + + try: + # Create torchvision transform pipeline + torchvision_transforms = v2.Compose( + [ + v2.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.1), + v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)), + v2.RandomRotation(degrees=10), # Small rotation + ] + ) + + dataset_with_torchvision = LeRobotDataset(repo_id=repo_id, image_transforms=torchvision_transforms) + + # Save examples with torchvision transforms + if len(dataset_with_torchvision) > 0: + sample = dataset_with_torchvision[0] + print("Saving torchvision transform examples:") + + for cam_key in dataset_with_torchvision.meta.camera_keys: + if cam_key in sample: + cam_name = cam_key.replace(".", "_").replace("/", "_") + save_image(sample[cam_key], f"{cam_name}_torchvision.png") + + except Exception as e: + print(f"Could not load dataset '{repo_id}': {e}") + + +def main(): + """Run all examples""" + print("LeRobot Dataset Image Transforms Examples") + + example_1_default_transforms() + example_2_custom_transforms() + example_3_torchvision_transforms() + + +if __name__ == "__main__": + main() diff --git a/examples/lekiwi/evaluate.py b/examples/lekiwi/evaluate.py index 57fb62e1084..8a62d92a9c6 100644 --- a/examples/lekiwi/evaluate.py +++ b/examples/lekiwi/evaluate.py @@ -1,31 +1,54 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.datasets.utils import hw_to_dataset_features from lerobot.policies.act.modeling_act import ACTPolicy -from lerobot.record import record_loop +from lerobot.policies.factory import make_pre_post_processors +from lerobot.processor import make_default_processors from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig +from lerobot.scripts.lerobot_record import record_loop +from lerobot.utils.constants import ACTION, OBS_STR from lerobot.utils.control_utils import init_keyboard_listener from lerobot.utils.utils import log_say -from lerobot.utils.visualization_utils import _init_rerun +from lerobot.utils.visualization_utils import init_rerun NUM_EPISODES = 2 FPS = 30 EPISODE_TIME_SEC = 60 TASK_DESCRIPTION = "My task description" +HF_MODEL_ID = "/" +HF_DATASET_ID = "/" -# Create the robot and teleoperator configurations +# Create the robot configuration & robot robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi") + robot = LeKiwiClient(robot_config) -policy = ACTPolicy.from_pretrained("/") +# Create policy +policy = ACTPolicy.from_pretrained(HF_MODEL_ID) # Configure the dataset features -action_features = hw_to_dataset_features(robot.action_features, "action") -obs_features = hw_to_dataset_features(robot.observation_features, "observation") +action_features = hw_to_dataset_features(robot.action_features, ACTION) +obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR) dataset_features = {**action_features, **obs_features} # Create the dataset dataset = LeRobotDataset.create( - repo_id="/", + repo_id=HF_DATASET_ID, fps=FPS, features=dataset_features, robot_type=robot.name, @@ -33,33 +56,52 @@ image_writer_threads=4, ) +# Build Policy Processors +preprocessor, postprocessor = make_pre_post_processors( + policy_cfg=policy, + pretrained_path=HF_MODEL_ID, + dataset_stats=dataset.meta.stats, + # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility. + preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}}, +) + +# Connect the robot # To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi` robot.connect() -_init_rerun(session_name="recording") +# TODO(Steven): Update this example to use pipelines +teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors() +# Initialize the keyboard listener and rerun visualization listener, events = init_keyboard_listener() +init_rerun(session_name="lekiwi_evaluate") if not robot.is_connected: raise ValueError("Robot is not connected!") +print("Starting evaluate loop...") recorded_episodes = 0 while recorded_episodes < NUM_EPISODES and not events["stop_recording"]: log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}") - # Run the policy inference loop + # Main record loop record_loop( robot=robot, events=events, fps=FPS, policy=policy, + preprocessor=preprocessor, # Pass the pre and post policy processors + postprocessor=postprocessor, dataset=dataset, control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, ) - # Logic for reset env + # Reset the environment if not stopping or re-recording if not events["stop_recording"] and ( (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"] ): @@ -71,6 +113,9 @@ control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, ) if events["rerecord_episode"]: @@ -80,11 +125,12 @@ dataset.clear_episode_buffer() continue + # Save episode dataset.save_episode() recorded_episodes += 1 -# Upload to hub and clean up -dataset.push_to_hub() - +# Clean up +log_say("Stop recording") robot.disconnect() listener.stop() +dataset.push_to_hub() diff --git a/examples/lekiwi/record.py b/examples/lekiwi/record.py index 11a716761c0..9070741bf46 100644 --- a/examples/lekiwi/record.py +++ b/examples/lekiwi/record.py @@ -1,37 +1,60 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.datasets.utils import hw_to_dataset_features -from lerobot.record import record_loop +from lerobot.processor import make_default_processors from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient +from lerobot.scripts.lerobot_record import record_loop from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig +from lerobot.utils.constants import ACTION, OBS_STR from lerobot.utils.control_utils import init_keyboard_listener from lerobot.utils.utils import log_say -from lerobot.utils.visualization_utils import _init_rerun +from lerobot.utils.visualization_utils import init_rerun -NUM_EPISODES = 3 +NUM_EPISODES = 2 FPS = 30 EPISODE_TIME_SEC = 30 RESET_TIME_SEC = 10 TASK_DESCRIPTION = "My task description" +HF_REPO_ID = "/" # Create the robot and teleoperator configurations robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi") leader_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm") keyboard_config = KeyboardTeleopConfig() +# Initialize the robot and teleoperator robot = LeKiwiClient(robot_config) leader_arm = SO100Leader(leader_arm_config) keyboard = KeyboardTeleop(keyboard_config) +# TODO(Steven): Update this example to use pipelines +teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors() + # Configure the dataset features -action_features = hw_to_dataset_features(robot.action_features, "action") -obs_features = hw_to_dataset_features(robot.observation_features, "observation") +action_features = hw_to_dataset_features(robot.action_features, ACTION) +obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR) dataset_features = {**action_features, **obs_features} # Create the dataset dataset = LeRobotDataset.create( - repo_id="/", + repo_id=HF_REPO_ID, fps=FPS, features=dataset_features, robot_type=robot.name, @@ -39,23 +62,25 @@ image_writer_threads=4, ) +# Connect the robot and teleoperator # To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi` robot.connect() leader_arm.connect() keyboard.connect() -_init_rerun(session_name="lekiwi_record") - +# Initialize the keyboard listener and rerun visualization listener, events = init_keyboard_listener() +init_rerun(session_name="lekiwi_record") if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected: - raise ValueError("Robot, leader arm of keyboard is not connected!") + raise ValueError("Robot or teleop is not connected!") +print("Starting record loop...") recorded_episodes = 0 while recorded_episodes < NUM_EPISODES and not events["stop_recording"]: log_say(f"Recording episode {recorded_episodes}") - # Run the record loop + # Main record loop record_loop( robot=robot, events=events, @@ -65,9 +90,12 @@ control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, ) - # Logic for reset env + # Reset the environment if not stopping or re-recording if not events["stop_recording"] and ( (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"] ): @@ -80,6 +108,9 @@ control_time_s=RESET_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, ) if events["rerecord_episode"]: @@ -89,13 +120,14 @@ dataset.clear_episode_buffer() continue + # Save episode dataset.save_episode() recorded_episodes += 1 -# Upload to hub and clean up -dataset.push_to_hub() - +# Clean up +log_say("Stop recording") robot.disconnect() leader_arm.disconnect() keyboard.disconnect() listener.stop() +dataset.push_to_hub() diff --git a/examples/lekiwi/replay.py b/examples/lekiwi/replay.py index 248354df94a..3ae9152866f 100644 --- a/examples/lekiwi/replay.py +++ b/examples/lekiwi/replay.py @@ -1,32 +1,60 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import time from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient +from lerobot.utils.constants import ACTION from lerobot.utils.robot_utils import busy_wait from lerobot.utils.utils import log_say EPISODE_IDX = 0 +# Initialize the robot config robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi") + +# Initialize the robot robot = LeKiwiClient(robot_config) +# Fetch the dataset to replay dataset = LeRobotDataset("/", episodes=[EPISODE_IDX]) -actions = dataset.hf_dataset.select_columns("action") +# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0 +episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX) +actions = episode_frames.select_columns(ACTION) +# Connect to the robot robot.connect() if not robot.is_connected: raise ValueError("Robot is not connected!") +print("Starting replay loop...") log_say(f"Replaying episode {EPISODE_IDX}") -for idx in range(dataset.num_frames): +for idx in range(len(episode_frames)): t0 = time.perf_counter() + # Get recorded action from dataset action = { - name: float(actions[idx]["action"][i]) for i, name in enumerate(dataset.features["action"]["names"]) + name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"]) } - robot.send_action(action) + + # Send action to robot + _ = robot.send_action(action) busy_wait(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) diff --git a/examples/lekiwi/teleoperate.py b/examples/lekiwi/teleoperate.py index 8358a2b93e6..6b430df4835 100644 --- a/examples/lekiwi/teleoperate.py +++ b/examples/lekiwi/teleoperate.py @@ -1,10 +1,26 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import time from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop, KeyboardTeleopConfig from lerobot.teleoperators.so100_leader import SO100Leader, SO100LeaderConfig from lerobot.utils.robot_utils import busy_wait -from lerobot.utils.visualization_utils import _init_rerun, log_rerun_data +from lerobot.utils.visualization_utils import init_rerun, log_rerun_data FPS = 30 @@ -13,35 +29,44 @@ teleop_arm_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm") keyboard_config = KeyboardTeleopConfig(id="my_laptop_keyboard") +# Initialize the robot and teleoperator robot = LeKiwiClient(robot_config) leader_arm = SO100Leader(teleop_arm_config) keyboard = KeyboardTeleop(keyboard_config) +# Connect to the robot and teleoperator # To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi` robot.connect() leader_arm.connect() keyboard.connect() -_init_rerun(session_name="lekiwi_teleop") +# Init rerun viewer +init_rerun(session_name="lekiwi_teleop") if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected: - raise ValueError("Robot, leader arm of keyboard is not connected!") + raise ValueError("Robot or teleop is not connected!") +print("Starting teleop loop...") while True: t0 = time.perf_counter() + # Get robot observation observation = robot.get_observation() + # Get teleop action + # Arm arm_action = leader_arm.get_action() arm_action = {f"arm_{k}": v for k, v in arm_action.items()} - + # Keyboard keyboard_keys = keyboard.get_action() base_action = robot._from_keyboard_to_base_action(keyboard_keys) - log_rerun_data(observation, {**arm_action, **base_action}) - action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action - robot.send_action(action) + # Send action to robot + _ = robot.send_action(action) + + # Visualize + log_rerun_data(observation=observation, action=action) busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0)) diff --git a/examples/phone_to_so100/evaluate.py b/examples/phone_to_so100/evaluate.py new file mode 100644 index 00000000000..0d53f117751 --- /dev/null +++ b/examples/phone_to_so100/evaluate.py @@ -0,0 +1,197 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig +from lerobot.configs.types import FeatureType, PolicyFeature +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features +from lerobot.datasets.utils import combine_feature_dicts +from lerobot.model.kinematics import RobotKinematics +from lerobot.policies.act.modeling_act import ACTPolicy +from lerobot.policies.factory import make_pre_post_processors +from lerobot.processor import ( + RobotAction, + RobotObservation, + RobotProcessorPipeline, + make_default_teleop_action_processor, +) +from lerobot.processor.converters import ( + observation_to_transition, + robot_action_observation_to_transition, + transition_to_observation, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + ForwardKinematicsJointsToEE, + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.scripts.lerobot_record import record_loop +from lerobot.utils.control_utils import init_keyboard_listener +from lerobot.utils.utils import log_say +from lerobot.utils.visualization_utils import init_rerun + +NUM_EPISODES = 5 +FPS = 30 +EPISODE_TIME_SEC = 60 +TASK_DESCRIPTION = "My task description" +HF_MODEL_ID = "/" +HF_DATASET_ID = "/" + +# Create the robot configuration & robot +camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)} +robot_config = SO100FollowerConfig( + port="/dev/tty.usbmodem58760434471", + id="my_awesome_follower_arm", + cameras=camera_config, + use_degrees=True, +) + +robot = SO100Follower(robot_config) + +# Create policy +policy = ACTPolicy.from_pretrained(HF_MODEL_ID) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(robot.bus.motors.keys()), +) + +# Build pipeline to convert EE action to joints action +robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=True, + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Build pipeline to convert joints observation to EE observation +robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation]( + steps=[ + ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())) + ], + to_transition=observation_to_transition, + to_output=transition_to_observation, +) + +# Create the dataset +dataset = LeRobotDataset.create( + repo_id=HF_DATASET_ID, + fps=FPS, + features=combine_feature_dicts( + aggregate_pipeline_dataset_features( + pipeline=robot_joints_to_ee_pose_processor, + initial_features=create_initial_features(observation=robot.observation_features), + use_videos=True, + ), + # User for now should be explicit on the feature keys that were used for record + # Alternatively, the user can pass the processor step that has the right features + aggregate_pipeline_dataset_features( + pipeline=make_default_teleop_action_processor(), + initial_features=create_initial_features( + action={ + f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,)) + for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"] + } + ), + use_videos=True, + ), + ), + robot_type=robot.name, + use_videos=True, + image_writer_threads=4, +) + +# Build Policy Processors +preprocessor, postprocessor = make_pre_post_processors( + policy_cfg=policy, + pretrained_path=HF_MODEL_ID, + dataset_stats=dataset.meta.stats, + # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility. + preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}}, +) + +# Connect the robot +robot.connect() + +# Initialize the keyboard listener and rerun visualization +listener, events = init_keyboard_listener() +init_rerun(session_name="phone_so100_evaluate") + +if not robot.is_connected: + raise ValueError("Robot is not connected!") + +print("Starting evaluate loop...") +episode_idx = 0 +for episode_idx in range(NUM_EPISODES): + log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}") + + # Main record loop + record_loop( + robot=robot, + events=events, + fps=FPS, + policy=policy, + preprocessor=preprocessor, # Pass the pre and post policy processors + postprocessor=postprocessor, + dataset=dataset, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=make_default_teleop_action_processor(), + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose_processor, + ) + + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=make_default_teleop_action_processor(), + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose_processor, + ) + + if events["rerecord_episode"]: + log_say("Re-record episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue + + # Save episode + dataset.save_episode() + episode_idx += 1 + +# Clean up +log_say("Stop recording") +robot.disconnect() +listener.stop() +dataset.push_to_hub() diff --git a/examples/phone_to_so100/record.py b/examples/phone_to_so100/record.py new file mode 100644 index 00000000000..d3ef293a717 --- /dev/null +++ b/examples/phone_to_so100/record.py @@ -0,0 +1,203 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features +from lerobot.datasets.utils import combine_feature_dicts +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline +from lerobot.processor.converters import ( + observation_to_transition, + robot_action_observation_to_transition, + transition_to_observation, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + EEBoundsAndSafety, + EEReferenceAndDelta, + ForwardKinematicsJointsToEE, + GripperVelocityToJoint, + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.scripts.lerobot_record import record_loop +from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS +from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction +from lerobot.teleoperators.phone.teleop_phone import Phone +from lerobot.utils.control_utils import init_keyboard_listener +from lerobot.utils.utils import log_say +from lerobot.utils.visualization_utils import init_rerun + +NUM_EPISODES = 2 +FPS = 30 +EPISODE_TIME_SEC = 60 +RESET_TIME_SEC = 30 +TASK_DESCRIPTION = "My task description" +HF_REPO_ID = "/" + +# Create the robot and teleoperator configurations +camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)} +robot_config = SO100FollowerConfig( + port="/dev/tty.usbmodem5A460814411", + id="my_awesome_follower_arm", + cameras=camera_config, + use_degrees=True, +) +teleop_config = PhoneConfig(phone_os=PhoneOS.IOS) # or PhoneOS.ANDROID + +# Initialize the robot and teleoperator +robot = SO100Follower(robot_config) +phone = Phone(teleop_config) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(robot.bus.motors.keys()), +) + +# Build pipeline to convert phone action to EE action +phone_to_robot_ee_pose_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + MapPhoneActionToRobotAction(platform=teleop_config.phone_os), + EEReferenceAndDelta( + kinematics=kinematics_solver, + end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5}, + motor_names=list(robot.bus.motors.keys()), + use_latched_reference=True, + ), + EEBoundsAndSafety( + end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, + max_ee_step_m=0.20, + ), + GripperVelocityToJoint(speed_factor=20.0), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Build pipeline to convert EE action to joints action +robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=True, + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Build pipeline to convert joint observation to EE observation +robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation]( + steps=[ + ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())) + ], + to_transition=observation_to_transition, + to_output=transition_to_observation, +) + +# Create the dataset +dataset = LeRobotDataset.create( + repo_id=HF_REPO_ID, + fps=FPS, + features=combine_feature_dicts( + # Run the feature contract of the pipelines + # This tells you how the features would look like after the pipeline steps + aggregate_pipeline_dataset_features( + pipeline=phone_to_robot_ee_pose_processor, + initial_features=create_initial_features(action=phone.action_features), + use_videos=True, + ), + aggregate_pipeline_dataset_features( + pipeline=robot_joints_to_ee_pose, + initial_features=create_initial_features(observation=robot.observation_features), + use_videos=True, + ), + ), + robot_type=robot.name, + use_videos=True, + image_writer_threads=4, +) + +# Connect the robot and teleoperator +robot.connect() +phone.connect() + +# Initialize the keyboard listener and rerun visualization +listener, events = init_keyboard_listener() +init_rerun(session_name="phone_so100_record") + +if not robot.is_connected or not phone.is_connected: + raise ValueError("Robot or teleop is not connected!") + + +print("Starting record loop. Move your phone to teleoperate the robot...") +episode_idx = 0 +while episode_idx < NUM_EPISODES and not events["stop_recording"]: + log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}") + + # Main record loop + record_loop( + robot=robot, + events=events, + fps=FPS, + teleop=phone, + dataset=dataset, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=phone_to_robot_ee_pose_processor, + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose, + ) + + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + teleop=phone, + control_time_s=RESET_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=phone_to_robot_ee_pose_processor, + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose, + ) + + if events["rerecord_episode"]: + log_say("Re-recording episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue + + # Save episode + dataset.save_episode() + episode_idx += 1 + +# Clean up +log_say("Stop recording") +robot.disconnect() +phone.disconnect() +listener.stop() +dataset.push_to_hub() diff --git a/examples/phone_to_so100/replay.py b/examples/phone_to_so100/replay.py new file mode 100644 index 00000000000..f1181143c10 --- /dev/null +++ b/examples/phone_to_so100/replay.py @@ -0,0 +1,100 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time + +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline +from lerobot.processor.converters import ( + robot_action_observation_to_transition, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.utils.constants import ACTION +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.utils import log_say + +EPISODE_IDX = 0 +HF_REPO_ID = "/" + +# Initialize the robot config +robot_config = SO100FollowerConfig( + port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True +) + +# Initialize the robot +robot = SO100Follower(robot_config) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(robot.bus.motors.keys()), +) + +# Build pipeline to convert EE action to joints action +robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=False, # Because replay is open loop + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Fetch the dataset to replay +dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX]) +# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0 +episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX) +actions = episode_frames.select_columns(ACTION) + +# Connect to the robot +robot.connect() + +if not robot.is_connected: + raise ValueError("Robot is not connected!") + +print("Starting replay loop...") +log_say(f"Replaying episode {EPISODE_IDX}") +for idx in range(len(episode_frames)): + t0 = time.perf_counter() + + # Get recorded action from dataset + ee_action = { + name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"]) + } + + # Get robot observation + robot_obs = robot.get_observation() + + # Dataset EE -> robot joints + joint_action = robot_ee_to_joints_processor((ee_action, robot_obs)) + + # Send action to robot + _ = robot.send_action(joint_action) + + busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0)) + +# Clean up +robot.disconnect() diff --git a/examples/phone_to_so100/teleoperate.py b/examples/phone_to_so100/teleoperate.py new file mode 100644 index 00000000000..783dce2429f --- /dev/null +++ b/examples/phone_to_so100/teleoperate.py @@ -0,0 +1,113 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specif + +import time + +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline +from lerobot.processor.converters import ( + robot_action_observation_to_transition, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + EEBoundsAndSafety, + EEReferenceAndDelta, + GripperVelocityToJoint, + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS +from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction +from lerobot.teleoperators.phone.teleop_phone import Phone +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.visualization_utils import init_rerun, log_rerun_data + +FPS = 30 + +# Initialize the robot and teleoperator +robot_config = SO100FollowerConfig( + port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True +) +teleop_config = PhoneConfig(phone_os=PhoneOS.IOS) # or PhoneOS.ANDROID + +# Initialize the robot and teleoperator +robot = SO100Follower(robot_config) +teleop_device = Phone(teleop_config) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(robot.bus.motors.keys()), +) + +# Build pipeline to convert phone action to ee pose action to joint action +phone_to_robot_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + MapPhoneActionToRobotAction(platform=teleop_config.phone_os), + EEReferenceAndDelta( + kinematics=kinematics_solver, + end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5}, + motor_names=list(robot.bus.motors.keys()), + use_latched_reference=True, + ), + EEBoundsAndSafety( + end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, + max_ee_step_m=0.10, + ), + GripperVelocityToJoint( + speed_factor=20.0, + ), + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=True, + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Connect to the robot and teleoperator +robot.connect() +teleop_device.connect() + +# Init rerun viewer +init_rerun(session_name="phone_so100_teleop") + +if not robot.is_connected or not teleop_device.is_connected: + raise ValueError("Robot or teleop is not connected!") + +print("Starting teleop loop. Move your phone to teleoperate the robot...") +while True: + t0 = time.perf_counter() + + # Get robot observation + robot_obs = robot.get_observation() + + # Get teleop action + phone_obs = teleop_device.get_action() + + # Phone -> EE pose -> Joints transition + joint_action = phone_to_robot_joints_processor((phone_obs, robot_obs)) + + # Send action to robot + _ = robot.send_action(joint_action) + + # Visualize + log_rerun_data(observation=phone_obs, action=joint_action) + + busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0)) diff --git a/examples/so100_to_so100_EE/evaluate.py b/examples/so100_to_so100_EE/evaluate.py new file mode 100644 index 00000000000..53a38544249 --- /dev/null +++ b/examples/so100_to_so100_EE/evaluate.py @@ -0,0 +1,198 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig +from lerobot.configs.types import FeatureType, PolicyFeature +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features +from lerobot.datasets.utils import combine_feature_dicts +from lerobot.model.kinematics import RobotKinematics +from lerobot.policies.act.modeling_act import ACTPolicy +from lerobot.policies.factory import make_pre_post_processors +from lerobot.processor import ( + RobotAction, + RobotObservation, + RobotProcessorPipeline, + make_default_teleop_action_processor, +) +from lerobot.processor.converters import ( + observation_to_transition, + robot_action_observation_to_transition, + transition_to_observation, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + ForwardKinematicsJointsToEE, + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.scripts.lerobot_record import record_loop +from lerobot.utils.control_utils import init_keyboard_listener +from lerobot.utils.utils import log_say +from lerobot.utils.visualization_utils import init_rerun + +NUM_EPISODES = 5 +FPS = 30 +EPISODE_TIME_SEC = 60 +TASK_DESCRIPTION = "My task description" +HF_MODEL_ID = "/" +HF_DATASET_ID = "/" + +# Create the robot configuration & robot +camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)} +robot_config = SO100FollowerConfig( + port="/dev/tty.usbmodem5A460814411", + id="my_awesome_follower_arm", + cameras=camera_config, + use_degrees=True, +) + +robot = SO100Follower(robot_config) + +# Create policy +policy = ACTPolicy.from_pretrained(HF_MODEL_ID) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(robot.bus.motors.keys()), +) + +# Build pipeline to convert EE action to joints action +robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=True, + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Build pipeline to convert joints observation to EE observation +robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation]( + steps=[ + ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())) + ], + to_transition=observation_to_transition, + to_output=transition_to_observation, +) + + +# Create the dataset +dataset = LeRobotDataset.create( + repo_id=HF_DATASET_ID, + fps=FPS, + features=combine_feature_dicts( + aggregate_pipeline_dataset_features( + pipeline=robot_joints_to_ee_pose_processor, + initial_features=create_initial_features(observation=robot.observation_features), + use_videos=True, + ), + # User for now should be explicit on the feature keys that were used for record + # Alternatively, the user can pass the processor step that has the right features + aggregate_pipeline_dataset_features( + pipeline=make_default_teleop_action_processor(), + initial_features=create_initial_features( + action={ + f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,)) + for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"] + } + ), + use_videos=True, + ), + ), + robot_type=robot.name, + use_videos=True, + image_writer_threads=4, +) + +# Build Policy Processors +preprocessor, postprocessor = make_pre_post_processors( + policy_cfg=policy, + pretrained_path=HF_MODEL_ID, + dataset_stats=dataset.meta.stats, + # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility. + preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}}, +) + +# Connect the robot and teleoperator +robot.connect() + +# Initialize the keyboard listener and rerun visualization +listener, events = init_keyboard_listener() +init_rerun(session_name="so100_so100_evaluate") + +if not robot.is_connected: + raise ValueError("Robot is not connected!") + +print("Starting evaluate loop...") +episode_idx = 0 +for episode_idx in range(NUM_EPISODES): + log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}") + + # Main record loop + record_loop( + robot=robot, + events=events, + fps=FPS, + policy=policy, + preprocessor=preprocessor, # Pass the pre and post policy processors + postprocessor=postprocessor, + dataset=dataset, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=make_default_teleop_action_processor(), + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose_processor, + ) + + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=make_default_teleop_action_processor(), + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose_processor, + ) + + if events["rerecord_episode"]: + log_say("Re-record episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue + + # Save episode + dataset.save_episode() + episode_idx += 1 + +# Clean up +log_say("Stop recording") +robot.disconnect() +listener.stop() +dataset.push_to_hub() diff --git a/examples/so100_to_so100_EE/record.py b/examples/so100_to_so100_EE/record.py new file mode 100644 index 00000000000..9ed6e51a992 --- /dev/null +++ b/examples/so100_to_so100_EE/record.py @@ -0,0 +1,202 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features +from lerobot.datasets.utils import combine_feature_dicts +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline +from lerobot.processor.converters import ( + observation_to_transition, + robot_action_observation_to_transition, + transition_to_observation, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + EEBoundsAndSafety, + ForwardKinematicsJointsToEE, + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.scripts.lerobot_record import record_loop +from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig +from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader +from lerobot.utils.control_utils import init_keyboard_listener +from lerobot.utils.utils import log_say +from lerobot.utils.visualization_utils import init_rerun + +NUM_EPISODES = 2 +FPS = 30 +EPISODE_TIME_SEC = 60 +RESET_TIME_SEC = 30 +TASK_DESCRIPTION = "My task description" +HF_REPO_ID = "/" + +# Create the robot and teleoperator configurations +camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)} +follower_config = SO100FollowerConfig( + port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", cameras=camera_config, use_degrees=True +) +leader_config = SO100LeaderConfig(port="/dev/tty.usbmodem5A460819811", id="my_awesome_leader_arm") + +# Initialize the robot and teleoperator +follower = SO100Follower(follower_config) +leader = SO100Leader(leader_config) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +follower_kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(follower.bus.motors.keys()), +) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +leader_kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(leader.bus.motors.keys()), +) + +# Build pipeline to convert follower joints to EE observation +follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation]( + steps=[ + ForwardKinematicsJointsToEE( + kinematics=follower_kinematics_solver, motor_names=list(follower.bus.motors.keys()) + ), + ], + to_transition=observation_to_transition, + to_output=transition_to_observation, +) + +# Build pipeline to convert leader joints to EE action +leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + ForwardKinematicsJointsToEE( + kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys()) + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Build pipeline to convert EE action to follower joints +ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + [ + EEBoundsAndSafety( + end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, + max_ee_step_m=0.10, + ), + InverseKinematicsEEToJoints( + kinematics=follower_kinematics_solver, + motor_names=list(follower.bus.motors.keys()), + initial_guess_current_joints=True, + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Create the dataset +dataset = LeRobotDataset.create( + repo_id=HF_REPO_ID, + fps=FPS, + features=combine_feature_dicts( + # Run the feature contract of the pipelines + # This tells you how the features would look like after the pipeline steps + aggregate_pipeline_dataset_features( + pipeline=leader_joints_to_ee, + initial_features=create_initial_features(action=leader.action_features), + use_videos=True, + ), + aggregate_pipeline_dataset_features( + pipeline=follower_joints_to_ee, + initial_features=create_initial_features(observation=follower.observation_features), + use_videos=True, + ), + ), + robot_type=follower.name, + use_videos=True, + image_writer_threads=4, +) + + +# Connect the robot and teleoperator +leader.connect() +follower.connect() + +# Initialize the keyboard listener and rerun visualization +listener, events = init_keyboard_listener() +init_rerun(session_name="recording_phone") + +if not leader.is_connected or not follower.is_connected: + raise ValueError("Robot or teleop is not connected!") + +print("Starting record loop...") +episode_idx = 0 +while episode_idx < NUM_EPISODES and not events["stop_recording"]: + log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}") + + # Main record loop + record_loop( + robot=follower, + events=events, + fps=FPS, + teleop=leader, + dataset=dataset, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=leader_joints_to_ee, + robot_action_processor=ee_to_follower_joints, + robot_observation_processor=follower_joints_to_ee, + ) + + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]): + log_say("Reset the environment") + record_loop( + robot=follower, + events=events, + fps=FPS, + teleop=leader, + control_time_s=RESET_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=leader_joints_to_ee, + robot_action_processor=ee_to_follower_joints, + robot_observation_processor=follower_joints_to_ee, + ) + + if events["rerecord_episode"]: + log_say("Re-recording episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue + + # Save episode + dataset.save_episode() + episode_idx += 1 + +# Clean up +log_say("Stop recording") +leader.disconnect() +follower.disconnect() +listener.stop() +dataset.push_to_hub() diff --git a/examples/so100_to_so100_EE/replay.py b/examples/so100_to_so100_EE/replay.py new file mode 100644 index 00000000000..ea78d4e66b6 --- /dev/null +++ b/examples/so100_to_so100_EE/replay.py @@ -0,0 +1,101 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import time + +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline +from lerobot.processor.converters import ( + robot_action_observation_to_transition, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.utils.constants import ACTION +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.utils import log_say + +EPISODE_IDX = 0 +HF_REPO_ID = "/" + +# Initialize the robot config +robot_config = SO100FollowerConfig( + port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True +) + +# Initialize the robot +robot = SO100Follower(robot_config) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(robot.bus.motors.keys()), +) + +# Build pipeline to convert EE action to joints action +robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[ + InverseKinematicsEEToJoints( + kinematics=kinematics_solver, + motor_names=list(robot.bus.motors.keys()), + initial_guess_current_joints=False, # Because replay is open loop + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Fetch the dataset to replay +dataset = LeRobotDataset(HF_REPO_ID, episodes=[EPISODE_IDX]) +# Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0 +episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == EPISODE_IDX) +actions = episode_frames.select_columns(ACTION) + +# Connect to the robot +robot.connect() + +if not robot.is_connected: + raise ValueError("Robot is not connected!") + +print("Starting replay loop...") +log_say(f"Replaying episode {EPISODE_IDX}") +for idx in range(len(episode_frames)): + t0 = time.perf_counter() + + # Get recorded action from dataset + ee_action = { + name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"]) + } + + # Get robot observation + robot_obs = robot.get_observation() + + # Dataset EE -> robot joints + joint_action = robot_ee_to_joints_processor((ee_action, robot_obs)) + + # Send action to robot + _ = robot.send_action(joint_action) + + busy_wait(1.0 / dataset.fps - (time.perf_counter() - t0)) + +# Clean up +robot.disconnect() diff --git a/examples/so100_to_so100_EE/teleoperate.py b/examples/so100_to_so100_EE/teleoperate.py new file mode 100644 index 00000000000..b1a8c8c27e4 --- /dev/null +++ b/examples/so100_to_so100_EE/teleoperate.py @@ -0,0 +1,121 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time + +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline +from lerobot.processor.converters import ( + robot_action_observation_to_transition, + robot_action_to_transition, + transition_to_robot_action, +) +from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + EEBoundsAndSafety, + ForwardKinematicsJointsToEE, + InverseKinematicsEEToJoints, +) +from lerobot.robots.so100_follower.so100_follower import SO100Follower +from lerobot.teleoperators.so100_leader.config_so100_leader import SO100LeaderConfig +from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.visualization_utils import init_rerun, log_rerun_data + +FPS = 30 + +# Initialize the robot and teleoperator config +follower_config = SO100FollowerConfig( + port="/dev/tty.usbmodem5A460814411", id="my_awesome_follower_arm", use_degrees=True +) +leader_config = SO100LeaderConfig(port="/dev/tty.usbmodem5A460819811", id="my_awesome_leader_arm") + +# Initialize the robot and teleoperator +follower = SO100Follower(follower_config) +leader = SO100Leader(leader_config) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +follower_kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(follower.bus.motors.keys()), +) + +# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf +leader_kinematics_solver = RobotKinematics( + urdf_path="./SO101/so101_new_calib.urdf", + target_frame_name="gripper_frame_link", + joint_names=list(leader.bus.motors.keys()), +) + +# Build pipeline to convert teleop joints to EE action +leader_to_ee = RobotProcessorPipeline[RobotAction, RobotAction]( + steps=[ + ForwardKinematicsJointsToEE( + kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys()) + ), + ], + to_transition=robot_action_to_transition, + to_output=transition_to_robot_action, +) + +# build pipeline to convert EE action to robot joints +ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + [ + EEBoundsAndSafety( + end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, + max_ee_step_m=0.10, + ), + InverseKinematicsEEToJoints( + kinematics=follower_kinematics_solver, + motor_names=list(follower.bus.motors.keys()), + initial_guess_current_joints=False, + ), + ], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, +) + +# Connect to the robot and teleoperator +follower.connect() +leader.connect() + +# Init rerun viewer +init_rerun(session_name="so100_so100_EE_teleop") + +print("Starting teleop loop...") +while True: + t0 = time.perf_counter() + + # Get robot observation + robot_obs = follower.get_observation() + + # Get teleop observation + leader_joints_obs = leader.get_action() + + # teleop joints -> teleop EE action + leader_ee_act = leader_to_ee(leader_joints_obs) + + # teleop EE -> robot joints + follower_joints_act = ee_to_follower_joints((leader_ee_act, robot_obs)) + + # Send action to robot + _ = follower.send_action(follower_joints_act) + + # Visualize + log_rerun_data(observation=leader_ee_act, action=follower_joints_act) + + busy_wait(max(1.0 / FPS - (time.perf_counter() - t0), 0.0)) diff --git a/examples/3_train_policy.py b/examples/training/train_policy.py similarity index 92% rename from examples/3_train_policy.py rename to examples/training/train_policy.py index f2de79db89d..16f2a4d87b1 100644 --- a/examples/3_train_policy.py +++ b/examples/training/train_policy.py @@ -12,11 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""This script demonstrates how to train Diffusion Policy on the PushT environment. - -Once you have trained a model with this script, you can try to evaluate it on -examples/2_evaluate_pretrained_policy.py -""" +"""This script demonstrates how to train Diffusion Policy on the PushT environment.""" from pathlib import Path @@ -27,6 +23,7 @@ from lerobot.datasets.utils import dataset_to_policy_features from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy +from lerobot.policies.factory import make_pre_post_processors def main(): @@ -56,9 +53,10 @@ def main(): cfg = DiffusionConfig(input_features=input_features, output_features=output_features) # We can now instantiate our policy with this config and the dataset stats. - policy = DiffusionPolicy(cfg, dataset_stats=dataset_metadata.stats) + policy = DiffusionPolicy(cfg) policy.train() policy.to(device) + preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats) # Another policy-dataset interaction is with the delta_timestamps. Each policy expects a given number frames # which can differ for inputs, outputs and rewards (if there are some). @@ -99,7 +97,7 @@ def main(): done = False while not done: for batch in dataloader: - batch = {k: (v.to(device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} + batch = preprocessor(batch) loss, _ = policy.forward(batch) loss.backward() optimizer.step() @@ -114,6 +112,8 @@ def main(): # Save a policy checkpoint. policy.save_pretrained(output_directory) + preprocessor.save_pretrained(output_directory) + postprocessor.save_pretrained(output_directory) if __name__ == "__main__": diff --git a/examples/5_train_with_streaming.py b/examples/training/train_with_streaming.py similarity index 82% rename from examples/5_train_with_streaming.py rename to examples/training/train_with_streaming.py index 17818410de2..185be5b13a5 100644 --- a/examples/5_train_with_streaming.py +++ b/examples/training/train_with_streaming.py @@ -13,23 +13,20 @@ # limitations under the License. """This script demonstrates how to train a Diffusion Policy on the PushT environment, -using a dataset processed in streaming mode. - -Once you have trained a model with this script, you can try to evaluate it on -examples/2_evaluate_pretrained_policy.py -""" +using a dataset processed in streaming mode.""" from pathlib import Path import torch from lerobot.configs.types import FeatureType -from lerobot.constants import ACTION from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset from lerobot.datasets.utils import dataset_to_policy_features from lerobot.policies.act.configuration_act import ACTConfig from lerobot.policies.act.modeling_act import ACTPolicy +from lerobot.policies.factory import make_pre_post_processors +from lerobot.utils.constants import ACTION def main(): @@ -50,9 +47,7 @@ def main(): training_steps = 10 log_freq = 1 - dataset_id = ( - "aractingi/droid_1.0.1" # 26M frames! Would require 4TB of disk space if installed locally (: - ) + dataset_id = "lerobot/droid_1.0.1" # 26M frames! Would require 4TB of disk space if installed locally (: dataset_metadata = LeRobotDatasetMetadata(dataset_id) features = dataset_to_policy_features(dataset_metadata.features) output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION} @@ -60,9 +55,10 @@ def main(): # We can now instantiate our policy with this config and the dataset stats. cfg = ACTConfig(input_features=input_features, output_features=output_features) - policy = ACTPolicy(cfg, dataset_stats=dataset_metadata.stats) + policy = ACTPolicy(cfg) policy.train() policy.to(device) + preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats) # Delta timestamps are used to (1) augment frames used during training and (2) supervise the policy. # Here, we use delta-timestamps to only provide ground truth actions for supervision @@ -89,13 +85,7 @@ def main(): done = False while not done: for batch in dataloader: - batch = { - k: (v.type(torch.float32) if isinstance(v, torch.Tensor) and v.dtype != torch.bool else v) - for k, v in batch.items() - } - batch = {k: (v.to(device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} - - # batch = {k: (v.to(device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} + batch = preprocessor(batch) loss, _ = policy.forward(batch) loss.backward() optimizer.step() @@ -110,6 +100,8 @@ def main(): # Save a policy checkpoint. policy.save_pretrained(output_directory) + preprocessor.save_pretrained(output_directory) + postprocessor.save_pretrained(output_directory) if __name__ == "__main__": diff --git a/pyproject.toml b/pyproject.toml index 7241a78f915..c67b481f09b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,20 +59,20 @@ keywords = ["lerobot", "huggingface", "robotics", "machine learning", "artifici dependencies = [ # Hugging Face dependencies - "datasets>=2.19.0,<=3.6.0", # TODO: Bumb dependency - "diffusers>=0.27.2", - "huggingface-hub[hf-transfer,cli]>=0.34.2", + "datasets>=4.0.0,<4.2.0", + "diffusers>=0.27.2,<0.36.0", + "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0", # Core dependencies - "cmake>=3.29.0.1", - "einops>=0.8.0", - "opencv-python-headless>=4.9.0", - "av>=14.2.0", - "jsonlines>=4.0.0", - "packaging>=24.2", - "pynput>=1.7.7", - "pyserial>=3.5", - "wandb>=0.20.0", + "cmake>=3.29.0.1,<4.2.0", + "einops>=0.8.0,<0.9.0", + "opencv-python-headless>=4.9.0,<4.13.0", + "av>=14.2.0,<16.0.0", + "jsonlines>=4.0.0,<5.0.0", + "packaging>=24.2,<26.0", + "pynput>=1.7.7,<1.9.0", + "pyserial>=3.5,<4.0", + "wandb>=0.20.0,<0.23.0", "torch>=2.2.1,<2.8.0", # TODO: Bumb dependency "torchcodec>=0.2.1,<0.6.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bumb dependency @@ -92,25 +92,26 @@ dependencies = [ [project.optional-dependencies] # Common -pygame-dep = ["pygame>=2.5.1"] -placo-dep = ["placo>=0.9.6"] -transformers-dep = ["transformers>=4.50.3,<4.52.0"] # TODO: Bumb dependency +pygame-dep = ["pygame>=2.5.1,<2.7.0"] +placo-dep = ["placo>=0.9.6,<0.10.0"] +transformers-dep = ["transformers>=4.53.0,<5.0.0"] grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"] # Motors -feetech = ["feetech-servo-sdk>=1.0.0"] -dynamixel = ["dynamixel-sdk>=3.7.31"] +feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"] +dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"] # Robots -gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0"] +gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"] hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"] -lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1"] -reachy2 = ["reachy2_sdk>=1.0.14"] +lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"] +reachy2 = ["reachy2_sdk>=1.0.14,<1.1.0"] kinematics = ["lerobot[placo-dep]"] intelrealsense = [ - "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'", - "pyrealsense2-macosx>=2.54 ; sys_platform == 'darwin'", + "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'", + "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'", ] +phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0"] # stretch = [ # "hello-robot-stretch-body>=0.7.27 ; sys_platform == 'linux'", # "pyrender @ git+https://github.com/mmatl/pyrender.git ; sys_platform == 'linux'", @@ -118,22 +119,24 @@ intelrealsense = [ # ] # TODO: Currently not supported # Policies -pi0 = ["lerobot[transformers-dep]"] -smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14", "accelerate>=1.7.0", "safetensors>=0.4.3"] -hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.9", "lerobot[grpcio-dep]", "lerobot[placo-dep]"] +pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi"] +smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"] +hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.11,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"] # Features -async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3"] +async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"] # Development -dev = ["pre-commit>=3.7.0", "debugpy>=1.8.1", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1"] -test = ["pytest>=8.1.0", "pytest-timeout>=2.4.0", "pytest-cov>=5.0.0", "mock-serial>=0.0.1 ; sys_platform != 'win32'"] -video_benchmark = ["scikit-image>=0.23.2", "pandas>=2.2.2"] +dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1"] +test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"] +video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"] # Simulation -aloha = ["gym-aloha>=0.1.1"] -pusht = ["gym-pusht>=0.1.5", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead -xarm = ["gym-xarm>=0.1.1"] +aloha = ["gym-aloha>=0.1.1,<0.2.0"] +pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead +xarm = ["gym-xarm>=0.1.1,<0.2.0"] +libero = ["lerobot[transformers-dep]", "libero @ git+https://github.com/huggingface/lerobot-libero.git@main#egg=libero"] + # All all = [ @@ -144,7 +147,7 @@ all = [ "lerobot[reachy2]", "lerobot[kinematics]", "lerobot[intelrealsense]", - "lerobot[pi0]", + "lerobot[pi]", "lerobot[smolvla]", "lerobot[hilserl]", "lerobot[async]", @@ -153,19 +156,25 @@ all = [ "lerobot[video_benchmark]", "lerobot[aloha]", "lerobot[pusht]", - "lerobot[xarm]" + "lerobot[xarm]", + "lerobot[phone]", + "lerobot[libero]", ] [project.scripts] -lerobot-calibrate="lerobot.calibrate:main" -lerobot-find-cameras="lerobot.find_cameras:main" -lerobot-find-port="lerobot.find_port:main" -lerobot-record="lerobot.record:main" -lerobot-replay="lerobot.replay:main" -lerobot-setup-motors="lerobot.setup_motors:main" -lerobot-teleoperate="lerobot.teleoperate:main" -lerobot-eval="lerobot.scripts.eval:main" -lerobot-train="lerobot.scripts.train:main" +lerobot-calibrate="lerobot.scripts.lerobot_calibrate:main" +lerobot-find-cameras="lerobot.scripts.lerobot_find_cameras:main" +lerobot-find-port="lerobot.scripts.lerobot_find_port:main" +lerobot-record="lerobot.scripts.lerobot_record:main" +lerobot-replay="lerobot.scripts.lerobot_replay:main" +lerobot-setup-motors="lerobot.scripts.lerobot_setup_motors:main" +lerobot-teleoperate="lerobot.scripts.lerobot_teleoperate:main" +lerobot-eval="lerobot.scripts.lerobot_eval:main" +lerobot-train="lerobot.scripts.lerobot_train:main" +lerobot-dataset-viz="lerobot.scripts.lerobot_dataset_viz:main" +lerobot-info="lerobot.scripts.lerobot_info:main" +lerobot-find-joint-limits="lerobot.scripts.lerobot_find_joint_limits:main" +lerobot-imgtransform-viz="lerobot.scripts.lerobot_imgtransform_viz:main" # ---------------- Tool Configurations ---------------- [tool.setuptools.packages.find] @@ -192,7 +201,7 @@ exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"] # N: pep8-naming # TODO: Uncomment rules when ready to use select = [ - "E", "W", "F", "I", "B", "C4", "T20", "N" # "SIM", "A", "S", "D", "RUF", "UP" + "E", "W", "F", "I", "B", "C4", "T20", "N", "UP", "SIM" #, "A", "S", "D", "RUF" ] ignore = [ "E501", # Line too long @@ -258,8 +267,87 @@ default.extend-ignore-identifiers-re = [ # color = true # paths = ["src/lerobot"] -# [tool.mypy] -# python_version = "3.10" +# TODO: Enable mypy gradually module by module across multiple PRs +# Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations + +[tool.mypy] +python_version = "3.10" +ignore_missing_imports = true +follow_imports = "skip" # warn_return_any = true # warn_unused_configs = true -# ignore_missing_imports = false +# strict = true +# disallow_untyped_defs = true +# disallow_incomplete_defs = true +# check_untyped_defs = true + +[[tool.mypy.overrides]] +module = "lerobot.*" +ignore_errors = true + +[[tool.mypy.overrides]] +module = "lerobot.envs.*" +# Enable type checking only for the envs module +ignore_errors = false + + +# [[tool.mypy.overrides]] +# module = "lerobot.utils.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.configs.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.optim.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.model.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.processor.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.datasets.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.cameras.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.motors.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.robots.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.teleoperators.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.policies.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.rl.*" +# ignore_errors = false + + +# [[tool.mypy.overrides]] +# module = "lerobot.async_inference.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.transport.*" +# ignore_errors = false + +# [[tool.mypy.overrides]] +# module = "lerobot.scripts.*" +# ignore_errors = false diff --git a/src/lerobot/scripts/server/configs.py b/src/lerobot/async_inference/configs.py similarity index 99% rename from src/lerobot/scripts/server/configs.py rename to src/lerobot/async_inference/configs.py index 5be46485ea4..24f889df17d 100644 --- a/src/lerobot/scripts/server/configs.py +++ b/src/lerobot/async_inference/configs.py @@ -18,7 +18,8 @@ import torch from lerobot.robots.config import RobotConfig -from lerobot.scripts.server.constants import ( + +from .constants import ( DEFAULT_FPS, DEFAULT_INFERENCE_LATENCY, DEFAULT_OBS_QUEUE_TIMEOUT, diff --git a/src/lerobot/scripts/server/constants.py b/src/lerobot/async_inference/constants.py similarity index 85% rename from src/lerobot/scripts/server/constants.py rename to src/lerobot/async_inference/constants.py index af983a80059..1b1dac0f57b 100644 --- a/src/lerobot/scripts/server/constants.py +++ b/src/lerobot/async_inference/constants.py @@ -23,7 +23,7 @@ DEFAULT_OBS_QUEUE_TIMEOUT = 2 # All action chunking policies -SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "pi0", "tdmpc", "vqbet"] +SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05"] # TODO: Add all other robots -SUPPORTED_ROBOTS = ["so100_follower", "so101_follower"] +SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so100_follower"] diff --git a/src/lerobot/scripts/server/helpers.py b/src/lerobot/async_inference/helpers.py similarity index 95% rename from src/lerobot/scripts/server/helpers.py rename to src/lerobot/async_inference/helpers.py index d8051b76e7f..54fad8c546a 100644 --- a/src/lerobot/scripts/server/helpers.py +++ b/src/lerobot/async_inference/helpers.py @@ -22,16 +22,22 @@ import torch from lerobot.configs.types import PolicyFeature -from lerobot.constants import OBS_IMAGES, OBS_STATE from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features # NOTE: Configs need to be loaded for the client to be able to instantiate the policy config -from lerobot.policies import ACTConfig, DiffusionConfig, PI0Config, SmolVLAConfig, VQBeTConfig # noqa: F401 +from lerobot.policies import ( # noqa: F401 + ACTConfig, + DiffusionConfig, + PI0Config, + PI05Config, + SmolVLAConfig, + VQBeTConfig, +) from lerobot.robots.robot import Robot +from lerobot.utils.constants import OBS_IMAGES, OBS_STATE, OBS_STR from lerobot.utils.utils import init_logging Action = torch.Tensor -ActionChunk = torch.Tensor # observation as received from the robot RawObservation = dict[str, torch.Tensor] @@ -46,7 +52,7 @@ def visualize_action_queue_size(action_queue_size: list[int]) -> None: import matplotlib.pyplot as plt - fig, ax = plt.subplots() + _, ax = plt.subplots() ax.set_title("Action Queue Size Over Time") ax.set_xlabel("Environment steps") ax.set_ylabel("Action Queue Size") @@ -66,7 +72,7 @@ def validate_robot_cameras_for_policy( def map_robot_keys_to_lerobot_features(robot: Robot) -> dict[str, dict]: - return hw_to_dataset_features(robot.observation_features, "observation", use_video=False) + return hw_to_dataset_features(robot.observation_features, OBS_STR, use_video=False) def is_image_key(k: str) -> bool: @@ -86,11 +92,11 @@ def resize_robot_observation_image(image: torch.tensor, resize_dims: tuple[int, return resized.squeeze(0) +# TODO(Steven): Consider implementing a pipeline step for this def raw_observation_to_observation( raw_observation: RawObservation, lerobot_features: dict[str, dict], policy_image_features: dict[str, PolicyFeature], - device: str, ) -> Observation: observation = {} @@ -99,9 +105,7 @@ def raw_observation_to_observation( if isinstance(v, torch.Tensor): # VLAs present natural-language instructions in observations if "image" in k: # Policy expects images in shape (B, C, H, W) - observation[k] = prepare_image(v).unsqueeze(0).to(device) - else: - observation[k] = v.to(device) + observation[k] = prepare_image(v).unsqueeze(0) else: observation[k] = v @@ -141,7 +145,7 @@ def make_lerobot_observation( lerobot_features: dict[str, dict], ) -> LeRobotObservation: """Make a lerobot observation from a raw observation.""" - return build_dataset_frame(lerobot_features, robot_obs, prefix="observation") + return build_dataset_frame(lerobot_features, robot_obs, prefix=OBS_STR) def prepare_raw_observation( diff --git a/src/lerobot/scripts/server/policy_server.py b/src/lerobot/async_inference/policy_server.py similarity index 78% rename from src/lerobot/scripts/server/policy_server.py rename to src/lerobot/async_inference/policy_server.py index 0ed446d3ad8..f7e00dea4ac 100644 --- a/src/lerobot/scripts/server/policy_server.py +++ b/src/lerobot/async_inference/policy_server.py @@ -15,7 +15,7 @@ """ Example: ```shell -python src/lerobot/scripts/server/policy_server.py \ +python -m lerobot.async_inference.policy_server \ --host=127.0.0.1 \ --port=8080 \ --fps=30 \ @@ -32,15 +32,26 @@ from dataclasses import asdict from pprint import pformat from queue import Empty, Queue +from typing import Any import draccus import grpc import torch -from lerobot.policies.factory import get_policy_class -from lerobot.scripts.server.configs import PolicyServerConfig -from lerobot.scripts.server.constants import SUPPORTED_POLICIES -from lerobot.scripts.server.helpers import ( +from lerobot.policies.factory import get_policy_class, make_pre_post_processors +from lerobot.processor import ( + PolicyAction, + PolicyProcessorPipeline, +) +from lerobot.transport import ( + services_pb2, # type: ignore + services_pb2_grpc, # type: ignore +) +from lerobot.transport.utils import receive_bytes_in_chunks + +from .configs import PolicyServerConfig +from .constants import SUPPORTED_POLICIES +from .helpers import ( FPSTracker, Observation, RemotePolicyConfig, @@ -50,11 +61,6 @@ observations_similar, raw_observation_to_observation, ) -from lerobot.transport import ( - services_pb2, # type: ignore - services_pb2_grpc, # type: ignore -) -from lerobot.transport.utils import receive_bytes_in_chunks class PolicyServer(services_pb2_grpc.AsyncInferenceServicer): @@ -81,6 +87,8 @@ def __init__(self, config: PolicyServerConfig): self.lerobot_features = None self.actions_per_chunk = None self.policy = None + self.preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]] | None = None + self.postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction] | None = None @property def running(self): @@ -145,6 +153,16 @@ def SendPolicyInstructions(self, request, context): # noqa: N802 start = time.perf_counter() self.policy = policy_class.from_pretrained(policy_specs.pretrained_name_or_path) self.policy.to(self.device) + + # Load preprocessor and postprocessor, overriding device to match requested device + device_override = {"device": self.device} + self.preprocessor, self.postprocessor = make_pre_post_processors( + self.policy.config, + pretrained_path=policy_specs.pretrained_name_or_path, + preprocessor_overrides={"device_processor": device_override}, + postprocessor_overrides={"device_processor": device_override}, + ) + end = time.perf_counter() self.logger.info(f"Time taken to put policy on {self.device}: {end - start:.4f} seconds") @@ -172,7 +190,7 @@ def SendObservations(self, request_iterator, context): # noqa: N802 # Calculate FPS metrics fps_metrics = self.fps_tracker.calculate_fps_metrics(obs_timestamp) - self.logger.info( + self.logger.debug( f"Received observation #{obs_timestep} | " f"Avg FPS: {fps_metrics['avg_fps']:.2f} | " # fps at which observations are received from client f"Target: {fps_metrics['target_fps']:.2f} | " @@ -188,7 +206,7 @@ def SendObservations(self, request_iterator, context): # noqa: N802 if not self._enqueue_observation( timed_observation # wrapping a RawObservation ): - self.logger.info(f"Observation #{obs_timestep} has been filtered out") + self.logger.debug(f"Observation #{obs_timestep} has been filtered out") return services_pb2.Empty() @@ -300,23 +318,6 @@ def _time_action_chunk(self, t_0: float, action_chunk: list[torch.Tensor], i_0: for i, action in enumerate(action_chunk) ] - def _prepare_observation(self, observation_t: TimedObservation) -> Observation: - """ - Prepare observation, ready for policy inference. - E.g.: To keep observation sampling rate high (and network packet tiny) we send int8 [0,255] images from the - client and then convert them to float32 [0,1] images here, before running inference. - """ - # RawObservation from robot.get_observation() - wrong keys, wrong dtype, wrong image shape - observation: Observation = raw_observation_to_observation( - observation_t.get_observation(), - self.lerobot_features, - self.policy_image_features, - self.device, - ) - # processed Observation - right keys, right dtype, right image shape - - return observation - def _get_action_chunk(self, observation: dict[str, torch.Tensor]) -> torch.Tensor: """Get an action chunk from the policy. The chunk contains only""" chunk = self.policy.predict_action_chunk(observation) @@ -326,44 +327,76 @@ def _get_action_chunk(self, observation: dict[str, torch.Tensor]) -> torch.Tenso return chunk[:, : self.actions_per_chunk, :] def _predict_action_chunk(self, observation_t: TimedObservation) -> list[TimedAction]: - """Predict an action chunk based on an observation""" - inference_starts = time.perf_counter() - + """Predict an action chunk based on an observation. + + Pipeline: + 1. Convert raw observation to LeRobot format + 2. Apply preprocessor (tokenization, normalization, batching, device placement) + 3. Run policy inference to get action chunk + 4. Apply postprocessor (unnormalization, device movement) + 5. Convert to TimedAction list + """ """1. Prepare observation""" - start_time = time.perf_counter() - observation = self._prepare_observation(observation_t) - preprocessing_time = time.perf_counter() - start_time + start_prepare = time.perf_counter() + observation: Observation = raw_observation_to_observation( + observation_t.get_observation(), + self.lerobot_features, + self.policy_image_features, + ) + prepare_time = time.perf_counter() - start_prepare + """2. Apply preprocessor""" + start_preprocess = time.perf_counter() + observation = self.preprocessor(observation) self.last_processed_obs: TimedObservation = observation_t + preprocessing_time = time.perf_counter() - start_preprocess - """2. Get action chunk""" - start_time = time.perf_counter() + """3. Get action chunk""" + start_inference = time.perf_counter() action_tensor = self._get_action_chunk(observation) - inference_time = time.perf_counter() - start_time - - """3. Post-inference processing""" - start_time = time.perf_counter() - # Move to CPU before serializing - action_tensor = action_tensor.cpu().squeeze(0) + inference_time = time.perf_counter() - start_inference + self.logger.info( + f"Preprocessing and inference took {inference_time:.4f}s, action shape: {action_tensor.shape}" + ) + """4. Apply postprocessor""" + # Apply postprocessor (handles unnormalization and device movement) + # Postprocessor expects (B, action_dim) per action, but we have (B, chunk_size, action_dim) + # So we process each action in the chunk individually + start_postprocess = time.perf_counter() + _, chunk_size, _ = action_tensor.shape + + # Process each action in the chunk + processed_actions = [] + for i in range(chunk_size): + # Extract action at timestep i: (B, action_dim) + single_action = action_tensor[:, i, :] + processed_action = self.postprocessor(single_action) + processed_actions.append(processed_action) + + # Stack back to (B, chunk_size, action_dim), then remove batch dim + action_tensor = torch.stack(processed_actions, dim=1).squeeze(0) + self.logger.debug(f"Postprocessed action shape: {action_tensor.shape}") + + """5. Convert to TimedAction list""" action_chunk = self._time_action_chunk( observation_t.get_timestamp(), list(action_tensor), observation_t.get_timestep() ) - postprocessing_time = time.perf_counter() - start_time - inference_stops = time.perf_counter() + postprocess_stops = time.perf_counter() + postprocessing_time = postprocess_stops - start_postprocess self.logger.info( - f"Observation {observation_t.get_timestep()} |" - f"Inference time: {1000 * (inference_stops - inference_starts):.2f}ms" + f"Observation {observation_t.get_timestep()} | " + f"Total time: {1000 * (postprocess_stops - start_prepare):.2f}ms" ) - # full-process latency breakdown for debugging purposes self.logger.debug( f"Observation {observation_t.get_timestep()} | " - f"Preprocessing time: {1000 * (preprocessing_time - inference_starts):.2f}ms | " - f"Inference time: {1000 * (inference_time - preprocessing_time):.2f}ms | " - f"Postprocessing time: {1000 * (postprocessing_time - inference_time):.2f}ms | " - f"Total time: {1000 * (postprocessing_time - inference_starts):.2f}ms" + f"Prepare time: {1000 * prepare_time:.2f}ms | " + f"Preprocessing time: {1000 * preprocessing_time:.2f}ms | " + f"Inference time: {1000 * inference_time:.2f}ms | " + f"Postprocessing time: {1000 * postprocessing_time:.2f}ms | " + f"Total time: {1000 * (postprocess_stops - start_prepare):.2f}ms" ) return action_chunk diff --git a/src/lerobot/scripts/server/robot_client.py b/src/lerobot/async_inference/robot_client.py similarity index 98% rename from src/lerobot/scripts/server/robot_client.py rename to src/lerobot/async_inference/robot_client.py index 939d5cea850..8c4425c6b5f 100644 --- a/src/lerobot/scripts/server/robot_client.py +++ b/src/lerobot/async_inference/robot_client.py @@ -15,7 +15,7 @@ """ Example command: ```shell -python src/lerobot/scripts/server/robot_client.py \ +python src/lerobot/async_inference/robot_client.py \ --robot.type=so100_follower \ --robot.port=/dev/tty.usbmodem58760431541 \ --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \ @@ -52,14 +52,21 @@ from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, + bi_so100_follower, koch_follower, make_robot_from_config, so100_follower, so101_follower, ) -from lerobot.scripts.server.configs import RobotClientConfig -from lerobot.scripts.server.constants import SUPPORTED_ROBOTS -from lerobot.scripts.server.helpers import ( +from lerobot.transport import ( + services_pb2, # type: ignore + services_pb2_grpc, # type: ignore +) +from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks + +from .configs import RobotClientConfig +from .constants import SUPPORTED_ROBOTS +from .helpers import ( Action, FPSTracker, Observation, @@ -72,11 +79,6 @@ validate_robot_cameras_for_policy, visualize_action_queue_size, ) -from lerobot.transport import ( - services_pb2, # type: ignore - services_pb2_grpc, # type: ignore -) -from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks class RobotClient: @@ -213,7 +215,7 @@ def send_observation( ) _ = self.stub.SendObservations(observation_iterator) obs_timestep = obs.get_timestep() - self.logger.info(f"Sent observation #{obs_timestep} | ") + self.logger.debug(f"Sent observation #{obs_timestep} | ") return True @@ -466,7 +468,7 @@ def control_loop(self, task: str, verbose: bool = False) -> tuple[Observation, A if self._ready_to_send_observation(): _captured_observation = self.control_loop_observation(task, verbose) - self.logger.info(f"Control loop (ms): {(time.perf_counter() - control_loop_start) * 1000:.2f}") + self.logger.debug(f"Control loop (ms): {(time.perf_counter() - control_loop_start) * 1000:.2f}") # Dynamically adjust sleep time to maintain the desired control frequency time.sleep(max(0, self.config.environment_dt - (time.perf_counter() - control_loop_start))) diff --git a/src/lerobot/cameras/opencv/camera_opencv.py b/src/lerobot/cameras/opencv/camera_opencv.py index 3665a909fed..50e55f0c22b 100644 --- a/src/lerobot/cameras/opencv/camera_opencv.py +++ b/src/lerobot/cameras/opencv/camera_opencv.py @@ -31,7 +31,7 @@ import cv2 import numpy as np -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..camera import Camera from ..utils import get_cv2_backend, get_cv2_rotation diff --git a/src/lerobot/cameras/reachy2_camera/reachy2_camera.py b/src/lerobot/cameras/reachy2_camera/reachy2_camera.py index 0daeb6bbb03..c96789f9698 100644 --- a/src/lerobot/cameras/reachy2_camera/reachy2_camera.py +++ b/src/lerobot/cameras/reachy2_camera/reachy2_camera.py @@ -31,7 +31,7 @@ from reachy2_sdk.media.camera import CameraView from reachy2_sdk.media.camera_manager import CameraManager -from lerobot.errors import DeviceNotConnectedError +from lerobot.utils.errors import DeviceNotConnectedError from ..camera import Camera from .configuration_reachy2_camera import ColorMode, Reachy2CameraConfig diff --git a/src/lerobot/cameras/realsense/camera_realsense.py b/src/lerobot/cameras/realsense/camera_realsense.py index 12ce89c9188..cc816e55253 100644 --- a/src/lerobot/cameras/realsense/camera_realsense.py +++ b/src/lerobot/cameras/realsense/camera_realsense.py @@ -29,7 +29,7 @@ except Exception as e: logging.info(f"Could not import realsense: {e}") -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..camera import Camera from ..configs import ColorMode diff --git a/src/lerobot/cameras/utils.py b/src/lerobot/cameras/utils.py index dfac33e17d9..aa6ff98b48a 100644 --- a/src/lerobot/cameras/utils.py +++ b/src/lerobot/cameras/utils.py @@ -15,19 +15,19 @@ # limitations under the License. import platform -from pathlib import Path -from typing import TypeAlias +from typing import cast + +from lerobot.utils.import_utils import make_device_from_device_class from .camera import Camera from .configs import CameraConfig, Cv2Rotation -IndexOrPath: TypeAlias = int | Path - def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[str, Camera]: - cameras = {} + cameras: dict[str, Camera] = {} for key, cfg in camera_configs.items(): + # TODO(Steven): Consider just using the make_device_from_device_class for all types if cfg.type == "opencv": from .opencv import OpenCVCamera @@ -44,7 +44,10 @@ def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[s cameras[key] = Reachy2Camera(cfg) else: - raise ValueError(f"The camera type '{cfg.type}' is not valid.") + try: + cameras[key] = cast(Camera, make_device_from_device_class(cfg)) + except Exception as e: + raise ValueError(f"Error creating camera {key} with config {cfg}: {e}") from e return cameras diff --git a/src/lerobot/configs/default.py b/src/lerobot/configs/default.py index 1bc2b8d169f..afd644e1c13 100644 --- a/src/lerobot/configs/default.py +++ b/src/lerobot/configs/default.py @@ -16,9 +16,6 @@ from dataclasses import dataclass, field -from lerobot import ( - policies, # noqa: F401 -) from lerobot.datasets.transforms import ImageTransformsConfig from lerobot.datasets.video_utils import get_safe_default_codec diff --git a/src/lerobot/configs/policies.py b/src/lerobot/configs/policies.py index f5fa727cf49..98dd4df3fa5 100644 --- a/src/lerobot/configs/policies.py +++ b/src/lerobot/configs/policies.py @@ -26,10 +26,10 @@ from huggingface_hub.constants import CONFIG_NAME from huggingface_hub.errors import HfHubHTTPError -from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature -from lerobot.constants import ACTION, OBS_STATE +from lerobot.configs.types import FeatureType, PolicyFeature from lerobot.optim.optimizers import OptimizerConfig from lerobot.optim.schedulers import LRSchedulerConfig +from lerobot.utils.constants import ACTION, OBS_STATE from lerobot.utils.hub import HubMixin from lerobot.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available @@ -53,7 +53,6 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): """ n_obs_steps: int = 1 - normalization_mapping: dict[str, NormalizationMode] = field(default_factory=dict) input_features: dict[str, PolicyFeature] = field(default_factory=dict) output_features: dict[str, PolicyFeature] = field(default_factory=dict) @@ -72,9 +71,11 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): tags: list[str] | None = None # Add tags to your policy on the hub. license: str | None = None + # Either the repo ID of a model hosted on the Hub or a path to a directory containing weights + # saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch. + pretrained_path: str | None = None def __post_init__(self): - self.pretrained_path = None if not self.device or not is_torch_device_available(self.device): auto_device = auto_select_torch_device() logging.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.") @@ -197,11 +198,10 @@ def from_pretrained( config = json.load(f) config.pop("type") - with tempfile.NamedTemporaryFile("w+") as f: + with tempfile.NamedTemporaryFile("w+", delete=False, suffix=".json") as f: json.dump(config, f) config_file = f.name - f.flush() - cli_overrides = policy_kwargs.pop("cli_overrides", []) - with draccus.config_type("json"): - return draccus.parse(orig_config.__class__, config_file, args=cli_overrides) + cli_overrides = policy_kwargs.pop("cli_overrides", []) + with draccus.config_type("json"): + return draccus.parse(orig_config.__class__, config_file, args=cli_overrides) diff --git a/src/lerobot/configs/types.py b/src/lerobot/configs/types.py index 6040ff70ba5..cb578060ef0 100644 --- a/src/lerobot/configs/types.py +++ b/src/lerobot/configs/types.py @@ -15,7 +15,6 @@ # https://stackoverflow.com/questions/24481852/serialising-an-enum-member-to-json from dataclasses import dataclass from enum import Enum -from typing import Any, Protocol class FeatureType(str, Enum): @@ -24,16 +23,20 @@ class FeatureType(str, Enum): ENV = "ENV" ACTION = "ACTION" REWARD = "REWARD" + LANGUAGE = "LANGUAGE" + + +class PipelineFeatureType(str, Enum): + ACTION = "ACTION" + OBSERVATION = "OBSERVATION" class NormalizationMode(str, Enum): MIN_MAX = "MIN_MAX" MEAN_STD = "MEAN_STD" IDENTITY = "IDENTITY" - - -class DictLike(Protocol): - def __getitem__(self, key: Any) -> Any: ... + QUANTILES = "QUANTILES" + QUANTILE10 = "QUANTILE10" @dataclass diff --git a/src/lerobot/datasets/aggregate.py b/src/lerobot/datasets/aggregate.py index 43d4ee2334e..803645f292e 100644 --- a/src/lerobot/datasets/aggregate.py +++ b/src/lerobot/datasets/aggregate.py @@ -93,14 +93,13 @@ def update_data_df(df, src_meta, dst_meta): pd.DataFrame: Updated DataFrame with adjusted indices. """ - def _update(row): - row["episode_index"] = row["episode_index"] + dst_meta.info["total_episodes"] - row["index"] = row["index"] + dst_meta.info["total_frames"] - task = src_meta.tasks.iloc[row["task_index"]].name - row["task_index"] = dst_meta.tasks.loc[task].task_index.item() - return row + df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"] + df["index"] = df["index"] + dst_meta.info["total_frames"] - return df.apply(_update, axis=1) + src_task_names = src_meta.tasks.index.take(df["task_index"].to_numpy()) + df["task_index"] = dst_meta.tasks.loc[src_task_names, "task_index"].to_numpy() + + return df def update_meta_data( @@ -126,27 +125,21 @@ def update_meta_data( pd.DataFrame: Updated DataFrame with adjusted indices and timestamps. """ - def _update(row): - row["meta/episodes/chunk_index"] = row["meta/episodes/chunk_index"] + meta_idx["chunk"] - row["meta/episodes/file_index"] = row["meta/episodes/file_index"] + meta_idx["file"] - row["data/chunk_index"] = row["data/chunk_index"] + data_idx["chunk"] - row["data/file_index"] = row["data/file_index"] + data_idx["file"] - for key, video_idx in videos_idx.items(): - row[f"videos/{key}/chunk_index"] = row[f"videos/{key}/chunk_index"] + video_idx["chunk"] - row[f"videos/{key}/file_index"] = row[f"videos/{key}/file_index"] + video_idx["file"] - row[f"videos/{key}/from_timestamp"] = ( - row[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"] - ) - row[f"videos/{key}/to_timestamp"] = ( - row[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"] - ) + df["meta/episodes/chunk_index"] = df["meta/episodes/chunk_index"] + meta_idx["chunk"] + df["meta/episodes/file_index"] = df["meta/episodes/file_index"] + meta_idx["file"] + df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"] + df["data/file_index"] = df["data/file_index"] + data_idx["file"] + for key, video_idx in videos_idx.items(): + df[f"videos/{key}/chunk_index"] = df[f"videos/{key}/chunk_index"] + video_idx["chunk"] + df[f"videos/{key}/file_index"] = df[f"videos/{key}/file_index"] + video_idx["file"] + df[f"videos/{key}/from_timestamp"] = df[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"] + df[f"videos/{key}/to_timestamp"] = df[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"] - row["dataset_from_index"] = row["dataset_from_index"] + dst_meta.info["total_frames"] - row["dataset_to_index"] = row["dataset_to_index"] + dst_meta.info["total_frames"] - row["episode_index"] = row["episode_index"] + dst_meta.info["total_episodes"] - return row + df["dataset_from_index"] = df["dataset_from_index"] + dst_meta.info["total_frames"] + df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info["total_frames"] + df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"] - return df.apply(_update, axis=1) + return df def aggregate_datasets( diff --git a/src/lerobot/datasets/backward_compatibility.py b/src/lerobot/datasets/backward_compatibility.py index 1d600434a28..ae95c5f7b53 100644 --- a/src/lerobot/datasets/backward_compatibility.py +++ b/src/lerobot/datasets/backward_compatibility.py @@ -23,6 +23,9 @@ python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id={repo_id} ``` +If you already have a converted version uploaded to the hub, then this error might be because of +an older version in your local cache. Consider deleting the cached version and retrying. + If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb) or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose). """ diff --git a/src/lerobot/datasets/compute_stats.py b/src/lerobot/datasets/compute_stats.py index bfe7b18b4e3..61e174d5caf 100644 --- a/src/lerobot/datasets/compute_stats.py +++ b/src/lerobot/datasets/compute_stats.py @@ -17,6 +17,179 @@ from lerobot.datasets.utils import load_image_as_numpy +DEFAULT_QUANTILES = [0.01, 0.10, 0.50, 0.90, 0.99] + + +class RunningQuantileStats: + """ + Maintains running statistics for batches of vectors, including mean, + standard deviation, min, max, and approximate quantiles. + + Statistics are computed per feature dimension and updated incrementally + as new batches are observed. Quantiles are estimated using histograms, + which adapt dynamically if the observed data range expands. + """ + + def __init__(self, quantile_list: list[float] | None = None, num_quantile_bins: int = 5000): + self._count = 0 + self._mean = None + self._mean_of_squares = None + self._min = None + self._max = None + self._histograms = None + self._bin_edges = None + self._num_quantile_bins = num_quantile_bins + + self._quantile_list = quantile_list + if self._quantile_list is None: + self._quantile_list = DEFAULT_QUANTILES + self._quantile_keys = [f"q{int(q * 100):02d}" for q in self._quantile_list] + + def update(self, batch: np.ndarray) -> None: + """Update the running statistics with a batch of vectors. + + Args: + batch: An array where all dimensions except the last are batch dimensions. + """ + batch = batch.reshape(-1, batch.shape[-1]) + num_elements, vector_length = batch.shape + + if self._count == 0: + self._mean = np.mean(batch, axis=0) + self._mean_of_squares = np.mean(batch**2, axis=0) + self._min = np.min(batch, axis=0) + self._max = np.max(batch, axis=0) + self._histograms = [np.zeros(self._num_quantile_bins) for _ in range(vector_length)] + self._bin_edges = [ + np.linspace(self._min[i] - 1e-10, self._max[i] + 1e-10, self._num_quantile_bins + 1) + for i in range(vector_length) + ] + else: + if vector_length != self._mean.size: + raise ValueError("The length of new vectors does not match the initialized vector length.") + + new_max = np.max(batch, axis=0) + new_min = np.min(batch, axis=0) + max_changed = np.any(new_max > self._max) + min_changed = np.any(new_min < self._min) + self._max = np.maximum(self._max, new_max) + self._min = np.minimum(self._min, new_min) + + if max_changed or min_changed: + self._adjust_histograms() + + self._count += num_elements + + batch_mean = np.mean(batch, axis=0) + batch_mean_of_squares = np.mean(batch**2, axis=0) + + # Update running mean and mean of squares + self._mean += (batch_mean - self._mean) * (num_elements / self._count) + self._mean_of_squares += (batch_mean_of_squares - self._mean_of_squares) * ( + num_elements / self._count + ) + + self._update_histograms(batch) + + def get_statistics(self) -> dict[str, np.ndarray]: + """Compute and return the statistics of the vectors processed so far. + + Args: + quantiles: List of quantiles to compute (e.g., [0.01, 0.10, 0.50, 0.90, 0.99]). If None, no quantiles computed. + + Returns: + Dictionary containing the computed statistics. + """ + if self._count < 2: + raise ValueError("Cannot compute statistics for less than 2 vectors.") + + variance = self._mean_of_squares - self._mean**2 + + stddev = np.sqrt(np.maximum(0, variance)) + + stats = { + "min": self._min.copy(), + "max": self._max.copy(), + "mean": self._mean.copy(), + "std": stddev, + "count": np.array([self._count]), + } + + quantile_results = self._compute_quantiles() + for i, q in enumerate(self._quantile_keys): + stats[q] = quantile_results[i] + + return stats + + def _adjust_histograms(self): + """Adjust histograms when min or max changes.""" + for i in range(len(self._histograms)): + old_edges = self._bin_edges[i] + old_hist = self._histograms[i] + + # Create new edges with small padding to ensure range coverage + padding = (self._max[i] - self._min[i]) * 1e-10 + new_edges = np.linspace( + self._min[i] - padding, self._max[i] + padding, self._num_quantile_bins + 1 + ) + + # Redistribute existing histogram counts to new bins + # We need to map each old bin center to the new bins + old_centers = (old_edges[:-1] + old_edges[1:]) / 2 + new_hist = np.zeros(self._num_quantile_bins) + + for old_center, count in zip(old_centers, old_hist, strict=False): + if count > 0: + # Find which new bin this old center belongs to + bin_idx = np.searchsorted(new_edges, old_center) - 1 + bin_idx = max(0, min(bin_idx, self._num_quantile_bins - 1)) + new_hist[bin_idx] += count + + self._histograms[i] = new_hist + self._bin_edges[i] = new_edges + + def _update_histograms(self, batch: np.ndarray) -> None: + """Update histograms with new vectors.""" + for i in range(batch.shape[1]): + hist, _ = np.histogram(batch[:, i], bins=self._bin_edges[i]) + self._histograms[i] += hist + + def _compute_quantiles(self) -> list[np.ndarray]: + """Compute quantiles based on histograms.""" + results = [] + for q in self._quantile_list: + target_count = q * self._count + q_values = [] + + for hist, edges in zip(self._histograms, self._bin_edges, strict=True): + q_value = self._compute_single_quantile(hist, edges, target_count) + q_values.append(q_value) + + results.append(np.array(q_values)) + return results + + def _compute_single_quantile(self, hist: np.ndarray, edges: np.ndarray, target_count: float) -> float: + """Compute a single quantile value from histogram and bin edges.""" + cumsum = np.cumsum(hist) + idx = np.searchsorted(cumsum, target_count) + + if idx == 0: + return edges[0] + if idx >= len(cumsum): + return edges[-1] + + # If not edge case, interpolate within the bin + count_before = cumsum[idx - 1] + count_in_bin = cumsum[idx] - count_before + + # If no samples in this bin, use the bin edge + if count_in_bin == 0: + return edges[idx] + + # Linear interpolation within the bin + fraction = (target_count - count_before) / count_in_bin + return edges[idx] + fraction * (edges[idx + 1] - edges[idx]) + def estimate_num_samples( dataset_len: int, min_num_samples: int = 100, max_num_samples: int = 10_000, power: float = 0.75 @@ -72,33 +245,282 @@ def sample_images(image_paths: list[str]) -> np.ndarray: return images -def get_feature_stats(array: np.ndarray, axis: tuple, keepdims: bool) -> dict[str, np.ndarray]: - return { - "min": np.min(array, axis=axis, keepdims=keepdims), - "max": np.max(array, axis=axis, keepdims=keepdims), - "mean": np.mean(array, axis=axis, keepdims=keepdims), - "std": np.std(array, axis=axis, keepdims=keepdims), - "count": np.array([len(array)]), +def _reshape_stats_by_axis( + stats: dict[str, np.ndarray], + axis: int | tuple[int, ...] | None, + keepdims: bool, + original_shape: tuple[int, ...], +) -> dict[str, np.ndarray]: + """Reshape all statistics to match NumPy's output conventions. + + Applies consistent reshaping to all statistics (except 'count') based on the + axis and keepdims parameters. This ensures statistics have the correct shape + for broadcasting with the original data. + + Args: + stats: Dictionary of computed statistics + axis: Axis or axes along which statistics were computed + keepdims: Whether to keep reduced dimensions as size-1 dimensions + original_shape: Shape of the original array + + Returns: + Dictionary with reshaped statistics + + Note: + The 'count' statistic is never reshaped as it represents metadata + rather than per-feature statistics. + """ + if axis == (1,) and not keepdims: + return stats + + result = {} + for key, value in stats.items(): + if key == "count": + result[key] = value + else: + result[key] = _reshape_single_stat(value, axis, keepdims, original_shape) + + return result + + +def _reshape_for_image_stats(value: np.ndarray, keepdims: bool) -> np.ndarray: + """Reshape statistics for image data (axis=(0,2,3)).""" + if keepdims and value.ndim == 1: + return value.reshape(1, -1, 1, 1) + return value + + +def _reshape_for_vector_stats( + value: np.ndarray, keepdims: bool, original_shape: tuple[int, ...] +) -> np.ndarray: + """Reshape statistics for vector data (axis=0 or axis=(0,)).""" + if not keepdims: + return value + + if len(original_shape) == 1 and value.ndim > 0: + return value.reshape(1) + elif len(original_shape) >= 2 and value.ndim == 1: + return value.reshape(1, -1) + return value + + +def _reshape_for_feature_stats(value: np.ndarray, keepdims: bool) -> np.ndarray: + """Reshape statistics for feature-wise computation (axis=(1,)).""" + if not keepdims: + return value + + if value.ndim == 0: + return value.reshape(1, 1) + elif value.ndim == 1: + return value.reshape(-1, 1) + return value + + +def _reshape_for_global_stats( + value: np.ndarray, keepdims: bool, original_shape: tuple[int, ...] +) -> np.ndarray | float: + """Reshape statistics for global reduction (axis=None).""" + if keepdims: + target_shape = tuple(1 for _ in original_shape) + return value.reshape(target_shape) + # Keep at least 1-D arrays to satisfy validator + return np.atleast_1d(value) + + +def _reshape_single_stat( + value: np.ndarray, axis: int | tuple[int, ...] | None, keepdims: bool, original_shape: tuple[int, ...] +) -> np.ndarray | float: + """Apply appropriate reshaping to a single statistic array. + + This function transforms statistic arrays to match expected output shapes + based on the axis configuration and keepdims parameter. + + Args: + value: The statistic array to reshape + axis: Axis or axes that were reduced during computation + keepdims: Whether to maintain reduced dimensions as size-1 dimensions + original_shape: Shape of the original data before reduction + + Returns: + Reshaped array following NumPy broadcasting conventions + + """ + if axis == (0, 2, 3): + return _reshape_for_image_stats(value, keepdims) + + if axis in [0, (0,)]: + return _reshape_for_vector_stats(value, keepdims, original_shape) + + if axis == (1,): + return _reshape_for_feature_stats(value, keepdims) + + if axis is None: + return _reshape_for_global_stats(value, keepdims, original_shape) + + return value + + +def _prepare_array_for_stats(array: np.ndarray, axis: int | tuple[int, ...] | None) -> tuple[np.ndarray, int]: + """Prepare array for statistics computation by reshaping according to axis. + + Args: + array: Input data array + axis: Axis or axes along which to compute statistics + + Returns: + Tuple of (reshaped_array, sample_count) + """ + if axis == (0, 2, 3): # Image data + batch_size, channels, height, width = array.shape + reshaped = array.transpose(0, 2, 3, 1).reshape(-1, channels) + return reshaped, batch_size + + if axis == 0 or axis == (0,): # Vector data + reshaped = array + if array.ndim == 1: + reshaped = array.reshape(-1, 1) + return reshaped, array.shape[0] + + if axis == (1,): # Feature-wise statistics + return array.T, array.shape[1] + + if axis is None: # Global statistics + reshaped = array.reshape(-1, 1) + # For backward compatibility, count represents the first dimension size + return reshaped, array.shape[0] if array.ndim > 0 else 1 + + raise ValueError(f"Unsupported axis configuration: {axis}") + + +def _compute_basic_stats( + array: np.ndarray, sample_count: int, quantile_list: list[float] | None = None +) -> dict[str, np.ndarray]: + """Compute basic statistics for arrays with insufficient samples for quantiles. + + Args: + array: Reshaped array ready for statistics computation + sample_count: Number of samples represented in the data + + Returns: + Dictionary with basic statistics and quantiles set to mean values + """ + if quantile_list is None: + quantile_list = DEFAULT_QUANTILES + quantile_list_keys = [f"q{int(q * 100):02d}" for q in quantile_list] + + stats = { + "min": np.min(array, axis=0), + "max": np.max(array, axis=0), + "mean": np.mean(array, axis=0), + "std": np.std(array, axis=0), + "count": np.array([sample_count]), } + for q in quantile_list_keys: + stats[q] = stats["mean"].copy() + + return stats + + +def get_feature_stats( + array: np.ndarray, + axis: int | tuple[int, ...] | None, + keepdims: bool, + quantile_list: list[float] | None = None, +) -> dict[str, np.ndarray]: + """Compute comprehensive statistics for array features along specified axes. + + This function calculates min, max, mean, std, and quantiles (1%, 10%, 50%, 90%, 99%) + for the input array along the specified axes. It handles different data layouts: + - Image data: axis=(0,2,3) computes per-channel statistics + - Vector data: axis=0 computes per-feature statistics + - Feature-wise: axis=1 computes statistics across features + - Global: axis=None computes statistics over entire array + + Args: + array: Input data array with shape appropriate for the specified axis + axis: Axis or axes along which to compute statistics + - (0, 2, 3): For image data (batch, channels, height, width) + - 0 or (0,): For vector/tabular data (samples, features) + - (1,): For computing across features + - None: For global statistics over entire array + keepdims: If True, reduced axes are kept as dimensions with size 1 + + Returns: + Dictionary containing: + - 'min': Minimum values + - 'max': Maximum values + - 'mean': Mean values + - 'std': Standard deviation + - 'count': Number of samples (always shape (1,)) + - 'q01', 'q10', 'q50', 'q90', 'q99': Quantile values + + """ + if quantile_list is None: + quantile_list = DEFAULT_QUANTILES + + original_shape = array.shape + reshaped, sample_count = _prepare_array_for_stats(array, axis) + + if reshaped.shape[0] < 2: + stats = _compute_basic_stats(reshaped, sample_count, quantile_list) + else: + running_stats = RunningQuantileStats() + running_stats.update(reshaped) + stats = running_stats.get_statistics() + stats["count"] = np.array([sample_count]) + + stats = _reshape_stats_by_axis(stats, axis, keepdims, original_shape) + return stats + + +def compute_episode_stats( + episode_data: dict[str, list[str] | np.ndarray], + features: dict, + quantile_list: list[float] | None = None, +) -> dict: + """Compute comprehensive statistics for all features in an episode. + + Processes different data types appropriately: + - Images/videos: Samples from paths, computes per-channel stats, normalizes to [0,1] + - Numerical arrays: Computes per-feature statistics + - Strings: Skipped (no statistics computed) + + Args: + episode_data: Dictionary mapping feature names to data + - For images/videos: list of file paths + - For numerical data: numpy arrays + features: Dictionary describing each feature's dtype and shape + + Returns: + Dictionary mapping feature names to their statistics dictionaries. + Each statistics dictionary contains min, max, mean, std, count, and quantiles. + + Note: + Image statistics are normalized to [0,1] range and have shape (3,1,1) for + per-channel values when dtype is 'image' or 'video'. + """ + if quantile_list is None: + quantile_list = DEFAULT_QUANTILES -def compute_episode_stats(episode_data: dict[str, list[str] | np.ndarray], features: dict) -> dict: ep_stats = {} for key, data in episode_data.items(): if features[key]["dtype"] == "string": - continue # HACK: we should receive np.arrays of strings - elif features[key]["dtype"] in ["image", "video"]: - ep_ft_array = sample_images(data) # data is a list of image paths - axes_to_reduce = (0, 2, 3) # keep channel dim + continue + + if features[key]["dtype"] in ["image", "video"]: + ep_ft_array = sample_images(data) + axes_to_reduce = (0, 2, 3) keepdims = True else: - ep_ft_array = data # data is already a np.ndarray - axes_to_reduce = 0 # compute stats over the first axis - keepdims = data.ndim == 1 # keep as np.array + ep_ft_array = data + axes_to_reduce = 0 + keepdims = data.ndim == 1 - ep_stats[key] = get_feature_stats(ep_ft_array, axis=axes_to_reduce, keepdims=keepdims) + ep_stats[key] = get_feature_stats( + ep_ft_array, axis=axes_to_reduce, keepdims=keepdims, quantile_list=quantile_list + ) - # finally, we normalize and remove batch dim for images if features[key]["dtype"] in ["image", "video"]: ep_stats[key] = { k: v if k == "count" else np.squeeze(v / 255.0, axis=0) for k, v in ep_stats[key].items() @@ -107,20 +529,37 @@ def compute_episode_stats(episode_data: dict[str, list[str] | np.ndarray], featu return ep_stats +def _validate_stat_value(value: np.ndarray, key: str, feature_key: str) -> None: + """Validate a single statistic value.""" + if not isinstance(value, np.ndarray): + raise ValueError( + f"Stats must be composed of numpy array, but key '{key}' of feature '{feature_key}' " + f"is of type '{type(value)}' instead." + ) + + if value.ndim == 0: + raise ValueError("Number of dimensions must be at least 1, and is 0 instead.") + + if key == "count" and value.shape != (1,): + raise ValueError(f"Shape of 'count' must be (1), but is {value.shape} instead.") + + if "image" in feature_key and key != "count" and value.shape != (3, 1, 1): + raise ValueError(f"Shape of quantile '{key}' must be (3,1,1), but is {value.shape} instead.") + + def _assert_type_and_shape(stats_list: list[dict[str, dict]]): - for i in range(len(stats_list)): - for fkey in stats_list[i]: - for k, v in stats_list[i][fkey].items(): - if not isinstance(v, np.ndarray): - raise ValueError( - f"Stats must be composed of numpy array, but key '{k}' of feature '{fkey}' is of type '{type(v)}' instead." - ) - if v.ndim == 0: - raise ValueError("Number of dimensions must be at least 1, and is 0 instead.") - if k == "count" and v.shape != (1,): - raise ValueError(f"Shape of 'count' must be (1), but is {v.shape} instead.") - if "image" in fkey and k != "count" and v.shape != (3, 1, 1): - raise ValueError(f"Shape of '{k}' must be (3,1,1), but is {v.shape} instead.") + """Validate that all statistics have correct types and shapes. + + Args: + stats_list: List of statistics dictionaries to validate + + Raises: + ValueError: If any statistic has incorrect type or shape + """ + for stats in stats_list: + for feature_key, feature_stats in stats.items(): + for stat_key, stat_value in feature_stats.items(): + _validate_stat_value(stat_value, stat_key, feature_key) def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]: @@ -143,7 +582,7 @@ def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, d weighted_variances = (variances + delta_means**2) * counts total_variance = weighted_variances.sum(axis=0) / total_count - return { + aggregated = { "min": np.min(np.stack([s["min"] for s in stats_ft_list]), axis=0), "max": np.max(np.stack([s["max"] for s in stats_ft_list]), axis=0), "mean": total_mean, @@ -151,6 +590,17 @@ def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, d "count": total_count, } + if stats_ft_list: + quantile_keys = [k for k in stats_ft_list[0] if k.startswith("q") and k[1:].isdigit()] + + for q_key in quantile_keys: + if all(q_key in s for s in stats_ft_list): + quantile_values = np.stack([s[q_key] for s in stats_ft_list]) + weighted_quantiles = quantile_values * counts + aggregated[q_key] = weighted_quantiles.sum(axis=0) / total_count + + return aggregated + def aggregate_stats(stats_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]: """Aggregate stats from multiple compute_stats outputs into a single set of stats. diff --git a/src/lerobot/datasets/factory.py b/src/lerobot/datasets/factory.py index a71e978bc53..f3ceb2b0c45 100644 --- a/src/lerobot/datasets/factory.py +++ b/src/lerobot/datasets/factory.py @@ -27,6 +27,7 @@ ) from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset from lerobot.datasets.transforms import ImageTransforms +from lerobot.utils.constants import ACTION, OBS_PREFIX, REWARD IMAGENET_STATS = { "mean": [[[0.485]], [[0.456]], [[0.406]]], # (c,1,1) @@ -54,11 +55,11 @@ def resolve_delta_timestamps( """ delta_timestamps = {} for key in ds_meta.features: - if key == "next.reward" and cfg.reward_delta_indices is not None: + if key == REWARD and cfg.reward_delta_indices is not None: delta_timestamps[key] = [i / ds_meta.fps for i in cfg.reward_delta_indices] - if key == "action" and cfg.action_delta_indices is not None: + if key == ACTION and cfg.action_delta_indices is not None: delta_timestamps[key] = [i / ds_meta.fps for i in cfg.action_delta_indices] - if key.startswith("observation.") and cfg.observation_delta_indices is not None: + if key.startswith(OBS_PREFIX) and cfg.observation_delta_indices is not None: delta_timestamps[key] = [i / ds_meta.fps for i in cfg.observation_delta_indices] if len(delta_timestamps) == 0: diff --git a/src/lerobot/datasets/image_writer.py b/src/lerobot/datasets/image_writer.py index 4a4e1ab0586..ee10df6e19c 100644 --- a/src/lerobot/datasets/image_writer.py +++ b/src/lerobot/datasets/image_writer.py @@ -68,7 +68,30 @@ def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) return PIL.Image.fromarray(image_array) -def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path): +def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1): + """ + Saves a NumPy array or PIL Image to a file. + + This function handles both NumPy arrays and PIL Image objects, converting + the former to a PIL Image before saving. It includes error handling for + the save operation. + + Args: + image (np.ndarray | PIL.Image.Image): The image data to save. + fpath (Path): The destination file path for the image. + compress_level (int, optional): The compression level for the saved + image, as used by PIL.Image.save(). Defaults to 1. + Refer to: https://github.com/huggingface/lerobot/pull/2135 + for more details on the default value rationale. + + Raises: + TypeError: If the input 'image' is not a NumPy array or a + PIL.Image.Image object. + + Side Effects: + Prints an error message to the console if the image writing process + fails for any reason. + """ try: if isinstance(image, np.ndarray): img = image_array_to_pil_image(image) @@ -76,7 +99,7 @@ def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path): img = image else: raise TypeError(f"Unsupported image type: {type(image)}") - img.save(fpath) + img.save(fpath, compress_level=compress_level) except Exception as e: print(f"Error writing image {fpath}: {e}") diff --git a/src/lerobot/datasets/lerobot_dataset.py b/src/lerobot/datasets/lerobot_dataset.py index 4ac7a841c9d..b661b21b038 100644 --- a/src/lerobot/datasets/lerobot_dataset.py +++ b/src/lerobot/datasets/lerobot_dataset.py @@ -31,7 +31,6 @@ from huggingface_hub import HfApi, snapshot_download from huggingface_hub.errors import RevisionNotFoundError -from lerobot.constants import HF_LEROBOT_HOME from lerobot.datasets.compute_stats import aggregate_stats, compute_episode_stats from lerobot.datasets.image_writer import AsyncImageWriter, write_image from lerobot.datasets.utils import ( @@ -79,6 +78,7 @@ get_video_duration_in_s, get_video_info, ) +from lerobot.utils.constants import HF_LEROBOT_HOME CODEBASE_VERSION = "v3.0" @@ -848,11 +848,6 @@ def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) - return item - def _add_padding_keys(self, item: dict, padding: dict[str, list[bool]]) -> dict: - for key, val in padding.items(): - item[key] = torch.BoolTensor(val) - return item - def __len__(self): return self.num_frames @@ -1032,7 +1027,7 @@ def save_episode(self, episode_data: dict | None = None) -> None: # Reset episode buffer and clean up temporary images (if not already deleted during video encoding) self.clear_episode_buffer(delete_images=len(self.meta.image_keys) > 0) - def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None): + def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None) -> None: """ Batch save videos for multiple episodes. @@ -1158,7 +1153,7 @@ def _save_episode_data(self, episode_buffer: dict) -> dict: } return metadata - def _save_episode_video(self, video_key: str, episode_index: int): + def _save_episode_video(self, video_key: str, episode_index: int) -> dict: # Encode episode frames into a temporary video ep_path = self._encode_temporary_episode_video(video_key, episode_index) ep_size_in_mb = get_video_size_in_mb(ep_path) @@ -1263,7 +1258,7 @@ def _wait_image_writer(self) -> None: if self.image_writer is not None: self.image_writer.wait_until_done() - def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> dict: + def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> Path: """ Use ffmpeg to convert frames stored as png into mp4 videos. Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding, @@ -1396,11 +1391,6 @@ def repo_id_to_index(self): """ return {repo_id: i for i, repo_id in enumerate(self.repo_ids)} - @property - def repo_index_to_id(self): - """Return the inverse mapping if repo_id_to_index.""" - return {v: k for k, v in self.repo_id_to_index} - @property def fps(self) -> int: """Frames per second used during data collection. @@ -1431,7 +1421,7 @@ def camera_keys(self) -> list[str]: """Keys to access image and video stream from cameras.""" keys = [] for key, feats in self.features.items(): - if isinstance(feats, (datasets.Image, VideoFrame)): + if isinstance(feats, (datasets.Image | VideoFrame)): keys.append(key) return keys diff --git a/src/lerobot/datasets/pipeline_features.py b/src/lerobot/datasets/pipeline_features.py new file mode 100644 index 00000000000..4fad7bd20b3 --- /dev/null +++ b/src/lerobot/datasets/pipeline_features.py @@ -0,0 +1,139 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +from collections.abc import Sequence +from typing import Any + +from lerobot.configs.types import PipelineFeatureType +from lerobot.datasets.utils import hw_to_dataset_features +from lerobot.processor import DataProcessorPipeline +from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR + + +def create_initial_features( + action: dict[str, Any] | None = None, observation: dict[str, Any] | None = None +) -> dict[PipelineFeatureType, dict[str, Any]]: + """ + Creates the initial features dict for the dataset from action and observation specs. + + Args: + action: A dictionary of action feature names to their types/shapes. + observation: A dictionary of observation feature names to their types/shapes. + + Returns: + The initial features dictionary structured by PipelineFeatureType. + """ + features = {PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: {}} + if action: + features[PipelineFeatureType.ACTION] = action + if observation: + features[PipelineFeatureType.OBSERVATION] = observation + return features + + +# Helper to filter state/action keys based on regex patterns. +def should_keep(key: str, patterns: tuple[str]) -> bool: + if patterns is None: + return True + return any(re.search(pat, key) for pat in patterns) + + +def strip_prefix(key: str, prefixes_to_strip: tuple[str]) -> str: + for prefix in prefixes_to_strip: + if key.startswith(prefix): + return key[len(prefix) :] + return key + + +# Define prefixes to strip from feature keys for clean names. +# Handles both fully qualified (e.g., "action.state") and short (e.g., "state") forms. +PREFIXES_TO_STRIP = tuple( + f"{token}." for const in (ACTION, OBS_STATE, OBS_IMAGES) for token in (const, const.split(".")[-1]) +) + + +def aggregate_pipeline_dataset_features( + pipeline: DataProcessorPipeline, + initial_features: dict[PipelineFeatureType, dict[str, Any]], + *, + use_videos: bool = True, + patterns: Sequence[str] | None = None, +) -> dict[str, dict]: + """ + Aggregates and filters pipeline features to create a dataset-ready features dictionary. + + This function transforms initial features using the pipeline, categorizes them as action or observations + (image or state), filters them based on `use_videos` and `patterns`, and finally + formats them for use with a Hugging Face LeRobot Dataset. + + Args: + pipeline: The DataProcessorPipeline to apply. + initial_features: A dictionary of raw feature specs for actions and observations. + use_videos: If False, image features are excluded. + patterns: A sequence of regex patterns to filter action and state features. + Image features are not affected by this filter. + + Returns: + A dictionary of features formatted for a Hugging Face LeRobot Dataset. + """ + all_features = pipeline.transform_features(initial_features) + + # Intermediate storage for categorized and filtered features. + processed_features: dict[str, dict[str, Any]] = { + ACTION: {}, + OBS_STR: {}, + } + images_token = OBS_IMAGES.split(".")[-1] + + # Iterate through all features transformed by the pipeline. + for ptype, feats in all_features.items(): + if ptype not in [PipelineFeatureType.ACTION, PipelineFeatureType.OBSERVATION]: + continue + + for key, value in feats.items(): + # 1. Categorize the feature. + is_action = ptype == PipelineFeatureType.ACTION + # Observations are classified as images if their key matches image-related tokens or if the shape of the feature is 3. + # All other observations are treated as state. + is_image = not is_action and ( + (isinstance(value, tuple) and len(value) == 3) + or ( + key.startswith(f"{OBS_IMAGES}.") + or key.startswith(f"{images_token}.") + or f".{images_token}." in key + ) + ) + + # 2. Apply filtering rules. + if is_image and not use_videos: + continue + if not is_image and not should_keep(key, patterns): + continue + + # 3. Add the feature to the appropriate group with a clean name. + name = strip_prefix(key, PREFIXES_TO_STRIP) + if is_action: + processed_features[ACTION][name] = value + else: + processed_features[OBS_STR][name] = value + + # Convert the processed features into the final dataset format. + dataset_features = {} + if processed_features[ACTION]: + dataset_features.update(hw_to_dataset_features(processed_features[ACTION], ACTION, use_videos)) + if processed_features[OBS_STR]: + dataset_features.update(hw_to_dataset_features(processed_features[OBS_STR], OBS_STR, use_videos)) + + return dataset_features diff --git a/src/lerobot/datasets/push_dataset_to_hub/utils.py b/src/lerobot/datasets/push_dataset_to_hub/utils.py index 5f6363a77be..48214e1bf27 100644 --- a/src/lerobot/datasets/push_dataset_to_hub/utils.py +++ b/src/lerobot/datasets/push_dataset_to_hub/utils.py @@ -13,67 +13,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import inspect -from concurrent.futures import ThreadPoolExecutor -from pathlib import Path import datasets -import numpy -import PIL import torch -from lerobot.datasets.video_utils import encode_video_frames - - -def concatenate_episodes(ep_dicts): - data_dict = {} - - keys = ep_dicts[0].keys() - for key in keys: - if torch.is_tensor(ep_dicts[0][key][0]): - data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts]) - else: - if key not in data_dict: - data_dict[key] = [] - for ep_dict in ep_dicts: - for x in ep_dict[key]: - data_dict[key].append(x) - - total_frames = data_dict["frame_index"].shape[0] - data_dict["index"] = torch.arange(0, total_frames, 1) - return data_dict - - -def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4): - out_dir = Path(out_dir) - out_dir.mkdir(parents=True, exist_ok=True) - - def save_image(img_array, i, out_dir): - img = PIL.Image.fromarray(img_array) - img.save(str(out_dir / f"frame_{i:06d}.png"), quality=100) - - num_images = len(imgs_array) - with ThreadPoolExecutor(max_workers=max_workers) as executor: - [executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)] - - -def get_default_encoding() -> dict: - """Returns the default ffmpeg encoding parameters used by `encode_video_frames`.""" - signature = inspect.signature(encode_video_frames) - return { - k: v.default - for k, v in signature.parameters.items() - if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"] - } - - -def check_repo_id(repo_id: str) -> None: - if len(repo_id.split("/")) != 2: - raise ValueError( - f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset - (e.g. 'lerobot/pusht'), but contains '{repo_id}'.""" - ) - # TODO(aliberts): remove def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> dict[str, torch.Tensor]: diff --git a/src/lerobot/datasets/streaming_dataset.py b/src/lerobot/datasets/streaming_dataset.py index e354c406010..454389d46f6 100644 --- a/src/lerobot/datasets/streaming_dataset.py +++ b/src/lerobot/datasets/streaming_dataset.py @@ -21,7 +21,6 @@ import torch from datasets import load_dataset -from lerobot.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDatasetMetadata from lerobot.datasets.utils import ( Backtrackable, @@ -38,6 +37,7 @@ VideoDecoderCache, decode_video_frames_torchcodec, ) +from lerobot.utils.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE class StreamingLeRobotDataset(torch.utils.data.IterableDataset): @@ -298,9 +298,7 @@ def _get_video_frame_padding_mask( return padding_mask - def make_frame( - self, dataset_iterator: Backtrackable, previous_dataset_iterator: Backtrackable | None = None - ) -> Generator: + def make_frame(self, dataset_iterator: Backtrackable) -> Generator: """Makes a frame starting from a dataset iterator""" item = next(dataset_iterator) item = item_to_torch(item) diff --git a/src/lerobot/datasets/transforms.py b/src/lerobot/datasets/transforms.py index f992275b7c8..f7072c72fb2 100644 --- a/src/lerobot/datasets/transforms.py +++ b/src/lerobot/datasets/transforms.py @@ -120,7 +120,7 @@ def __init__(self, sharpness: float | Sequence[float]) -> None: self.sharpness = self._check_input(sharpness) def _check_input(self, sharpness): - if isinstance(sharpness, (int, float)): + if isinstance(sharpness, (int | float)): if sharpness < 0: raise ValueError("If sharpness is a single number, it must be non negative.") sharpness = [1.0 - sharpness, 1.0 + sharpness] diff --git a/src/lerobot/datasets/utils.py b/src/lerobot/datasets/utils.py index c840d5bc1f0..a2f2850141a 100644 --- a/src/lerobot/datasets/utils.py +++ b/src/lerobot/datasets/utils.py @@ -21,7 +21,7 @@ from collections.abc import Iterable, Iterator from pathlib import Path from pprint import pformat -from typing import Any, Deque, Generic, TypeVar +from typing import Any, Generic, TypeVar import datasets import numpy as np @@ -43,6 +43,7 @@ BackwardCompatibilityError, ForwardCompatibilityError, ) +from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_STR from lerobot.utils.utils import is_valid_numpy_dtype_string DEFAULT_CHUNK_SIZE = 1000 # Max number of files per chunk @@ -66,18 +67,6 @@ LEGACY_EPISODES_PATH = "meta/episodes.jsonl" LEGACY_EPISODES_STATS_PATH = "meta/episodes_stats.jsonl" LEGACY_TASKS_PATH = "meta/tasks.jsonl" -LEGACY_DEFAULT_VIDEO_PATH = "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4" -LEGACY_DEFAULT_PARQUET_PATH = "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet" - -DATASET_CARD_TEMPLATE = """ ---- -# Metadata will go there ---- -This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). - -## {} - -""" DEFAULT_FEATURES = { "timestamp": {"dtype": "float32", "shape": (1,), "names": None}, @@ -150,14 +139,20 @@ def get_video_size_in_mb(mp4_path: Path) -> float: def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict: - """Flatten a nested dictionary structure by collapsing nested keys into one key with a separator. + """Flatten a nested dictionary by joining keys with a separator. - For example: - ``` - >>> dct = {"a": {"b": 1, "c": {"d": 2}}, "e": 3}` - >>> print(flatten_dict(dct)) - {"a/b": 1, "a/c/d": 2, "e": 3} - ``` + Example: + >>> dct = {"a": {"b": 1, "c": {"d": 2}}, "e": 3} + >>> print(flatten_dict(dct)) + {'a/b': 1, 'a/c/d': 2, 'e': 3} + + Args: + d (dict): The dictionary to flatten. + parent_key (str): The base key to prepend to the keys in this level. + sep (str): The separator to use between keys. + + Returns: + dict: A flattened dictionary. """ items = [] for k, v in d.items(): @@ -170,6 +165,20 @@ def flatten_dict(d: dict, parent_key: str = "", sep: str = "/") -> dict: def unflatten_dict(d: dict, sep: str = "/") -> dict: + """Unflatten a dictionary with delimited keys into a nested dictionary. + + Example: + >>> flat_dct = {"a/b": 1, "a/c/d": 2, "e": 3} + >>> print(unflatten_dict(flat_dct)) + {'a': {'b': 1, 'c': {'d': 2}}, 'e': 3} + + Args: + d (dict): A dictionary with flattened keys. + sep (str): The separator used in the keys. + + Returns: + dict: A nested dictionary. + """ outdict = {} for key, value in d.items(): parts = key.split(sep) @@ -183,15 +192,28 @@ def unflatten_dict(d: dict, sep: str = "/") -> dict: def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict: + """Serialize a dictionary containing tensors or numpy arrays to be JSON-compatible. + + Converts torch.Tensor, np.ndarray, and np.generic types to lists or native Python types. + + Args: + stats (dict): A dictionary that may contain non-serializable numeric types. + + Returns: + dict: A dictionary with all values converted to JSON-serializable types. + + Raises: + NotImplementedError: If a value has an unsupported type. + """ serialized_dict = {} for key, value in flatten_dict(stats).items(): - if isinstance(value, (torch.Tensor, np.ndarray)): + if isinstance(value, (torch.Tensor | np.ndarray)): serialized_dict[key] = value.tolist() - elif isinstance(value, list) and isinstance(value[0], (int, float, list)): + elif isinstance(value, list) and isinstance(value[0], (int | float | list)): serialized_dict[key] = value elif isinstance(value, np.generic): serialized_dict[key] = value.item() - elif isinstance(value, (int, float)): + elif isinstance(value, (int | float)): serialized_dict[key] = value else: raise NotImplementedError(f"The value '{value}' of type '{type(value)}' is not supported.") @@ -199,6 +221,17 @@ def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict: def embed_images(dataset: datasets.Dataset) -> datasets.Dataset: + """Embed image bytes into the dataset table before saving to Parquet. + + This function prepares a Hugging Face dataset for serialization by converting + image objects into an embedded format that can be stored in Arrow/Parquet. + + Args: + dataset (datasets.Dataset): The input dataset, possibly containing image features. + + Returns: + datasets.Dataset: The dataset with images embedded in the table storage. + """ # Embed image bytes into the table before saving to parquet format = dataset.format dataset = dataset.with_format("arrow") @@ -208,11 +241,27 @@ def embed_images(dataset: datasets.Dataset) -> datasets.Dataset: def load_json(fpath: Path) -> Any: + """Load data from a JSON file. + + Args: + fpath (Path): Path to the JSON file. + + Returns: + Any: The data loaded from the JSON file. + """ with open(fpath) as f: return json.load(f) def write_json(data: dict, fpath: Path) -> None: + """Write data to a JSON file. + + Creates parent directories if they don't exist. + + Args: + data (dict): The dictionary to write. + fpath (Path): The path to the output JSON file. + """ fpath.parent.mkdir(exist_ok=True, parents=True) with open(fpath, "w") as f: json.dump(data, f, indent=4, ensure_ascii=False) @@ -223,6 +272,16 @@ def write_info(info: dict, local_dir: Path) -> None: def load_info(local_dir: Path) -> dict: + """Load dataset info metadata from its standard file path. + + Also converts shape lists to tuples for consistency. + + Args: + local_dir (Path): The root directory of the dataset. + + Returns: + dict: The dataset information dictionary. + """ info = load_json(local_dir / INFO_PATH) for ft in info["features"].values(): ft["shape"] = tuple(ft["shape"]) @@ -230,16 +289,40 @@ def load_info(local_dir: Path) -> dict: def write_stats(stats: dict, local_dir: Path) -> None: + """Serialize and write dataset statistics to their standard file path. + + Args: + stats (dict): The statistics dictionary (can contain tensors/numpy arrays). + local_dir (Path): The root directory of the dataset. + """ serialized_stats = serialize_dict(stats) write_json(serialized_stats, local_dir / STATS_PATH) def cast_stats_to_numpy(stats: dict) -> dict[str, dict[str, np.ndarray]]: + """Recursively cast numerical values in a stats dictionary to numpy arrays. + + Args: + stats (dict): The statistics dictionary. + + Returns: + dict: The statistics dictionary with values cast to numpy arrays. + """ stats = {key: np.array(value) for key, value in flatten_dict(stats).items()} return unflatten_dict(stats) def load_stats(local_dir: Path) -> dict[str, dict[str, np.ndarray]] | None: + """Load dataset statistics and cast numerical values to numpy arrays. + + Returns None if the stats file doesn't exist. + + Args: + local_dir (Path): The root directory of the dataset. + + Returns: + A dictionary of statistics or None if the file is not found. + """ if not (local_dir / STATS_PATH).exists(): return None stats = load_json(local_dir / STATS_PATH) @@ -288,15 +371,21 @@ def load_episodes(local_dir: Path) -> datasets.Dataset: return episodes -def backward_compatible_episodes_stats( - stats: dict[str, dict[str, np.ndarray]], episodes: list[int] -) -> dict[int, dict[str, dict[str, np.ndarray]]]: - return dict.fromkeys(episodes, stats) - - def load_image_as_numpy( fpath: str | Path, dtype: np.dtype = np.float32, channel_first: bool = True ) -> np.ndarray: + """Load an image from a file into a numpy array. + + Args: + fpath (str | Path): Path to the image file. + dtype (np.dtype): The desired data type of the output array. If floating, + pixels are scaled to [0, 1]. + channel_first (bool): If True, converts the image to (C, H, W) format. + Otherwise, it remains in (H, W, C) format. + + Returns: + np.ndarray: The image as a numpy array. + """ img = PILImage.open(fpath).convert("RGB") img_array = np.array(img, dtype=dtype) if channel_first: # (H, W, C) -> (C, H, W) @@ -307,10 +396,19 @@ def load_image_as_numpy( def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]: - """Get a transform function that convert items from Hugging Face dataset (pyarrow) - to torch tensors. Importantly, images are converted from PIL, which corresponds to - a channel last representation (h w c) of uint8 type, to a torch image representation - with channel first (c h w) of float32 type in range [0,1]. + """Convert a batch from a Hugging Face dataset to torch tensors. + + This transform function converts items from Hugging Face dataset format (pyarrow) + to torch tensors. Importantly, images are converted from PIL objects (H, W, C, uint8) + to a torch image representation (C, H, W, float32) in the range [0, 1]. Other + types are converted to torch.tensor. + + Args: + items_dict (dict): A dictionary representing a batch of data from a + Hugging Face dataset. + + Returns: + dict: The batch with items converted to torch tensors. """ for key in items_dict: first_item = items_dict[key][0] @@ -325,6 +423,14 @@ def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[to def is_valid_version(version: str) -> bool: + """Check if a string is a valid PEP 440 version. + + Args: + version (str): The version string to check. + + Returns: + bool: True if the version string is valid, False otherwise. + """ try: packaging.version.parse(version) return True @@ -338,6 +444,18 @@ def check_version_compatibility( current_version: str | packaging.version.Version, enforce_breaking_major: bool = True, ) -> None: + """Check for version compatibility between a dataset and the current codebase. + + Args: + repo_id (str): The repository ID for logging purposes. + version_to_check (str | packaging.version.Version): The version of the dataset. + current_version (str | packaging.version.Version): The current version of the codebase. + enforce_breaking_major (bool): If True, raise an error on major version mismatch. + + Raises: + BackwardCompatibilityError: If the dataset version is from a newer, incompatible + major version of the codebase. + """ v_check = ( packaging.version.parse(version_to_check) if not isinstance(version_to_check, packaging.version.Version) @@ -355,7 +473,14 @@ def check_version_compatibility( def get_repo_versions(repo_id: str) -> list[packaging.version.Version]: - """Returns available valid versions (branches and tags) on given repo.""" + """Return available valid versions (branches and tags) on a given Hub repo. + + Args: + repo_id (str): The repository ID on the Hugging Face Hub. + + Returns: + list[packaging.version.Version]: A list of valid versions found. + """ api = HfApi() repo_refs = api.list_repo_refs(repo_id, repo_type="dataset") repo_refs = [b.name for b in repo_refs.branches + repo_refs.tags] @@ -368,9 +493,22 @@ def get_repo_versions(repo_id: str) -> list[packaging.version.Version]: def get_safe_version(repo_id: str, version: str | packaging.version.Version) -> str: - """ - Returns the version if available on repo or the latest compatible one. - Otherwise, will throw a `CompatibilityError`. + """Return the specified version if available on repo, or the latest compatible one. + + If the exact version is not found, it looks for the latest version with the + same major version number that is less than or equal to the target minor version. + + Args: + repo_id (str): The repository ID on the Hugging Face Hub. + version (str | packaging.version.Version): The target version. + + Returns: + str: The safe version string (e.g., "v1.2.3") to use as a revision. + + Raises: + RevisionNotFoundError: If the repo has no version tags. + BackwardCompatibilityError: If only older major versions are available. + ForwardCompatibilityError: If only newer major versions are available. """ target_version = ( packaging.version.parse(version) if not isinstance(version, packaging.version.Version) else version @@ -412,6 +550,17 @@ def get_safe_version(repo_id: str, version: str | packaging.version.Version) -> def get_hf_features_from_features(features: dict) -> datasets.Features: + """Convert a LeRobot features dictionary to a `datasets.Features` object. + + Args: + features (dict): A LeRobot-style feature dictionary. + + Returns: + datasets.Features: The corresponding Hugging Face `datasets.Features` object. + + Raises: + ValueError: If a feature has an unsupported shape. + """ hf_features = {} for key, ft in features.items(): if ft["dtype"] == "video": @@ -439,6 +588,14 @@ def get_hf_features_from_features(features: dict) -> datasets.Features: def _validate_feature_names(features: dict[str, dict]) -> None: + """Validate that feature names do not contain invalid characters. + + Args: + features (dict): The LeRobot features dictionary. + + Raises: + ValueError: If any feature name contains '/'. + """ invalid_features = {name: ft for name, ft in features.items() if "/" in name} if invalid_features: raise ValueError(f"Feature names should not contain '/'. Found '/' in '{invalid_features}'.") @@ -447,18 +604,38 @@ def _validate_feature_names(features: dict[str, dict]) -> None: def hw_to_dataset_features( hw_features: dict[str, type | tuple], prefix: str, use_video: bool = True ) -> dict[str, dict]: + """Convert hardware-specific features to a LeRobot dataset feature dictionary. + + This function takes a dictionary describing hardware outputs (like joint states + or camera image shapes) and formats it into the standard LeRobot feature + specification. + + Args: + hw_features (dict): Dictionary mapping feature names to their type (float for + joints) or shape (tuple for images). + prefix (str): The prefix to add to the feature keys (e.g., "observation" + or "action"). + use_video (bool): If True, image features are marked as "video", otherwise "image". + + Returns: + dict: A LeRobot features dictionary. + """ features = {} - joint_fts = {key: ftype for key, ftype in hw_features.items() if ftype is float} + joint_fts = { + key: ftype + for key, ftype in hw_features.items() + if ftype is float or (isinstance(ftype, PolicyFeature) and ftype.type != FeatureType.VISUAL) + } cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)} - if joint_fts and prefix == "action": + if joint_fts and prefix == ACTION: features[prefix] = { "dtype": "float32", "shape": (len(joint_fts),), "names": list(joint_fts), } - if joint_fts and prefix == "observation": + if joint_fts and prefix == OBS_STR: features[f"{prefix}.state"] = { "dtype": "float32", "shape": (len(joint_fts),), @@ -479,6 +656,20 @@ def hw_to_dataset_features( def build_dataset_frame( ds_features: dict[str, dict], values: dict[str, Any], prefix: str ) -> dict[str, np.ndarray]: + """Construct a single data frame from raw values based on dataset features. + + A "frame" is a dictionary containing all the data for a single timestep, + formatted as numpy arrays according to the feature specification. + + Args: + ds_features (dict): The LeRobot dataset features dictionary. + values (dict): A dictionary of raw values from the hardware/environment. + prefix (str): The prefix to filter features by (e.g., "observation" + or "action"). + + Returns: + dict: A dictionary representing a single frame of data. + """ frame = {} for key, ft in ds_features.items(): if key in DEFAULT_FEATURES or not key.startswith(prefix): @@ -492,6 +683,21 @@ def build_dataset_frame( def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFeature]: + """Convert dataset features to policy features. + + This function transforms the dataset's feature specification into a format + that a policy can use, classifying features by type (e.g., visual, state, + action) and ensuring correct shapes (e.g., channel-first for images). + + Args: + features (dict): The LeRobot dataset features dictionary. + + Returns: + dict: A dictionary mapping feature keys to `PolicyFeature` objects. + + Raises: + ValueError: If an image feature does not have a 3D shape. + """ # TODO(aliberts): Implement "type" in dataset features and simplify this policy_features = {} for key, ft in features.items(): @@ -505,11 +711,11 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets. if names[2] in ["channel", "channels"]: # (h, w, c) -> (c, h, w) shape = (shape[2], shape[0], shape[1]) - elif key == "observation.environment_state": + elif key == OBS_ENV_STATE: type = FeatureType.ENV - elif key.startswith("observation"): + elif key.startswith(OBS_STR): type = FeatureType.STATE - elif key.startswith("action"): + elif key.startswith(ACTION): type = FeatureType.ACTION else: continue @@ -522,6 +728,58 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea return policy_features +def combine_feature_dicts(*dicts: dict) -> dict: + """Merge LeRobot grouped feature dicts. + + - For 1D numeric specs (dtype not image/video/string) with "names": we merge the names and recompute the shape. + - For others (e.g. `observation.images.*`), the last one wins (if they are identical). + + Args: + *dicts: A variable number of LeRobot feature dictionaries to merge. + + Returns: + dict: A single merged feature dictionary. + + Raises: + ValueError: If there's a dtype mismatch for a feature being merged. + """ + out: dict = {} + for d in dicts: + for key, value in d.items(): + if not isinstance(value, dict): + out[key] = value + continue + + dtype = value.get("dtype") + shape = value.get("shape") + is_vector = ( + dtype not in ("image", "video", "string") + and isinstance(shape, tuple) + and len(shape) == 1 + and "names" in value + ) + + if is_vector: + # Initialize or retrieve the accumulating dict for this feature key + target = out.setdefault(key, {"dtype": dtype, "names": [], "shape": (0,)}) + # Ensure consistent data types across merged entries + if "dtype" in target and dtype != target["dtype"]: + raise ValueError(f"dtype mismatch for '{key}': {target['dtype']} vs {dtype}") + + # Merge feature names: append only new ones to preserve order without duplicates + seen = set(target["names"]) + for n in value["names"]: + if n not in seen: + target["names"].append(n) + seen.add(n) + # Recompute the shape to reflect the updated number of features + target["shape"] = (len(target["names"]),) + else: + # For images/videos and non-1D entries: override with the latest definition + out[key] = value + return out + + def create_empty_dataset_info( codebase_version: str, fps: int, @@ -532,6 +790,18 @@ def create_empty_dataset_info( data_files_size_in_mb: int | None = None, video_files_size_in_mb: int | None = None, ) -> dict: + """Create a template dictionary for a new dataset's `info.json`. + + Args: + codebase_version (str): The version of the LeRobot codebase. + fps (int): The frames per second of the data. + features (dict): The LeRobot features dictionary for the dataset. + use_videos (bool): Whether the dataset will store videos. + robot_type (str | None): The type of robot used, if any. + + Returns: + dict: A dictionary with the initial dataset metadata. + """ return { "codebase_version": codebase_version, "robot_type": robot_type, @@ -552,9 +822,23 @@ def create_empty_dataset_info( def check_delta_timestamps( delta_timestamps: dict[str, list[float]], fps: int, tolerance_s: float, raise_value_error: bool = True ) -> bool: - """This will check if all the values in delta_timestamps are multiples of 1/fps +/- tolerance. - This is to ensure that these delta_timestamps added to any timestamp from a dataset will themselves be - actual timestamps from the dataset. + """Check if delta timestamps are multiples of 1/fps +/- tolerance. + + This ensures that adding these delta timestamps to any existing timestamp in + the dataset will result in a value that aligns with the dataset's frame rate. + + Args: + delta_timestamps (dict): A dictionary where values are lists of time + deltas in seconds. + fps (int): The frames per second of the dataset. + tolerance_s (float): The allowed tolerance in seconds. + raise_value_error (bool): If True, raises an error on failure. + + Returns: + bool: True if all deltas are valid, False otherwise. + + Raises: + ValueError: If any delta is outside the tolerance and `raise_value_error` is True. """ outside_tolerance = {} for key, delta_ts in delta_timestamps.items(): @@ -580,6 +864,15 @@ def check_delta_timestamps( def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dict[str, list[int]]: + """Convert delta timestamps in seconds to delta indices in frames. + + Args: + delta_timestamps (dict): A dictionary of time deltas in seconds. + fps (int): The frames per second of the dataset. + + Returns: + dict: A dictionary of frame delta indices. + """ delta_indices = {} for key, delta_ts in delta_timestamps.items(): delta_indices[key] = [round(d * fps) for d in delta_ts] @@ -588,9 +881,17 @@ def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dic def cycle(iterable: Any) -> Iterator[Any]: - """The equivalent of itertools.cycle, but safe for Pytorch dataloaders. + """Create a dataloader-safe cyclical iterator. + + This is an equivalent of `itertools.cycle` but is safe for use with + PyTorch DataLoaders with multiple workers. + See https://github.com/pytorch/pytorch/issues/23900 for details. - See https://github.com/pytorch/pytorch/issues/23900 for information on why itertools.cycle is not safe. + Args: + iterable: The iterable to cycle over. + + Yields: + Items from the iterable, restarting from the beginning when exhausted. """ iterator = iter(iterable) while True: @@ -601,8 +902,14 @@ def cycle(iterable: Any) -> Iterator[Any]: def create_branch(repo_id: str, *, branch: str, repo_type: str | None = None) -> None: - """Create a branch on a existing Hugging Face repo. Delete the branch if it already - exists before creating it. + """Create a branch on an existing Hugging Face repo. + + Deletes the branch if it already exists before creating it. + + Args: + repo_id (str): The ID of the repository. + branch (str): The name of the branch to create. + repo_type (str | None): The type of the repository (e.g., "dataset"). """ api = HfApi() @@ -620,9 +927,20 @@ def create_lerobot_dataset_card( dataset_info: dict | None = None, **kwargs, ) -> DatasetCard: - """ - Keyword arguments will be used to replace values in src/lerobot/datasets/card_template.md. - Note: If specified, license must be one of https://huggingface.co/docs/hub/repositories-licenses. + """Create a `DatasetCard` for a LeRobot dataset. + + Keyword arguments are used to replace values in the card template. + Note: If specified, `license` must be a valid license identifier from + https://huggingface.co/docs/hub/repositories-licenses. + + Args: + tags (list | None): A list of tags to add to the dataset card. + dataset_info (dict | None): The dataset's info dictionary, which will + be displayed on the card. + **kwargs: Additional keyword arguments to populate the card template. + + Returns: + DatasetCard: The generated dataset card object. """ card_tags = ["LeRobot"] @@ -675,6 +993,15 @@ def validate_frame(frame: dict, features: dict) -> None: def validate_features_presence(actual_features: set[str], expected_features: set[str]) -> str: + """Check for missing or extra features in a frame. + + Args: + actual_features (set[str]): The set of feature names present in the frame. + expected_features (set[str]): The set of feature names expected in the frame. + + Returns: + str: An error message string if there's a mismatch, otherwise an empty string. + """ error_message = "" missing_features = expected_features - actual_features extra_features = actual_features - expected_features @@ -692,6 +1019,19 @@ def validate_features_presence(actual_features: set[str], expected_features: set def validate_feature_dtype_and_shape( name: str, feature: dict, value: np.ndarray | PILImage.Image | str ) -> str: + """Validate the dtype and shape of a single feature's value. + + Args: + name (str): The name of the feature. + feature (dict): The feature specification from the LeRobot features dictionary. + value: The value of the feature to validate. + + Returns: + str: An error message if validation fails, otherwise an empty string. + + Raises: + NotImplementedError: If the feature dtype is not supported for validation. + """ expected_dtype = feature["dtype"] expected_shape = feature["shape"] if is_valid_numpy_dtype_string(expected_dtype): @@ -707,6 +1047,17 @@ def validate_feature_dtype_and_shape( def validate_feature_numpy_array( name: str, expected_dtype: str, expected_shape: list[int], value: np.ndarray ) -> str: + """Validate a feature that is expected to be a numpy array. + + Args: + name (str): The name of the feature. + expected_dtype (str): The expected numpy dtype as a string. + expected_shape (list[int]): The expected shape. + value (np.ndarray): The numpy array to validate. + + Returns: + str: An error message if validation fails, otherwise an empty string. + """ error_message = "" if isinstance(value, np.ndarray): actual_dtype = value.dtype @@ -726,6 +1077,18 @@ def validate_feature_numpy_array( def validate_feature_image_or_video( name: str, expected_shape: list[str], value: np.ndarray | PILImage.Image ) -> str: + """Validate a feature that is expected to be an image or video frame. + + Accepts `np.ndarray` (channel-first or channel-last) or `PIL.Image.Image`. + + Args: + name (str): The name of the feature. + expected_shape (list[str]): The expected shape (C, H, W). + value: The image data to validate. + + Returns: + str: An error message if validation fails, otherwise an empty string. + """ # Note: The check of pixels range ([0,1] for float and [0,255] for uint8) is done by the image writer threads. error_message = "" if isinstance(value, np.ndarray): @@ -742,12 +1105,35 @@ def validate_feature_image_or_video( def validate_feature_string(name: str, value: str) -> str: + """Validate a feature that is expected to be a string. + + Args: + name (str): The name of the feature. + value (str): The value to validate. + + Returns: + str: An error message if validation fails, otherwise an empty string. + """ if not isinstance(value, str): return f"The feature '{name}' is expected to be of type 'str', but type '{type(value)}' provided instead.\n" return "" def validate_episode_buffer(episode_buffer: dict, total_episodes: int, features: dict) -> None: + """Validate the episode buffer before it's written to disk. + + Ensures the buffer has the required keys, contains at least one frame, and + has features consistent with the dataset's specification. + + Args: + episode_buffer (dict): The buffer containing data for a single episode. + total_episodes (int): The current total number of episodes in the dataset. + features (dict): The LeRobot features dictionary for the dataset. + + Raises: + ValueError: If the buffer is invalid. + NotImplementedError: If the episode index is manually set and doesn't match. + """ if "size" not in episode_buffer: raise ValueError("size key not found in episode_buffer") @@ -793,7 +1179,7 @@ def item_to_torch(item: dict) -> dict: dict: Dictionary with all tensor-like items converted to torch.Tensor. """ for key, val in item.items(): - if isinstance(val, (np.ndarray, list)) and key not in ["task"]: + if isinstance(val, (np.ndarray | list)) and key not in ["task"]: # Convert numpy arrays and lists to torch tensors item[key] = torch.tensor(val) return item @@ -867,8 +1253,8 @@ def __init__(self, iterable: Iterable[T], *, history: int = 1, lookahead: int = raise ValueError("lookahead must be > 0") self._source: Iterator[T] = iter(iterable) - self._back_buf: Deque[T] = deque(maxlen=history) - self._ahead_buf: Deque[T] = deque(maxlen=lookahead) if lookahead > 0 else deque() + self._back_buf: deque[T] = deque(maxlen=history) + self._ahead_buf: deque[T] = deque(maxlen=lookahead) if lookahead > 0 else deque() self._cursor: int = 0 self._history = history self._lookahead = lookahead @@ -942,12 +1328,6 @@ def history(self) -> list[T]: # When cursor<0, slice so the order remains chronological return list(self._back_buf)[: self._cursor or None] - def lookahead_buffer(self) -> list[T]: - """ - Return a copy of the current lookahead buffer. - """ - return list(self._ahead_buf) - def can_peek_back(self, steps: int = 1) -> bool: """ Check if we can go back `steps` items without raising an IndexError. @@ -973,31 +1353,6 @@ def can_peek_ahead(self, steps: int = 1) -> bool: except StopIteration: return False - def reset_cursor(self) -> None: - """ - Reset cursor to the most recent position (equivalent to calling next() - until you're back to the latest item). - """ - self._cursor = 0 - - def clear_ahead_buffer(self) -> None: - """ - Clear the ahead buffer, discarding any pre-fetched items. - """ - self._ahead_buf.clear() - - def switch_source_iterable(self, new_source: Iterable[T]) -> None: - """ - Switch the source of the backtrackable to a new iterable, keeping the history. - - This is useful when iterating over a sequence of datasets. The history from the - previous source is kept, but the lookahead buffer is cleared. The cursor is reset - to the present. - """ - self._source = iter(new_source) - self.clear_ahead_buffer() - self.reset_cursor() - def safe_shard(dataset: datasets.IterableDataset, index: int, num_shards: int) -> datasets.Dataset: """ diff --git a/src/lerobot/datasets/v30/augment_dataset_quantile_stats.py b/src/lerobot/datasets/v30/augment_dataset_quantile_stats.py new file mode 100644 index 00000000000..900a43a4f0d --- /dev/null +++ b/src/lerobot/datasets/v30/augment_dataset_quantile_stats.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script augments existing LeRobot datasets with quantile statistics. + +Most datasets created before the quantile feature was added do not contain +quantile statistics (q01, q10, q50, q90, q99) in their metadata. This script: + +1. Loads an existing LeRobot dataset in v3.0 format +2. Checks if it already contains quantile statistics +3. If missing, computes quantile statistics for all features +4. Updates the dataset metadata with the new quantile statistics + +Usage: + +```bash +python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \ + --repo-id=lerobot/pusht \ +``` +""" + +import argparse +import concurrent.futures +import logging +from pathlib import Path + +import numpy as np +import torch +from huggingface_hub import HfApi +from requests import HTTPError +from tqdm import tqdm + +from lerobot.datasets.compute_stats import DEFAULT_QUANTILES, aggregate_stats, get_feature_stats +from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset +from lerobot.datasets.utils import write_stats +from lerobot.utils.utils import init_logging + + +def has_quantile_stats(stats: dict[str, dict] | None, quantile_list_keys: list[str] | None = None) -> bool: + """Check if dataset statistics already contain quantile information. + + Args: + stats: Dataset statistics dictionary + + Returns: + True if quantile statistics are present, False otherwise + """ + if quantile_list_keys is None: + quantile_list_keys = [f"q{int(q * 100):02d}" for q in DEFAULT_QUANTILES] + + if stats is None: + return False + + for feature_stats in stats.values(): + if any(q_key in feature_stats for q_key in quantile_list_keys): + return True + + return False + + +def process_single_episode(dataset: LeRobotDataset, episode_idx: int) -> dict: + """Process a single episode and return its statistics. + + Args: + dataset: The LeRobot dataset + episode_idx: Index of the episode to process + + Returns: + Dictionary containing episode statistics + """ + logging.info(f"Computing stats for episode {episode_idx}") + + start_idx = dataset.meta.episodes[episode_idx]["dataset_from_index"] + end_idx = dataset.meta.episodes[episode_idx]["dataset_to_index"] + + collected_data: dict[str, list] = {} + for idx in range(start_idx, end_idx): + item = dataset[idx] + for key, value in item.items(): + if key not in dataset.features: + continue + + if key not in collected_data: + collected_data[key] = [] + collected_data[key].append(value) + + ep_stats = {} + for key, data_list in collected_data.items(): + if dataset.features[key]["dtype"] == "string": + continue + + data = torch.stack(data_list).cpu().numpy() + if dataset.features[key]["dtype"] in ["image", "video"]: + if data.dtype == np.uint8: + data = data.astype(np.float32) / 255.0 + + axes_to_reduce = (0, 2, 3) + keepdims = True + else: + axes_to_reduce = 0 + keepdims = data.ndim == 1 + + ep_stats[key] = get_feature_stats( + data, axis=axes_to_reduce, keepdims=keepdims, quantile_list=DEFAULT_QUANTILES + ) + + if dataset.features[key]["dtype"] in ["image", "video"]: + ep_stats[key] = { + k: v if k == "count" else np.squeeze(v, axis=0) for k, v in ep_stats[key].items() + } + + return ep_stats + + +def compute_quantile_stats_for_dataset(dataset: LeRobotDataset) -> dict[str, dict]: + """Compute quantile statistics for all episodes in the dataset. + + Args: + dataset: The LeRobot dataset to compute statistics for + + Returns: + Dictionary containing aggregated statistics with quantiles + + Note: + Video decoding operations are not thread-safe, so we process episodes sequentially + when video keys are present. For datasets without videos, we use parallel processing + with ThreadPoolExecutor for better performance. + """ + logging.info(f"Computing quantile statistics for dataset with {dataset.num_episodes} episodes") + + episode_stats_list = [] + has_videos = len(dataset.meta.video_keys) > 0 + + if has_videos: + logging.info("Dataset contains video keys - using sequential processing for thread safety") + for episode_idx in tqdm(range(dataset.num_episodes), desc="Processing episodes"): + ep_stats = process_single_episode(dataset, episode_idx) + episode_stats_list.append(ep_stats) + else: + logging.info("Dataset has no video keys - using parallel processing for better performance") + max_workers = min(dataset.num_episodes, 16) + + with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: + future_to_episode = { + executor.submit(process_single_episode, dataset, episode_idx): episode_idx + for episode_idx in range(dataset.num_episodes) + } + + episode_results = {} + with tqdm(total=dataset.num_episodes, desc="Processing episodes") as pbar: + for future in concurrent.futures.as_completed(future_to_episode): + episode_idx = future_to_episode[future] + ep_stats = future.result() + episode_results[episode_idx] = ep_stats + pbar.update(1) + + for episode_idx in range(dataset.num_episodes): + if episode_idx in episode_results: + episode_stats_list.append(episode_results[episode_idx]) + + if not episode_stats_list: + raise ValueError("No episode data found for computing statistics") + + logging.info(f"Aggregating statistics from {len(episode_stats_list)} episodes") + return aggregate_stats(episode_stats_list) + + +def augment_dataset_with_quantile_stats( + repo_id: str, + root: str | Path | None = None, + overwrite: bool = False, +) -> None: + """Augment a dataset with quantile statistics if they are missing. + + Args: + repo_id: Repository ID of the dataset + root: Local root directory for the dataset + overwrite: Overwrite existing quantile statistics if they already exist + """ + logging.info(f"Loading dataset: {repo_id}") + dataset = LeRobotDataset( + repo_id=repo_id, + root=root, + ) + + if not overwrite and has_quantile_stats(dataset.meta.stats): + logging.info("Dataset already contains quantile statistics. No action needed.") + return + + logging.info("Dataset does not contain quantile statistics. Computing them now...") + + new_stats = compute_quantile_stats_for_dataset(dataset) + + logging.info("Updating dataset metadata with new quantile statistics") + dataset.meta.stats = new_stats + + write_stats(new_stats, dataset.meta.root) + + logging.info("Successfully updated dataset with quantile statistics") + dataset.push_to_hub() + + hub_api = HfApi() + try: + hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset") + except HTTPError as e: + logging.info(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})") + pass + hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=None, repo_type="dataset") + + +def main(): + """Main function to run the augmentation script.""" + parser = argparse.ArgumentParser(description="Augment LeRobot dataset with quantile statistics") + + parser.add_argument( + "--repo-id", + type=str, + required=True, + help="Repository ID of the dataset (e.g., 'lerobot/pusht')", + ) + + parser.add_argument( + "--root", + type=str, + help="Local root directory for the dataset", + ) + parser.add_argument( + "--overwrite", + action="store_true", + help="Overwrite existing quantile statistics if they already exist", + ) + + args = parser.parse_args() + root = Path(args.root) if args.root else None + + init_logging() + + augment_dataset_with_quantile_stats( + repo_id=args.repo_id, + root=root, + overwrite=args.overwrite, + ) + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py b/src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py index 96bdc18973f..42ab2f6425a 100644 --- a/src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py +++ b/src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py @@ -26,14 +26,24 @@ Usage: +Convert a dataset from the hub: ```bash python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \ --repo-id=lerobot/pusht ``` +Convert a local dataset (works in place): +```bash +python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \ + --repo-id=lerobot/pusht \ + --root=/path/to/local/dataset/directory + --push-to-hub=false +``` + """ import argparse +import logging import shutil from pathlib import Path from typing import Any @@ -46,7 +56,6 @@ from huggingface_hub import HfApi, snapshot_download from requests import HTTPError -from lerobot.constants import HF_LEROBOT_HOME from lerobot.datasets.compute_stats import aggregate_stats from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset from lerobot.datasets.utils import ( @@ -71,9 +80,11 @@ write_tasks, ) from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s +from lerobot.utils.constants import HF_LEROBOT_HOME +from lerobot.utils.utils import init_logging V21 = "v2.1" - +V30 = "v3.0" """ ------------------------- @@ -143,7 +154,19 @@ def legacy_load_tasks(local_dir: Path) -> tuple[dict, dict]: return tasks, task_to_task_index +def validate_local_dataset_version(local_path: Path) -> None: + """Validate that the local dataset has the expected v2.1 version.""" + info = load_info(local_path) + dataset_version = info.get("codebase_version", "unknown") + if dataset_version != V21: + raise ValueError( + f"Local dataset has codebase version '{dataset_version}', expected '{V21}'. " + f"This script is specifically for converting v2.1 datasets to v3.0." + ) + + def convert_tasks(root, new_root): + logging.info(f"Converting tasks from {root} to {new_root}") tasks, _ = legacy_load_tasks(root) task_indices = tasks.keys() task_strings = tasks.values() @@ -185,7 +208,10 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int): num_frames = 0 paths_to_cat = [] episodes_metadata = [] - for ep_path in ep_paths: + + logging.info(f"Converting data files from {len(ep_paths)} episodes") + + for ep_path in tqdm.tqdm(ep_paths, desc="convert data files"): ep_size_in_mb = get_parquet_file_size_in_mb(ep_path) ep_num_frames = get_parquet_num_frames(ep_path) ep_metadata = { @@ -209,7 +235,6 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int): # Reset for the next file size_in_mb = ep_size_in_mb - num_frames = ep_num_frames paths_to_cat = [ep_path] chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE) @@ -236,6 +261,8 @@ def get_image_keys(root): def convert_videos(root: Path, new_root: Path, video_file_size_in_mb: int): + logging.info(f"Converting videos from {root} to {new_root}") + video_keys = get_video_keys(root) if len(video_keys) == 0: return None @@ -254,7 +281,7 @@ def convert_videos(root: Path, new_root: Path, video_file_size_in_mb: int): episods_metadata = [] num_cameras = len(video_keys) num_episodes = num_eps_per_cam[0] - for ep_idx in range(num_episodes): + for ep_idx in tqdm.tqdm(range(num_episodes), desc="convert videos"): # Sanity check ep_ids = [eps_metadata_per_cam[cam_idx][ep_idx]["episode_index"] for cam_idx in range(num_cameras)] ep_ids += [ep_idx] @@ -281,6 +308,7 @@ def convert_videos_of_camera(root: Path, new_root: Path, video_key: str, video_f duration_in_s = 0.0 paths_to_cat = [] episodes_metadata = [] + for ep_path in tqdm.tqdm(ep_paths, desc=f"convert videos of {video_key}"): ep_size_in_mb = get_video_size_in_mb(ep_path) ep_duration_in_s = get_video_duration_in_s(ep_path) @@ -374,6 +402,8 @@ def generate_episode_metadata_dict( def convert_episodes_metadata(root, new_root, episodes_metadata, episodes_video_metadata=None): + logging.info(f"Converting episodes metadata from {root} to {new_root}") + episodes_legacy_metadata = legacy_load_episodes(root) episodes_stats = legacy_load_episodes_stats(root) @@ -397,14 +427,15 @@ def convert_episodes_metadata(root, new_root, episodes_metadata, episodes_video_ def convert_info(root, new_root, data_file_size_in_mb, video_file_size_in_mb): info = load_info(root) - info["codebase_version"] = "v3.0" + info["codebase_version"] = V30 del info["total_chunks"] del info["total_videos"] info["data_files_size_in_mb"] = data_file_size_in_mb info["video_files_size_in_mb"] = video_file_size_in_mb info["data_path"] = DEFAULT_DATA_PATH - info["video_path"] = DEFAULT_VIDEO_PATH - info["fps"] = float(info["fps"]) + info["video_path"] = DEFAULT_VIDEO_PATH if info["video_path"] is not None else None + info["fps"] = int(info["fps"]) + logging.info(f"Converting info from {root} to {new_root}") for key in info["features"]: if info["features"][key]["dtype"] == "video": # already has fps in video_info @@ -418,16 +449,36 @@ def convert_dataset( branch: str | None = None, data_file_size_in_mb: int | None = None, video_file_size_in_mb: int | None = None, + root: str | Path | None = None, + push_to_hub: bool = True, + force_conversion: bool = False, ): - root = HF_LEROBOT_HOME / repo_id - old_root = HF_LEROBOT_HOME / f"{repo_id}_old" - new_root = HF_LEROBOT_HOME / f"{repo_id}_v30" - if data_file_size_in_mb is None: data_file_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB if video_file_size_in_mb is None: video_file_size_in_mb = DEFAULT_VIDEO_FILE_SIZE_IN_MB + # First check if the dataset already has a v3.0 version + if root is None and not force_conversion: + try: + print("Trying to download v3.0 version of the dataset from the hub...") + snapshot_download(repo_id, repo_type="dataset", revision=V30, local_dir=HF_LEROBOT_HOME / repo_id) + return + except Exception: + print("Dataset does not have an uploaded v3.0 version. Continuing with conversion.") + + # Set root based on whether local dataset path is provided + use_local_dataset = False + root = HF_LEROBOT_HOME / repo_id if root is None else Path(root) / repo_id + if root.exists(): + validate_local_dataset_version(root) + use_local_dataset = True + print(f"Using local dataset at {root}") + + old_root = root.parent / f"{root.name}_old" + new_root = root.parent / f"{root.name}_v30" + + # Handle old_root cleanup if both old_root and root exist if old_root.is_dir() and root.is_dir(): shutil.rmtree(str(root)) shutil.move(str(old_root), str(root)) @@ -435,12 +486,13 @@ def convert_dataset( if new_root.is_dir(): shutil.rmtree(new_root) - snapshot_download( - repo_id, - repo_type="dataset", - revision=V21, - local_dir=root, - ) + if not use_local_dataset: + snapshot_download( + repo_id, + repo_type="dataset", + revision=V21, + local_dir=root, + ) convert_info(root, new_root, data_file_size_in_mb, video_file_size_in_mb) convert_tasks(root, new_root) @@ -451,24 +503,26 @@ def convert_dataset( shutil.move(str(root), str(old_root)) shutil.move(str(new_root), str(root)) - hub_api = HfApi() - try: - hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset") - except HTTPError as e: - print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})") - pass - hub_api.delete_files( - delete_patterns=["data/chunk*/episode_*", "meta/*.jsonl", "videos/chunk*"], - repo_id=repo_id, - revision=branch, - repo_type="dataset", - ) - hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset") + if push_to_hub: + hub_api = HfApi() + try: + hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset") + except HTTPError as e: + print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})") + pass + hub_api.delete_files( + delete_patterns=["data/chunk*/episode_*", "meta/*.jsonl", "videos/chunk*"], + repo_id=repo_id, + revision=branch, + repo_type="dataset", + ) + hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset") - LeRobotDataset(repo_id).push_to_hub() + LeRobotDataset(repo_id).push_to_hub() if __name__ == "__main__": + init_logging() parser = argparse.ArgumentParser() parser.add_argument( "--repo-id", @@ -495,6 +549,23 @@ def convert_dataset( default=None, help="File size in MB. Defaults to 100 for data and 500 for videos.", ) + parser.add_argument( + "--root", + type=str, + default=None, + help="Local directory to use for downloading/writing the dataset.", + ) + parser.add_argument( + "--push-to-hub", + type=lambda input: input.lower() == "true", + default=True, + help="Push the converted dataset to the hub.", + ) + parser.add_argument( + "--force-conversion", + action="store_true", + help="Force conversion even if the dataset already has a v3.0 version.", + ) args = parser.parse_args() convert_dataset(**vars(args)) diff --git a/src/lerobot/datasets/video_utils.py b/src/lerobot/datasets/video_utils.py index 9da89022b3f..1d4f07c769a 100644 --- a/src/lerobot/datasets/video_utils.py +++ b/src/lerobot/datasets/video_utils.py @@ -428,7 +428,7 @@ def concatenate_video_files( with tempfile.NamedTemporaryFile(mode="w", suffix=".ffconcat", delete=False) as tmp_concatenate_file: tmp_concatenate_file.write("ffconcat version 1.0\n") for input_path in input_video_paths: - tmp_concatenate_file.write(f"file '{str(input_path)}'\n") + tmp_concatenate_file.write(f"file '{str(input_path.resolve())}'\n") tmp_concatenate_file.flush() tmp_concatenate_path = tmp_concatenate_file.name @@ -437,7 +437,9 @@ def concatenate_video_files( tmp_concatenate_path, mode="r", format="concat", options={"safe": "0"} ) # safe = 0 allows absolute paths as well as relative paths - tmp_output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name + with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_named_file: + tmp_output_video_path = tmp_named_file.name + output_container = av.open( tmp_output_video_path, mode="w", options={"movflags": "faststart"} ) # faststart is to move the metadata to the beginning of the file to speed up loading @@ -449,11 +451,6 @@ def concatenate_video_files( stream_map[input_stream.index] = output_container.add_stream_from_template( template=input_stream, opaque=True ) - stream_map[ - input_stream.index - ].time_base = ( - input_stream.time_base - ) # set the time base to the input stream time base (missing in the codec context) # Demux + remux packets (no re-encode) for packet in input_container.demux(): @@ -585,19 +582,6 @@ def get_video_pixel_channels(pix_fmt: str) -> int: raise ValueError("Unknown format") -def get_image_pixel_channels(image: Image): - if image.mode == "L": - return 1 # Grayscale - elif image.mode == "LA": - return 2 # Grayscale + Alpha - elif image.mode == "RGB": - return 3 # RGB - elif image.mode == "RGBA": - return 4 # RGBA - else: - raise ValueError("Unknown format") - - def get_video_duration_in_s(video_path: Path | str) -> float: """ Get the duration of a video file in seconds using PyAV. diff --git a/src/lerobot/envs/configs.py b/src/lerobot/envs/configs.py index 35797c6edfc..0daaaf9fd1f 100644 --- a/src/lerobot/envs/configs.py +++ b/src/lerobot/envs/configs.py @@ -19,9 +19,9 @@ import draccus from lerobot.configs.types import FeatureType, PolicyFeature -from lerobot.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE from lerobot.robots import RobotConfig from lerobot.teleoperators.config import TeleoperatorConfig +from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE @dataclass @@ -30,6 +30,8 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC): fps: int = 30 features: dict[str, PolicyFeature] = field(default_factory=dict) features_map: dict[str, str] = field(default_factory=dict) + max_parallel_tasks: int = 1 + disable_env_checker: bool = True @property def type(self) -> str: @@ -51,12 +53,12 @@ class AlohaEnv(EnvConfig): render_mode: str = "rgb_array" features: dict[str, PolicyFeature] = field( default_factory=lambda: { - "action": PolicyFeature(type=FeatureType.ACTION, shape=(14,)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(14,)), } ) features_map: dict[str, str] = field( default_factory=lambda: { - "action": ACTION, + ACTION: ACTION, "agent_pos": OBS_STATE, "top": f"{OBS_IMAGE}.top", "pixels/top": f"{OBS_IMAGES}.top", @@ -91,13 +93,13 @@ class PushtEnv(EnvConfig): visualization_height: int = 384 features: dict[str, PolicyFeature] = field( default_factory=lambda: { - "action": PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), "agent_pos": PolicyFeature(type=FeatureType.STATE, shape=(2,)), } ) features_map: dict[str, str] = field( default_factory=lambda: { - "action": ACTION, + ACTION: ACTION, "agent_pos": OBS_STATE, "environment_state": OBS_ENV_STATE, "pixels": OBS_IMAGE, @@ -133,13 +135,13 @@ class XarmEnv(EnvConfig): visualization_height: int = 384 features: dict[str, PolicyFeature] = field( default_factory=lambda: { - "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(4,)), "pixels": PolicyFeature(type=FeatureType.VISUAL, shape=(84, 84, 3)), } ) features_map: dict[str, str] = field( default_factory=lambda: { - "action": ACTION, + ACTION: ACTION, "agent_pos": OBS_STATE, "pixels": OBS_IMAGE, } @@ -161,33 +163,69 @@ def gym_kwargs(self) -> dict: @dataclass -class VideoRecordConfig: - """Configuration for video recording in ManiSkill environments.""" +class ImagePreprocessingConfig: + crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None + resize_size: tuple[int, int] | None = None + - enabled: bool = False - record_dir: str = "videos" - trajectory_name: str = "trajectory" +@dataclass +class RewardClassifierConfig: + """Configuration for reward classification.""" + + pretrained_path: str | None = None + success_threshold: float = 0.5 + success_reward: float = 1.0 @dataclass -class EnvTransformConfig: - """Configuration for environment wrappers.""" +class InverseKinematicsConfig: + """Configuration for inverse kinematics processing.""" + + urdf_path: str | None = None + target_frame_name: str | None = None + end_effector_bounds: dict[str, list[float]] | None = None + end_effector_step_sizes: dict[str, float] | None = None + + +@dataclass +class ObservationConfig: + """Configuration for observation processing.""" - # ee_action_space_params: EEActionSpaceConfig = field(default_factory=EEActionSpaceConfig) - control_mode: str = "gamepad" - display_cameras: bool = False add_joint_velocity_to_observation: bool = False add_current_to_observation: bool = False - add_ee_pose_to_observation: bool = False - crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None - resize_size: tuple[int, int] | None = None - control_time_s: float = 20.0 - fixed_reset_joint_positions: Any | None = None - reset_time_s: float = 5.0 + display_cameras: bool = False + + +@dataclass +class GripperConfig: + """Configuration for gripper control and penalties.""" + use_gripper: bool = True - gripper_quantization_threshold: float | None = 0.8 gripper_penalty: float = 0.0 - gripper_penalty_in_reward: bool = False + + +@dataclass +class ResetConfig: + """Configuration for environment reset behavior.""" + + fixed_reset_joint_positions: Any | None = None + reset_time_s: float = 5.0 + control_time_s: float = 20.0 + terminate_on_success: bool = True + + +@dataclass +class HILSerlProcessorConfig: + """Configuration for environment processing pipeline.""" + + control_mode: str = "gamepad" + observation: ObservationConfig | None = None + image_preprocessing: ImagePreprocessingConfig | None = None + gripper: GripperConfig | None = None + reset: ResetConfig | None = None + inverse_kinematics: InverseKinematicsConfig | None = None + reward_classifier: RewardClassifierConfig | None = None + max_gripper_pos: float | None = 100.0 @EnvConfig.register_subclass(name="gym_manipulator") @@ -197,77 +235,62 @@ class HILSerlRobotEnvConfig(EnvConfig): robot: RobotConfig | None = None teleop: TeleoperatorConfig | None = None - wrapper: EnvTransformConfig | None = None - fps: int = 10 + processor: HILSerlProcessorConfig = field(default_factory=HILSerlProcessorConfig) + name: str = "real_robot" - mode: str | None = None # Either "record", "replay", None - repo_id: str | None = None - dataset_root: str | None = None - task: str | None = "" - num_episodes: int = 10 # only for record mode - episode: int = 0 - device: str = "cuda" - push_to_hub: bool = True - pretrained_policy_name_or_path: str | None = None - reward_classifier_pretrained_path: str | None = None - # For the reward classifier, to record more positive examples after a success - number_of_steps_after_success: int = 0 @property def gym_kwargs(self) -> dict: return {} -@EnvConfig.register_subclass("hil") +@EnvConfig.register_subclass("libero") @dataclass -class HILEnvConfig(EnvConfig): - """Configuration for the HIL environment.""" - - name: str = "PandaPickCube" - task: str | None = "PandaPickCubeKeyboard-v0" - use_viewer: bool = True - gripper_penalty: float = 0.0 - use_gamepad: bool = True - state_dim: int = 18 - action_dim: int = 4 - fps: int = 100 - episode_length: int = 100 - video_record: VideoRecordConfig = field(default_factory=VideoRecordConfig) +class LiberoEnv(EnvConfig): + task: str = "libero_10" # can also choose libero_spatial, libero_object, etc. + fps: int = 30 + episode_length: int = 520 + obs_type: str = "pixels_agent_pos" + render_mode: str = "rgb_array" + camera_name: str = "agentview_image,robot0_eye_in_hand_image" + init_states: bool = True + camera_name_mapping: dict[str, str] | None = None features: dict[str, PolicyFeature] = field( default_factory=lambda: { - "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)), - "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), - "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(18,)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)), } ) features_map: dict[str, str] = field( default_factory=lambda: { - "action": ACTION, - "observation.image": OBS_IMAGE, - "observation.state": OBS_STATE, + ACTION: ACTION, + "agent_pos": OBS_STATE, + "pixels/agentview_image": f"{OBS_IMAGES}.image", + "pixels/robot0_eye_in_hand_image": f"{OBS_IMAGES}.image2", } ) - ################# args from hilserlrobotenv - reward_classifier_pretrained_path: str | None = None - robot_config: RobotConfig | None = None - teleop_config: TeleoperatorConfig | None = None - wrapper: EnvTransformConfig | None = None - mode: str | None = None # Either "record", "replay", None - repo_id: str | None = None - dataset_root: str | None = None - num_episodes: int = 10 # only for record mode - episode: int = 0 - device: str = "cuda" - push_to_hub: bool = True - pretrained_policy_name_or_path: str | None = None - # For the reward classifier, to record more positive examples after a success - number_of_steps_after_success: int = 0 - ############################ + + def __post_init__(self): + if self.obs_type == "pixels": + self.features["pixels/agentview_image"] = PolicyFeature( + type=FeatureType.VISUAL, shape=(360, 360, 3) + ) + self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature( + type=FeatureType.VISUAL, shape=(360, 360, 3) + ) + elif self.obs_type == "pixels_agent_pos": + self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(8,)) + self.features["pixels/agentview_image"] = PolicyFeature( + type=FeatureType.VISUAL, shape=(360, 360, 3) + ) + self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature( + type=FeatureType.VISUAL, shape=(360, 360, 3) + ) + else: + raise ValueError(f"Unsupported obs_type: {self.obs_type}") @property def gym_kwargs(self) -> dict: return { - "use_viewer": self.use_viewer, - "use_gamepad": self.use_gamepad, - "gripper_penalty": self.gripper_penalty, + "obs_type": self.obs_type, + "render_mode": self.render_mode, } diff --git a/src/lerobot/envs/factory.py b/src/lerobot/envs/factory.py index dc6d96d61af..c27f01b65b1 100644 --- a/src/lerobot/envs/factory.py +++ b/src/lerobot/envs/factory.py @@ -17,7 +17,7 @@ import gymnasium as gym -from lerobot.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv +from lerobot.envs.configs import AlohaEnv, EnvConfig, LiberoEnv, PushtEnv, XarmEnv def make_env_config(env_type: str, **kwargs) -> EnvConfig: @@ -27,13 +27,15 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig: return PushtEnv(**kwargs) elif env_type == "xarm": return XarmEnv(**kwargs) - elif env_type == "hil": - return HILEnvConfig(**kwargs) + elif env_type == "libero": + return LiberoEnv(**kwargs) else: raise ValueError(f"Policy type '{env_type}' is not available.") -def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> gym.vector.VectorEnv | None: +def make_env( + cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False +) -> dict[str, dict[int, gym.vector.VectorEnv]]: """Makes a gym vector environment according to the config. Args: @@ -47,13 +49,33 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g ModuleNotFoundError: If the requested env package is not installed Returns: - gym.vector.VectorEnv: The parallelized gym.env instance. + dict[str, dict[int, gym.vector.VectorEnv]]: + A mapping from suite name to indexed vectorized environments. + - For multi-task benchmarks (e.g., LIBERO): one entry per suite, and one vec env per task_id. + - For single-task environments: a single suite entry (cfg.type) with task_id=0. + """ if n_envs < 1: - raise ValueError("`n_envs must be at least 1") + raise ValueError("`n_envs` must be at least 1") - package_name = f"gym_{cfg.type}" + env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv + + if "libero" in cfg.type: + from lerobot.envs.libero import create_libero_envs + + if cfg.task is None: + raise ValueError("LiberoEnv requires a task to be specified") + return create_libero_envs( + task=cfg.task, + n_envs=n_envs, + camera_name=cfg.camera_name, + init_states=cfg.init_states, + gym_kwargs=cfg.gym_kwargs, + env_cls=env_cls, + ) + + package_name = f"gym_{cfg.type}" try: importlib.import_module(package_name) except ModuleNotFoundError as e: @@ -62,10 +84,11 @@ def make_env(cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False) -> g gym_handle = f"{package_name}/{cfg.task}" - # batched version of the env that returns an observation of shape (b, c) - env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv - env = env_cls( - [lambda: gym.make(gym_handle, disable_env_checker=True, **cfg.gym_kwargs) for _ in range(n_envs)] - ) + def _make_one(): + return gym.make(gym_handle, disable_env_checker=cfg.disable_env_checker, **(cfg.gym_kwargs or {})) + + vec = env_cls([_make_one for _ in range(n_envs)]) - return env + # normalize to {suite: {task_id: vec_env}} for consistency + suite_name = cfg.type # e.g., "pusht", "aloha" + return {suite_name: {0: vec}} diff --git a/src/lerobot/envs/libero.py b/src/lerobot/envs/libero.py new file mode 100644 index 00000000000..99ec6712ff1 --- /dev/null +++ b/src/lerobot/envs/libero.py @@ -0,0 +1,377 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import os +from collections import defaultdict +from collections.abc import Callable, Iterable, Mapping, Sequence +from functools import partial +from pathlib import Path +from typing import Any + +import gymnasium as gym +import numpy as np +import torch +from gymnasium import spaces +from libero.libero import benchmark, get_libero_path +from libero.libero.envs import OffScreenRenderEnv +from robosuite.utils.transform_utils import quat2axisangle + + +def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]: + """Normalize camera_name into a non-empty list of strings.""" + if isinstance(camera_name, str): + cams = [c.strip() for c in camera_name.split(",") if c.strip()] + elif isinstance(camera_name, (list | tuple)): + cams = [str(c).strip() for c in camera_name if str(c).strip()] + else: + raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}") + if not cams: + raise ValueError("camera_name resolved to an empty list.") + return cams + + +def _get_suite(name: str) -> benchmark.Benchmark: + """Instantiate a LIBERO suite by name with clear validation.""" + bench = benchmark.get_benchmark_dict() + if name not in bench: + raise ValueError(f"Unknown LIBERO suite '{name}'. Available: {', '.join(sorted(bench.keys()))}") + suite = bench[name]() + if not getattr(suite, "tasks", None): + raise ValueError(f"Suite '{name}' has no tasks.") + return suite + + +def _select_task_ids(total_tasks: int, task_ids: Iterable[int] | None) -> list[int]: + """Validate/normalize task ids. If None → all tasks.""" + if task_ids is None: + return list(range(total_tasks)) + ids = sorted({int(t) for t in task_ids}) + for t in ids: + if t < 0 or t >= total_tasks: + raise ValueError(f"task_id {t} out of range [0, {total_tasks - 1}].") + return ids + + +def get_task_init_states(task_suite: Any, i: int) -> np.ndarray: + init_states_path = ( + Path(get_libero_path("init_states")) + / task_suite.tasks[i].problem_folder + / task_suite.tasks[i].init_states_file + ) + init_states = torch.load(init_states_path, weights_only=False) # nosec B614 + return init_states + + +def get_libero_dummy_action(): + """Get dummy/no-op action, used to roll out the simulation while the robot does nothing.""" + return [0, 0, 0, 0, 0, 0, -1] + + +OBS_STATE_DIM = 8 +ACTION_DIM = 7 +AGENT_POS_LOW = -1000.0 +AGENT_POS_HIGH = 1000.0 +ACTION_LOW = -1.0 +ACTION_HIGH = 1.0 +TASK_SUITE_MAX_STEPS: dict[str, int] = { + "libero_spatial": 280, # longest training demo has 193 steps + "libero_object": 280, # longest training demo has 254 steps + "libero_goal": 300, # longest training demo has 270 steps + "libero_10": 520, # longest training demo has 505 steps + "libero_90": 400, # longest training demo has 373 steps +} + + +class LiberoEnv(gym.Env): + metadata = {"render_modes": ["rgb_array"], "render_fps": 80} + + def __init__( + self, + task_suite: Any, + task_id: int, + task_suite_name: str, + camera_name: str | Sequence[str] = "agentview_image,robot0_eye_in_hand_image", + obs_type: str = "pixels", + render_mode: str = "rgb_array", + observation_width: int = 256, + observation_height: int = 256, + visualization_width: int = 640, + visualization_height: int = 480, + init_states: bool = True, + episode_index: int = 0, + camera_name_mapping: dict[str, str] | None = None, + num_steps_wait: int = 10, + ): + super().__init__() + self.task_id = task_id + self.obs_type = obs_type + self.render_mode = render_mode + self.observation_width = observation_width + self.observation_height = observation_height + self.visualization_width = visualization_width + self.visualization_height = visualization_height + self.init_states = init_states + self.camera_name = _parse_camera_names( + camera_name + ) # agentview_image (main) or robot0_eye_in_hand_image (wrist) + + # Map raw camera names to "image1" and "image2". + # The preprocessing step `preprocess_observation` will then prefix these with `.images.*`, + # following the LeRobot convention (e.g., `observation.images.image`, `observation.images.image2`). + # This ensures the policy consistently receives observations in the + # expected format regardless of the original camera naming. + if camera_name_mapping is None: + camera_name_mapping = { + "agentview_image": "image", + "robot0_eye_in_hand_image": "image2", + } + self.camera_name_mapping = camera_name_mapping + self.num_steps_wait = num_steps_wait + self.episode_index = episode_index + # Load once and keep + self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None + self._init_state_id = self.episode_index # tie each sub-env to a fixed init state + + self._env = self._make_envs_task(task_suite, self.task_id) + default_steps = 500 + self._max_episode_steps = TASK_SUITE_MAX_STEPS.get(task_suite_name, default_steps) + + images = {} + for cam in self.camera_name: + images[self.camera_name_mapping[cam]] = spaces.Box( + low=0, + high=255, + shape=(self.observation_height, self.observation_width, 3), + dtype=np.uint8, + ) + + if self.obs_type == "state": + raise NotImplementedError( + "The 'state' observation type is not supported in LiberoEnv. " + "Please switch to an image-based obs_type (e.g. 'pixels', 'pixels_agent_pos')." + ) + + elif self.obs_type == "pixels": + self.observation_space = spaces.Dict( + { + "pixels": spaces.Dict(images), + } + ) + elif self.obs_type == "pixels_agent_pos": + self.observation_space = spaces.Dict( + { + "pixels": spaces.Dict(images), + "agent_pos": spaces.Box( + low=AGENT_POS_LOW, + high=AGENT_POS_HIGH, + shape=(OBS_STATE_DIM,), + dtype=np.float64, + ), + } + ) + + self.action_space = spaces.Box( + low=ACTION_LOW, high=ACTION_HIGH, shape=(ACTION_DIM,), dtype=np.float32 + ) + + def render(self): + raw_obs = self._env.env._get_observations() + image = self._format_raw_obs(raw_obs)["pixels"]["image"] + return image + + def _make_envs_task(self, task_suite: Any, task_id: int = 0): + task = task_suite.get_task(task_id) + self.task = task.name + self.task_description = task.language + task_bddl_file = os.path.join(get_libero_path("bddl_files"), task.problem_folder, task.bddl_file) + + env_args = { + "bddl_file_name": task_bddl_file, + "camera_heights": self.observation_height, + "camera_widths": self.observation_width, + } + env = OffScreenRenderEnv(**env_args) + env.reset() + return env + + def _format_raw_obs(self, raw_obs: dict[str, Any]) -> dict[str, Any]: + images = {} + for camera_name in self.camera_name: + image = raw_obs[camera_name] + image = image[::-1, ::-1] # rotate 180 degrees + images[self.camera_name_mapping[camera_name]] = image + state = np.concatenate( + ( + raw_obs["robot0_eef_pos"], + quat2axisangle(raw_obs["robot0_eef_quat"]), + raw_obs["robot0_gripper_qpos"], + ) + ) + agent_pos = state + if self.obs_type == "pixels": + return {"pixels": images.copy()} + if self.obs_type == "pixels_agent_pos": + return { + "pixels": images.copy(), + "agent_pos": agent_pos, + } + raise NotImplementedError( + f"The observation type '{self.obs_type}' is not supported in LiberoEnv. " + "Please switch to an image-based obs_type (e.g. 'pixels', 'pixels_agent_pos')." + ) + + def reset(self, seed=None, **kwargs): + super().reset(seed=seed) + self._env.seed(seed) + if self.init_states and self._init_states is not None: + self._env.set_init_state(self._init_states[self._init_state_id]) + raw_obs = self._env.reset() + + # After reset, objects may be unstable (slightly floating, intersecting, etc.). + # Step the simulator with a no-op action for a few frames so everything settles. + # Increasing this value can improve determinism and reproducibility across resets. + for _ in range(self.num_steps_wait): + raw_obs, _, _, _ = self._env.step(get_libero_dummy_action()) + observation = self._format_raw_obs(raw_obs) + info = {"is_success": False} + return observation, info + + def step(self, action: np.ndarray) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]: + if action.ndim != 1: + raise ValueError( + f"Expected action to be 1-D (shape (action_dim,)), " + f"but got shape {action.shape} with ndim={action.ndim}" + ) + raw_obs, reward, done, info = self._env.step(action) + + is_success = self._env.check_success() + terminated = done or is_success + info["is_success"] = is_success + + observation = self._format_raw_obs(raw_obs) + if done: + self.reset() + info.update( + { + "task": self.task, + "task_id": self.task_id, + "done": done, + "is_success": is_success, + } + ) + truncated = False + return observation, reward, terminated, truncated, info + + def close(self): + self._env.close() + + +def _make_env_fns( + *, + suite, + suite_name: str, + task_id: int, + n_envs: int, + camera_names: list[str], + init_states: bool, + gym_kwargs: Mapping[str, Any], +) -> list[Callable[[], LiberoEnv]]: + """Build n_envs factory callables for a single (suite, task_id).""" + + def _make_env(episode_index: int, **kwargs) -> LiberoEnv: + local_kwargs = dict(kwargs) + return LiberoEnv( + task_suite=suite, + task_id=task_id, + task_suite_name=suite_name, + camera_name=camera_names, + init_states=init_states, + episode_index=episode_index, + **local_kwargs, + ) + + fns: list[Callable[[], LiberoEnv]] = [] + for episode_index in range(n_envs): + fns.append(partial(_make_env, episode_index, **gym_kwargs)) + return fns + + +# ---- Main API ---------------------------------------------------------------- + + +def create_libero_envs( + task: str, + n_envs: int, + gym_kwargs: dict[str, Any] | None = None, + camera_name: str | Sequence[str] = "agentview_image,robot0_eye_in_hand_image", + init_states: bool = True, + env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None, +) -> dict[str, dict[int, Any]]: + """ + Create vectorized LIBERO environments with a consistent return shape. + + Returns: + dict[suite_name][task_id] -> vec_env (env_cls([...]) with exactly n_envs factories) + Notes: + - n_envs is the number of rollouts *per task* (episode_index = 0..n_envs-1). + - `task` can be a single suite or a comma-separated list of suites. + - You may pass `task_ids` (list[int]) inside `gym_kwargs` to restrict tasks per suite. + """ + if env_cls is None or not callable(env_cls): + raise ValueError("env_cls must be a callable that wraps a list of environment factory callables.") + if not isinstance(n_envs, int) or n_envs <= 0: + raise ValueError(f"n_envs must be a positive int; got {n_envs}.") + + gym_kwargs = dict(gym_kwargs or {}) + task_ids_filter = gym_kwargs.pop("task_ids", None) # optional: limit to specific tasks + + camera_names = _parse_camera_names(camera_name) + suite_names = [s.strip() for s in str(task).split(",") if s.strip()] + if not suite_names: + raise ValueError("`task` must contain at least one LIBERO suite name.") + + print( + f"Creating LIBERO envs | suites={suite_names} | n_envs(per task)={n_envs} | init_states={init_states}" + ) + if task_ids_filter is not None: + print(f"Restricting to task_ids={task_ids_filter}") + + out: dict[str, dict[int, Any]] = defaultdict(dict) + + for suite_name in suite_names: + suite = _get_suite(suite_name) + total = len(suite.tasks) + selected = _select_task_ids(total, task_ids_filter) + + if not selected: + raise ValueError(f"No tasks selected for suite '{suite_name}' (available: {total}).") + + for tid in selected: + fns = _make_env_fns( + suite=suite, + suite_name=suite_name, + task_id=tid, + n_envs=n_envs, + camera_names=camera_names, + init_states=init_states, + gym_kwargs=gym_kwargs, + ) + out[suite_name][tid] = env_cls(fns) + print(f"Built vec env | suite={suite_name} | task_id={tid} | n_envs={n_envs}") + + # return plain dicts for predictability + return {suite: dict(task_map) for suite, task_map in out.items()} diff --git a/src/lerobot/envs/utils.py b/src/lerobot/envs/utils.py index 00676a01104..5584e0bff0f 100644 --- a/src/lerobot/envs/utils.py +++ b/src/lerobot/envs/utils.py @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. import warnings +from collections.abc import Mapping, Sequence +from functools import singledispatch from typing import Any import einops @@ -24,6 +26,7 @@ from lerobot.configs.types import FeatureType, PolicyFeature from lerobot.envs.configs import EnvConfig +from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE from lerobot.utils.utils import get_channel_first_image_shape @@ -39,44 +42,44 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten return_observations = {} if "pixels" in observations: if isinstance(observations["pixels"], dict): - imgs = {f"observation.images.{key}": img for key, img in observations["pixels"].items()} + imgs = {f"{OBS_IMAGES}.{key}": img for key, img in observations["pixels"].items()} else: - imgs = {"observation.image": observations["pixels"]} + imgs = {OBS_IMAGE: observations["pixels"]} for imgkey, img in imgs.items(): # TODO(aliberts, rcadene): use transforms.ToTensor()? - img = torch.from_numpy(img) + img_tensor = torch.from_numpy(img) # When preprocessing observations in a non-vectorized environment, we need to add a batch dimension. # This is the case for human-in-the-loop RL where there is only one environment. - if img.ndim == 3: - img = img.unsqueeze(0) + if img_tensor.ndim == 3: + img_tensor = img_tensor.unsqueeze(0) # sanity check that images are channel last - _, h, w, c = img.shape - assert c < h and c < w, f"expect channel last images, but instead got {img.shape=}" + _, h, w, c = img_tensor.shape + assert c < h and c < w, f"expect channel last images, but instead got {img_tensor.shape=}" # sanity check that images are uint8 - assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}" + assert img_tensor.dtype == torch.uint8, f"expect torch.uint8, but instead {img_tensor.dtype=}" # convert to channel first of type float32 in range [0,1] - img = einops.rearrange(img, "b h w c -> b c h w").contiguous() - img = img.type(torch.float32) - img /= 255 + img_tensor = einops.rearrange(img_tensor, "b h w c -> b c h w").contiguous() + img_tensor = img_tensor.type(torch.float32) + img_tensor /= 255 - return_observations[imgkey] = img + return_observations[imgkey] = img_tensor if "environment_state" in observations: env_state = torch.from_numpy(observations["environment_state"]).float() if env_state.dim() == 1: env_state = env_state.unsqueeze(0) - return_observations["observation.environment_state"] = env_state + return_observations[OBS_ENV_STATE] = env_state # TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing agent_pos = torch.from_numpy(observations["agent_pos"]).float() if agent_pos.dim() == 1: agent_pos = agent_pos.unsqueeze(0) - return_observations["observation.state"] = agent_pos + return_observations[OBS_STATE] = agent_pos return return_observations @@ -127,10 +130,68 @@ def check_env_attributes_and_types(env: gym.vector.VectorEnv) -> None: def add_envs_task(env: gym.vector.VectorEnv, observation: dict[str, Any]) -> dict[str, Any]: """Adds task feature to the observation dict with respect to the first environment attribute.""" if hasattr(env.envs[0], "task_description"): - observation["task"] = env.call("task_description") + task_result = env.call("task_description") + + if isinstance(task_result, tuple): + task_result = list(task_result) + + if not isinstance(task_result, list): + raise TypeError(f"Expected task_description to return a list, got {type(task_result)}") + if not all(isinstance(item, str) for item in task_result): + raise TypeError("All items in task_description result must be strings") + + observation["task"] = task_result elif hasattr(env.envs[0], "task"): - observation["task"] = env.call("task") + task_result = env.call("task") + + if isinstance(task_result, tuple): + task_result = list(task_result) + + if not isinstance(task_result, list): + raise TypeError(f"Expected task to return a list, got {type(task_result)}") + if not all(isinstance(item, str) for item in task_result): + raise TypeError("All items in task result must be strings") + + observation["task"] = task_result else: # For envs without language instructions, e.g. aloha transfer cube and etc. num_envs = observation[list(observation.keys())[0]].shape[0] observation["task"] = ["" for _ in range(num_envs)] return observation + + +def _close_single_env(env: Any) -> None: + try: + env.close() + except Exception as exc: + print(f"Exception while closing env {env}: {exc}") + + +@singledispatch +def close_envs(obj: Any) -> None: + """Default: raise if the type is not recognized.""" + raise NotImplementedError(f"close_envs not implemented for type {type(obj).__name__}") + + +@close_envs.register +def _(env: Mapping) -> None: + for v in env.values(): + if isinstance(v, Mapping): + close_envs(v) + elif hasattr(v, "close"): + _close_single_env(v) + + +@close_envs.register +def _(envs: Sequence) -> None: + if isinstance(envs, (str | bytes)): + return + for v in envs: + if isinstance(v, Mapping) or isinstance(v, Sequence) and not isinstance(v, (str | bytes)): + close_envs(v) + elif hasattr(v, "close"): + _close_single_env(v) + + +@close_envs.register +def _(env: gym.Env) -> None: + _close_single_env(env) diff --git a/src/lerobot/motors/__init__.py b/src/lerobot/motors/__init__.py index dfbfbaee8fc..850ef33d74e 100644 --- a/src/lerobot/motors/__init__.py +++ b/src/lerobot/motors/__init__.py @@ -1 +1,17 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from .motors_bus import Motor, MotorCalibration, MotorNormMode, MotorsBus diff --git a/src/lerobot/motors/dynamixel/dynamixel.py b/src/lerobot/motors/dynamixel/dynamixel.py index 1113ec0f7e2..e1d4e09637e 100644 --- a/src/lerobot/motors/dynamixel/dynamixel.py +++ b/src/lerobot/motors/dynamixel/dynamixel.py @@ -22,7 +22,7 @@ from copy import deepcopy from enum import Enum -from lerobot.utils.encoding_utils import decode_twos_complement, encode_twos_complement +from lerobot.motors.encoding_utils import decode_twos_complement, encode_twos_complement from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address from .tables import ( diff --git a/src/lerobot/utils/encoding_utils.py b/src/lerobot/motors/encoding_utils.py similarity index 100% rename from src/lerobot/utils/encoding_utils.py rename to src/lerobot/motors/encoding_utils.py diff --git a/src/lerobot/motors/feetech/feetech.py b/src/lerobot/motors/feetech/feetech.py index 88d45ba394d..2ea57af1283 100644 --- a/src/lerobot/motors/feetech/feetech.py +++ b/src/lerobot/motors/feetech/feetech.py @@ -17,7 +17,7 @@ from enum import Enum from pprint import pformat -from lerobot.utils.encoding_utils import decode_sign_magnitude, encode_sign_magnitude +from lerobot.motors.encoding_utils import decode_sign_magnitude, encode_sign_magnitude from ..motors_bus import Motor, MotorCalibration, MotorsBus, NameOrID, Value, get_address from .tables import ( diff --git a/src/lerobot/motors/motors_bus.py b/src/lerobot/motors/motors_bus.py index 97830fc35f0..17eaa8063d7 100644 --- a/src/lerobot/motors/motors_bus.py +++ b/src/lerobot/motors/motors_bus.py @@ -32,7 +32,7 @@ from deepdiff import DeepDiff from tqdm import tqdm -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.utils.utils import enter_pressed, move_cursor_up NameOrID: TypeAlias = str | int @@ -99,12 +99,6 @@ class Motor: norm_mode: MotorNormMode -class JointOutOfRangeError(Exception): - def __init__(self, message="Joint is out of range"): - self.message = message - super().__init__(self.message) - - class PortHandler(Protocol): def __init__(self, port_name): self.is_open: bool @@ -348,7 +342,7 @@ def _get_motors_list(self, motors: str | list[str] | None) -> list[str]: raise TypeError(motors) def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> list[str]: - if isinstance(values, (int, float)): + if isinstance(values, (int | float)): return dict.fromkeys(self.ids, values) elif isinstance(values, dict): return {self.motors[motor].id: val for motor, val in values.items()} @@ -675,7 +669,7 @@ def reset_calibration(self, motors: NameOrID | list[NameOrID] | None = None) -> """ if motors is None: motors = list(self.motors) - elif isinstance(motors, (str, int)): + elif isinstance(motors, (str | int)): motors = [motors] elif not isinstance(motors, list): raise TypeError(motors) @@ -703,7 +697,7 @@ def set_half_turn_homings(self, motors: NameOrID | list[NameOrID] | None = None) """ if motors is None: motors = list(self.motors) - elif isinstance(motors, (str, int)): + elif isinstance(motors, (str | int)): motors = [motors] elif not isinstance(motors, list): raise TypeError(motors) @@ -739,7 +733,7 @@ def record_ranges_of_motion( """ if motors is None: motors = list(self.motors) - elif isinstance(motors, (str, int)): + elif isinstance(motors, (str | int)): motors = [motors] elif not isinstance(motors, list): raise TypeError(motors) diff --git a/src/lerobot/optim/optimizers.py b/src/lerobot/optim/optimizers.py index ece4dc157a2..f2bd0df4288 100644 --- a/src/lerobot/optim/optimizers.py +++ b/src/lerobot/optim/optimizers.py @@ -22,11 +22,11 @@ import torch from safetensors.torch import load_file, save_file -from lerobot.constants import ( +from lerobot.datasets.utils import flatten_dict, unflatten_dict, write_json +from lerobot.utils.constants import ( OPTIMIZER_PARAM_GROUPS, OPTIMIZER_STATE, ) -from lerobot.datasets.utils import flatten_dict, unflatten_dict, write_json from lerobot.utils.io_utils import deserialize_json_into_object diff --git a/src/lerobot/optim/schedulers.py b/src/lerobot/optim/schedulers.py index d080181755f..55ee62e4014 100644 --- a/src/lerobot/optim/schedulers.py +++ b/src/lerobot/optim/schedulers.py @@ -22,8 +22,8 @@ from torch.optim import Optimizer from torch.optim.lr_scheduler import LambdaLR, LRScheduler -from lerobot.constants import SCHEDULER_STATE from lerobot.datasets.utils import write_json +from lerobot.utils.constants import SCHEDULER_STATE from lerobot.utils.io_utils import deserialize_json_into_object diff --git a/src/lerobot/policies/__init__.py b/src/lerobot/policies/__init__.py index 9cb0f62341a..49f1e0f9559 100644 --- a/src/lerobot/policies/__init__.py +++ b/src/lerobot/policies/__init__.py @@ -15,6 +15,18 @@ from .act.configuration_act import ACTConfig as ACTConfig from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig from .pi0.configuration_pi0 import PI0Config as PI0Config +from .pi05.configuration_pi05 import PI05Config as PI05Config from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig +from .smolvla.processor_smolvla import SmolVLANewLineProcessor from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig + +__all__ = [ + "ACTConfig", + "DiffusionConfig", + "PI0Config", + "PI05Config", + "SmolVLAConfig", + "TDMPCConfig", + "VQBeTConfig", +] diff --git a/src/lerobot/policies/act/modeling_act.py b/src/lerobot/policies/act/modeling_act.py index cfd549b25a7..4d2890ba61c 100644 --- a/src/lerobot/policies/act/modeling_act.py +++ b/src/lerobot/policies/act/modeling_act.py @@ -33,10 +33,9 @@ from torchvision.models._utils import IntermediateLayerGetter from torchvision.ops.misc import FrozenBatchNorm2d -from lerobot.constants import ACTION, OBS_IMAGES from lerobot.policies.act.configuration_act import ACTConfig -from lerobot.policies.normalize import Normalize, Unnormalize from lerobot.policies.pretrained import PreTrainedPolicy +from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE class ACTPolicy(PreTrainedPolicy): @@ -51,27 +50,16 @@ class ACTPolicy(PreTrainedPolicy): def __init__( self, config: ACTConfig, - dataset_stats: dict[str, dict[str, Tensor]] | None = None, ): """ Args: config: Policy configuration class instance or None, in which case the default instantiation of the configuration class is used. - dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected - that they will be passed with a call to `load_state_dict` before the policy is used. """ super().__init__(config) config.validate_features() self.config = config - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.model = ACT(config) if config.temporal_ensemble_coeff is not None: @@ -137,23 +125,19 @@ def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: """Predict a chunk of actions given environment observations.""" self.eval() - batch = self.normalize_inputs(batch) if self.config.image_features: batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch[OBS_IMAGES] = [batch[key] for key in self.config.image_features] actions = self.model(batch)[0] - actions = self.unnormalize_outputs({ACTION: actions})[ACTION] return actions def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: """Run the batch through the model and compute the loss for training or validation.""" - batch = self.normalize_inputs(batch) if self.config.image_features: batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch[OBS_IMAGES] = [batch[key] for key in self.config.image_features] - batch = self.normalize_targets(batch) actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch) l1_loss = ( @@ -410,25 +394,22 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tenso latent dimension. """ if self.config.use_vae and self.training: - assert "action" in batch, ( + assert ACTION in batch, ( "actions must be provided when using the variational objective in training mode." ) - if "observation.images" in batch: - batch_size = batch["observation.images"][0].shape[0] - else: - batch_size = batch["observation.environment_state"].shape[0] + batch_size = batch[OBS_IMAGES][0].shape[0] if OBS_IMAGES in batch else batch[OBS_ENV_STATE].shape[0] # Prepare the latent for input to the transformer encoder. - if self.config.use_vae and "action" in batch and self.training: + if self.config.use_vae and ACTION in batch and self.training: # Prepare the input to the VAE encoder: [cls, *joint_space_configuration, *action_sequence]. cls_embed = einops.repeat( self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size ) # (B, 1, D) if self.config.robot_state_feature: - robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"]) + robot_state_embed = self.vae_encoder_robot_state_input_proj(batch[OBS_STATE]) robot_state_embed = robot_state_embed.unsqueeze(1) # (B, 1, D) - action_embed = self.vae_encoder_action_input_proj(batch["action"]) # (B, S, D) + action_embed = self.vae_encoder_action_input_proj(batch[ACTION]) # (B, S, D) if self.config.robot_state_feature: vae_encoder_input = [cls_embed, robot_state_embed, action_embed] # (B, S+2, D) @@ -446,7 +427,7 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tenso cls_joint_is_pad = torch.full( (batch_size, 2 if self.config.robot_state_feature else 1), False, - device=batch["observation.state"].device, + device=batch[OBS_STATE].device, ) key_padding_mask = torch.cat( [cls_joint_is_pad, batch["action_is_pad"]], axis=1 @@ -470,7 +451,7 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tenso mu = log_sigma_x2 = None # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use buffer latent_sample = torch.zeros([batch_size, self.config.latent_dim], dtype=torch.float32).to( - batch["observation.state"].device + batch[OBS_STATE].device ) # Prepare transformer encoder inputs. @@ -478,18 +459,16 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tenso encoder_in_pos_embed = list(self.encoder_1d_feature_pos_embed.weight.unsqueeze(1)) # Robot state token. if self.config.robot_state_feature: - encoder_in_tokens.append(self.encoder_robot_state_input_proj(batch["observation.state"])) + encoder_in_tokens.append(self.encoder_robot_state_input_proj(batch[OBS_STATE])) # Environment state token. if self.config.env_state_feature: - encoder_in_tokens.append( - self.encoder_env_state_input_proj(batch["observation.environment_state"]) - ) + encoder_in_tokens.append(self.encoder_env_state_input_proj(batch[OBS_ENV_STATE])) if self.config.image_features: # For a list of images, the H and W may vary but H*W is constant. # NOTE: If modifying this section, verify on MPS devices that # gradients remain stable (no explosions or NaNs). - for img in batch["observation.images"]: + for img in batch[OBS_IMAGES]: cam_features = self.backbone(img)["feature_map"] cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype) cam_features = self.encoder_img_feat_input_proj(cam_features) diff --git a/src/lerobot/policies/act/processor_act.py b/src/lerobot/policies/act/processor_act.py new file mode 100644 index 00000000000..727b18cef56 --- /dev/null +++ b/src/lerobot/policies/act/processor_act.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python + +# Copyright 2024 Tony Z. Zhao and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any + +import torch + +from lerobot.policies.act.configuration_act import ACTConfig +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + RenameObservationsProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def make_act_pre_post_processors( + config: ACTConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """Creates the pre- and post-processing pipelines for the ACT policy. + + The pre-processing pipeline handles normalization, batching, and device placement for the model inputs. + The post-processing pipeline handles unnormalization and moves the model outputs back to the CPU. + + Args: + config (ACTConfig): The ACT policy configuration object. + dataset_stats (dict[str, dict[str, torch.Tensor]] | None): A dictionary containing dataset + statistics (e.g., mean and std) used for normalization. Defaults to None. + + Returns: + tuple[PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], PolicyProcessorPipeline[PolicyAction, PolicyAction]]: A tuple containing the + pre-processor pipeline and the post-processor pipeline. + """ + + input_steps = [ + RenameObservationsProcessorStep(rename_map={}), + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + device=config.device, + ), + ] + output_steps = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/diffusion/modeling_diffusion.py b/src/lerobot/policies/diffusion/modeling_diffusion.py index 85d4d598118..3ab6719cb29 100644 --- a/src/lerobot/policies/diffusion/modeling_diffusion.py +++ b/src/lerobot/policies/diffusion/modeling_diffusion.py @@ -33,9 +33,7 @@ from diffusers.schedulers.scheduling_ddpm import DDPMScheduler from torch import Tensor, nn -from lerobot.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig -from lerobot.policies.normalize import Normalize, Unnormalize from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.utils import ( get_device_from_parameters, @@ -43,6 +41,7 @@ get_output_shape, populate_queues, ) +from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGES, OBS_STATE class DiffusionPolicy(PreTrainedPolicy): @@ -57,7 +56,6 @@ class DiffusionPolicy(PreTrainedPolicy): def __init__( self, config: DiffusionConfig, - dataset_stats: dict[str, dict[str, Tensor]] | None = None, ): """ Args: @@ -70,14 +68,6 @@ def __init__( config.validate_features() self.config = config - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - # queues are populated during rollout of the policy, they contain the n latest observations and actions self._queues = None @@ -91,28 +81,25 @@ def get_optim_params(self) -> dict: def reset(self): """Clear observation and action queues. Should be called on `env.reset()`""" self._queues = { - "observation.state": deque(maxlen=self.config.n_obs_steps), - "action": deque(maxlen=self.config.n_action_steps), + OBS_STATE: deque(maxlen=self.config.n_obs_steps), + ACTION: deque(maxlen=self.config.n_action_steps), } if self.config.image_features: - self._queues["observation.images"] = deque(maxlen=self.config.n_obs_steps) + self._queues[OBS_IMAGES] = deque(maxlen=self.config.n_obs_steps) if self.config.env_state_feature: - self._queues["observation.environment_state"] = deque(maxlen=self.config.n_obs_steps) + self._queues[OBS_ENV_STATE] = deque(maxlen=self.config.n_obs_steps) @torch.no_grad() - def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: + def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor: """Predict a chunk of actions given environment observations.""" # stack n latest observations from the queue batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues} - actions = self.diffusion.generate_actions(batch) - - # TODO(rcadene): make above methods return output dictionary? - actions = self.unnormalize_outputs({ACTION: actions})[ACTION] + actions = self.diffusion.generate_actions(batch, noise=noise) return actions @torch.no_grad() - def select_action(self, batch: dict[str, Tensor]) -> Tensor: + def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor: """Select a single action given environment observations. This method handles caching a history of observations and an action trajectory generated by the @@ -137,7 +124,6 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: if ACTION in batch: batch.pop(ACTION) - batch = self.normalize_inputs(batch) if self.config.image_features: batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4) @@ -145,7 +131,7 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: self._queues = populate_queues(self._queues, batch) if len(self._queues[ACTION]) == 0: - actions = self.predict_action_chunk(batch) + actions = self.predict_action_chunk(batch, noise=noise) self._queues[ACTION].extend(actions.transpose(0, 1)) action = self._queues[ACTION].popleft() @@ -153,11 +139,9 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, None]: """Run the batch through the model and compute the loss for training or validation.""" - batch = self.normalize_inputs(batch) if self.config.image_features: batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4) - batch = self.normalize_targets(batch) loss = self.diffusion.compute_loss(batch) # no output_dict so returning None return loss, None @@ -215,17 +199,25 @@ def __init__(self, config: DiffusionConfig): # ========= inference ============ def conditional_sample( - self, batch_size: int, global_cond: Tensor | None = None, generator: torch.Generator | None = None + self, + batch_size: int, + global_cond: Tensor | None = None, + generator: torch.Generator | None = None, + noise: Tensor | None = None, ) -> Tensor: device = get_device_from_parameters(self) dtype = get_dtype_from_parameters(self) # Sample prior. - sample = torch.randn( - size=(batch_size, self.config.horizon, self.config.action_feature.shape[0]), - dtype=dtype, - device=device, - generator=generator, + sample = ( + noise + if noise is not None + else torch.randn( + size=(batch_size, self.config.horizon, self.config.action_feature.shape[0]), + dtype=dtype, + device=device, + generator=generator, + ) ) self.noise_scheduler.set_timesteps(self.num_inference_steps) @@ -250,7 +242,7 @@ def _prepare_global_conditioning(self, batch: dict[str, Tensor]) -> Tensor: if self.config.image_features: if self.config.use_separate_rgb_encoder_per_camera: # Combine batch and sequence dims while rearranging to make the camera index dimension first. - images_per_camera = einops.rearrange(batch["observation.images"], "b s n ... -> n (b s) ...") + images_per_camera = einops.rearrange(batch[OBS_IMAGES], "b s n ... -> n (b s) ...") img_features_list = torch.cat( [ encoder(images) @@ -265,7 +257,7 @@ def _prepare_global_conditioning(self, batch: dict[str, Tensor]) -> Tensor: else: # Combine batch, sequence, and "which camera" dims before passing to shared encoder. img_features = self.rgb_encoder( - einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...") + einops.rearrange(batch[OBS_IMAGES], "b s n ... -> (b s n) ...") ) # Separate batch dim and sequence dim back out. The camera index dim gets absorbed into the # feature dim (effectively concatenating the camera features). @@ -280,7 +272,7 @@ def _prepare_global_conditioning(self, batch: dict[str, Tensor]) -> Tensor: # Concatenate features then flatten to (B, global_cond_dim). return torch.cat(global_cond_feats, dim=-1).flatten(start_dim=1) - def generate_actions(self, batch: dict[str, Tensor]) -> Tensor: + def generate_actions(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor: """ This function expects `batch` to have: { @@ -291,14 +283,14 @@ def generate_actions(self, batch: dict[str, Tensor]) -> Tensor: "observation.environment_state": (B, n_obs_steps, environment_dim) } """ - batch_size, n_obs_steps = batch["observation.state"].shape[:2] + batch_size, n_obs_steps = batch[OBS_STATE].shape[:2] assert n_obs_steps == self.config.n_obs_steps # Encode image features and concatenate them all together along with the state vector. global_cond = self._prepare_global_conditioning(batch) # (B, global_cond_dim) # run sampling - actions = self.conditional_sample(batch_size, global_cond=global_cond) + actions = self.conditional_sample(batch_size, global_cond=global_cond, noise=noise) # Extract `n_action_steps` steps worth of actions (from the current observation). start = n_obs_steps - 1 @@ -322,10 +314,10 @@ def compute_loss(self, batch: dict[str, Tensor]) -> Tensor: } """ # Input validation. - assert set(batch).issuperset({"observation.state", "action", "action_is_pad"}) - assert "observation.images" in batch or "observation.environment_state" in batch - n_obs_steps = batch["observation.state"].shape[1] - horizon = batch["action"].shape[1] + assert set(batch).issuperset({OBS_STATE, ACTION, "action_is_pad"}) + assert OBS_IMAGES in batch or OBS_ENV_STATE in batch + n_obs_steps = batch[OBS_STATE].shape[1] + horizon = batch[ACTION].shape[1] assert horizon == self.config.horizon assert n_obs_steps == self.config.n_obs_steps @@ -333,7 +325,7 @@ def compute_loss(self, batch: dict[str, Tensor]) -> Tensor: global_cond = self._prepare_global_conditioning(batch) # (B, global_cond_dim) # Forward diffusion. - trajectory = batch["action"] + trajectory = batch[ACTION] # Sample noise to add to the trajectory. eps = torch.randn(trajectory.shape, device=trajectory.device) # Sample a random noising timestep for each item in the batch. @@ -354,7 +346,7 @@ def compute_loss(self, batch: dict[str, Tensor]) -> Tensor: if self.config.prediction_type == "epsilon": target = eps elif self.config.prediction_type == "sample": - target = batch["action"] + target = batch[ACTION] else: raise ValueError(f"Unsupported prediction type {self.config.prediction_type}") diff --git a/src/lerobot/policies/diffusion/processor_diffusion.py b/src/lerobot/policies/diffusion/processor_diffusion.py new file mode 100644 index 00000000000..a7799be64b6 --- /dev/null +++ b/src/lerobot/policies/diffusion/processor_diffusion.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python + +# Copyright 2024 Columbia Artificial Intelligence, Robotics Lab, +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any + +import torch + +from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + RenameObservationsProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def make_diffusion_pre_post_processors( + config: DiffusionConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for a diffusion policy. + + The pre-processing pipeline prepares the input data for the model by: + 1. Renaming features. + 2. Normalizing the input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Moving the data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving the data to the CPU. + 2. Unnormalizing the output features to their original scale. + + Args: + config: The configuration object for the diffusion policy, + containing feature definitions, normalization mappings, and device information. + dataset_stats: A dictionary of statistics used for normalization. + Defaults to None. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + input_steps = [ + RenameObservationsProcessorStep(rename_map={}), + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + ] + output_steps = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/factory.py b/src/lerobot/policies/factory.py index ef56bdb613d..ac76baf9f31 100644 --- a/src/lerobot/policies/factory.py +++ b/src/lerobot/policies/factory.py @@ -14,9 +14,13 @@ # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + import logging +from typing import Any, TypedDict -from torch import nn +import torch +from typing_extensions import Unpack from lerobot.configs.policies import PreTrainedConfig from lerobot.configs.types import FeatureType @@ -28,16 +32,40 @@ from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig from lerobot.policies.pi0.configuration_pi0 import PI0Config from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig +from lerobot.policies.pi05.configuration_pi05 import PI05Config from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.sac.configuration_sac import SACConfig from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig +from lerobot.processor import PolicyAction, PolicyProcessorPipeline +from lerobot.processor.converters import ( + batch_to_transition, + policy_action_to_transition, + transition_to_batch, + transition_to_policy_action, +) +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def get_policy_class(name: str) -> type[PreTrainedPolicy]: + """ + Retrieves a policy class by its registered name. + + This function uses dynamic imports to avoid loading all policy classes into memory + at once, improving startup time and reducing dependencies. + Args: + name: The name of the policy. Supported names are "tdmpc", "diffusion", "act", + "vqbet", "pi0", "pi0fast", "sac", "reward_classifier", "smolvla". -def get_policy_class(name: str) -> PreTrainedPolicy: - """Get the policy's class and config class given a name (matching the policy class' `name` attribute).""" + Returns: + The policy class corresponding to the given name. + + Raises: + NotImplementedError: If the policy name is not recognized. + """ if name == "tdmpc": from lerobot.policies.tdmpc.modeling_tdmpc import TDMPCPolicy @@ -54,14 +82,18 @@ def get_policy_class(name: str) -> PreTrainedPolicy: from lerobot.policies.vqbet.modeling_vqbet import VQBeTPolicy return VQBeTPolicy + elif name == "pi0fast": + from lerobot.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy + + return PI0FASTPolicy elif name == "pi0": from lerobot.policies.pi0.modeling_pi0 import PI0Policy return PI0Policy - elif name == "pi0fast": - from lerobot.policies.pi0fast.modeling_pi0fast import PI0FASTPolicy + elif name == "pi05": + from lerobot.policies.pi05.modeling_pi05 import PI05Policy - return PI0FASTPolicy + return PI05Policy elif name == "sac": from lerobot.policies.sac.modeling_sac import SACPolicy @@ -79,6 +111,24 @@ def get_policy_class(name: str) -> PreTrainedPolicy: def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig: + """ + Instantiates a policy configuration object based on the policy type. + + This factory function simplifies the creation of policy configuration objects by + mapping a string identifier to the corresponding config class. + + Args: + policy_type: The type of the policy. Supported types include "tdmpc", + "diffusion", "act", "vqbet", "pi0", "pi0fast", "sac", "smolvla", + "reward_classifier". + **kwargs: Keyword arguments to be passed to the configuration class constructor. + + Returns: + An instance of a `PreTrainedConfig` subclass. + + Raises: + ValueError: If the `policy_type` is not recognized. + """ if policy_type == "tdmpc": return TDMPCConfig(**kwargs) elif policy_type == "diffusion": @@ -87,10 +137,12 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig: return ACTConfig(**kwargs) elif policy_type == "vqbet": return VQBeTConfig(**kwargs) - elif policy_type == "pi0": - return PI0Config(**kwargs) elif policy_type == "pi0fast": return PI0FASTConfig(**kwargs) + elif policy_type == "pi0": + return PI0Config(**kwargs) + elif policy_type == "pi05": + return PI05Config(**kwargs) elif policy_type == "sac": return SACConfig(**kwargs) elif policy_type == "smolvla": @@ -101,30 +153,195 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig: raise ValueError(f"Policy type '{policy_type}' is not available.") +class ProcessorConfigKwargs(TypedDict, total=False): + """ + A TypedDict defining the keyword arguments for processor configuration. + + This provides type hints for the optional arguments passed to `make_pre_post_processors`, + improving code clarity and enabling static analysis. + + Attributes: + preprocessor_config_filename: The filename for the preprocessor configuration. + postprocessor_config_filename: The filename for the postprocessor configuration. + preprocessor_overrides: A dictionary of overrides for the preprocessor configuration. + postprocessor_overrides: A dictionary of overrides for the postprocessor configuration. + dataset_stats: Dataset statistics for normalization. + """ + + preprocessor_config_filename: str | None + postprocessor_config_filename: str | None + preprocessor_overrides: dict[str, Any] | None + postprocessor_overrides: dict[str, Any] | None + dataset_stats: dict[str, dict[str, torch.Tensor]] | None + + +def make_pre_post_processors( + policy_cfg: PreTrainedConfig, + pretrained_path: str | None = None, + **kwargs: Unpack[ProcessorConfigKwargs], +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Create or load pre- and post-processor pipelines for a given policy. + + This function acts as a factory. It can either load existing processor pipelines + from a pretrained path or create new ones from scratch based on the policy + configuration. Each policy type has a dedicated factory function for its + processors (e.g., `make_tdmpc_pre_post_processors`). + + Args: + policy_cfg: The configuration of the policy for which to create processors. + pretrained_path: An optional path to load pretrained processor pipelines from. + If provided, pipelines are loaded from this path. + **kwargs: Keyword arguments for processor configuration, as defined in + `ProcessorConfigKwargs`. + + Returns: + A tuple containing the input (pre-processor) and output (post-processor) pipelines. + + Raises: + NotImplementedError: If a processor factory is not implemented for the given + policy configuration type. + """ + if pretrained_path: + return ( + PolicyProcessorPipeline.from_pretrained( + pretrained_model_name_or_path=pretrained_path, + config_filename=kwargs.get( + "preprocessor_config_filename", f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json" + ), + overrides=kwargs.get("preprocessor_overrides", {}), + to_transition=batch_to_transition, + to_output=transition_to_batch, + ), + PolicyProcessorPipeline.from_pretrained( + pretrained_model_name_or_path=pretrained_path, + config_filename=kwargs.get( + "postprocessor_config_filename", f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json" + ), + overrides=kwargs.get("postprocessor_overrides", {}), + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) + + # Create a new processor based on policy type + if isinstance(policy_cfg, TDMPCConfig): + from lerobot.policies.tdmpc.processor_tdmpc import make_tdmpc_pre_post_processors + + processors = make_tdmpc_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, DiffusionConfig): + from lerobot.policies.diffusion.processor_diffusion import make_diffusion_pre_post_processors + + processors = make_diffusion_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, ACTConfig): + from lerobot.policies.act.processor_act import make_act_pre_post_processors + + processors = make_act_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, VQBeTConfig): + from lerobot.policies.vqbet.processor_vqbet import make_vqbet_pre_post_processors + + processors = make_vqbet_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, PI0FASTConfig): + from lerobot.policies.pi0fast.processor_pi0fast import make_pi0fast_pre_post_processors + + processors = make_pi0fast_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, PI0Config): + from lerobot.policies.pi0.processor_pi0 import make_pi0_pre_post_processors + + processors = make_pi0_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, PI05Config): + from lerobot.policies.pi05.processor_pi05 import make_pi05_pre_post_processors + + processors = make_pi05_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, SACConfig): + from lerobot.policies.sac.processor_sac import make_sac_pre_post_processors + + processors = make_sac_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, RewardClassifierConfig): + from lerobot.policies.sac.reward_model.processor_classifier import make_classifier_processor + + processors = make_classifier_processor( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + elif isinstance(policy_cfg, SmolVLAConfig): + from lerobot.policies.smolvla.processor_smolvla import make_smolvla_pre_post_processors + + processors = make_smolvla_pre_post_processors( + config=policy_cfg, + dataset_stats=kwargs.get("dataset_stats"), + ) + + else: + raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.") + + return processors + + def make_policy( cfg: PreTrainedConfig, ds_meta: LeRobotDatasetMetadata | None = None, env_cfg: EnvConfig | None = None, ) -> PreTrainedPolicy: - """Make an instance of a policy class. + """ + Instantiate a policy model. - This function exists because (for now) we need to parse features from either a dataset or an environment - in order to properly dimension and instantiate a policy for that dataset or environment. + This factory function handles the logic of creating a policy, which requires + determining the input and output feature shapes. These shapes can be derived + either from a `LeRobotDatasetMetadata` object or an `EnvConfig` object. The function + can either initialize a new policy from scratch or load a pretrained one. Args: - cfg (PreTrainedConfig): The config of the policy to make. If `pretrained_path` is set, the policy will - be loaded with the weights from that path. - ds_meta (LeRobotDatasetMetadata | None, optional): Dataset metadata to take input/output shapes and - statistics to use for (un)normalization of inputs/outputs in the policy. Defaults to None. - env_cfg (EnvConfig | None, optional): The config of a gym environment to parse features from. Must be - provided if ds_meta is not. Defaults to None. - - Raises: - ValueError: Either ds_meta or env and env_cfg must be provided. - NotImplementedError: if the policy.type is 'vqbet' and the policy device 'mps' (due to an incompatibility) + cfg: The configuration for the policy to be created. If `cfg.pretrained_path` is + set, the policy will be loaded with weights from that path. + ds_meta: Dataset metadata used to infer feature shapes and types. Also provides + statistics for normalization layers. + env_cfg: Environment configuration used to infer feature shapes and types. + One of `ds_meta` or `env_cfg` must be provided. Returns: - PreTrainedPolicy: _description_ + An instantiated and device-placed policy model. + + Raises: + ValueError: If both or neither of `ds_meta` and `env_cfg` are provided. + NotImplementedError: If attempting to use an unsupported policy-backend + combination (e.g., VQBeT with 'mps'). """ if bool(ds_meta) == bool(env_cfg): raise ValueError("Either one of a dataset metadata or a sim env must be provided.") @@ -147,7 +364,6 @@ def make_policy( kwargs = {} if ds_meta is not None: features = dataset_to_policy_features(ds_meta.features) - kwargs["dataset_stats"] = ds_meta.stats else: if not cfg.pretrained_path: logging.warning( @@ -155,6 +371,8 @@ def make_policy( "rather than a dataset. Normalization modules inside the policy will have infinite values " "by default without stats from a dataset." ) + if env_cfg is None: + raise ValueError("env_cfg cannot be None when ds_meta is not provided") features = env_to_policy_features(env_cfg) cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION} @@ -171,7 +389,7 @@ def make_policy( policy = policy_cls(**kwargs) policy.to(cfg.device) - assert isinstance(policy, nn.Module) + assert isinstance(policy, torch.nn.Module) # policy = torch.compile(policy, mode="reduce-overhead") diff --git a/src/lerobot/policies/normalize.py b/src/lerobot/policies/normalize.py deleted file mode 100644 index 119055873ca..00000000000 --- a/src/lerobot/policies/normalize.py +++ /dev/null @@ -1,420 +0,0 @@ -#!/usr/bin/env python - -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import numpy as np -import torch -from torch import Tensor, nn - -from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature - - -def create_stats_buffers( - features: dict[str, PolicyFeature], - norm_map: dict[str, NormalizationMode], - stats: dict[str, dict[str, Tensor]] | None = None, -) -> dict[str, dict[str, nn.ParameterDict]]: - """ - Create buffers per modality (e.g. "observation.image", "action") containing their mean, std, min, max - statistics. - - Args: (see Normalize and Unnormalize) - - Returns: - dict: A dictionary where keys are modalities and values are `nn.ParameterDict` containing - `nn.Parameters` set to `requires_grad=False`, suitable to not be updated during backpropagation. - """ - stats_buffers = {} - - for key, ft in features.items(): - norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY) - if norm_mode is NormalizationMode.IDENTITY: - continue - - assert isinstance(norm_mode, NormalizationMode) - - shape = tuple(ft.shape) - - if ft.type is FeatureType.VISUAL: - # sanity checks - assert len(shape) == 3, f"number of dimensions of {key} != 3 ({shape=}" - c, h, w = shape - assert c < h and c < w, f"{key} is not channel first ({shape=})" - # override image shape to be invariant to height and width - shape = (c, 1, 1) - - # Note: we initialize mean, std, min, max to infinity. They should be overwritten - # downstream by `stats` or `policy.load_state_dict`, as expected. During forward, - # we assert they are not infinity anymore. - - buffer = {} - if norm_mode is NormalizationMode.MEAN_STD: - mean = torch.ones(shape, dtype=torch.float32) * torch.inf - std = torch.ones(shape, dtype=torch.float32) * torch.inf - buffer = nn.ParameterDict( - { - "mean": nn.Parameter(mean, requires_grad=False), - "std": nn.Parameter(std, requires_grad=False), - } - ) - elif norm_mode is NormalizationMode.MIN_MAX: - min = torch.ones(shape, dtype=torch.float32) * torch.inf - max = torch.ones(shape, dtype=torch.float32) * torch.inf - buffer = nn.ParameterDict( - { - "min": nn.Parameter(min, requires_grad=False), - "max": nn.Parameter(max, requires_grad=False), - } - ) - - # TODO(aliberts, rcadene): harmonize this to only use one framework (np or torch) - if stats: - if isinstance(stats[key]["mean"], np.ndarray): - if norm_mode is NormalizationMode.MEAN_STD: - buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32) - buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32) - elif norm_mode is NormalizationMode.MIN_MAX: - buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32) - buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32) - elif isinstance(stats[key]["mean"], torch.Tensor): - # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated - # tensors anywhere (for example, when we use the same stats for normalization and - # unnormalization). See the logic here - # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97. - if norm_mode is NormalizationMode.MEAN_STD: - buffer["mean"].data = stats[key]["mean"].clone().to(dtype=torch.float32) - buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32) - elif norm_mode is NormalizationMode.MIN_MAX: - buffer["min"].data = stats[key]["min"].clone().to(dtype=torch.float32) - buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32) - else: - type_ = type(stats[key]["mean"]) - raise ValueError(f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead.") - - stats_buffers[key] = buffer - return stats_buffers - - -def _no_stats_error_str(name: str) -> str: - return ( - f"`{name}` is infinity. You should either initialize with `stats` as an argument, or use a " - "pretrained model." - ) - - -class Normalize(nn.Module): - """Normalizes data (e.g. "observation.image") for more stable and faster convergence during training.""" - - def __init__( - self, - features: dict[str, PolicyFeature], - norm_map: dict[str, NormalizationMode], - stats: dict[str, dict[str, Tensor]] | None = None, - ): - """ - Args: - shapes (dict): A dictionary where keys are input modalities (e.g. "observation.image") and values - are their shapes (e.g. `[3,96,96]`]). These shapes are used to create the tensor buffer containing - mean, std, min, max statistics. If the provided `shapes` contain keys related to images, the shape - is adjusted to be invariant to height and width, assuming a channel-first (c, h, w) format. - modes (dict): A dictionary where keys are output modalities (e.g. "observation.image") and values - are their normalization modes among: - - "mean_std": subtract the mean and divide by standard deviation. - - "min_max": map to [-1, 1] range. - stats (dict, optional): A dictionary where keys are output modalities (e.g. "observation.image") - and values are dictionaries of statistic types and their values (e.g. - `{"mean": torch.randn(3,1,1)}, "std": torch.randn(3,1,1)}`). If provided, as expected for - training the model for the first time, these statistics will overwrite the default buffers. If - not provided, as expected for finetuning or evaluation, the default buffers should to be - overwritten by a call to `policy.load_state_dict(state_dict)`. That way, initializing the - dataset is not needed to get the stats, since they are already in the policy state_dict. - """ - super().__init__() - self.features = features - self.norm_map = norm_map - self.stats = stats - stats_buffers = create_stats_buffers(features, norm_map, stats) - for key, buffer in stats_buffers.items(): - setattr(self, "buffer_" + key.replace(".", "_"), buffer) - - # TODO(rcadene): should we remove torch.no_grad? - @torch.no_grad() - def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: - # TODO: Remove this shallow copy - batch = dict(batch) # shallow copy avoids mutating the input batch - for key, ft in self.features.items(): - if key not in batch: - # FIXME(aliberts, rcadene): This might lead to silent fail! - continue - - norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) - if norm_mode is NormalizationMode.IDENTITY: - continue - - buffer = getattr(self, "buffer_" + key.replace(".", "_")) - - if norm_mode is NormalizationMode.MEAN_STD: - mean = buffer["mean"] - std = buffer["std"] - assert not torch.isinf(mean).any(), _no_stats_error_str("mean") - assert not torch.isinf(std).any(), _no_stats_error_str("std") - batch[key] = (batch[key] - mean) / (std + 1e-8) - elif norm_mode is NormalizationMode.MIN_MAX: - min = buffer["min"] - max = buffer["max"] - assert not torch.isinf(min).any(), _no_stats_error_str("min") - assert not torch.isinf(max).any(), _no_stats_error_str("max") - # normalize to [0,1] - batch[key] = (batch[key] - min) / (max - min + 1e-8) - # normalize to [-1, 1] - batch[key] = batch[key] * 2 - 1 - else: - raise ValueError(norm_mode) - return batch - - -class Unnormalize(nn.Module): - """ - Similar to `Normalize` but unnormalizes output data (e.g. `{"action": torch.randn(b,c)}`) in their - original range used by the environment. - """ - - def __init__( - self, - features: dict[str, PolicyFeature], - norm_map: dict[str, NormalizationMode], - stats: dict[str, dict[str, Tensor]] | None = None, - ): - """ - Args: - shapes (dict): A dictionary where keys are input modalities (e.g. "observation.image") and values - are their shapes (e.g. `[3,96,96]`]). These shapes are used to create the tensor buffer containing - mean, std, min, max statistics. If the provided `shapes` contain keys related to images, the shape - is adjusted to be invariant to height and width, assuming a channel-first (c, h, w) format. - modes (dict): A dictionary where keys are output modalities (e.g. "observation.image") and values - are their normalization modes among: - - "mean_std": subtract the mean and divide by standard deviation. - - "min_max": map to [-1, 1] range. - stats (dict, optional): A dictionary where keys are output modalities (e.g. "observation.image") - and values are dictionaries of statistic types and their values (e.g. - `{"mean": torch.randn(3,1,1)}, "std": torch.randn(3,1,1)}`). If provided, as expected for - training the model for the first time, these statistics will overwrite the default buffers. If - not provided, as expected for finetuning or evaluation, the default buffers should to be - overwritten by a call to `policy.load_state_dict(state_dict)`. That way, initializing the - dataset is not needed to get the stats, since they are already in the policy state_dict. - """ - super().__init__() - self.features = features - self.norm_map = norm_map - self.stats = stats - # `self.buffer_observation_state["mean"]` contains `torch.tensor(state_dim)` - stats_buffers = create_stats_buffers(features, norm_map, stats) - for key, buffer in stats_buffers.items(): - setattr(self, "buffer_" + key.replace(".", "_"), buffer) - - # TODO(rcadene): should we remove torch.no_grad? - @torch.no_grad() - def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: - batch = dict(batch) # shallow copy avoids mutating the input batch - for key, ft in self.features.items(): - if key not in batch: - continue - - norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) - if norm_mode is NormalizationMode.IDENTITY: - continue - - buffer = getattr(self, "buffer_" + key.replace(".", "_")) - - if norm_mode is NormalizationMode.MEAN_STD: - mean = buffer["mean"] - std = buffer["std"] - assert not torch.isinf(mean).any(), _no_stats_error_str("mean") - assert not torch.isinf(std).any(), _no_stats_error_str("std") - batch[key] = batch[key] * std + mean - elif norm_mode is NormalizationMode.MIN_MAX: - min = buffer["min"] - max = buffer["max"] - assert not torch.isinf(min).any(), _no_stats_error_str("min") - assert not torch.isinf(max).any(), _no_stats_error_str("max") - batch[key] = (batch[key] + 1) / 2 - batch[key] = batch[key] * (max - min) + min - else: - raise ValueError(norm_mode) - return batch - - -# TODO (azouitine): We should replace all normalization on the policies with register_buffer normalization -# and remove the `Normalize` and `Unnormalize` classes. -def _initialize_stats_buffers( - module: nn.Module, - features: dict[str, PolicyFeature], - norm_map: dict[str, NormalizationMode], - stats: dict[str, dict[str, Tensor]] | None = None, -) -> None: - """Register statistics buffers (mean/std or min/max) on the given *module*. - - The logic matches the previous constructors of `NormalizeBuffer` and `UnnormalizeBuffer`, - but is factored out so it can be reused by both classes and stay in sync. - """ - for key, ft in features.items(): - norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY) - if norm_mode is NormalizationMode.IDENTITY: - continue - - shape: tuple[int, ...] = tuple(ft.shape) - if ft.type is FeatureType.VISUAL: - # reduce spatial dimensions, keep channel dimension only - c, *_ = shape - shape = (c, 1, 1) - - prefix = key.replace(".", "_") - - if norm_mode is NormalizationMode.MEAN_STD: - mean = torch.full(shape, torch.inf, dtype=torch.float32) - std = torch.full(shape, torch.inf, dtype=torch.float32) - - if stats and key in stats and "mean" in stats[key] and "std" in stats[key]: - mean_data = stats[key]["mean"] - std_data = stats[key]["std"] - if isinstance(mean_data, torch.Tensor): - # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated - # tensors anywhere (for example, when we use the same stats for normalization and - # unnormalization). See the logic here - # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97. - mean = mean_data.clone().to(dtype=torch.float32) - std = std_data.clone().to(dtype=torch.float32) - else: - raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).") - - module.register_buffer(f"{prefix}_mean", mean) - module.register_buffer(f"{prefix}_std", std) - continue - - if norm_mode is NormalizationMode.MIN_MAX: - min_val = torch.full(shape, torch.inf, dtype=torch.float32) - max_val = torch.full(shape, torch.inf, dtype=torch.float32) - - if stats and key in stats and "min" in stats[key] and "max" in stats[key]: - min_data = stats[key]["min"] - max_data = stats[key]["max"] - if isinstance(min_data, torch.Tensor): - min_val = min_data.clone().to(dtype=torch.float32) - max_val = max_data.clone().to(dtype=torch.float32) - else: - raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).") - - module.register_buffer(f"{prefix}_min", min_val) - module.register_buffer(f"{prefix}_max", max_val) - continue - - raise ValueError(norm_mode) - - -class NormalizeBuffer(nn.Module): - """Same as `Normalize` but statistics are stored as registered buffers rather than parameters.""" - - def __init__( - self, - features: dict[str, PolicyFeature], - norm_map: dict[str, NormalizationMode], - stats: dict[str, dict[str, Tensor]] | None = None, - ): - super().__init__() - self.features = features - self.norm_map = norm_map - - _initialize_stats_buffers(self, features, norm_map, stats) - - def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: - batch = dict(batch) - for key, ft in self.features.items(): - if key not in batch: - continue - - norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) - if norm_mode is NormalizationMode.IDENTITY: - continue - - prefix = key.replace(".", "_") - - if norm_mode is NormalizationMode.MEAN_STD: - mean = getattr(self, f"{prefix}_mean") - std = getattr(self, f"{prefix}_std") - assert not torch.isinf(mean).any(), _no_stats_error_str("mean") - assert not torch.isinf(std).any(), _no_stats_error_str("std") - batch[key] = (batch[key] - mean) / (std + 1e-8) - continue - - if norm_mode is NormalizationMode.MIN_MAX: - min_val = getattr(self, f"{prefix}_min") - max_val = getattr(self, f"{prefix}_max") - assert not torch.isinf(min_val).any(), _no_stats_error_str("min") - assert not torch.isinf(max_val).any(), _no_stats_error_str("max") - batch[key] = (batch[key] - min_val) / (max_val - min_val + 1e-8) - batch[key] = batch[key] * 2 - 1 - continue - - raise ValueError(norm_mode) - - return batch - - -class UnnormalizeBuffer(nn.Module): - """Inverse operation of `NormalizeBuffer`. Uses registered buffers for statistics.""" - - def __init__( - self, - features: dict[str, PolicyFeature], - norm_map: dict[str, NormalizationMode], - stats: dict[str, dict[str, Tensor]] | None = None, - ): - super().__init__() - self.features = features - self.norm_map = norm_map - - _initialize_stats_buffers(self, features, norm_map, stats) - - def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: - # batch = dict(batch) - for key, ft in self.features.items(): - if key not in batch: - continue - - norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY) - if norm_mode is NormalizationMode.IDENTITY: - continue - - prefix = key.replace(".", "_") - - if norm_mode is NormalizationMode.MEAN_STD: - mean = getattr(self, f"{prefix}_mean") - std = getattr(self, f"{prefix}_std") - assert not torch.isinf(mean).any(), _no_stats_error_str("mean") - assert not torch.isinf(std).any(), _no_stats_error_str("std") - batch[key] = batch[key] * std + mean - continue - - if norm_mode is NormalizationMode.MIN_MAX: - min_val = getattr(self, f"{prefix}_min") - max_val = getattr(self, f"{prefix}_max") - assert not torch.isinf(min_val).any(), _no_stats_error_str("min") - assert not torch.isinf(max_val).any(), _no_stats_error_str("max") - batch[key] = (batch[key] + 1) / 2 - batch[key] = batch[key] * (max_val - min_val) + min_val - continue - - raise ValueError(norm_mode) - - return batch diff --git a/src/lerobot/policies/pi0/README.md b/src/lerobot/policies/pi0/README.md new file mode 100644 index 00000000000..65b331e5112 --- /dev/null +++ b/src/lerobot/policies/pi0/README.md @@ -0,0 +1,49 @@ +# π₀ (pi0) + +This repository contains the Hugging Face port of **π₀**, adapted from [OpenPI](https://github.com/Physical-Intelligence/openpi) by the Physical Intelligence. +It is designed as a **Vision-Language-Action model for general robot control**. + +--- + +## Model Overview + +| Feature | π₀ | π₀.₅ | +| -------------------- | ------------------------------------------------------ | ----------------------------------------- | +| Time Conditioning | Concatenates time with actions via `action_time_mlp_*` | Uses `time_mlp_*` for AdaRMS conditioning | +| AdaRMS | Not used | Used in action expert | +| Tokenizer Length | 48 tokens | 200 tokens | +| Discrete State Input | False (Uses `state_proj` layer) | True | +| Parameter Count | Higher (includes state embedding) | Lower (no state embedding) | + +--- + +## Citation + +If you use this work, please cite both **OpenPI** and the π₀ paper: + +```bibtex +@misc{openpi2024, + author = {Physical Intelligence Lab}, + title = {OpenPI: PyTorch Implementation of π0 and π0.5 Policies}, + year = {2024}, + publisher = {GitHub}, + howpublished = {\url{https://github.com/Physical-Intelligence/openpi}}, + license = {Apache-2.0} +} + +@misc{black2024pi0visionlanguageactionflowmodel, + title = {π₀: A Vision-Language-Action Flow Model for General Robot Control}, + author = {Kevin Black and Noah Brown and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Lucy Xiaoyang Shi and James Tanner and Quan Vuong and Anna Walling and Haohuan Wang and Ury Zhilinsky}, + year = {2024}, + eprint = {2410.24164}, + archivePrefix= {arXiv}, + primaryClass = {cs.LG}, + url = {https://arxiv.org/abs/2410.24164}, +} +``` + +--- + +## License + +This port follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi). diff --git a/src/lerobot/policies/pi0/__init__.py b/src/lerobot/policies/pi0/__init__.py new file mode 100644 index 00000000000..ea3095b4ec9 --- /dev/null +++ b/src/lerobot/policies/pi0/__init__.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .configuration_pi0 import PI0Config +from .modeling_pi0 import PI0Policy +from .processor_pi0 import make_pi0_pre_post_processors + +__all__ = ["PI0Config", "PI0Policy", "make_pi0_pre_post_processors"] diff --git a/src/lerobot/policies/pi0/configuration_pi0.py b/src/lerobot/policies/pi0/configuration_pi0.py index c9728e418b8..cc1cda9d841 100644 --- a/src/lerobot/policies/pi0/configuration_pi0.py +++ b/src/lerobot/policies/pi0/configuration_pi0.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -17,19 +19,40 @@ from lerobot.configs.policies import PreTrainedConfig from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature from lerobot.optim.optimizers import AdamWConfig -from lerobot.optim.schedulers import ( - CosineDecayWithWarmupSchedulerConfig, -) +from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig +from lerobot.utils.constants import OBS_IMAGES @PreTrainedConfig.register_subclass("pi0") @dataclass class PI0Config(PreTrainedConfig): - # Input / output structure. + paligemma_variant: str = "gemma_2b" + action_expert_variant: str = "gemma_300m" + dtype: str = "float32" # Options: "bfloat16", "float32" + n_obs_steps: int = 1 - chunk_size: int = 50 - n_action_steps: int = 50 + chunk_size: int = 50 # Number of action steps to predict, in openpi called "action_horizon" + n_action_steps: int = 50 # Number of action steps to execute + + # Shorter state and action vectors will be padded to these dimensions + max_state_dim: int = 32 + max_action_dim: int = 32 + + # Flow matching parameters: see openpi `PI0Pytorch` + num_inference_steps: int = 10 # Number of denoising steps during inference + time_sampling_beta_alpha: float = 1.5 + time_sampling_beta_beta: float = 1.0 + time_sampling_scale: float = 0.999 + time_sampling_offset: float = 0.001 + min_period: float = 4e-3 + max_period: float = 4.0 + + image_resolution: tuple[int, int] = (224, 224) # see openpi `preprocessing_pytorch.py` + + # Add empty images. Used to add empty cameras when no image features are present. + empty_cameras: int = 0 + # Normalization normalization_mapping: dict[str, NormalizationMode] = field( default_factory=lambda: { "VISUAL": NormalizationMode.IDENTITY, @@ -38,94 +61,75 @@ class PI0Config(PreTrainedConfig): } ) - # Shorter state and action vectors will be padded - max_state_dim: int = 32 - max_action_dim: int = 32 - - # Image preprocessing - resize_imgs_with_padding: tuple[int, int] = (224, 224) - - # Add empty images. Used by pi0_aloha_sim which adds the empty - # left and right wrist cameras in addition to the top camera. - empty_cameras: int = 0 - - # Converts the joint and gripper values from the standard Aloha space to - # the space used by the pi internal runtime which was used to train the base model. - adapt_to_pi_aloha: bool = False - - # Converts joint dimensions to deltas with respect to the current state before passing to the model. - # Gripper dimensions will remain in absolute values. - use_delta_joint_actions_aloha: bool = False + # Training settings + gradient_checkpointing: bool = False # Enable gradient checkpointing for memory optimization + compile_model: bool = False # Whether to use torch.compile for model optimization + compile_mode: str = "max-autotune" # Torch compile mode + device: str | None = None # Device to use for the model (None = auto-detect) - # Tokenizer - tokenizer_max_length: int = 48 - - # Projector - proj_width: int = 1024 - - # Decoding - num_steps: int = 10 - - # Attention utils - use_cache: bool = True - attention_implementation: str = "eager" # or fa2, flex - - # Finetuning settings - freeze_vision_encoder: bool = True - train_expert_only: bool = False - train_state_proj: bool = True - - # Training presets - optimizer_lr: float = 2.5e-5 + # Optimizer settings: see openpi `AdamW`` + optimizer_lr: float = 2.5e-5 # see openpi `CosineDecaySchedule: peak_lr` optimizer_betas: tuple[float, float] = (0.9, 0.95) optimizer_eps: float = 1e-8 - optimizer_weight_decay: float = 1e-10 + optimizer_weight_decay: float = 0.01 + optimizer_grad_clip_norm: float = 1.0 + # Scheduler settings: see openpi `CosineDecaySchedule` scheduler_warmup_steps: int = 1_000 scheduler_decay_steps: int = 30_000 scheduler_decay_lr: float = 2.5e-6 - # TODO: Add EMA + tokenizer_max_length: int = 48 # see openpi `__post_init__` def __post_init__(self): super().__post_init__() - # TODO(Steven): Validate device and amp? in all policy configs? - """Input validation (not exhaustive).""" + # Validate configuration if self.n_action_steps > self.chunk_size: raise ValueError( - f"The chunk size is the upper bound for the number of action steps per model invocation. Got " - f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`." - ) - if self.n_obs_steps != 1: - raise ValueError( - f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`" + f"n_action_steps ({self.n_action_steps}) cannot be greater than chunk_size ({self.chunk_size})" ) - if self.use_delta_joint_actions_aloha: - raise NotImplementedError( - "`use_delta_joint_actions_aloha` is used by pi0 for aloha real models. It is not ported yet in LeRobot." - ) + if self.paligemma_variant not in ["gemma_300m", "gemma_2b"]: + raise ValueError(f"Invalid paligemma_variant: {self.paligemma_variant}") - def validate_features(self) -> None: - # TODO: implement value error - # if not self.image_features and not self.env_state_feature: - # raise ValueError("You must provide at least one image or the environment state among the inputs.") + if self.action_expert_variant not in ["gemma_300m", "gemma_2b"]: + raise ValueError(f"Invalid action_expert_variant: {self.action_expert_variant}") + + if self.dtype not in ["bfloat16", "float32"]: + raise ValueError(f"Invalid dtype: {self.dtype}") + def validate_features(self) -> None: + """Validate and set up input/output features.""" for i in range(self.empty_cameras): - key = f"observation.images.empty_camera_{i}" + key = f"{OBS_IMAGES}.empty_camera_{i}" empty_camera = PolicyFeature( type=FeatureType.VISUAL, - shape=(3, 480, 640), + shape=(3, *self.image_resolution), # Use configured image resolution ) self.input_features[key] = empty_camera + if "observation.state" not in self.input_features: + state_feature = PolicyFeature( + type=FeatureType.STATE, + shape=(self.max_state_dim,), # Padded to max_state_dim + ) + self.input_features["observation.state"] = state_feature + + if "action" not in self.output_features: + action_feature = PolicyFeature( + type=FeatureType.ACTION, + shape=(self.max_action_dim,), # Padded to max_action_dim + ) + self.output_features["action"] = action_feature + def get_optimizer_preset(self) -> AdamWConfig: return AdamWConfig( lr=self.optimizer_lr, betas=self.optimizer_betas, eps=self.optimizer_eps, weight_decay=self.optimizer_weight_decay, + grad_clip_norm=self.optimizer_grad_clip_norm, ) def get_scheduler_preset(self): diff --git a/src/lerobot/policies/pi0/conversion_scripts/benchmark.py b/src/lerobot/policies/pi0/conversion_scripts/benchmark.py deleted file mode 100644 index c1a4882449f..00000000000 --- a/src/lerobot/policies/pi0/conversion_scripts/benchmark.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch - -from lerobot.configs.policies import PreTrainedConfig -from lerobot.datasets.lerobot_dataset import LeRobotDataset -from lerobot.policies.factory import make_policy - -torch.backends.cudnn.benchmark = True - - -def main(): - device = "cuda" - dataset_repo_id = "danaaubakirova/koch_test" - # model_name = "pi0_base" - # ckpt_torch_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}_pytorch" - ckpt_torch_dir = "lerobot/pi0" - - dataset = LeRobotDataset(dataset_repo_id, episodes=[0]) - - dataloader = torch.utils.data.DataLoader( - dataset, - num_workers=0, - batch_size=1, - ) - - batch = next(iter(dataloader)) - - # To device - for k in batch: - if isinstance(batch[k], torch.Tensor): - batch[k] = batch[k].to(device=device, dtype=torch.float32) - - cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir) - cfg.pretrained_path = ckpt_torch_dir - policy = make_policy(cfg, ds_meta=dataset.meta) - - # policy = torch.compile(policy, mode="reduce-overhead") - - warmup_iters = 10 - benchmark_iters = 30 - - # Warmup - for _ in range(warmup_iters): - torch.cuda.synchronize() - policy.select_action(batch) - policy.reset() - torch.cuda.synchronize() - - # Benchmark - start_event = torch.cuda.Event(enable_timing=True) - end_event = torch.cuda.Event(enable_timing=True) - - start_event.record() - for _ in range(benchmark_iters): - policy.select_action(batch) - policy.reset() - end_event.record() - - # Synchronize and measure time - torch.cuda.synchronize() - elapsed_time_ms = start_event.elapsed_time(end_event) - - avg_time_per_iter = elapsed_time_ms / benchmark_iters - print(f"Average execution time per iteration: {avg_time_per_iter:.3f} ms") - - -if __name__ == "__main__": - with torch.inference_mode(): - main() diff --git a/src/lerobot/policies/pi0/conversion_scripts/compare_with_jax.py b/src/lerobot/policies/pi0/conversion_scripts/compare_with_jax.py deleted file mode 100644 index c0c2e48169a..00000000000 --- a/src/lerobot/policies/pi0/conversion_scripts/compare_with_jax.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -import pickle -from pathlib import Path - -import torch - -from lerobot.configs.policies import PreTrainedConfig -from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata -from lerobot.policies.factory import make_policy - - -def display(tensor: torch.Tensor): - if tensor.dtype == torch.bool: - tensor = tensor.float() - print(f"Shape: {tensor.shape}") - print(f"Mean: {tensor.mean().item()}") - print(f"Std: {tensor.std().item()}") - print(f"Min: {tensor.min().item()}") - print(f"Max: {tensor.max().item()}") - - -def main(): - num_motors = 14 - device = "cuda" - # model_name = "pi0_aloha_towel" - model_name = "pi0_aloha_sim" - - if model_name == "pi0_aloha_towel": - dataset_repo_id = "lerobot/aloha_static_towel" - else: - dataset_repo_id = "lerobot/aloha_sim_transfer_cube_human" - - ckpt_torch_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}_pytorch" - ckpt_jax_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}" - save_dir = Path(f"../openpi/data/{model_name}/save") - - with open(save_dir / "example.pkl", "rb") as f: - example = pickle.load(f) - with open(save_dir / "outputs.pkl", "rb") as f: - outputs = pickle.load(f) - with open(save_dir / "noise.pkl", "rb") as f: - noise = pickle.load(f) - - with open(ckpt_jax_dir / "assets/norm_stats.json") as f: - norm_stats = json.load(f) - - # Override stats - dataset_meta = LeRobotDatasetMetadata(dataset_repo_id) - dataset_meta.stats["observation.state"]["mean"] = torch.tensor( - norm_stats["norm_stats"]["state"]["mean"][:num_motors], dtype=torch.float32 - ) - dataset_meta.stats["observation.state"]["std"] = torch.tensor( - norm_stats["norm_stats"]["state"]["std"][:num_motors], dtype=torch.float32 - ) - - # Create LeRobot batch from Jax - batch = {} - for cam_key, uint_chw_array in example["images"].items(): - batch[f"observation.images.{cam_key}"] = torch.from_numpy(uint_chw_array) / 255.0 - batch["observation.state"] = torch.from_numpy(example["state"]) - batch["action"] = torch.from_numpy(outputs["actions"]) - batch["task"] = example["prompt"] - - if model_name == "pi0_aloha_towel": - del batch["observation.images.cam_low"] - elif model_name == "pi0_aloha_sim": - batch["observation.images.top"] = batch["observation.images.cam_high"] - del batch["observation.images.cam_high"] - - # Batchify - for key in batch: - if isinstance(batch[key], torch.Tensor): - batch[key] = batch[key].unsqueeze(0) - elif isinstance(batch[key], str): - batch[key] = [batch[key]] - else: - raise ValueError(f"{key}, {batch[key]}") - - # To device - for k in batch: - if isinstance(batch[k], torch.Tensor): - batch[k] = batch[k].to(device=device, dtype=torch.float32) - - noise = torch.from_numpy(noise).to(device=device, dtype=torch.float32) - - from lerobot import policies # noqa - - cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir) - cfg.pretrained_path = ckpt_torch_dir - policy = make_policy(cfg, dataset_meta) - - # loss_dict = policy.forward(batch, noise=noise, time=time_beta) - # loss_dict["loss"].backward() - # print("losses") - # display(loss_dict["losses_after_forward"]) - # print("pi_losses") - # display(pi_losses) - - actions = [] - for _ in range(50): - action = policy.select_action(batch, noise=noise) - actions.append(action) - - actions = torch.stack(actions, dim=1) - pi_actions = batch["action"] - print("actions") - display(actions) - print() - print("pi_actions") - display(pi_actions) - print("atol=3e-2", torch.allclose(actions, pi_actions, atol=3e-2)) - print("atol=2e-2", torch.allclose(actions, pi_actions, atol=2e-2)) - print("atol=1e-2", torch.allclose(actions, pi_actions, atol=1e-2)) - - -if __name__ == "__main__": - main() diff --git a/src/lerobot/policies/pi0/conversion_scripts/conversion_utils.py b/src/lerobot/policies/pi0/conversion_scripts/conversion_utils.py deleted file mode 100644 index 8835da31efd..00000000000 --- a/src/lerobot/policies/pi0/conversion_scripts/conversion_utils.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from transformers import GemmaConfig, PaliGemmaConfig - - -def get_paligemma_config(precision: str): - config = { - "image_token_index": None, - "pad_token_id": 0, - "bos_token_id": 2, - "eos_token_id": 1, - } - - # image_sizes = {"2b-test": 224, "3b-224px": 224, "3b-448px": 448, "3b-896px": 896} - - image_size = 224 # image_sizes[variant] - patch_size = 14 - num_image_tokens = (image_size**2) // (patch_size**2) - - config["image_token_index"] = 257152 - text_config = { - "vocab_size": 257152, - "num_hidden_layers": 18, - "num_key_value_heads": 1, - "head_dim": 256, - "torch_dtype": precision, - "hidden_size": 2048, - "hidden_activation": "gelu_pytorch_tanh", - "num_attention_heads": 8, - "intermediate_size": 16384, - "is_encoder_decoder": False, - } - vision_config = { - "torch_dtype": precision, - "image_size": image_size, - "patch_size": patch_size, - "num_image_tokens": num_image_tokens, - "hidden_size": 1152, - "intermediate_size": 4304, - "num_hidden_layers": 27, - "num_attention_heads": 16, - "projector_hidden_act": "gelu_fast", - "vision_use_head": False, - } - final_config = PaliGemmaConfig(text_config=text_config, vision_config=vision_config, **config) - return final_config - - -def get_gemma_config(precision: str): - config = { - "image_token_index": None, - "pad_token_id": 0, - "bos_token_id": 2, - "eos_token_id": 1, - } - - config["image_token_index"] = 257152 - text_config = { - "vocab_size": 257152, - "num_hidden_layers": 18, - "num_key_value_heads": 1, - "head_dim": 256, - "torch_dtype": precision, - "hidden_size": 1024, - "hidden_activation": "gelu_pytorch_tanh", - "num_attention_heads": 8, - "intermediate_size": 4096, - "is_encoder_decoder": False, - } - final_config = GemmaConfig() - final_config.update(text_config) - return final_config diff --git a/src/lerobot/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py b/src/lerobot/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py deleted file mode 100644 index 742c9ab3f71..00000000000 --- a/src/lerobot/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py +++ /dev/null @@ -1,437 +0,0 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Convert pi0 parameters from Jax to Pytorch - -Follow [README of openpi](https://github.com/Physical-Intelligence/openpi) to create a new environment -and install the required libraries. - -```bash -cd ~/code/openpi -source .venv/bin/activate -``` - -Example downloading parameters: -```bash -python ->>> import openpi.shared.download as download ->>> path='s3://openpi-assets/checkpoints/pi0_base/params' ->>> download.maybe_download(path) -``` - -Converting pi0_base: -```python -python -m lerobot.policies.pi0.conversion_scripts.convert_pi0_to_hf_lerobot \ - --checkpoint_dir /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_base/params \ - --output_path /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_base_pytorch -``` - -```python -python -m lerobot.policies.pi0.conversion_scripts.convert_pi0_to_hf_lerobot \ - --checkpoint_dir /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim/params \ - --output_path /home/remi_cadene/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim_pytorch -``` -""" - -import argparse -import pathlib - -import jax -import numpy as np -import orbax.checkpoint as ocp -import torch -from jax.sharding import SingleDeviceSharding - -from lerobot.policies.pi0.configuration_pi0 import PI0Config -from lerobot.policies.pi0.conversion_scripts.conversion_utils import ( - get_gemma_config, - get_paligemma_config, -) -from lerobot.policies.pi0.modeling_pi0 import PI0Policy - -PRECISIONS = {"bfloat16": torch.bfloat16, "float32": torch.float32, "float16": torch.float16} - - -def slice_paligemma_state_dict(state_dict, config): - suffix = "/value" if "img/embedding/kernel/value" in state_dict else "" - - # fmt: off - # patch embeddings - state_dict["paligemma.vision_tower.vision_model.embeddings.patch_embedding.weight"] = state_dict.pop(f"img/embedding/kernel{suffix}").transpose( - 3, 2, 0, 1 - ) - state_dict["paligemma.vision_tower.vision_model.embeddings.patch_embedding.bias"] = state_dict.pop(f"img/embedding/bias{suffix}") - # positional embeddings - state_dict["paligemma.vision_tower.vision_model.embeddings.position_embedding.weight"] = state_dict.pop(f"img/pos_embedding{suffix}").reshape( - -1, config.vision_config.hidden_size - ) - - # extract vision layers to be sliced at index 0. There are 27 layers in the base model. - encoderblock_layernorm0_scale = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_0/scale{suffix}") - encoderblock_layernorm0_bias = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_0/bias{suffix}") - encoderblock_layernorm1_scale = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_1/scale{suffix}") - encoderblock_layernorm1_bias = state_dict.pop(f"img/Transformer/encoderblock/LayerNorm_1/bias{suffix}") - - encoderblock_mlp_dense0_kernel= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_0/kernel{suffix}") - encoderblock_mlp_dense0_bias= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_0/bias{suffix}") - encoderblock_mlp_dense1_kernel= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_1/kernel{suffix}") - encoderblock_mlp_dense1_bias= state_dict.pop(f"img/Transformer/encoderblock/MlpBlock_0/Dense_1/bias{suffix}") - - encoderblock_attention_0_key_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/kernel{suffix}") - encoderblock_attention_0_key_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/key/bias{suffix}") - encoderblock_attention_0_value_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/kernel{suffix}") - encoderblock_attention_0_value_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/value/bias{suffix}") - encoderblock_attention_0_query_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/kernel{suffix}") - encoderblock_attention_0_query_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/query/bias{suffix}") - encoderblock_attention_0_out_kernel = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/kernel{suffix}") - encoderblock_attention_0_out_bias = state_dict.pop(f"img/Transformer/encoderblock/MultiHeadDotProductAttention_0/out/bias{suffix}") - - for i in range(config.vision_config.num_hidden_layers): - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm1.weight"] = encoderblock_layernorm0_scale[i].transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm1.bias"] = encoderblock_layernorm0_bias[i] - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm2.weight"] = encoderblock_layernorm1_scale[i].transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.layer_norm2.bias"] = encoderblock_layernorm1_bias[i] - - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.weight"] = encoderblock_mlp_dense0_kernel[i].transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc1.bias"] = encoderblock_mlp_dense0_bias[i] - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.weight"] = encoderblock_mlp_dense1_kernel[i].transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.mlp.fc2.bias"] = encoderblock_mlp_dense1_bias[i] - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.weight"] = encoderblock_attention_0_key_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.k_proj.bias"] = encoderblock_attention_0_key_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.weight"] = encoderblock_attention_0_value_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.v_proj.bias"] = encoderblock_attention_0_value_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.weight"] = encoderblock_attention_0_query_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.q_proj.bias"] = encoderblock_attention_0_query_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.weight"] = encoderblock_attention_0_out_kernel[i].reshape(-1, config.vision_config.hidden_size).transpose() - state_dict[f"paligemma.vision_tower.vision_model.encoder.layers.{i}.self_attn.out_proj.bias"] = encoderblock_attention_0_out_bias[i].reshape(-1, config.vision_config.hidden_size).reshape(-1) - - state_dict["paligemma.vision_tower.vision_model.post_layernorm.weight"] = state_dict.pop(f"img/Transformer/encoder_norm/scale{suffix}").transpose() - state_dict["paligemma.vision_tower.vision_model.post_layernorm.bias"] = state_dict.pop(f"img/Transformer/encoder_norm/bias{suffix}") - - # multimodal projector - - state_dict['paligemma.multi_modal_projector.linear.weight'] = state_dict.pop(f"img/head/kernel{suffix}").transpose() - state_dict['paligemma.multi_modal_projector.linear.bias'] = state_dict.pop(f"img/head/bias{suffix}") - - # text decoder (gemma) - embedding_vector = state_dict.pop(f"llm/embedder/input_embedding{suffix}") - state_dict["paligemma.language_model.model.embed_tokens.weight"] = embedding_vector - - # pop the einsum attention + mlp representations. There are 18 layers in gemma-2b. - - llm_attention_attn_vec_einsum = state_dict.pop(f"llm/layers/attn/attn_vec_einsum/w{suffix}") - llm_attention_kv_einsum = state_dict.pop(f"llm/layers/attn/kv_einsum/w{suffix}") - llm_attention_q_einsum = state_dict.pop(f"llm/layers/attn/q_einsum/w{suffix}") - - llm_mlp_gating_einsum = state_dict.pop(f"llm/layers/mlp/gating_einsum{suffix}") - llm_mlp_linear = state_dict.pop(f"llm/layers/mlp/linear{suffix}") - # TODO verify correctness of layer norm loading - - llm_input_layernorm = state_dict.pop(f"llm/layers/pre_attention_norm/scale{suffix}") - llm_post_attention_layernorm = state_dict.pop(f"llm/layers/pre_ffw_norm/scale{suffix}") - - for i in range(config.text_config.num_hidden_layers): - # llm_attention_q_einsum[i].shape = (8, 2048, 256) - q_proj_weight_reshaped = llm_attention_q_einsum[i].transpose(0, 2, 1).reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size) - - state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.q_proj.weight"] = q_proj_weight_reshaped - - # llm_attention_kv_einsum[i, 0, 0].shape = (2048, 256) - k_proj_weight_reshaped = llm_attention_kv_einsum[i, 0, 0].transpose() - state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.k_proj.weight"] = k_proj_weight_reshaped - # llm_attention_kv_einsum[i, 1, 0].shape = (2048, 256) - v_proj_weight_reshaped = llm_attention_kv_einsum[i, 1, 0].transpose() - state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.v_proj.weight"] = v_proj_weight_reshaped - - # output projection. - - # llm_attention_attn_vec_einsum[i].shape = (8, 256, 2048) - o_proj_weight_reshaped = llm_attention_attn_vec_einsum[i].transpose(2, 0, 1).reshape(config.text_config.num_attention_heads * config.text_config.head_dim, config.text_config.hidden_size) - - state_dict[f"paligemma.language_model.model.layers.{i}.self_attn.o_proj.weight"] = o_proj_weight_reshaped - # mlp layers - gate_proj_weight = llm_mlp_gating_einsum[i, 0] - state_dict[f"paligemma.language_model.model.layers.{i}.mlp.gate_proj.weight"] = gate_proj_weight.transpose() - up_proj_weight = llm_mlp_gating_einsum[i, 1] - state_dict[f"paligemma.language_model.model.layers.{i}.mlp.up_proj.weight"] = up_proj_weight.transpose() - state_dict[f"paligemma.language_model.model.layers.{i}.mlp.down_proj.weight"] = llm_mlp_linear[i].transpose() - state_dict[f"paligemma.language_model.model.layers.{i}.input_layernorm.weight"] = llm_input_layernorm[i] - state_dict[f"paligemma.language_model.model.layers.{i}.post_attention_layernorm.weight"] = llm_post_attention_layernorm[i] - - state_dict["paligemma.language_model.model.norm.weight"] = state_dict.pop(f"llm/final_norm/scale{suffix}") - state_dict["paligemma.language_model.lm_head.weight"] = embedding_vector # weights are tied. - - # fmt: on - expert_dict = {} - final_state_dict = {} - for key, value in state_dict.items(): - if key not in [ - f"llm/final_norm_1/scale{suffix}", - f"llm/layers/attn/attn_vec_einsum_1/w{suffix}", - f"llm/layers/attn/kv_einsum_1/w{suffix}", - f"llm/layers/attn/q_einsum_1/w{suffix}", - f"llm/layers/mlp_1/gating_einsum{suffix}", - f"llm/layers/mlp_1/linear{suffix}", - f"llm/layers/pre_attention_norm_1/scale{suffix}", - f"llm/layers/pre_ffw_norm_1/scale{suffix}", - ]: - final_state_dict[key] = torch.from_numpy(value) - else: - expert_dict[key] = value - - return final_state_dict, expert_dict - - -def slice_gemma_state_dict(state_dict, config, num_expert=1): - # fmt: off - # text decoder (gemma) - # no embedding vector, the expert just has the decoder layers - - embedding_vector = torch.zeros([config.vocab_size, config.hidden_size]) - state_dict["gemma_expert.model.embed_tokens.weight"] = embedding_vector - - # pop the einsum attention + mlp representations. There are 18 layers in gemma-2b. - - suffix = "/value" if f"llm/layers/attn/attn_vec_einsum_{num_expert}/w/value" in state_dict else "" - - llm_attention_attn_vec_einsum = state_dict.pop(f"llm/layers/attn/attn_vec_einsum_{num_expert}/w{suffix}") - llm_attention_kv_einsum = state_dict.pop(f"llm/layers/attn/kv_einsum_{num_expert}/w{suffix}") - llm_attention_q_einsum = state_dict.pop(f"llm/layers/attn/q_einsum_{num_expert}/w{suffix}") - - llm_mlp_gating_einsum = state_dict.pop(f"llm/layers/mlp_{num_expert}/gating_einsum{suffix}") - llm_mlp_linear = state_dict.pop(f"llm/layers/mlp_{num_expert}/linear{suffix}") - # TODO verify correctness of layer norm loading - - llm_input_layernorm = state_dict.pop(f"llm/layers/pre_attention_norm_{num_expert}/scale{suffix}") - llm_post_attention_layernorm = state_dict.pop(f"llm/layers/pre_ffw_norm_{num_expert}/scale{suffix}") - - for i in range(config.num_hidden_layers): - q_proj_weight_reshaped = llm_attention_q_einsum[i].transpose(0, 2, 1).reshape(config.num_attention_heads * config.head_dim, config.hidden_size) - - state_dict[f"gemma_expert.model.layers.{i}.self_attn.q_proj.weight"] = q_proj_weight_reshaped - - k_proj_weight_reshaped = llm_attention_kv_einsum[i, 0, 0].transpose() - state_dict[f"gemma_expert.model.layers.{i}.self_attn.k_proj.weight"] = k_proj_weight_reshaped - v_proj_weight_reshaped = llm_attention_kv_einsum[i, 1, 0].transpose() - state_dict[f"gemma_expert.model.layers.{i}.self_attn.v_proj.weight"] = v_proj_weight_reshaped - - # output projection. - - # llm_attention_attn_vec_einsum[i].shape = (8, 256, 1024) - o_proj_weight_reshaped = llm_attention_attn_vec_einsum[i].reshape(config.num_attention_heads * config.head_dim, config.hidden_size).transpose(1,0)# .transpose(2, 0, 1).reshape(config.num_attention_heads * config.head_dim, config.hidden_size).transpose(1, 0) - - state_dict[f"gemma_expert.model.layers.{i}.self_attn.o_proj.weight"] = o_proj_weight_reshaped - # mlp layers - gate_proj_weight = llm_mlp_gating_einsum[i, 0] - state_dict[f"gemma_expert.model.layers.{i}.mlp.gate_proj.weight"] = gate_proj_weight.transpose() - up_proj_weight = llm_mlp_gating_einsum[i, 1] - state_dict[f"gemma_expert.model.layers.{i}.mlp.up_proj.weight"] = up_proj_weight.transpose() - state_dict[f"gemma_expert.model.layers.{i}.mlp.down_proj.weight"] = llm_mlp_linear[i].transpose() - state_dict[f"gemma_expert.model.layers.{i}.input_layernorm.weight"] = llm_input_layernorm[i] - state_dict[f"gemma_expert.model.layers.{i}.post_attention_layernorm.weight"] = llm_post_attention_layernorm[i] - - state_dict["gemma_expert.model.norm.weight"] = state_dict.pop(f"llm/final_norm_{num_expert}/scale{suffix}") - state_dict["gemma_expert.lm_head.weight"] = embedding_vector # weights are tied. (and zeros here) - - # fmt: on - final_state_dict = {} - for key, value in state_dict.items(): - if not isinstance(value, torch.Tensor): - final_state_dict[key] = torch.from_numpy(value) - else: - final_state_dict[key] = value - return final_state_dict - - -def flatten_for_memory(tree, parent_key=""): - out = {} - for k, v in tree.items(): - new_key = f"{parent_key}/{k}" if parent_key else k - if isinstance(v, dict): - out.update(flatten_for_memory(v, new_key)) - else: - out[new_key] = np.array(v) # Ensure conversion to np.array for consistency - return out - - -def flatten_for_npz(tree, parent_key=""): - out = {} - for k, v in tree.items(): - new_key = f"{parent_key}/{k}" if parent_key else k - if isinstance(v, dict): - out.update(flatten_for_npz(v, new_key)) - else: - # bf16/f32 here? - out[new_key] = np.array(v) - return out - - -def slice_initial_orbax_checkpoint(checkpoint_dir: str): - params_path = pathlib.Path(checkpoint_dir).resolve() - checkpointer = ocp.PyTreeCheckpointer() - - metadata = checkpointer.metadata(params_path) - print("Metadata keys:", list(metadata.keys())) - - params_name = "params" - - item = {params_name: metadata[params_name]} - device = jax.local_devices()[0] # Use the first local device - sharding = SingleDeviceSharding(device) - restored = checkpointer.restore( - params_path, - ocp.args.PyTreeRestore( - item=item, - restore_args=jax.tree_util.tree_map( - lambda _: ocp.ArrayRestoreArgs( - restore_type=jax.Array, # or np.ndarray, but bf16 is annoying about it - sharding=sharding, - ), - item, - ), - transforms={}, - ), - ) - params = restored[params_name] - - # get params for PaliGemma - pali_params = params["PaliGemma"] - del params["PaliGemma"] - pali_params_flat = flatten_for_npz(pali_params) - return {"paligemma_params": pali_params_flat, "projection_params": params} - - -def update_keys_with_prefix(d: dict, prefix: str) -> dict: - """Update dictionary keys by adding a prefix.""" - return {f"{prefix}{key}": value for key, value in d.items()} - - -def convert_pi0_checkpoint(checkpoint_dir: str, precision: str, tokenizer_id: str, output_path: str): - # Break down orbax ckpts - they are in OCDBT - initial_params = slice_initial_orbax_checkpoint(checkpoint_dir=checkpoint_dir) - # process projection params - keys = [ - "state_proj", - "action_in_proj", - "action_out_proj", - "action_time_mlp_in", - "action_time_mlp_out", - ] - - projection_params = {} - for key in keys: - kernel_params = initial_params["projection_params"][key]["kernel"] - bias_params = initial_params["projection_params"][key]["bias"] - if isinstance(kernel_params, dict): - weight = kernel_params["value"] - bias = bias_params["value"] - else: - weight = kernel_params - bias = bias_params - projection_params[f"{key}.weight"] = torch.from_numpy(np.array(weight)).T - projection_params[f"{key}.bias"] = torch.from_numpy(np.array(bias)) - - # Process PaliGemma weights - paligemma_config = get_paligemma_config(precision) - paligemma_params, gemma_raw_dictionary = slice_paligemma_state_dict( - initial_params["paligemma_params"], paligemma_config - ) - - # Process Gemma weights (at this stage they are unused) - gemma_config = get_gemma_config(precision) - gemma_params = slice_gemma_state_dict(gemma_raw_dictionary, config=gemma_config) - - # Instantiate model from configs - - if "pi0_aloha_sim" in checkpoint_dir: - pi0_config = PI0Config( - empty_cameras=2, - adapt_to_pi_aloha=True, - use_delta_joint_actions_aloha=False, - ) - elif "pi0_aloha_towel" in checkpoint_dir: - pi0_config = PI0Config( - adapt_to_pi_aloha=True, - use_delta_joint_actions_aloha=True, - ) - elif "pi0_base" in checkpoint_dir: - pi0_config = PI0Config( - empty_cameras=0, - adapt_to_pi_aloha=False, - use_delta_joint_actions_aloha=False, - ) - else: - raise ValueError() - - # gemma_config=gemma_config, paligemma_config=paligemma_config) - pi0_model = PI0Policy(pi0_config) - - paligemma_params = update_keys_with_prefix(paligemma_params, "model.paligemma_with_expert.") - gemma_params = update_keys_with_prefix(gemma_params, "model.paligemma_with_expert.") - projection_params = update_keys_with_prefix(projection_params, "model.") - - # load state dict - torch_dtype = PRECISIONS[precision] - pi0_model.load_state_dict({**paligemma_params, **gemma_params, **projection_params}) - pi0_model = pi0_model.to(torch_dtype) - # pi0_tokenizer = AutoTokenizer.from_pretrained(tokenizer_id) - - pi0_model.save_pretrained(output_path, safe_serialization=True) - # pi0_tokenizer.save_pretrained(output_path, dtype=torch_dtype) - - # assert that model loads properly - del pi0_model - PI0Policy.from_pretrained(output_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--checkpoint_dir", - default="/raid/pablo/.cache/openpi/openpi-assets/checkpoints/pi0_aloha_sim/params", - type=str, - help="Path to the ocdbt checkpoint", - ) - - parser.add_argument( - "--precision", - choices=["float32", "bfloat16", "float16"], - default="float32", - type=str, - help="Precision identifier for model conversion - should match the base checkpoint precision.", - ) - # tokenizer is identical to paligemma, it appears - - parser.add_argument( - "--tokenizer_hub_id", - default="google/paligemma-3b-pt-224", - type=str, - help="Hub path to the tokenizer to save", - ) - - parser.add_argument( - "--output_path", - required=True, - type=str, - help="Path to save converted weights to", - ) - - args = parser.parse_args() - convert_pi0_checkpoint( - checkpoint_dir=args.checkpoint_dir, - precision=args.precision, - tokenizer_id=args.tokenizer_hub_id, - output_path=args.output_path, - ) diff --git a/src/lerobot/policies/pi0/flex_attention.py b/src/lerobot/policies/pi0/flex_attention.py deleted file mode 100644 index 35628cddb40..00000000000 --- a/src/lerobot/policies/pi0/flex_attention.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import torch.nn.functional as F # noqa: N812 -from packaging.version import Version - -if Version(torch.__version__) > Version("2.5.0"): - # Ffex attention is only available from torch 2.5 onwards - from torch.nn.attention.flex_attention import ( - _mask_mod_signature, - _round_up_to_multiple, - create_block_mask, - create_mask, - flex_attention, - ) - - -# @torch.compile(dynamic=False) -def flex_attention_forward( - attention_mask: torch.Tensor, - batch_size: int, - head_dim: int, - query_states: torch.Tensor, - key_states: torch.Tensor, - value_states: torch.Tensor, - scaling=None, -): - """ - This is defined out of classes to make compile happy. - """ - - original_dtype = query_states.dtype - num_att_heads = 8 - num_key_value_heads = 1 - num_key_value_groups = num_att_heads // num_key_value_heads - - key_states = key_states[:, :, :, None, :] - key_states = key_states.expand( - batch_size, key_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim - ) - key_states = key_states.reshape( - batch_size, key_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim - ) - - value_states = value_states[:, :, :, None, :] - value_states = value_states.expand( - batch_size, value_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim - ) - value_states = value_states.reshape( - batch_size, value_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim - ) - - query_states = query_states.transpose(1, 2) - key_states = key_states.transpose(1, 2) - value_states = value_states.transpose(1, 2) - - query_states = query_states.to(torch.float32) - key_states = key_states.to(torch.float32) - value_states = value_states.to(torch.float32) - - causal_mask = attention_mask - if causal_mask is not None: - causal_mask = causal_mask[:, None, :, : key_states.shape[2]] - - if causal_mask.shape[1] == 1 and query_states.shape[1] > 1: - causal_mask = causal_mask.expand(-1, query_states.shape[1], -1, -1) - - def precomputed_mask_factory(precomputed_mask: torch.Tensor) -> _mask_mod_signature: - def mask_mod(b, h, q_idx, kv_idx): - # Danger zone: if b,h,q_idx,kv_idx exceed the shape, device-side assert occurs. - return precomputed_mask[b][h][q_idx][kv_idx] - - return mask_mod - - b_mask, h_mask, q_len, kv_len = causal_mask.shape # The shape of your mask - - block_size = 128 - q_len_rounded = _round_up_to_multiple(q_len, block_size) - kv_len_rounded = _round_up_to_multiple(kv_len, block_size) - - # *CRITICAL* we do need to expand here, else we get a CUDA index error - - pad_q = q_len_rounded - q_len - pad_k = kv_len_rounded - kv_len - - padded_causal_mask = F.pad(causal_mask, (0, pad_k, 0, pad_q), value=0.0) - mask_mod_fn_orig = precomputed_mask_factory(padded_causal_mask) - - mask_4d = create_mask( - mod_fn=mask_mod_fn_orig, - B=b_mask, - H=h_mask, - Q_LEN=q_len_rounded, - KV_LEN=kv_len_rounded, - device=causal_mask.device, - _compile=False, - ) - - mask_mod_fn_padded = precomputed_mask_factory(mask_4d) - block_mask = create_block_mask( - mask_mod=mask_mod_fn_padded, - B=b_mask, - H=h_mask, - Q_LEN=q_len_rounded, - KV_LEN=kv_len_rounded, - BLOCK_SIZE=block_size, - device=causal_mask.device, - _compile=False, - ) - - # mask is applied inside the kernel, ideally more efficiently than score_mod. - attn_output, attention_weights = flex_attention( - query_states, - key_states, - value_states, - block_mask=block_mask, - enable_gqa=True, # because we shaped query/key states for GQA - scale=head_dim**-0.5 if scaling is None else scaling, - return_lse=True, - ) - - attn_output = attn_output.to(dtype=original_dtype) - attn_output = attn_output.transpose(1, 2).contiguous() # [B, Q_LEN, H, head_dim] - attn_output = attn_output.reshape( - batch_size, - -1, - attn_output.shape[2] * attn_output.shape[3], # merges [H, head_dim] - ) - return attn_output diff --git a/src/lerobot/policies/pi0/modeling_pi0.py b/src/lerobot/policies/pi0/modeling_pi0.py index de41e2bd48e..a2dcdaea37f 100644 --- a/src/lerobot/policies/pi0/modeling_pi0.py +++ b/src/lerobot/policies/pi0/modeling_pi0.py @@ -14,64 +14,58 @@ # See the License for the specific language governing permissions and # limitations under the License. -""" -π0: A Vision-Language-Action Flow Model for General Robot Control - -[Paper](https://www.physicalintelligence.company/download/pi0.pdf) -[Jax code](https://github.com/Physical-Intelligence/openpi) - -Designed by Physical Intelligence. Ported from Jax by Hugging Face. -Disclaimer: It is not expected to perform as well as the original implementation. - -Install pi0 extra dependencies: -```bash -pip install -e ".[pi0]" -``` - -Example of finetuning the pi0 pretrained model (`pi0_base` in `openpi`): -```bash -lerobot-train \ ---policy.path=lerobot/pi0 \ ---dataset.repo_id=danaaubakirova/koch_test -``` - -Example of finetuning the pi0 neural network with PaliGemma and expert Gemma -pretrained with VLM default parameters before pi0 finetuning: -```bash -lerobot-train \ ---policy.type=pi0 \ ---dataset.repo_id=danaaubakirova/koch_test -``` - -Example of using the pi0 pretrained model outside LeRobot training framework: -```python -policy = Pi0Policy.from_pretrained("lerobot/pi0") -``` - -""" - +import builtins +import logging import math from collections import deque +from pathlib import Path +from typing import TYPE_CHECKING, Literal import torch import torch.nn.functional as F # noqa: N812 from torch import Tensor, nn -from transformers import AutoTokenizer -from lerobot.constants import ACTION, OBS_STATE -from lerobot.policies.normalize import Normalize, Unnormalize +from lerobot.utils.import_utils import _transformers_available + +# Conditional import for type checking and lazy loading +if TYPE_CHECKING or _transformers_available: + from transformers.models.auto import CONFIG_MAPPING + from transformers.models.gemma import modeling_gemma + from transformers.models.gemma.modeling_gemma import GemmaForCausalLM + from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration +else: + CONFIG_MAPPING = None + modeling_gemma = None + GemmaForCausalLM = None + PaliGemmaForConditionalGeneration = None + +from lerobot.configs.policies import PreTrainedConfig from lerobot.policies.pi0.configuration_pi0 import PI0Config -from lerobot.policies.pi0.paligemma_with_expert import ( - PaliGemmaWithExpertConfig, - PaliGemmaWithExpertModel, +from lerobot.policies.pretrained import PreTrainedPolicy, T +from lerobot.utils.constants import ( + ACTION, + OBS_LANGUAGE_ATTENTION_MASK, + OBS_LANGUAGE_TOKENS, + OBS_STATE, + OPENPI_ATTENTION_MASK_VALUE, ) -from lerobot.policies.pretrained import PreTrainedPolicy -from lerobot.policies.utils import log_model_loading_keys -from lerobot.utils.utils import get_safe_dtype, init_logging -def create_sinusoidal_pos_embedding( - time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu" +def get_safe_dtype(target_dtype, device_type): + """Get a safe dtype for the given device type.""" + if device_type == "mps" and target_dtype == torch.float64: + return torch.float32 + if device_type == "cpu": + # CPU doesn't support bfloat16, use float32 instead + if target_dtype == torch.bfloat16: + return torch.float32 + if target_dtype == torch.float64: + return torch.float64 + return target_dtype + + +def create_sinusoidal_pos_embedding( # see openpi `create_sinusoidal_pos_embedding` (exact copy) + time: torch.Tensor, dimension: int, min_period: float, max_period: float, device="cpu" ) -> Tensor: """Computes sine-cosine positional embedding vectors for scalar positions.""" if dimension % 2 != 0: @@ -87,11 +81,17 @@ def create_sinusoidal_pos_embedding( # Compute the outer product scaling_factor = 1.0 / period * 2 * math.pi sin_input = scaling_factor[None, :] * time[:, None] - pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1) - return pos_emb + return torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1) + + +def sample_beta(alpha, beta, bsize, device): # see openpi `sample_beta` (exact copy) + alpha_t = torch.as_tensor(alpha, dtype=torch.float32, device=device) + beta_t = torch.as_tensor(beta, dtype=torch.float32, device=device) + dist = torch.distributions.Beta(alpha_t, beta_t) + return dist.sample((bsize,)) -def make_att_2d_masks(pad_masks, att_masks): +def make_att_2d_masks(pad_masks, att_masks): # see openpi `make_att_2d_masks` (exact copy) """Copied from big_vision. Tokens can attend to valid inputs tokens which have a cumulative mask_ar @@ -120,542 +120,514 @@ def make_att_2d_masks(pad_masks, att_masks): cumsum = torch.cumsum(att_masks, dim=1) att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None] pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None] - att_2d_masks = att_2d_masks & pad_2d_masks - return att_2d_masks - - -def resize_with_pad(img, width, height, pad_value=-1): - # assume no-op when width height fits already - if img.ndim != 4: - raise ValueError(f"(b,c,h,w) expected, but {img.shape}") - - cur_height, cur_width = img.shape[2:] - - ratio = max(cur_width / width, cur_height / height) - resized_height = int(cur_height / ratio) - resized_width = int(cur_width / ratio) - resized_img = F.interpolate( - img, size=(resized_height, resized_width), mode="bilinear", align_corners=False - ) - - pad_height = max(0, int(height - resized_height)) - pad_width = max(0, int(width - resized_width)) - - # pad on left and top of image - padded_img = F.pad(resized_img, (pad_width, 0, pad_height, 0), value=pad_value) - return padded_img + return att_2d_masks & pad_2d_masks def pad_vector(vector, new_dim): - """Can be (batch_size x sequence_length x features_dimension) + """Pad the last dimension of a vector to new_dim with zeros. + + Can be (batch_size x sequence_length x features_dimension) or (batch_size x features_dimension) """ - if vector.shape[-1] == new_dim: + if vector.shape[-1] >= new_dim: return vector - shape = list(vector.shape) - current_dim = shape[-1] - shape[-1] = new_dim - new_vector = torch.zeros(*shape, dtype=vector.dtype, device=vector.device) - new_vector[..., :current_dim] = vector - return new_vector - - -def normalize(x, min_val, max_val): - return (x - min_val) / (max_val - min_val) - - -def unnormalize(x, min_val, max_val): - return x * (max_val - min_val) + min_val + return F.pad(vector, (0, new_dim - vector.shape[-1])) -def safe_arcsin(value): - # This ensures that the input stays within - # [−1,1] to avoid invalid values for arcsin - return torch.arcsin(torch.clamp(value, -1.0, 1.0)) +def resize_with_pad_torch( # see openpi `resize_with_pad_torch` (exact copy) + images: torch.Tensor, + height: int, + width: int, + mode: str = "bilinear", +) -> torch.Tensor: + """PyTorch version of resize_with_pad. Resizes an image to a target height and width without distortion + by padding with black. If the image is float32, it must be in the range [-1, 1]. + Args: + images: Tensor of shape [*b, h, w, c] or [*b, c, h, w] + height: Target height + width: Target width + mode: Interpolation mode ('bilinear', 'nearest', etc.) -def aloha_gripper_to_angular(value): - # Aloha transforms the gripper positions into a linear space. The following code - # reverses this transformation to be consistent with pi0 which is pretrained in - # angular space. - # - # These values are coming from the Aloha code: - # PUPPET_GRIPPER_POSITION_OPEN, PUPPET_GRIPPER_POSITION_CLOSED - value = unnormalize(value, min_val=0.01844, max_val=0.05800) - - # This is the inverse of the angular to linear transformation inside the Interbotix code. - def linear_to_radian(linear_position, arm_length, horn_radius): - value = (horn_radius**2 + linear_position**2 - arm_length**2) / (2 * horn_radius * linear_position) - return safe_arcsin(value) - - # The constants are taken from the Interbotix code. - value = linear_to_radian(value, arm_length=0.036, horn_radius=0.022) - - # Normalize to [0, 1]. - # The values 0.4 and 1.5 were measured on an actual Trossen robot. - return normalize(value, min_val=0.4, max_val=1.5) - - -def aloha_gripper_from_angular(value): - # Convert from the gripper position used by pi0 to the gripper position that is used by Aloha. - # Note that the units are still angular but the range is different. - - # The values 0.4 and 1.5 were measured on an actual Trossen robot. - value = unnormalize(value, min_val=0.4, max_val=1.5) + Returns: + Resized and padded tensor with same shape format as input + """ + # Check if input is in channels-last format [*b, h, w, c] or channels-first [*b, c, h, w] + if images.shape[-1] <= 4: # Assume channels-last format + channels_last = True + if images.dim() == 3: + images = images.unsqueeze(0) # Add batch dimension + images = images.permute(0, 3, 1, 2) # [b, h, w, c] -> [b, c, h, w] + else: + channels_last = False + if images.dim() == 3: + images = images.unsqueeze(0) # Add batch dimension + + batch_size, channels, cur_height, cur_width = images.shape + + # Calculate resize ratio + ratio = max(cur_width / width, cur_height / height) + resized_height = int(cur_height / ratio) + resized_width = int(cur_width / ratio) - # These values are coming from the Aloha code: - # PUPPET_GRIPPER_JOINT_OPEN, PUPPET_GRIPPER_JOINT_CLOSE - return normalize(value, min_val=-0.6213, max_val=1.4910) + # Resize + resized_images = F.interpolate( + images, + size=(resized_height, resized_width), + mode=mode, + align_corners=False if mode == "bilinear" else None, + ) + # Handle dtype-specific clipping + if images.dtype == torch.uint8: + resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8) + elif images.dtype == torch.float32: + resized_images = resized_images.clamp(-1.0, 1.0) + else: + raise ValueError(f"Unsupported image dtype: {images.dtype}") + + # Calculate padding + pad_h0, remainder_h = divmod(height - resized_height, 2) + pad_h1 = pad_h0 + remainder_h + pad_w0, remainder_w = divmod(width - resized_width, 2) + pad_w1 = pad_w0 + remainder_w + + # Pad + constant_value = 0 if images.dtype == torch.uint8 else -1.0 + padded_images = F.pad( + resized_images, + (pad_w0, pad_w1, pad_h0, pad_h1), # left, right, top, bottom + mode="constant", + value=constant_value, + ) -def aloha_gripper_from_angular_inv(value): - # Directly inverts the gripper_from_angular function. - value = unnormalize(value, min_val=-0.6213, max_val=1.4910) - return normalize(value, min_val=0.4, max_val=1.5) + # Convert back to original format if needed + if channels_last: + padded_images = padded_images.permute(0, 2, 3, 1) # [b, c, h, w] -> [b, h, w, c] + + return padded_images + + +# Define the complete layer computation function for gradient checkpointing +def compute_layer_complete( + layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert +): + models = [paligemma.language_model, gemma_expert.model] + query_states = [] + key_states = [] + value_states = [] + gates = [] + for i, hidden_states in enumerate(inputs_embeds): + layer = models[i].layers[layer_idx] + hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i]) # noqa: PLW2901 + gates.append(gate) + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, layer.self_attn.head_dim) + query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + query_states.append(query_state) + key_states.append(key_state) + value_states.append(value_state) + # Concatenate and process attention + query_states = torch.cat(query_states, dim=2) + key_states = torch.cat(key_states, dim=2) + value_states = torch.cat(value_states, dim=2) + dummy_tensor = torch.zeros( + query_states.shape[0], + query_states.shape[2], + query_states.shape[-1], + device=query_states.device, + dtype=query_states.dtype, + ) + cos, sin = paligemma.model.language_model.rotary_emb(dummy_tensor, position_ids) + query_states, key_states = modeling_gemma.apply_rotary_pos_emb( + query_states, key_states, cos, sin, unsqueeze_dim=1 + ) + batch_size = query_states.shape[0] + scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling + # Attention computation + att_output, _ = modeling_gemma.eager_attention_forward( + paligemma.language_model.layers[layer_idx].self_attn, + query_states, + key_states, + value_states, + attention_mask, + scaling, + ) + # Get head_dim from the current layer, not from the model + head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim + att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim) + # Process layer outputs + outputs_embeds = [] + start_pos = 0 + for i, hidden_states in enumerate(inputs_embeds): + layer = models[i].layers[layer_idx] + end_pos = start_pos + hidden_states.shape[1] + if att_output.dtype != layer.self_attn.o_proj.weight.dtype: + att_output = att_output.to(layer.self_attn.o_proj.weight.dtype) + out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos]) + # first residual + out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i]) # noqa: SLF001 + after_first_residual = out_emb.clone() + out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i]) + # Convert to bfloat16 if the next layer (mlp) uses bfloat16 + if layer.mlp.up_proj.weight.dtype == torch.bfloat16: + out_emb = out_emb.to(dtype=torch.bfloat16) + out_emb = layer.mlp(out_emb) + # second residual + out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate) # noqa: SLF001 + outputs_embeds.append(out_emb) + start_pos = end_pos + return outputs_embeds + + +class GemmaConfig: # see openpi `gemma.py: Config` + """Configuration for Gemma model variants.""" + + def __init__(self, width, depth, mlp_dim, num_heads, num_kv_heads, head_dim): + self.width = width + self.depth = depth + self.mlp_dim = mlp_dim + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = head_dim + + +def get_gemma_config(variant: str) -> GemmaConfig: # see openpi `gemma.py: get_config` + """Returns config for specified gemma variant.""" + if variant == "gemma_300m": + return GemmaConfig( + width=1024, + depth=18, + mlp_dim=4096, + num_heads=8, + num_kv_heads=1, + head_dim=256, + ) + elif variant == "gemma_2b": + return GemmaConfig( + width=2048, + depth=18, + mlp_dim=16_384, + num_heads=8, + num_kv_heads=1, + head_dim=256, + ) + else: + raise ValueError(f"Unknown variant: {variant}") -class PI0Policy(PreTrainedPolicy): - """Wrapper class around PI0FlowMatching model to train and run inference within LeRobot.""" - - config_class = PI0Config - name = "pi0" +class PaliGemmaWithExpertModel( + nn.Module +): # see openpi `gemma_pytorch.py: PaliGemmaWithExpertModel` this class is almost a exact copy of PaliGemmaWithExpertModel in openpi + """PaliGemma model with action expert for PI0.""" def __init__( self, - config: PI0Config, - dataset_stats: dict[str, dict[str, Tensor]] | None = None, + vlm_config, + action_expert_config, + use_adarms=None, + precision: Literal["bfloat16", "float32"] = "bfloat16", ): - """ - Args: - config: Policy configuration class instance or None, in which case the default instantiation of - the configuration class is used. - dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected - that they will be passed with a call to `load_state_dict` before the policy is used. - """ + if use_adarms is None: + use_adarms = [False, False] + super().__init__() - super().__init__(config) - config.validate_features() - self.config = config - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats + vlm_config_hf = CONFIG_MAPPING["paligemma"]() + vlm_config_hf._vocab_size = 257152 # noqa: SLF001 + vlm_config_hf.image_token_index = 257152 + vlm_config_hf.text_config.hidden_size = vlm_config.width + vlm_config_hf.text_config.intermediate_size = vlm_config.mlp_dim + vlm_config_hf.text_config.num_attention_heads = vlm_config.num_heads + vlm_config_hf.text_config.head_dim = vlm_config.head_dim + vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth + vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads + vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh" + vlm_config_hf.text_config.torch_dtype = "float32" + vlm_config_hf.text_config.vocab_size = 257152 + vlm_config_hf.text_config.use_adarms = use_adarms[0] + vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None + vlm_config_hf.vision_config.intermediate_size = 4304 + vlm_config_hf.vision_config.projection_dim = 2048 + vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast" + vlm_config_hf.vision_config.torch_dtype = "float32" + + action_expert_config_hf = CONFIG_MAPPING["gemma"]( + head_dim=action_expert_config.head_dim, + hidden_size=action_expert_config.width, + intermediate_size=action_expert_config.mlp_dim, + num_attention_heads=action_expert_config.num_heads, + num_hidden_layers=action_expert_config.depth, + num_key_value_heads=action_expert_config.num_kv_heads, + vocab_size=257152, + hidden_activation="gelu_pytorch_tanh", + torch_dtype="float32", + use_adarms=use_adarms[1], + adarms_cond_dim=action_expert_config.width if use_adarms[1] else None, ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - - self.language_tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224") - self.model = PI0FlowMatching(config) - - self.reset() - - def reset(self): - """This should be called whenever the environment is reset.""" - self._action_queue = deque([], maxlen=self.config.n_action_steps) - @classmethod - def _transform_state_dict_keys(cls, state_dict: dict) -> dict: - """ - Transform state dict keys to match expected model structure. - - Transformations: - - model.paligemma_with_expert.paligemma.language_model.lm_head -> - model.paligemma_with_expert.paligemma.lm_head - - model.paligemma_with_expert.paligemma.language_model.model -> - model.paligemma_with_expert.paligemma.model.language_model - - model.paligemma_with_expert.paligemma.vision_tower -> - model.paligemma_with_expert.paligemma.model.vision_tower - - model.paligemma_with_expert.paligemma.multi_modal_projector -> - model.paligemma_with_expert.paligemma.model.multi_modal_projector - - Also handles tied weights between lm_head.weight and - embed_tokens.weight. - """ - import re - - transformed_dict = {} - - transformations = [ - ( - re.compile(r"\.paligemma_with_expert\.paligemma\.language_model\.lm_head"), - ".paligemma_with_expert.paligemma.lm_head", - ), - ( - re.compile(r"\.paligemma_with_expert\.paligemma\.language_model\.model"), - ".paligemma_with_expert.paligemma.model.language_model", - ), - ( - re.compile(r"\.paligemma_with_expert\.paligemma\.vision_tower"), - ".paligemma_with_expert.paligemma.model.vision_tower", - ), - ( - re.compile(r"\.paligemma_with_expert\.paligemma\.multi_modal_projector"), - ".paligemma_with_expert.paligemma.model.multi_modal_projector", - ), + self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf) + self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf) + self.gemma_expert.model.embed_tokens = None + + self.to_bfloat16_for_selected_params(precision) + + def to_bfloat16_for_selected_params(self, precision: Literal["bfloat16", "float32"] = "bfloat16"): + if precision == "bfloat16": + self.to(dtype=torch.bfloat16) + elif precision == "float32": + self.to(dtype=torch.float32) + return + else: + raise ValueError(f"Invalid precision: {precision}") + + params_to_keep_float32 = [ + "vision_tower.vision_model.embeddings.patch_embedding.weight", + "vision_tower.vision_model.embeddings.patch_embedding.bias", + "vision_tower.vision_model.embeddings.position_embedding.weight", + "input_layernorm", + "post_attention_layernorm", + "model.norm", ] - for key, value in state_dict.items(): - new_key = key - for pattern, replacement in transformations: - new_key = pattern.sub(replacement, new_key) - transformed_dict[new_key] = value - - # Handle tied weights: lm_head.weight and embed_tokens.weight share memory - lm_head_key = None - embed_tokens_key = None - - for key in transformed_dict: - if key.endswith(".paligemma_with_expert.paligemma.lm_head.weight"): - lm_head_key = key - elif key.endswith(".paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"): - embed_tokens_key = key - if lm_head_key and embed_tokens_key: - break - - if lm_head_key and not embed_tokens_key: - embed_tokens_key = lm_head_key.replace( - ".lm_head.weight", ".model.language_model.embed_tokens.weight" - ) - transformed_dict[embed_tokens_key] = transformed_dict[lm_head_key] - elif embed_tokens_key and not lm_head_key: - lm_head_key = embed_tokens_key.replace( - ".model.language_model.embed_tokens.weight", ".lm_head.weight" - ) - transformed_dict[lm_head_key] = transformed_dict[embed_tokens_key] - - return transformed_dict - - @classmethod - def _load_as_safetensor( - cls, model: "PI0Policy", model_file: str, map_location: str, strict: bool - ) -> "PI0Policy": - """Override to apply key transformations before loading.""" - from safetensors.torch import load_file - - init_logging() - # Load the state dict from file safely - state_dict = load_file(model_file, device=map_location) - - # Apply key transformations - transformed_state_dict = cls._transform_state_dict_keys(state_dict) - - # Load the transformed state dict - msg = model.load_state_dict(transformed_state_dict, strict=strict) - - # Log message - log_model_loading_keys(msg.missing_keys, msg.unexpected_keys) - return model - - def get_optim_params(self) -> dict: - return self.parameters() - - @classmethod - def from_pretrained(cls, *args, **kwargs): - """Override the from_pretrained method to display important disclaimer.""" - print( - "⚠️ DISCLAIMER: The PI0 model is ported from JAX by the Hugging Face team. \n" - " It is not expected to perform as well as the original implementation. \n" - " Original implementation: https://github.com/Physical-Intelligence/openpi" - ) - return super().from_pretrained(*args, **kwargs) - - @torch.no_grad() - def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: - """Predict a chunk of actions given environment observations.""" - raise NotImplementedError("Currently not implemented for PI0") - - @torch.no_grad() - def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor: - """Select a single action given environment observations. - - This method wraps `select_actions` in order to return one action at a time for execution in the - environment. It works by managing the actions in a queue and only calling `select_actions` when the - queue is empty. - """ - self.eval() - - if self.config.adapt_to_pi_aloha: - batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) + for name, param in self.named_parameters(): + if any(selector in name for selector in params_to_keep_float32): + param.data = param.data.to(dtype=torch.float32) - batch = self.normalize_inputs(batch) + def embed_image(self, image: torch.Tensor): + return self.paligemma.model.get_image_features(image) - # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by - # querying the policy. - if len(self._action_queue) == 0: - images, img_masks = self.prepare_images(batch) - state = self.prepare_state(batch) - lang_tokens, lang_masks = self.prepare_language(batch) + def embed_language_tokens(self, tokens: torch.Tensor): + return self.paligemma.language_model.embed_tokens(tokens) - actions = self.model.sample_actions( - images, img_masks, lang_tokens, lang_masks, state, noise=noise + def forward( + self, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + adarms_cond: list[torch.Tensor] | None = None, + ): + if adarms_cond is None: + adarms_cond = [None, None] + if inputs_embeds[1] is None: + prefix_output = self.paligemma.language_model.forward( + inputs_embeds=inputs_embeds[0], + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + adarms_cond=adarms_cond[0] if adarms_cond is not None else None, ) - - # Unpad actions - original_action_dim = self.config.action_feature.shape[0] - actions = actions[:, :, :original_action_dim] - - actions = self.unnormalize_outputs({"action": actions})["action"] - - if self.config.adapt_to_pi_aloha: - actions = self._pi_aloha_encode_actions(actions) - - # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue - # effectively has shape (n_action_steps, batch_size, *), hence the transpose. - self._action_queue.extend(actions.transpose(0, 1)) - return self._action_queue.popleft() - - def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> tuple[Tensor, dict[str, Tensor]]: - """Do a full training forward pass to compute the loss""" - if self.config.adapt_to_pi_aloha: - batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) - batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION]) - - batch = self.normalize_inputs(batch) - batch = self.normalize_targets(batch) - - images, img_masks = self.prepare_images(batch) - state = self.prepare_state(batch) - lang_tokens, lang_masks = self.prepare_language(batch) - actions = self.prepare_action(batch) - actions_is_pad = batch.get("action_is_pad") - - loss_dict = {} - losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time) - loss_dict["losses_after_forward"] = losses.clone() - - if actions_is_pad is not None: - in_episode_bound = ~actions_is_pad - losses = losses * in_episode_bound.unsqueeze(-1) - loss_dict["losses_after_in_ep_bound"] = losses.clone() - - # Remove padding - losses = losses[:, :, : self.config.max_action_dim] - loss_dict["losses_after_rm_padding"] = losses.clone() - - # For backward pass - loss = losses.mean() - # For logging - loss_dict["l2_loss"] = loss.item() - - return loss, loss_dict - - def prepare_images(self, batch): - """Apply Pi0 preprocessing to the images, like resizing to 224x224 and padding to keep aspect ratio, and - convert pixel range from [0.0, 1.0] to [-1.0, 1.0] as requested by SigLIP. - """ - images = [] - img_masks = [] - - present_img_keys = [key for key in self.config.image_features if key in batch] - missing_img_keys = [key for key in self.config.image_features if key not in batch] - - if len(present_img_keys) == 0: - raise ValueError( - f"All image features are missing from the batch. At least one expected. (batch: {batch.keys()}) (image_features:{self.config.image_features})" + prefix_past_key_values = prefix_output.past_key_values + prefix_output = prefix_output.last_hidden_state + suffix_output = None + elif inputs_embeds[0] is None: + suffix_output = self.gemma_expert.model.forward( + inputs_embeds=inputs_embeds[1], + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + adarms_cond=adarms_cond[1] if adarms_cond is not None else None, ) - - # Preprocess image features present in the batch - for key in present_img_keys: - img = batch[key] - - if self.config.resize_imgs_with_padding is not None: - img = resize_with_pad(img, *self.config.resize_imgs_with_padding, pad_value=0) - - # Normalize from range [0,1] to [-1,1] as expected by siglip - img = img * 2.0 - 1.0 - - bsize = img.shape[0] - device = img.device - mask = torch.ones(bsize, dtype=torch.bool, device=device) - images.append(img) - img_masks.append(mask) - - # Create image features not present in the batch - # as fully 0 padded images. - for num_empty_cameras in range(len(missing_img_keys)): - if num_empty_cameras >= self.config.empty_cameras: - break - img = torch.ones_like(img) * -1 - mask = torch.zeros_like(mask) - images.append(img) - img_masks.append(mask) - - return images, img_masks - - def prepare_language(self, batch) -> tuple[Tensor, Tensor]: - """Tokenize the text input""" - device = batch[OBS_STATE].device - tasks = batch["task"] - - # PaliGemma prompt has to end with a new line - tasks = [task if task.endswith("\n") else f"{task}\n" for task in tasks] - - tokenized_prompt = self.language_tokenizer.__call__( - tasks, - padding="max_length", - padding_side="right", - max_length=self.config.tokenizer_max_length, - return_tensors="pt", - ) - lang_tokens = tokenized_prompt["input_ids"].to(device=device) - lang_masks = tokenized_prompt["attention_mask"].to(device=device, dtype=torch.bool) - - return lang_tokens, lang_masks - - def _pi_aloha_decode_state(self, state): - # Flip the joints. - for motor_idx in [1, 2, 8, 9]: - state[:, motor_idx] *= -1 - # Reverse the gripper transformation that is being applied by the Aloha runtime. - for motor_idx in [6, 13]: - state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx]) - return state - - def _pi_aloha_encode_actions(self, actions): - # Flip the joints. - for motor_idx in [1, 2, 8, 9]: - actions[:, :, motor_idx] *= -1 - # Reverse the gripper transformation that is being applied by the Aloha runtime. - for motor_idx in [6, 13]: - actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx]) - return actions - - def _pi_aloha_encode_actions_inv(self, actions): - # Flip the joints again. - for motor_idx in [1, 2, 8, 9]: - actions[:, :, motor_idx] *= -1 - # Reverse the gripper transformation that is being applied by the Aloha runtime. - for motor_idx in [6, 13]: - actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx]) - return actions - - def prepare_state(self, batch): - """Pad state""" - state = pad_vector(batch[OBS_STATE], self.config.max_state_dim) - return state - - def prepare_action(self, batch): - """Pad action""" - actions = pad_vector(batch[ACTION], self.config.max_action_dim) - return actions - - -class PI0FlowMatching(nn.Module): - """ - π0: A Vision-Language-Action Flow Model for General Robot Control - - [Paper](https://www.physicalintelligence.company/download/pi0.pdf) - [Jax code](https://github.com/Physical-Intelligence/openpi) - - Designed by Physical Intelligence. Ported from Jax by Hugging Face. - ┌──────────────────────────────┐ - │ actions │ - │ ▲ │ - │ ┌┴─────┐ │ - │ kv cache │Gemma │ │ - │ ┌──────────►│Expert│ │ - │ │ │ │ │ - │ ┌┴────────┐ │x 10 │ │ - │ │ │ └▲──▲──┘ │ - │ │PaliGemma│ │ │ │ - │ │ │ │ robot state │ - │ │ │ noise │ - │ └▲──▲─────┘ │ - │ │ │ │ - │ │ image(s) │ - │ language tokens │ - └──────────────────────────────┘ - """ - - def __init__(self, config): + suffix_output = suffix_output.last_hidden_state + prefix_output = None + prefix_past_key_values = None + else: + models = [self.paligemma.language_model, self.gemma_expert.model] + num_layers = self.paligemma.config.text_config.num_hidden_layers + + # Check if gradient checkpointing is enabled for any of the models + use_gradient_checkpointing = ( + hasattr(self.gemma_expert.model, "gradient_checkpointing") + and self.gemma_expert.model.gradient_checkpointing + and self.training + ) or (hasattr(self, "gradient_checkpointing") and self.gradient_checkpointing and self.training) + + # Process all layers with gradient checkpointing if enabled + for layer_idx in range(num_layers): + if use_gradient_checkpointing: + inputs_embeds = torch.utils.checkpoint.checkpoint( + compute_layer_complete, + layer_idx, + inputs_embeds, + attention_mask, + position_ids, + adarms_cond, + use_reentrant=False, + preserve_rng_state=False, + paligemma=self.paligemma, + gemma_expert=self.gemma_expert, + ) + else: + inputs_embeds = compute_layer_complete( + layer_idx, + inputs_embeds, + attention_mask, + position_ids, + adarms_cond, + paligemma=self.paligemma, + gemma_expert=self.gemma_expert, + ) + + # final norm + def compute_final_norms(inputs_embeds, adarms_cond): + outputs_embeds = [] + for i, hidden_states in enumerate(inputs_embeds): + out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i]) + outputs_embeds.append(out_emb) + return outputs_embeds + + # Apply gradient checkpointing to final norm if enabled + if use_gradient_checkpointing: + outputs_embeds = torch.utils.checkpoint.checkpoint( + compute_final_norms, + inputs_embeds, + adarms_cond, + use_reentrant=False, + preserve_rng_state=False, + ) + else: + outputs_embeds = compute_final_norms(inputs_embeds, adarms_cond) + + prefix_output = outputs_embeds[0] + suffix_output = outputs_embeds[1] + prefix_past_key_values = None + + return [prefix_output, suffix_output], prefix_past_key_values + + +class PI0Pytorch(nn.Module): # see openpi `PI0Pytorch` + """Core PI0 PyTorch model.""" + + def __init__(self, config: PI0Config): super().__init__() self.config = config - paligemma_with_export_config = PaliGemmaWithExpertConfig( - freeze_vision_encoder=self.config.freeze_vision_encoder, - train_expert_only=self.config.train_expert_only, - attention_implementation=self.config.attention_implementation, - ) - self.paligemma_with_expert = PaliGemmaWithExpertModel(paligemma_with_export_config) - - # Projections are float32 - self.state_proj = nn.Linear(self.config.max_state_dim, self.config.proj_width) - self.action_in_proj = nn.Linear(self.config.max_action_dim, self.config.proj_width) - self.action_out_proj = nn.Linear(self.config.proj_width, self.config.max_action_dim) + paligemma_config = get_gemma_config(config.paligemma_variant) + action_expert_config = get_gemma_config(config.action_expert_variant) - self.action_time_mlp_in = nn.Linear(self.config.proj_width * 2, self.config.proj_width) - self.action_time_mlp_out = nn.Linear(self.config.proj_width, self.config.proj_width) + self.paligemma_with_expert = PaliGemmaWithExpertModel( + paligemma_config, + action_expert_config, + use_adarms=[False, False], + precision=config.dtype, + ) - self.set_requires_grad() + self.action_in_proj = nn.Linear(config.max_action_dim, action_expert_config.width) + self.action_out_proj = nn.Linear(action_expert_config.width, config.max_action_dim) + + self.state_proj = nn.Linear(config.max_state_dim, action_expert_config.width) + self.action_time_mlp_in = nn.Linear(2 * action_expert_config.width, action_expert_config.width) + self.action_time_mlp_out = nn.Linear(action_expert_config.width, action_expert_config.width) + + # Initialize gradient checkpointing flag + self.gradient_checkpointing_enabled = False + + # Compile model if requested + if config.compile_model: + torch.set_float32_matmul_precision("high") + self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode) + # Also compile the main forward pass used during training + self.forward = torch.compile(self.forward, mode=config.compile_mode) + + msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues""" + + try: + from transformers.models.siglip import check + + if not check.check_whether_transformers_replace_is_installed_correctly(): + raise ValueError(msg) + except ImportError: + raise ValueError(msg) from None + + def gradient_checkpointing_enable(self): + """Enable gradient checkpointing for memory optimization.""" + self.gradient_checkpointing_enabled = True + self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True + self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True + self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True + logging.info("Enabled gradient checkpointing for PI0Pytorch model") + + def gradient_checkpointing_disable(self): + """Disable gradient checkpointing.""" + self.gradient_checkpointing_enabled = False + self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False + self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False + self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False + logging.info("Disabled gradient checkpointing for PI0Pytorch model") + + def _apply_checkpoint(self, func, *args, **kwargs): + """Helper method to apply gradient checkpointing if enabled.""" + if self.gradient_checkpointing_enabled and self.training: + return torch.utils.checkpoint.checkpoint( + func, *args, use_reentrant=False, preserve_rng_state=False, **kwargs + ) + return func(*args, **kwargs) - def set_requires_grad(self): - for params in self.state_proj.parameters(): - params.requires_grad = self.config.train_state_proj + def _prepare_attention_masks_4d(self, att_2d_masks): + """Helper method to prepare 4D attention masks for transformer.""" + att_2d_masks_4d = att_2d_masks[:, None, :, :] + return torch.where(att_2d_masks_4d, 0.0, OPENPI_ATTENTION_MASK_VALUE) def sample_noise(self, shape, device): - noise = torch.normal( + return torch.normal( mean=0.0, std=1.0, size=shape, dtype=torch.float32, device=device, ) - return noise def sample_time(self, bsize, device): - beta_dist = torch.distributions.Beta(concentration1=1.5, concentration0=1.0) - time_beta = beta_dist.sample((bsize,)).to(device=device, dtype=torch.float32) - time = time_beta * 0.999 + 0.001 - return time + time_beta = sample_beta( + self.config.time_sampling_beta_alpha, self.config.time_sampling_beta_beta, bsize, device + ) + time = time_beta * self.config.time_sampling_scale + self.config.time_sampling_offset + return time.to(dtype=torch.float32, device=device) def embed_prefix( self, images, img_masks, lang_tokens, lang_masks ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Embed images with SigLIP and language tokens with embedding layer to prepare - for PaliGemma transformer processing. - """ - # TODO: avoid list in python and torch.cat ; prefer pre-allocation with torch.empty + """Embed images with SigLIP and language tokens with embedding layer.""" embs = [] pad_masks = [] att_masks = [] - # TODO: remove for loop - for ( - img, - img_mask, - ) in zip(images, img_masks, strict=False): - img_emb = self.paligemma_with_expert.embed_image(img) - img_emb = img_emb.to(dtype=torch.bfloat16) + # Process images + for img, img_mask in zip(images, img_masks, strict=True): - # Normalize image embeddings - img_emb_dim = img_emb.shape[-1] - img_emb = img_emb * torch.tensor(img_emb_dim**0.5, dtype=img_emb.dtype, device=img_emb.device) + def image_embed_func(img): + return self.paligemma_with_expert.embed_image(img) + img_emb = self._apply_checkpoint(image_embed_func, img) bsize, num_img_embs = img_emb.shape[:2] - img_mask = img_mask[:, None].expand(bsize, num_img_embs) embs.append(img_emb) - pad_masks.append(img_mask) - - # Create attention masks so that image tokens attend to each other + pad_masks.append(img_mask[:, None].expand(bsize, num_img_embs)) att_masks += [0] * num_img_embs - lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens) - - # Normalize language embeddings - lang_emb_dim = lang_emb.shape[-1] - lang_emb = lang_emb * math.sqrt(lang_emb_dim) + # Process language tokens + def lang_embed_func(lang_tokens): + lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens) + lang_emb_dim = lang_emb.shape[-1] + return lang_emb * math.sqrt(lang_emb_dim) + lang_emb = self._apply_checkpoint(lang_embed_func, lang_tokens) embs.append(lang_emb) pad_masks.append(lang_masks) - # full attention between image and language inputs num_lang_embs = lang_emb.shape[1] att_masks += [0] * num_lang_embs embs = torch.cat(embs, dim=1) pad_masks = torch.cat(pad_masks, dim=1) att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device) + + bsize = pad_masks.shape[0] att_masks = att_masks[None, :].expand(bsize, len(att_masks)) return embs, pad_masks, att_masks @@ -666,57 +638,67 @@ def embed_suffix(self, state, noisy_actions, timestep): pad_masks = [] att_masks = [] - # Embed state - state_emb = self.state_proj(state) - state_emb = state_emb.to(dtype=torch.bfloat16) + if self.state_proj.weight.dtype == torch.float32: + state = state.to(torch.float32) + + def state_proj_func(state): + return self.state_proj(state) + + state_emb = self._apply_checkpoint(state_proj_func, state) embs.append(state_emb[:, None, :]) bsize = state_emb.shape[0] - dtype = state_emb.dtype device = state_emb.device state_mask = torch.ones(bsize, 1, dtype=torch.bool, device=device) pad_masks.append(state_mask) - - # Set attention masks so that image and language inputs do not attend to state or actions att_masks += [1] - # Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1] + # Embed timestep using sine-cosine positional encoding time_emb = create_sinusoidal_pos_embedding( - timestep, self.config.proj_width, min_period=4e-3, max_period=4.0, device=device + timestep, + self.action_in_proj.out_features, + min_period=self.config.min_period, + max_period=self.config.max_period, + device=timestep.device, ) - time_emb = time_emb.type(dtype=dtype) + time_emb = time_emb.type(dtype=timestep.dtype) # Fuse timestep + action information using an MLP - action_emb = self.action_in_proj(noisy_actions) + def action_proj_func(noisy_actions): + return self.action_in_proj(noisy_actions) + + action_emb = self._apply_checkpoint(action_proj_func, noisy_actions) time_emb = time_emb[:, None, :].expand_as(action_emb) action_time_emb = torch.cat([action_emb, time_emb], dim=2) - action_time_emb = self.action_time_mlp_in(action_time_emb) - action_time_emb = F.silu(action_time_emb) # swish == silu - action_time_emb = self.action_time_mlp_out(action_time_emb) + def mlp_func(action_time_emb): + x = self.action_time_mlp_in(action_time_emb) + x = F.silu(x) + return self.action_time_mlp_out(x) - # Add to input tokens - embs.append(action_time_emb) + action_time_emb = self._apply_checkpoint(mlp_func, action_time_emb) + adarms_cond = None + embs.append(action_time_emb) bsize, action_time_dim = action_time_emb.shape[:2] - action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=device) + action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=timestep.device) pad_masks.append(action_time_mask) # Set attention masks so that image, language and state inputs do not attend to action tokens - att_masks += [1] + ([0] * (self.config.n_action_steps - 1)) + att_masks += [1] + ([0] * (self.config.chunk_size - 1)) embs = torch.cat(embs, dim=1) pad_masks = torch.cat(pad_masks, dim=1) att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device) att_masks = att_masks[None, :].expand(bsize, len(att_masks)) - return embs, pad_masks, att_masks + return embs, pad_masks, att_masks, adarms_cond def forward( self, images, img_masks, lang_tokens, lang_masks, state, actions, noise=None, time=None ) -> Tensor: - """Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)""" + """Do a full training forward pass and compute the loss.""" if noise is None: noise = self.sample_noise(actions.shape, actions.device) @@ -730,7 +712,14 @@ def forward( prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix( images, img_masks, lang_tokens, lang_masks ) - suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(state, x_t, time) + suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, time) + + if ( + self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype + == torch.bfloat16 + ): + suffix_embs = suffix_embs.to(dtype=torch.bfloat16) + prefix_embs = prefix_embs.to(dtype=torch.bfloat16) pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1) att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1) @@ -738,29 +727,51 @@ def forward( att_2d_masks = make_att_2d_masks(pad_masks, att_masks) position_ids = torch.cumsum(pad_masks, dim=1) - 1 - (_, suffix_out), _ = self.paligemma_with_expert.forward( - attention_mask=att_2d_masks, - position_ids=position_ids, - past_key_values=None, - inputs_embeds=[prefix_embs, suffix_embs], - use_cache=False, - fill_kv_cache=False, + att_2d_masks_4d = self._prepare_attention_masks_4d(att_2d_masks) + + def forward_func(prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond): + (_, suffix_out), _ = self.paligemma_with_expert.forward( + attention_mask=att_2d_masks_4d, + position_ids=position_ids, + past_key_values=None, + inputs_embeds=[prefix_embs, suffix_embs], + use_cache=False, + adarms_cond=[None, adarms_cond], + ) + return suffix_out + + suffix_out = self._apply_checkpoint( + forward_func, prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond ) - suffix_out = suffix_out[:, -self.config.n_action_steps :] - # Original openpi code, upcast attention output + + suffix_out = suffix_out[:, -self.config.chunk_size :] suffix_out = suffix_out.to(dtype=torch.float32) - v_t = self.action_out_proj(suffix_out) - losses = F.mse_loss(u_t, v_t, reduction="none") - return losses + def action_out_proj_func(suffix_out): + return self.action_out_proj(suffix_out) + + v_t = self._apply_checkpoint(action_out_proj_func, suffix_out) + + return F.mse_loss(u_t, v_t, reduction="none") + + @torch.no_grad() # see openpi `sample_actions` (slightly adapted) + def sample_actions( + self, images, img_masks, lang_tokens, lang_masks, state, noise=None, num_steps=None + ) -> Tensor: + """Do a full inference forward and compute the action.""" + if num_steps is None: + num_steps = self.config.num_inference_steps - def sample_actions(self, images, img_masks, lang_tokens, lang_masks, state, noise=None) -> Tensor: - """Do a full inference forward and compute the action (batch_size x num_steps x num_motors)""" bsize = state.shape[0] device = state.device if noise is None: - actions_shape = (bsize, self.config.n_action_steps, self.config.max_action_dim) + # Sample noise with padded dimension as expected by action_in_proj + actions_shape = ( + bsize, + self.config.chunk_size, + self.config.max_action_dim, + ) # Use config max_action_dim for internal processing noise = self.sample_noise(actions_shape, device) prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix( @@ -769,17 +780,18 @@ def sample_actions(self, images, img_masks, lang_tokens, lang_masks, state, nois prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks) prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1 - # Compute image and language key value cache + prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks) + self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager" # noqa: SLF001 + _, past_key_values = self.paligemma_with_expert.forward( - attention_mask=prefix_att_2d_masks, + attention_mask=prefix_att_2d_masks_4d, position_ids=prefix_position_ids, past_key_values=None, inputs_embeds=[prefix_embs, None], - use_cache=self.config.use_cache, - fill_kv_cache=True, + use_cache=True, ) - dt = -1.0 / self.config.num_steps + dt = -1.0 / num_steps dt = torch.tensor(dt, dtype=torch.float32, device=device) x_t = noise @@ -793,10 +805,9 @@ def sample_actions(self, images, img_masks, lang_tokens, lang_masks, state, nois x_t, expanded_time, ) - - # Euler step - x_t += dt * v_t + x_t = x_t + dt * v_t time += dt + return x_t def denoise_step( @@ -808,30 +819,374 @@ def denoise_step( timestep, ): """Apply one denoising step of the noise `x_t` at a given timestep.""" - suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(state, x_t, timestep) + suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, timestep) suffix_len = suffix_pad_masks.shape[1] batch_size = prefix_pad_masks.shape[0] prefix_len = prefix_pad_masks.shape[1] - prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len) + prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len) suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks) - full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2) prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None] position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1 + full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks) + self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager" # noqa: SLF001 + outputs_embeds, _ = self.paligemma_with_expert.forward( - attention_mask=full_att_2d_masks, + attention_mask=full_att_2d_masks_4d, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=[None, suffix_embs], - use_cache=self.config.use_cache, - fill_kv_cache=False, + use_cache=False, + adarms_cond=[None, adarms_cond], ) + suffix_out = outputs_embeds[1] - suffix_out = suffix_out[:, -self.config.n_action_steps :] + suffix_out = suffix_out[:, -self.config.chunk_size :] suffix_out = suffix_out.to(dtype=torch.float32) - v_t = self.action_out_proj(suffix_out) - return v_t + return self.action_out_proj(suffix_out) + + +class PI0Policy(PreTrainedPolicy): + """PI0 OpenPI Policy for LeRobot.""" + + config_class = PI0Config + name = "pi0" + + def __init__( + self, + config: PI0Config, + ): + """ + Args: + config: Policy configuration class instance. + """ + super().__init__(config) + config.validate_features() + self.config = config + + # Initialize the core PI0 model + self.model = PI0Pytorch(config) + + # Enable gradient checkpointing if requested + if config.gradient_checkpointing: + self.model.gradient_checkpointing_enable() + + self.model.to(config.device) + + self.reset() + + @classmethod + def from_pretrained( + cls: builtins.type[T], + pretrained_name_or_path: str | Path, + *, + config: PreTrainedConfig | None = None, + force_download: bool = False, + resume_download: bool | None = None, + proxies: dict | None = None, + token: str | bool | None = None, + cache_dir: str | Path | None = None, + local_files_only: bool = False, + revision: str | None = None, + strict: bool = True, + **kwargs, + ) -> T: + """Override the from_pretrained method to handle key remapping and display important disclaimer.""" + print( + "The PI05 model is a direct port of the OpenPI implementation. \n" + "This implementation follows the original OpenPI structure for compatibility. \n" + "Original implementation: https://github.com/Physical-Intelligence/openpi" + ) + if pretrained_name_or_path is None: + raise ValueError("pretrained_name_or_path is required") + + # Use provided config if available, otherwise create default config + if config is None: + config = PreTrainedConfig.from_pretrained( + pretrained_name_or_path=pretrained_name_or_path, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + token=token, + cache_dir=cache_dir, + local_files_only=local_files_only, + revision=revision, + **kwargs, + ) + + # Initialize model without loading weights + # Check if dataset_stats were provided in kwargs + model = cls(config, **kwargs) + + # Now manually load and remap the state dict + try: + # Try to load the pytorch_model.bin or model.safetensors file + print(f"Loading model from: {pretrained_name_or_path}") + try: + from transformers.utils import cached_file + + # Try safetensors first + resolved_file = cached_file( + pretrained_name_or_path, + "model.safetensors", + cache_dir=kwargs.get("cache_dir"), + force_download=kwargs.get("force_download", False), + resume_download=kwargs.get("resume_download"), + proxies=kwargs.get("proxies"), + use_auth_token=kwargs.get("use_auth_token"), + revision=kwargs.get("revision"), + local_files_only=kwargs.get("local_files_only", False), + ) + from safetensors.torch import load_file + + original_state_dict = load_file(resolved_file) + print("✓ Loaded state dict from model.safetensors") + except Exception as e: + print(f"Could not load state dict from remote files: {e}") + print("Returning model without loading pretrained weights") + return model + + # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys` + fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config) + + # Then add "model." prefix for all keys that don't already have it + remapped_state_dict = {} + remap_count = 0 + + for key, value in fixed_state_dict.items(): + if not key.startswith("model."): + new_key = f"model.{key}" + remapped_state_dict[new_key] = value + remap_count += 1 + else: + remapped_state_dict[key] = value + + if remap_count > 0: + print(f"Remapped {remap_count} state dict keys") + + # Load the remapped state dict into the model + missing_keys, unexpected_keys = model.load_state_dict(remapped_state_dict, strict=strict) + + if missing_keys: + print(f"Missing keys when loading state dict: {len(missing_keys)} keys") + if len(missing_keys) <= 5: + for key in missing_keys: + print(f" - {key}") + else: + for key in missing_keys[:5]: + print(f" - {key}") + print(f" ... and {len(missing_keys) - 5} more") + + if unexpected_keys: + print(f"Unexpected keys when loading state dict: {len(unexpected_keys)} keys") + if len(unexpected_keys) <= 5: + for key in unexpected_keys: + print(f" - {key}") + else: + for key in unexpected_keys[:5]: + print(f" - {key}") + print(f" ... and {len(unexpected_keys) - 5} more") + + if not missing_keys and not unexpected_keys: + print("All keys loaded successfully!") + + except Exception as e: + print(f"Warning: Could not remap state dict keys: {e}") + + return model + + def _fix_pytorch_state_dict_keys( + self, state_dict, model_config + ): # see openpi `BaseModelConfig, _fix_pytorch_state_dict_keys` + """Fix state dict keys to match current model architecture.""" + import re + + fixed_state_dict = {} + + for key, value in state_dict.items(): + new_key = key + + # Handle layer norm structure changes: .weight -> .dense.weight + .dense.bias + # For gemma expert layers + if re.match( + r"paligemma_with_expert\.gemma_expert\.model\.layers\.\d+\.(input_layernorm|post_attention_layernorm)\.weight", + key, + ): + # Check if the model actually has adaRMS enabled for the expert + expert_uses_adarms = getattr( + self.model.paligemma_with_expert.gemma_expert.config, "use_adarms", False + ) + if expert_uses_adarms: + logging.warning(f"Skipping layer norm key (adaRMS mismatch): {key}") + continue + + if re.match(r"paligemma_with_expert\.gemma_expert\.model\.norm\.weight", key): + # Check if the model actually has adaRMS enabled for the expert + expert_uses_adarms = getattr( + self.model.paligemma_with_expert.gemma_expert.config, "use_adarms", False + ) + if expert_uses_adarms: + logging.warning(f"Skipping norm key (adaRMS mismatch): {key}") + continue + + # Handle MLP naming changes for pi0 + # non-pi05 model expects action_time_mlp_*, but checkpoint might have time_mlp_* + if key.startswith("time_mlp_in."): + new_key = key.replace("time_mlp_in.", "action_time_mlp_in.") + elif key.startswith("time_mlp_out."): + new_key = key.replace("time_mlp_out.", "action_time_mlp_out.") + + # Handle vision tower embedding layer potential differences + if "patch_embedding" in key: + # Some checkpoints might have this, but current model expects different structure + logging.warning(f"Vision embedding key might need handling: {key}") + + fixed_state_dict[new_key] = value + + return fixed_state_dict + + def get_optim_params(self) -> dict: + return self.parameters() + + def reset(self): + """Reset internal state - called when environment resets.""" + self._action_queue = deque(maxlen=self.config.n_action_steps) + self._queues = { + ACTION: deque(maxlen=self.config.n_action_steps), + } + + def _preprocess_images(self, batch: dict[str, Tensor]) -> tuple[list[Tensor], list[Tensor]]: + """Preprocess images for the model. + + Images from LeRobot are typically in [B, C, H, W] format and normalized to [0, 1]. + PaliGemma expects images in [B, C, H, W] format and normalized to [-1, 1]. + """ + images = [] + img_masks = [] + + # Get device from model parameters + device = next(self.parameters()).device + + present_img_keys = [key for key in self.config.image_features if key in batch] + missing_img_keys = [key for key in self.config.image_features if key not in batch] + + if len(present_img_keys) == 0: + raise ValueError( + f"All image features are missing from the batch. At least one expected. " + f"(batch: {batch.keys()}) (image_features: {self.config.image_features})" + ) + + for key in present_img_keys: + img = batch[key] + + # Ensure tensor is on the same device as the model + if img.device != device: + img = img.to(device) + + # Ensure float32 dtype for consistency + if img.dtype != torch.float32: + img = img.to(torch.float32) + + # from openpi preprocess_observation_pytorch: Handle both [B, C, H, W] and [B, H, W, C] formats + is_channels_first = img.shape[1] == 3 # Check if channels are in dimension 1 + + if is_channels_first: + # Convert [B, C, H, W] to [B, H, W, C] for processing + img = img.permute(0, 2, 3, 1) + + # from openpi preprocess_observation_pytorch: Resize with padding if needed + if img.shape[1:3] != self.config.image_resolution: + img = resize_with_pad_torch(img, *self.config.image_resolution) + + # Normalize from [0,1] to [-1,1] as expected by siglip + img = img * 2.0 - 1.0 + + # from openpi preprocess_observation_pytorch: Convert back to [B, C, H, W] format if it was originally channels-first + if is_channels_first: + img = img.permute(0, 3, 1, 2) # [B, H, W, C] -> [B, C, H, W] + + images.append(img) + # Create mask (all ones for real images) + bsize = img.shape[0] + mask = torch.ones(bsize, dtype=torch.bool, device=device) + img_masks.append(mask) + + # Create image features not present in the batch as fully 0 padded images + for _num_empty_cameras in range(len(missing_img_keys)): + img = torch.ones_like(img) * -1 # padded with -1 for SigLIP + mask = torch.zeros_like(mask) # mask is zero for empty cameras + images.append(img) + img_masks.append(mask) + + return images, img_masks + + def prepare_state(self, batch): + """Pad state""" + state = pad_vector(batch[OBS_STATE], self.config.max_state_dim) + return state + + def prepare_action(self, batch): + """Pad action""" + actions = pad_vector(batch[ACTION], self.config.max_action_dim) + return actions + + @torch.no_grad() + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select a single action given environment observations.""" + self.eval() + + # Action queue logic for n_action_steps > 1 + if len(self._action_queue) == 0: + actions = self.predict_action_chunk(batch)[:, : self.config.n_action_steps] + # Transpose to get shape (n_action_steps, batch_size, action_dim) + self._action_queue.extend(actions.transpose(0, 1)) + + return self._action_queue.popleft() + + @torch.no_grad() + def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: + """Predict a chunk of actions given environment observations.""" + self.eval() + + # Prepare inputs + images, img_masks = self._preprocess_images(batch) + lang_tokens, lang_masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"] + state = self.prepare_state(batch) + + # Sample actions using the model + actions = self.model.sample_actions(images, img_masks, lang_tokens, lang_masks, state) + + # Unpad actions to actual action dimension + original_action_dim = self.config.output_features[ACTION].shape[0] + actions = actions[:, :, :original_action_dim] + + return actions + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: + """Run the batch through the model and compute the loss for training.""" + + # Prepare inputs + images, img_masks = self._preprocess_images(batch) + lang_tokens, lang_masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"] + state = self.prepare_state(batch) + actions = self.prepare_action(batch) + + # Compute loss + losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions) + + # Truncate losses to actual action dimensions + original_action_dim = self.config.output_features[ACTION].shape[0] + losses = losses[:, :, :original_action_dim] + + loss = losses.mean() + + loss_dict = { + "loss": loss.item(), + "loss_per_dim": losses.mean(dim=[0, 1]).detach().cpu().numpy().tolist(), + } + + return loss, loss_dict diff --git a/src/lerobot/policies/pi0/paligemma_with_expert.py b/src/lerobot/policies/pi0/paligemma_with_expert.py deleted file mode 100644 index edc34b7c560..00000000000 --- a/src/lerobot/policies/pi0/paligemma_with_expert.py +++ /dev/null @@ -1,420 +0,0 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -import torch.version -from pytest import Cache -from torch import nn -from transformers import ( - AutoConfig, - GemmaForCausalLM, - PaliGemmaForConditionalGeneration, - PretrainedConfig, - PreTrainedModel, -) -from transformers.models.auto import CONFIG_MAPPING - -from lerobot.policies.pi0.flex_attention import flex_attention_forward - - -def apply_rope(x, positions, max_wavelength=10_000): - """ - Applies RoPE positions [B, L] to x [B, L, H, D]. - """ - d_half = x.shape[-1] // 2 - device = x.device - dtype = x.dtype - x = x.to(torch.float32) - - freq_exponents = (2.0 / x.shape[-1]) * torch.arange(d_half, dtype=torch.float32, device=device) - timescale = max_wavelength**freq_exponents - radians = positions[..., None].to(torch.float32) / timescale[None, None, :].to(torch.float32) - - radians = radians[..., None, :] - - sin = torch.sin(radians) # .to(dtype=dtype) - cos = torch.cos(radians) # .to(dtype=dtype) - - x1, x2 = x.split(d_half, dim=-1) - res = torch.empty_like(x) - res[..., :d_half] = x1 * cos - x2 * sin - res[..., d_half:] = x2 * cos + x1 * sin - - return res.to(dtype) - - -class PaliGemmaWithExpertConfig(PretrainedConfig): - model_type = "PaliGemmaWithExpertModel" - sub_configs = {"paligemma_config": AutoConfig, "gemma_expert_config": AutoConfig} - - def __init__( - self, - paligemma_config: dict | None = None, - gemma_expert_config: dict | None = None, - freeze_vision_encoder: bool = True, - train_expert_only: bool = True, - attention_implementation: str = "eager", - **kwargs, - ): - self.freeze_vision_encoder = freeze_vision_encoder - self.train_expert_only = train_expert_only - self.attention_implementation = attention_implementation - - if paligemma_config is None: - # Default config from Pi0 - self.paligemma_config = CONFIG_MAPPING["paligemma"]( - transformers_version="4.48.1", - _vocab_size=257152, - bos_token_id=2, - eos_token_id=1, - hidden_size=2048, - image_token_index=257152, - model_type="paligemma", - pad_token_id=0, - projection_dim=2048, - text_config={ - "hidden_activation": "gelu_pytorch_tanh", - "hidden_size": 2048, - "intermediate_size": 16384, - "model_type": "gemma", - "num_attention_heads": 8, - "num_hidden_layers": 18, - "num_image_tokens": 256, - "num_key_value_heads": 1, - "torch_dtype": "float32", - "vocab_size": 257152, - }, - vision_config={ - "hidden_size": 1152, - "intermediate_size": 4304, - "model_type": "siglip_vision_model", - "num_attention_heads": 16, - "num_hidden_layers": 27, - "num_image_tokens": 256, - "patch_size": 14, - "projection_dim": 2048, - "projector_hidden_act": "gelu_fast", - "torch_dtype": "float32", - "vision_use_head": False, - }, - ) - elif isinstance(self.paligemma_config, dict): - # Override Pi0 default config for PaliGemma - if "model_type" not in gemma_expert_config: - paligemma_config["model_type"] = "paligemma" - - cfg_cls = CONFIG_MAPPING[paligemma_config["model_type"]] - self.paligemma_config = cfg_cls(**paligemma_config) - - if gemma_expert_config is None: - # Default config from Pi0 - self.gemma_expert_config = CONFIG_MAPPING["gemma"]( - attention_bias=False, - attention_dropout=0.0, - bos_token_id=2, - eos_token_id=1, - head_dim=256, - hidden_act="gelu_pytorch_tanh", - hidden_activation="gelu_pytorch_tanh", - hidden_size=1024, - initializer_range=0.02, - intermediate_size=4096, - max_position_embeddings=8192, - model_type="gemma", - num_attention_heads=8, - num_hidden_layers=18, - num_key_value_heads=1, - pad_token_id=0, - rms_norm_eps=1e-06, - rope_theta=10000.0, - torch_dtype="float32", - transformers_version="4.48.1", - use_cache=True, - vocab_size=257152, - ) - elif isinstance(self.gemma_expert_config, dict): - # Override Pi0 default config for Gemma Expert - if "model_type" not in gemma_expert_config: - gemma_expert_config["model_type"] = "gemma" - - cfg_cls = CONFIG_MAPPING[paligemma_config["model_type"]] - self.gemma_expert_config = cfg_cls(**gemma_expert_config) - - super().__init__(**kwargs) - - def __post_init__(self): - super().__post_init__() - if self.train_expert_only and not self.freeze_vision_encoder: - raise ValueError( - "You set `freeze_vision_encoder=False` and `train_expert_only=True` which are not compatible." - ) - - if self.attention_implementation not in ["eager", "fa2", "flex"]: - raise ValueError( - f"Wrong value provided for `attention_implementation` ({self.attention_implementation}). Expected 'eager', 'fa2' or 'flex'." - ) - - -class PaliGemmaWithExpertModel(PreTrainedModel): - config_class = PaliGemmaWithExpertConfig - - def __init__(self, config: PaliGemmaWithExpertConfig): - super().__init__(config=config) - self.config = config - self.paligemma = PaliGemmaForConditionalGeneration(config=config.paligemma_config) - self.gemma_expert = GemmaForCausalLM(config=config.gemma_expert_config) - # Remove unused embed_tokens - self.gemma_expert.model.embed_tokens = None - - self.to_bfloat16_like_physical_intelligence() - self.set_requires_grad() - - def set_requires_grad(self): - if self.config.freeze_vision_encoder: - self.paligemma.vision_tower.eval() - for params in self.paligemma.vision_tower.parameters(): - params.requires_grad = False - - if self.config.train_expert_only: - self.paligemma.eval() - for params in self.paligemma.parameters(): - params.requires_grad = False - - def train(self, mode: bool = True): - super().train(mode) - - if self.config.freeze_vision_encoder: - self.paligemma.vision_tower.eval() - - if self.config.train_expert_only: - self.paligemma.eval() - - def to_bfloat16_like_physical_intelligence(self): - self.paligemma = self.paligemma.to(dtype=torch.bfloat16) - - params_to_change_dtype = [ - "language_model.model.layers", - "gemma_expert.model.layers", - "vision_tower", - "multi_modal", - ] - for name, param in self.named_parameters(): - if any(selector in name for selector in params_to_change_dtype): - param.data = param.data.to(dtype=torch.bfloat16) - - def embed_image(self, image: torch.Tensor): - # Handle different transformers versions - if hasattr(self.paligemma, "get_image_features"): - return self.paligemma.get_image_features(image) - else: - return self.paligemma.model.get_image_features(image) - - def embed_language_tokens(self, tokens: torch.Tensor): - return self.paligemma.language_model.embed_tokens(tokens) - - # TODO: break down this huge forward into modules or functions - def forward( - self, - attention_mask: torch.Tensor | None = None, - position_ids: torch.LongTensor | None = None, - past_key_values: list[torch.FloatTensor] | Cache | None = None, - inputs_embeds: list[torch.FloatTensor] = None, - use_cache: bool | None = None, - fill_kv_cache: bool | None = None, - ): - models = [self.paligemma.language_model, self.gemma_expert.model] - - for hidden_states in inputs_embeds: - # TODO this is very inefficient - # dtype is always the same, batch size too (if > 1 len) - # device could be trickier in multi gpu edge cases but that's it - if hidden_states is None: - continue - batch_size = hidden_states.shape[0] - - # RMSNorm - num_layers = self.paligemma.config.text_config.num_hidden_layers - head_dim = self.paligemma.config.text_config.head_dim - for layer_idx in range(num_layers): - query_states = [] - key_states = [] - value_states = [] - for i, hidden_states in enumerate(inputs_embeds): - if hidden_states is None: - continue - layer = models[i].layers[layer_idx] - # normalizer = torch.tensor(models[i].config.hidden_size**0.5, dtype=hidden_states.dtype) - # hidden_states = hidden_states * normalizer - hidden_states = layer.input_layernorm(hidden_states) - - input_shape = hidden_states.shape[:-1] - hidden_shape = (*input_shape, -1, layer.self_attn.head_dim) - - hidden_states = hidden_states.to(dtype=torch.bfloat16) - query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape) - key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape) - value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape) - - query_states.append(query_state) - key_states.append(key_state) - value_states.append(value_state) - - # B,L,H,D with L sequence length, H number of heads, D head dim - # concatenate on the number of embeddings/tokens - query_states = torch.cat(query_states, dim=1) - key_states = torch.cat(key_states, dim=1) - value_states = torch.cat(value_states, dim=1) - - query_states = apply_rope(query_states, position_ids) - key_states = apply_rope(key_states, position_ids) - - if use_cache and past_key_values is None: - past_key_values = {} - - if use_cache: - if fill_kv_cache: - past_key_values[layer_idx] = { - "key_states": key_states, - "value_states": value_states, - } - else: - # TODO here, some optimization can be done - similar to a `StaticCache` we can declare the `max_len` before. - # so we create an empty cache, with just one cuda malloc, and if (in autoregressive case) we reach - # the max len, then we (for instance) double the cache size. This implementation already exists - # in `transformers`. (molbap) - key_states = torch.cat([past_key_values[layer_idx]["key_states"], key_states], dim=1) - value_states = torch.cat( - [past_key_values[layer_idx]["value_states"], value_states], dim=1 - ) - - attention_interface = self.get_attention_interface() - att_output = attention_interface( - attention_mask, batch_size, head_dim, query_states, key_states, value_states - ) - att_output = att_output.to(dtype=torch.bfloat16) - - # first part of att_output is prefix (up to sequence length, [:, 0:prefix_seq_len]) - outputs_embeds = [] - start = 0 - for i, hidden_states in enumerate(inputs_embeds): - layer = models[i].layers[layer_idx] - - if hidden_states is not None: - end = start + hidden_states.shape[1] - - if att_output.dtype != layer.self_attn.o_proj.weight.dtype: - att_output = att_output.to(layer.self_attn.o_proj.weight.dtype) - out_emb = layer.self_attn.o_proj(att_output[:, start:end]) - - # TODO: first dropout (by default 0.0) - - # first residual - out_emb += hidden_states - after_first_residual = out_emb.clone() - - out_emb = layer.post_attention_layernorm(out_emb) - out_emb = layer.mlp(out_emb) - - # TODO: second dropout (by default 0.0) - - # second residual - out_emb += after_first_residual - - outputs_embeds.append(out_emb) - - start = end - else: - outputs_embeds.append(None) - - inputs_embeds = outputs_embeds - - # final norm - outputs_embeds = [] - for i, hidden_states in enumerate(inputs_embeds): - if hidden_states is not None: - out_emb = models[i].norm(hidden_states) - outputs_embeds.append(out_emb) - else: - outputs_embeds.append(None) - - return outputs_embeds, past_key_values - - def get_attention_interface(self): - if self.config.attention_implementation == "fa2": - attention_interface = self.flash_attention_forward - elif self.config.attention_implementation == "flex": - attention_interface = flex_attention_forward - else: - attention_interface = self.eager_attention_forward - return attention_interface - - def flash_attention_forward( - self, attention_mask, batch_size, head_dim, query_states, key_states, value_states - ): - raise NotImplementedError("FA2 is not implemented (yet)") - - def eager_attention_forward( - self, attention_mask, batch_size, head_dim, query_states, key_states, value_states - ): - num_att_heads = self.config.paligemma_config.text_config.num_attention_heads - num_key_value_heads = self.config.paligemma_config.text_config.num_key_value_heads - num_key_value_groups = num_att_heads // num_key_value_heads - - # query_states: batch_size, sequence_length, num_att_head, head_dim - # key_states: batch_size, sequence_length, num_key_value_head, head_dim - # value_states: batch_size, sequence_length, num_key_value_head, head_dim - sequence_length = key_states.shape[1] - - key_states = key_states[:, :, :, None, :].expand( - batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim - ) - key_states = key_states.reshape( - batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim - ) - - value_states = value_states[:, :, :, None, :].expand( - batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim - ) - value_states = value_states.reshape( - batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim - ) - - # Attention here is upcasted to float32 to match the original eager implementation. - - query_states = query_states.to(dtype=torch.float32) - key_states = key_states.to(dtype=torch.float32) - - query_states = query_states.transpose(1, 2) - key_states = key_states.transpose(1, 2) - - att_weights = torch.matmul(query_states, key_states.transpose(2, 3)) - att_weights *= head_dim**-0.5 - big_neg = -2.3819763e38 # See gemma/modules.py - - masked_att_weights = torch.where(attention_mask[:, None, :, :], att_weights, big_neg) - - probs = nn.functional.softmax(masked_att_weights, dim=-1) - probs = probs.to(dtype=value_states.dtype) - - # probs: batch_size, num_key_value_head, num_att_head, sequence_length, sequence_length - # value_states: batch_size, sequence_length, num_att_heads, head_dim - - att_output = torch.matmul(probs, value_states.permute(0, 2, 1, 3)) - - att_output = att_output.permute(0, 2, 1, 3) - # we use -1 because sequence length can change - att_output = att_output.reshape(batch_size, -1, num_key_value_heads * num_key_value_groups * head_dim) - - return att_output diff --git a/src/lerobot/policies/pi0/processor_pi0.py b/src/lerobot/policies/pi0/processor_pi0.py new file mode 100644 index 00000000000..50f5dec83ae --- /dev/null +++ b/src/lerobot/policies/pi0/processor_pi0.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any + +import torch + +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.policies.pi0.configuration_pi0 import PI0Config +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + ComplementaryDataProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + ProcessorStep, + ProcessorStepRegistry, + RenameObservationsProcessorStep, + TokenizerProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +@ProcessorStepRegistry.register(name="pi0_new_line_processor") +class Pi0NewLineProcessor(ComplementaryDataProcessorStep): + """ + Ensures that the task description string ends with a newline character. + + This processing step is required for compatibility with the PaliGemma tokenizer, + which expects a newline at the end of the text prompt. It handles both single + strings and lists of strings for the 'task' key in complementary data. + """ + + def complementary_data(self, complementary_data): + """ + Adds a newline to the 'task' field if it doesn't already have one. + + Args: + complementary_data: A dictionary that may contain a 'task' key with a + string or list of strings. + + Returns: + A new dictionary with the modified 'task' field. + """ + if "task" not in complementary_data: + return complementary_data + + task = complementary_data["task"] + if task is None: + return complementary_data + + new_complementary_data = dict(complementary_data) + + # Handle both string and list of strings + if isinstance(task, str): + # Single string: add newline if not present + if not task.endswith("\n"): + new_complementary_data["task"] = f"{task}\n" + elif isinstance(task, list) and all(isinstance(t, str) for t in task): + # List of strings: add newline to each if not present + new_complementary_data["task"] = [t if t.endswith("\n") else f"{t}\n" for t in task] + # If task is neither string nor list of strings, leave unchanged + + return new_complementary_data + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + This step does not alter the feature definitions. + + Args: + features: The input feature dictionary. + + Returns: + The unchanged feature dictionary. + """ + return features + + +def make_pi0_pre_post_processors( + config: PI0Config, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the PI0 policy. + + The pre-processing pipeline prepares input data for the model by: + 1. Renaming features to match pretrained configurations. + 2. Normalizing input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Appending a newline character to the task description for tokenizer compatibility. + 5. Tokenizing the text prompt using the PaliGemma tokenizer. + 6. Moving all data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving data to the CPU. + 2. Unnormalizing the output features to their original scale. + + Args: + config: The configuration object for the PI0 policy. + dataset_stats: A dictionary of statistics for normalization. + preprocessor_kwargs: Additional arguments for the pre-processor pipeline. + postprocessor_kwargs: Additional arguments for the post-processor pipeline. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + # Add remaining processors + input_steps: list[ProcessorStep] = [ + RenameObservationsProcessorStep(rename_map={}), # To mimic the same processor as pretrained one + AddBatchDimensionProcessorStep(), + Pi0NewLineProcessor(), # Add newlines before tokenization for PaliGemma + TokenizerProcessorStep( + tokenizer_name="google/paligemma-3b-pt-224", + max_length=config.tokenizer_max_length, + padding_side="right", + padding="max_length", + ), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + ] + + output_steps: list[ProcessorStep] = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/pi05/README.md b/src/lerobot/policies/pi05/README.md new file mode 100644 index 00000000000..2ae69d978d3 --- /dev/null +++ b/src/lerobot/policies/pi05/README.md @@ -0,0 +1,49 @@ +# π₀.₅ (pi05) + +This repository contains the Hugging Face port of **π₀.₅**, adapted from [OpenPI](https://github.com/Physical-Intelligence/openpi) by the Physical Intelligence. +It is designed as a **Vision-Language-Action model with open-world generalization**. + +--- + +## Model Overview + +| Feature | π₀ | π₀.₅ | +| -------------------- | ------------------------------------------------------ | ----------------------------------------- | +| Time Conditioning | Concatenates time with actions via `action_time_mlp_*` | Uses `time_mlp_*` for AdaRMS conditioning | +| AdaRMS | Not used | Used in action expert | +| Tokenizer Length | 48 tokens | 200 tokens | +| Discrete State Input | False (Uses `state_proj` layer) | True | +| Parameter Count | Higher (includes state embedding) | Lower (no state embedding) | + +--- + +## Citation + +If you use this work, please cite both **OpenPI** and the π₀.₅ paper: + +```bibtex +@misc{openpi2024, + author = {Physical Intelligence Lab}, + title = {OpenPI: PyTorch Implementation of π0 and π0.5 Policies}, + year = {2024}, + publisher = {GitHub}, + howpublished = {\url{https://github.com/Physical-Intelligence/openpi}}, + license = {Apache-2.0} +} + +@misc{intelligence2025pi05visionlanguageactionmodelopenworld, + title = {π₀.₅: a Vision-Language-Action Model with Open-World Generalization}, + author = {Physical Intelligence and Kevin Black and Noah Brown and James Darpinian and Karan Dhabalia and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Manuel Y. Galliker and Dibya Ghosh and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and James Tanner and Quan Vuong and Homer Walke and Anna Walling and Haohuan Wang and Lili Yu and Ury Zhilinsky}, + year = {2025}, + eprint = {2504.16054}, + archivePrefix= {arXiv}, + primaryClass = {cs.LG}, + url = {https://arxiv.org/abs/2504.16054}, +} +``` + +--- + +## License + +This port follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi). diff --git a/src/lerobot/policies/pi05/__init__.py b/src/lerobot/policies/pi05/__init__.py new file mode 100644 index 00000000000..4f9a9de4af6 --- /dev/null +++ b/src/lerobot/policies/pi05/__init__.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .configuration_pi05 import PI05Config +from .modeling_pi05 import PI05Policy +from .processor_pi05 import make_pi05_pre_post_processors + +__all__ = ["PI05Config", "PI05Policy", "make_pi05_pre_post_processors"] diff --git a/src/lerobot/policies/pi05/configuration_pi05.py b/src/lerobot/policies/pi05/configuration_pi05.py new file mode 100644 index 00000000000..7c1e950b0bd --- /dev/null +++ b/src/lerobot/policies/pi05/configuration_pi05.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.configs.policies import PreTrainedConfig +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.optim.optimizers import AdamWConfig +from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig + + +@PreTrainedConfig.register_subclass("pi05") +@dataclass +class PI05Config(PreTrainedConfig): + paligemma_variant: str = "gemma_2b" + action_expert_variant: str = "gemma_300m" + dtype: str = "float32" # Options: "bfloat16", "float32" + + n_obs_steps: int = 1 + chunk_size: int = 50 # Number of action steps to predict, in openpi called "action_horizon" + n_action_steps: int = 50 # Number of action steps to execute + + # Shorter state and action vectors will be padded to these dimensions + max_state_dim: int = 32 + max_action_dim: int = 32 + + # Flow matching parameters: see openpi `PI0Pytorch` + num_inference_steps: int = 10 + time_sampling_beta_alpha: float = 1.5 + time_sampling_beta_beta: float = 1.0 + time_sampling_scale: float = 0.999 + time_sampling_offset: float = 0.001 + min_period: float = 4e-3 + max_period: float = 4.0 + + image_resolution: tuple[int, int] = (224, 224) # see openpi `preprocessing_pytorch.py` + + # Add empty images. Used to add empty cameras when no image features are present. + empty_cameras: int = 0 + + tokenizer_max_length: int = 200 # see openpi `__post_init__` + + normalization_mapping: dict[str, NormalizationMode] = field( + default_factory=lambda: { + "VISUAL": NormalizationMode.IDENTITY, + "STATE": NormalizationMode.QUANTILES, # Pi0.5 uses quantiles for state + "ACTION": NormalizationMode.QUANTILES, # Pi0.5 uses quantiles for action + } + ) + + # Training settings + gradient_checkpointing: bool = False # Enable gradient checkpointing for memory optimization + compile_model: bool = False # Whether to use torch.compile for model optimization + compile_mode: str = "max-autotune" # Torch compile mode + device: str | None = None # Device to use for the model (None = auto-detect) + + # Optimizer settings: see openpi `AdamW` + optimizer_lr: float = 2.5e-5 # see openpi `CosineDecaySchedule: peak_lr` + optimizer_betas: tuple[float, float] = (0.9, 0.95) + optimizer_eps: float = 1e-8 + optimizer_weight_decay: float = 0.01 + optimizer_grad_clip_norm: float = 1.0 + + # Scheduler settings: see openpi `CosineDecaySchedule` + scheduler_warmup_steps: int = 1_000 + scheduler_decay_steps: int = 30_000 + scheduler_decay_lr: float = 2.5e-6 + + tokenizer_max_length: int = 200 # see openpi `__post_init__` + + def __post_init__(self): + super().__post_init__() + + # Validate configuration + if self.n_action_steps > self.chunk_size: + raise ValueError( + f"n_action_steps ({self.n_action_steps}) cannot be greater than chunk_size ({self.chunk_size})" + ) + + if self.paligemma_variant not in ["gemma_300m", "gemma_2b"]: + raise ValueError(f"Invalid paligemma_variant: {self.paligemma_variant}") + + if self.action_expert_variant not in ["gemma_300m", "gemma_2b"]: + raise ValueError(f"Invalid action_expert_variant: {self.action_expert_variant}") + + if self.dtype not in ["bfloat16", "float32"]: + raise ValueError(f"Invalid dtype: {self.dtype}") + + def validate_features(self) -> None: + """Validate and set up input/output features.""" + for i in range(self.empty_cameras): + key = f"observation.images.empty_camera_{i}" + empty_camera = PolicyFeature( + type=FeatureType.VISUAL, + shape=(3, *self.image_resolution), # Use configured image resolution + ) + self.input_features[key] = empty_camera + + if "observation.state" not in self.input_features: + state_feature = PolicyFeature( + type=FeatureType.STATE, + shape=(self.max_state_dim,), # Padded to max_state_dim + ) + self.input_features["observation.state"] = state_feature + + if "action" not in self.output_features: + action_feature = PolicyFeature( + type=FeatureType.ACTION, + shape=(self.max_action_dim,), # Padded to max_action_dim + ) + self.output_features["action"] = action_feature + + def get_optimizer_preset(self) -> AdamWConfig: + return AdamWConfig( + lr=self.optimizer_lr, + betas=self.optimizer_betas, + eps=self.optimizer_eps, + weight_decay=self.optimizer_weight_decay, + grad_clip_norm=self.optimizer_grad_clip_norm, + ) + + def get_scheduler_preset(self): + return CosineDecayWithWarmupSchedulerConfig( + peak_lr=self.optimizer_lr, + decay_lr=self.scheduler_decay_lr, + num_warmup_steps=self.scheduler_warmup_steps, + num_decay_steps=self.scheduler_decay_steps, + ) + + @property + def observation_delta_indices(self) -> None: + return None + + @property + def action_delta_indices(self) -> list: + return list(range(self.chunk_size)) + + @property + def reward_delta_indices(self) -> None: + return None diff --git a/src/lerobot/policies/pi05/modeling_pi05.py b/src/lerobot/policies/pi05/modeling_pi05.py new file mode 100644 index 00000000000..93ca5fa82a6 --- /dev/null +++ b/src/lerobot/policies/pi05/modeling_pi05.py @@ -0,0 +1,1163 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import builtins +import logging +import math +from collections import deque +from pathlib import Path +from typing import TYPE_CHECKING, Literal + +import torch +import torch.nn.functional as F # noqa: N812 +from torch import Tensor, nn + +from lerobot.utils.import_utils import _transformers_available + +# Conditional import for type checking and lazy loading +if TYPE_CHECKING or _transformers_available: + from transformers.models.auto import CONFIG_MAPPING + from transformers.models.gemma import modeling_gemma + from transformers.models.gemma.modeling_gemma import GemmaForCausalLM + from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration +else: + CONFIG_MAPPING = None + modeling_gemma = None + GemmaForCausalLM = None + PaliGemmaForConditionalGeneration = None + +from lerobot.configs.policies import PreTrainedConfig +from lerobot.policies.pi05.configuration_pi05 import PI05Config +from lerobot.policies.pretrained import PreTrainedPolicy, T +from lerobot.utils.constants import ( + ACTION, + OBS_LANGUAGE_ATTENTION_MASK, + OBS_LANGUAGE_TOKENS, + OPENPI_ATTENTION_MASK_VALUE, +) + + +def get_safe_dtype(target_dtype, device_type): + """Get a safe dtype for the given device type.""" + if device_type == "mps" and target_dtype == torch.float64: + return torch.float32 + if device_type == "cpu": + # CPU doesn't support bfloat16, use float32 instead + if target_dtype == torch.bfloat16: + return torch.float32 + if target_dtype == torch.float64: + return torch.float64 + return target_dtype + + +def create_sinusoidal_pos_embedding( # see openpi `create_sinusoidal_pos_embedding` (exact copy) + time: torch.Tensor, dimension: int, min_period: float, max_period: float, device="cpu" +) -> Tensor: + """Computes sine-cosine positional embedding vectors for scalar positions.""" + if dimension % 2 != 0: + raise ValueError(f"dimension ({dimension}) must be divisible by 2") + + if time.ndim != 1: + raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.") + + dtype = get_safe_dtype(torch.float64, device.type) + fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device) + period = min_period * (max_period / min_period) ** fraction + + # Compute the outer product + scaling_factor = 1.0 / period * 2 * math.pi + sin_input = scaling_factor[None, :] * time[:, None] + return torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1) + + +def sample_beta(alpha, beta, bsize, device): # see openpi `sample_beta` (exact copy) + alpha_t = torch.as_tensor(alpha, dtype=torch.float32, device=device) + beta_t = torch.as_tensor(beta, dtype=torch.float32, device=device) + dist = torch.distributions.Beta(alpha_t, beta_t) + return dist.sample((bsize,)) + + +def make_att_2d_masks(pad_masks, att_masks): # see openpi `make_att_2d_masks` (exact copy) + """Copied from big_vision. + + Tokens can attend to valid inputs tokens which have a cumulative mask_ar + smaller or equal to theirs. This way `mask_ar` int[B, N] can be used to + setup several types of attention, for example: + + [[1 1 1 1 1 1]]: pure causal attention. + + [[0 0 0 1 1 1]]: prefix-lm attention. The first 3 tokens can attend between + themselves and the last 3 tokens have a causal attention. The first + entry could also be a 1 without changing behaviour. + + [[1 0 1 0 1 0 0 1 0 0]]: causal attention between 4 blocks. Tokens of a + block can attend all previous blocks and all tokens on the same block. + + Args: + input_mask: bool[B, N] true if its part of the input, false if padding. + mask_ar: int32[B, N] mask that's 1 where previous tokens cannot depend on + it and 0 where it shares the same attention mask as the previous token. + """ + if att_masks.ndim != 2: + raise ValueError(att_masks.ndim) + if pad_masks.ndim != 2: + raise ValueError(pad_masks.ndim) + + cumsum = torch.cumsum(att_masks, dim=1) + att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None] + pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None] + return att_2d_masks & pad_2d_masks + + +def pad_vector(vector, new_dim): + """Pad the last dimension of a vector to new_dim with zeros. + + Can be (batch_size x sequence_length x features_dimension) + or (batch_size x features_dimension) + """ + if vector.shape[-1] >= new_dim: + return vector + return F.pad(vector, (0, new_dim - vector.shape[-1])) + + +def resize_with_pad_torch( # see openpi `resize_with_pad_torch` (exact copy) + images: torch.Tensor, + height: int, + width: int, + mode: str = "bilinear", +) -> torch.Tensor: + """PyTorch version of resize_with_pad. Resizes an image to a target height and width without distortion + by padding with black. If the image is float32, it must be in the range [-1, 1]. + + Args: + images: Tensor of shape [*b, h, w, c] or [*b, c, h, w] + height: Target height + width: Target width + mode: Interpolation mode ('bilinear', 'nearest', etc.) + + Returns: + Resized and padded tensor with same shape format as input + """ + # Check if input is in channels-last format [*b, h, w, c] or channels-first [*b, c, h, w] + if images.shape[-1] <= 4: # Assume channels-last format + channels_last = True + if images.dim() == 3: + images = images.unsqueeze(0) # Add batch dimension + images = images.permute(0, 3, 1, 2) # [b, h, w, c] -> [b, c, h, w] + else: + channels_last = False + if images.dim() == 3: + images = images.unsqueeze(0) # Add batch dimension + + batch_size, channels, cur_height, cur_width = images.shape + + # Calculate resize ratio + ratio = max(cur_width / width, cur_height / height) + resized_height = int(cur_height / ratio) + resized_width = int(cur_width / ratio) + + # Resize + resized_images = F.interpolate( + images, + size=(resized_height, resized_width), + mode=mode, + align_corners=False if mode == "bilinear" else None, + ) + + # Handle dtype-specific clipping + if images.dtype == torch.uint8: + resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8) + elif images.dtype == torch.float32: + resized_images = resized_images.clamp(-1.0, 1.0) + else: + raise ValueError(f"Unsupported image dtype: {images.dtype}") + + # Calculate padding + pad_h0, remainder_h = divmod(height - resized_height, 2) + pad_h1 = pad_h0 + remainder_h + pad_w0, remainder_w = divmod(width - resized_width, 2) + pad_w1 = pad_w0 + remainder_w + + # Pad + constant_value = 0 if images.dtype == torch.uint8 else -1.0 + padded_images = F.pad( + resized_images, + (pad_w0, pad_w1, pad_h0, pad_h1), # left, right, top, bottom + mode="constant", + value=constant_value, + ) + + # Convert back to original format if needed + if channels_last: + padded_images = padded_images.permute(0, 2, 3, 1) # [b, c, h, w] -> [b, h, w, c] + + return padded_images + + +# Define the complete layer computation function for gradient checkpointing +def compute_layer_complete( + layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert +): + models = [paligemma.language_model, gemma_expert.model] + query_states = [] + key_states = [] + value_states = [] + gates = [] + for i, hidden_states in enumerate(inputs_embeds): + layer = models[i].layers[layer_idx] + hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i]) # noqa: PLW2901 + gates.append(gate) + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, layer.self_attn.head_dim) + query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + query_states.append(query_state) + key_states.append(key_state) + value_states.append(value_state) + # Concatenate and process attention + query_states = torch.cat(query_states, dim=2) + key_states = torch.cat(key_states, dim=2) + value_states = torch.cat(value_states, dim=2) + dummy_tensor = torch.zeros( + query_states.shape[0], + query_states.shape[2], + query_states.shape[-1], + device=query_states.device, + dtype=query_states.dtype, + ) + cos, sin = paligemma.model.language_model.rotary_emb(dummy_tensor, position_ids) + query_states, key_states = modeling_gemma.apply_rotary_pos_emb( + query_states, key_states, cos, sin, unsqueeze_dim=1 + ) + batch_size = query_states.shape[0] + scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling + # Attention computation + att_output, _ = modeling_gemma.eager_attention_forward( + paligemma.language_model.layers[layer_idx].self_attn, + query_states, + key_states, + value_states, + attention_mask, + scaling, + ) + # Get head_dim from the current layer, not from the model + head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim + att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim) + # Process layer outputs + outputs_embeds = [] + start_pos = 0 + for i, hidden_states in enumerate(inputs_embeds): + layer = models[i].layers[layer_idx] + end_pos = start_pos + hidden_states.shape[1] + if att_output.dtype != layer.self_attn.o_proj.weight.dtype: + att_output = att_output.to(layer.self_attn.o_proj.weight.dtype) + out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos]) + # first residual + out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i]) # noqa: SLF001 + after_first_residual = out_emb.clone() + out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i]) + # Convert to bfloat16 if the next layer (mlp) uses bfloat16 + if layer.mlp.up_proj.weight.dtype == torch.bfloat16: + out_emb = out_emb.to(dtype=torch.bfloat16) + out_emb = layer.mlp(out_emb) + # second residual + out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate) # noqa: SLF001 + outputs_embeds.append(out_emb) + start_pos = end_pos + return outputs_embeds + + +class GemmaConfig: # see openpi `gemma.py: Config` + """Configuration for Gemma model variants.""" + + def __init__(self, width, depth, mlp_dim, num_heads, num_kv_heads, head_dim): + self.width = width + self.depth = depth + self.mlp_dim = mlp_dim + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = head_dim + + +def get_gemma_config(variant: str) -> GemmaConfig: # see openpi `gemma.py: get_config` + """Returns config for specified gemma variant.""" + if variant == "gemma_300m": + return GemmaConfig( + width=1024, + depth=18, + mlp_dim=4096, + num_heads=8, + num_kv_heads=1, + head_dim=256, + ) + elif variant == "gemma_2b": + return GemmaConfig( + width=2048, + depth=18, + mlp_dim=16_384, + num_heads=8, + num_kv_heads=1, + head_dim=256, + ) + else: + raise ValueError(f"Unknown variant: {variant}") + + +class PaliGemmaWithExpertModel( + nn.Module +): # see openpi `gemma_pytorch.py: PaliGemmaWithExpertModel` this class is almost a exact copy of PaliGemmaWithExpertModel in openpi + """PaliGemma model with action expert for PI05.""" + + def __init__( + self, + vlm_config, + action_expert_config, + use_adarms=None, + precision: Literal["bfloat16", "float32"] = "bfloat16", + ): + if use_adarms is None: + use_adarms = [False, False] + super().__init__() + + vlm_config_hf = CONFIG_MAPPING["paligemma"]() + vlm_config_hf._vocab_size = 257152 # noqa: SLF001 + vlm_config_hf.image_token_index = 257152 + vlm_config_hf.text_config.hidden_size = vlm_config.width + vlm_config_hf.text_config.intermediate_size = vlm_config.mlp_dim + vlm_config_hf.text_config.num_attention_heads = vlm_config.num_heads + vlm_config_hf.text_config.head_dim = vlm_config.head_dim + vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth + vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads + vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh" + vlm_config_hf.text_config.torch_dtype = "float32" + vlm_config_hf.text_config.vocab_size = 257152 + vlm_config_hf.text_config.use_adarms = use_adarms[0] + vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None + vlm_config_hf.vision_config.intermediate_size = 4304 + vlm_config_hf.vision_config.projection_dim = 2048 + vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast" + vlm_config_hf.vision_config.torch_dtype = "float32" + + action_expert_config_hf = CONFIG_MAPPING["gemma"]( + head_dim=action_expert_config.head_dim, + hidden_size=action_expert_config.width, + intermediate_size=action_expert_config.mlp_dim, + num_attention_heads=action_expert_config.num_heads, + num_hidden_layers=action_expert_config.depth, + num_key_value_heads=action_expert_config.num_kv_heads, + vocab_size=257152, + hidden_activation="gelu_pytorch_tanh", + torch_dtype="float32", + use_adarms=use_adarms[1], + adarms_cond_dim=action_expert_config.width if use_adarms[1] else None, + ) + + self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf) + self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf) + self.gemma_expert.model.embed_tokens = None + + self.to_bfloat16_for_selected_params(precision) + + def to_bfloat16_for_selected_params(self, precision: Literal["bfloat16", "float32"] = "bfloat16"): + if precision == "bfloat16": + self.to(dtype=torch.bfloat16) + elif precision == "float32": + self.to(dtype=torch.float32) + return + else: + raise ValueError(f"Invalid precision: {precision}") + + params_to_keep_float32 = [ + "vision_tower.vision_model.embeddings.patch_embedding.weight", + "vision_tower.vision_model.embeddings.patch_embedding.bias", + "vision_tower.vision_model.embeddings.position_embedding.weight", + "input_layernorm", + "post_attention_layernorm", + "model.norm", + ] + + for name, param in self.named_parameters(): + if any(selector in name for selector in params_to_keep_float32): + param.data = param.data.to(dtype=torch.float32) + + def embed_image(self, image: torch.Tensor): + return self.paligemma.model.get_image_features(image) + + def embed_language_tokens(self, tokens: torch.Tensor): + return self.paligemma.language_model.embed_tokens(tokens) + + def forward( + self, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + adarms_cond: list[torch.Tensor] | None = None, + ): + if adarms_cond is None: + adarms_cond = [None, None] + if inputs_embeds[1] is None: + prefix_output = self.paligemma.language_model.forward( + inputs_embeds=inputs_embeds[0], + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + adarms_cond=adarms_cond[0] if adarms_cond is not None else None, + ) + prefix_past_key_values = prefix_output.past_key_values + prefix_output = prefix_output.last_hidden_state + suffix_output = None + elif inputs_embeds[0] is None: + suffix_output = self.gemma_expert.model.forward( + inputs_embeds=inputs_embeds[1], + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + adarms_cond=adarms_cond[1] if adarms_cond is not None else None, + ) + suffix_output = suffix_output.last_hidden_state + prefix_output = None + prefix_past_key_values = None + else: + models = [self.paligemma.language_model, self.gemma_expert.model] + num_layers = self.paligemma.config.text_config.num_hidden_layers + + # Check if gradient checkpointing is enabled for any of the models + use_gradient_checkpointing = ( + hasattr(self.gemma_expert.model, "gradient_checkpointing") + and self.gemma_expert.model.gradient_checkpointing + and self.training + ) or (hasattr(self, "gradient_checkpointing") and self.gradient_checkpointing and self.training) + + # Process all layers with gradient checkpointing if enabled + for layer_idx in range(num_layers): + if use_gradient_checkpointing: + inputs_embeds = torch.utils.checkpoint.checkpoint( + compute_layer_complete, + layer_idx, + inputs_embeds, + attention_mask, + position_ids, + adarms_cond, + use_reentrant=False, + preserve_rng_state=False, + paligemma=self.paligemma, + gemma_expert=self.gemma_expert, + ) + else: + inputs_embeds = compute_layer_complete( + layer_idx, + inputs_embeds, + attention_mask, + position_ids, + adarms_cond, + paligemma=self.paligemma, + gemma_expert=self.gemma_expert, + ) + + # final norm + def compute_final_norms(inputs_embeds, adarms_cond): + outputs_embeds = [] + for i, hidden_states in enumerate(inputs_embeds): + out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i]) + outputs_embeds.append(out_emb) + return outputs_embeds + + # Apply gradient checkpointing to final norm if enabled + if use_gradient_checkpointing: + outputs_embeds = torch.utils.checkpoint.checkpoint( + compute_final_norms, + inputs_embeds, + adarms_cond, + use_reentrant=False, + preserve_rng_state=False, + ) + else: + outputs_embeds = compute_final_norms(inputs_embeds, adarms_cond) + + prefix_output = outputs_embeds[0] + suffix_output = outputs_embeds[1] + prefix_past_key_values = None + + return [prefix_output, suffix_output], prefix_past_key_values + + +class PI05Pytorch(nn.Module): # see openpi `PI0Pytorch` + """Core PI05 PyTorch model.""" + + def __init__(self, config: PI05Config): + super().__init__() + self.config = config + + paligemma_config = get_gemma_config(config.paligemma_variant) + action_expert_config = get_gemma_config(config.action_expert_variant) + + self.paligemma_with_expert = PaliGemmaWithExpertModel( + paligemma_config, + action_expert_config, + use_adarms=[False, True], + precision=config.dtype, + ) + + self.action_in_proj = nn.Linear(config.max_action_dim, action_expert_config.width) + self.action_out_proj = nn.Linear(action_expert_config.width, config.max_action_dim) + + self.time_mlp_in = nn.Linear(action_expert_config.width, action_expert_config.width) + self.time_mlp_out = nn.Linear(action_expert_config.width, action_expert_config.width) + + # Initialize gradient checkpointing flag + self.gradient_checkpointing_enabled = False + + # Compile model if requested + if config.compile_model: + torch.set_float32_matmul_precision("high") + self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode) + + msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues""" + + try: + from transformers.models.siglip import check + + if not check.check_whether_transformers_replace_is_installed_correctly(): + raise ValueError(msg) + except ImportError: + raise ValueError(msg) from None + + def gradient_checkpointing_enable(self): + """Enable gradient checkpointing for memory optimization.""" + self.gradient_checkpointing_enabled = True + self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True + self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True + self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True + logging.info("Enabled gradient checkpointing for PI05Pytorch model") + + def gradient_checkpointing_disable(self): + """Disable gradient checkpointing.""" + self.gradient_checkpointing_enabled = False + self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False + self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False + self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False + logging.info("Disabled gradient checkpointing for PI05Pytorch model") + + def _apply_checkpoint(self, func, *args, **kwargs): + """Helper method to apply gradient checkpointing if enabled.""" + if self.gradient_checkpointing_enabled and self.training: + return torch.utils.checkpoint.checkpoint( + func, *args, use_reentrant=False, preserve_rng_state=False, **kwargs + ) + return func(*args, **kwargs) + + def _prepare_attention_masks_4d(self, att_2d_masks): + """Helper method to prepare 4D attention masks for transformer.""" + att_2d_masks_4d = att_2d_masks[:, None, :, :] + return torch.where(att_2d_masks_4d, 0.0, OPENPI_ATTENTION_MASK_VALUE) + + def sample_noise(self, shape, device): + return torch.normal( + mean=0.0, + std=1.0, + size=shape, + dtype=torch.float32, + device=device, + ) + + def sample_time(self, bsize, device): + time_beta = sample_beta( + self.config.time_sampling_beta_alpha, self.config.time_sampling_beta_beta, bsize, device + ) + time = time_beta * self.config.time_sampling_scale + self.config.time_sampling_offset + return time.to(dtype=torch.float32, device=device) + + def embed_prefix( + self, images, img_masks, tokens, masks + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Embed images with SigLIP and language tokens with embedding layer.""" + embs = [] + pad_masks = [] + att_masks = [] + + # Process images + for img, img_mask in zip(images, img_masks, strict=True): + + def image_embed_func(img): + return self.paligemma_with_expert.embed_image(img) + + img_emb = self._apply_checkpoint(image_embed_func, img) + bsize, num_img_embs = img_emb.shape[:2] + + embs.append(img_emb) + pad_masks.append(img_mask[:, None].expand(bsize, num_img_embs)) + att_masks += [0] * num_img_embs + + # Process language tokens + def lang_embed_func(tokens): + lang_emb = self.paligemma_with_expert.embed_language_tokens(tokens) + lang_emb_dim = lang_emb.shape[-1] + return lang_emb * math.sqrt(lang_emb_dim) + + lang_emb = self._apply_checkpoint(lang_embed_func, tokens) + embs.append(lang_emb) + pad_masks.append(masks) + + num_lang_embs = lang_emb.shape[1] + att_masks += [0] * num_lang_embs + + embs = torch.cat(embs, dim=1) + pad_masks = torch.cat(pad_masks, dim=1) + att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device) + + bsize = pad_masks.shape[0] + att_masks = att_masks[None, :].expand(bsize, len(att_masks)) + + return embs, pad_masks, att_masks + + def embed_suffix(self, noisy_actions, timestep): + """Embed noisy_actions, timestep to prepare for Expert Gemma processing.""" + embs = [] + pad_masks = [] + att_masks = [] + + # Embed timestep using sine-cosine positional encoding + time_emb = create_sinusoidal_pos_embedding( + timestep, + self.action_in_proj.out_features, + min_period=self.config.min_period, + max_period=self.config.max_period, + device=timestep.device, + ) + time_emb = time_emb.type(dtype=timestep.dtype) + + # Fuse timestep + action information using an MLP + def action_proj_func(noisy_actions): + return self.action_in_proj(noisy_actions) + + action_emb = self._apply_checkpoint(action_proj_func, noisy_actions) + + def time_mlp_func(time_emb): + x = self.time_mlp_in(time_emb) + x = F.silu(x) + x = self.time_mlp_out(x) + return F.silu(x) + + time_emb = self._apply_checkpoint(time_mlp_func, time_emb) + action_time_emb = action_emb + adarms_cond = time_emb + + embs.append(action_time_emb) + bsize, action_time_dim = action_time_emb.shape[:2] + action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=timestep.device) + pad_masks.append(action_time_mask) + + # Set attention masks so that image, language and state inputs do not attend to action tokens + att_masks += [1] + ([0] * (self.config.chunk_size - 1)) + + embs = torch.cat(embs, dim=1) + pad_masks = torch.cat(pad_masks, dim=1) + att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device) + att_masks = att_masks[None, :].expand(bsize, len(att_masks)) + + return embs, pad_masks, att_masks, adarms_cond + + def forward(self, images, img_masks, tokens, masks, actions, noise=None, time=None) -> Tensor: + """Do a full training forward pass and compute the loss.""" + if noise is None: + noise = self.sample_noise(actions.shape, actions.device) + + if time is None: + time = self.sample_time(actions.shape[0], actions.device) + + time_expanded = time[:, None, None] + x_t = time_expanded * noise + (1 - time_expanded) * actions + u_t = noise - actions + + prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(images, img_masks, tokens, masks) + suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(x_t, time) + + if ( + self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype + == torch.bfloat16 + ): + suffix_embs = suffix_embs.to(dtype=torch.bfloat16) + prefix_embs = prefix_embs.to(dtype=torch.bfloat16) + + pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1) + att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1) + + att_2d_masks = make_att_2d_masks(pad_masks, att_masks) + position_ids = torch.cumsum(pad_masks, dim=1) - 1 + + att_2d_masks_4d = self._prepare_attention_masks_4d(att_2d_masks) + + def forward_func(prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond): + (_, suffix_out), _ = self.paligemma_with_expert.forward( + attention_mask=att_2d_masks_4d, + position_ids=position_ids, + past_key_values=None, + inputs_embeds=[prefix_embs, suffix_embs], + use_cache=False, + adarms_cond=[None, adarms_cond], + ) + return suffix_out + + suffix_out = self._apply_checkpoint( + forward_func, prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond + ) + + suffix_out = suffix_out[:, -self.config.chunk_size :] + suffix_out = suffix_out.to(dtype=torch.float32) + + def action_out_proj_func(suffix_out): + return self.action_out_proj(suffix_out) + + v_t = self._apply_checkpoint(action_out_proj_func, suffix_out) + + return F.mse_loss(u_t, v_t, reduction="none") + + @torch.no_grad() # see openpi `sample_actions` (slightly adapted) + def sample_actions(self, images, img_masks, tokens, masks, noise=None, num_steps=None) -> Tensor: + """Do a full inference forward and compute the action.""" + if num_steps is None: + num_steps = self.config.num_inference_steps + + bsize = tokens.shape[0] + device = tokens.device + + if noise is None: + # Sample noise with padded dimension as expected by action_in_proj + actions_shape = ( + bsize, + self.config.chunk_size, + self.config.max_action_dim, + ) # Use config max_action_dim for internal processing + noise = self.sample_noise(actions_shape, device) + + prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(images, img_masks, tokens, masks) + prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks) + prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1 + + prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks) + self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager" # noqa: SLF001 + + _, past_key_values = self.paligemma_with_expert.forward( + attention_mask=prefix_att_2d_masks_4d, + position_ids=prefix_position_ids, + past_key_values=None, + inputs_embeds=[prefix_embs, None], + use_cache=True, + ) + + dt = -1.0 / num_steps + dt = torch.tensor(dt, dtype=torch.float32, device=device) + + x_t = noise + time = torch.tensor(1.0, dtype=torch.float32, device=device) + while time >= -dt / 2: + expanded_time = time.expand(bsize) + v_t = self.denoise_step( + prefix_pad_masks, + past_key_values, + x_t, + expanded_time, + ) + x_t = x_t + dt * v_t + time += dt + + return x_t + + def denoise_step( + self, + prefix_pad_masks, + past_key_values, + x_t, + timestep, + ): + """Apply one denoising step of the noise `x_t` at a given timestep.""" + suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(x_t, timestep) + + suffix_len = suffix_pad_masks.shape[1] + batch_size = prefix_pad_masks.shape[0] + prefix_len = prefix_pad_masks.shape[1] + + prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len) + suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks) + full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2) + + prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None] + position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1 + + full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks) + self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager" # noqa: SLF001 + + outputs_embeds, _ = self.paligemma_with_expert.forward( + attention_mask=full_att_2d_masks_4d, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=[None, suffix_embs], + use_cache=False, + adarms_cond=[None, adarms_cond], + ) + + suffix_out = outputs_embeds[1] + suffix_out = suffix_out[:, -self.config.chunk_size :] + suffix_out = suffix_out.to(dtype=torch.float32) + return self.action_out_proj(suffix_out) + + +class PI05Policy(PreTrainedPolicy): + """PI05 Policy for LeRobot.""" + + config_class = PI05Config + name = "pi05" + + def __init__( + self, + config: PI05Config, + ): + """ + Args: + config: Policy configuration class instance. + """ + super().__init__(config) + config.validate_features() + self.config = config + + # Initialize the core PI05 model + self.model = PI05Pytorch(config) + + # Enable gradient checkpointing if requested + if config.gradient_checkpointing: + self.model.gradient_checkpointing_enable() + + self.model.to(config.device) + + self.reset() + + @classmethod + def from_pretrained( + cls: builtins.type[T], + pretrained_name_or_path: str | Path, + *, + config: PreTrainedConfig | None = None, + force_download: bool = False, + resume_download: bool | None = None, + proxies: dict | None = None, + token: str | bool | None = None, + cache_dir: str | Path | None = None, + local_files_only: bool = False, + revision: str | None = None, + strict: bool = True, + **kwargs, + ) -> T: + """Override the from_pretrained method to handle key remapping and display important disclaimer.""" + print( + "The PI05 model is a direct port of the OpenPI implementation. \n" + "This implementation follows the original OpenPI structure for compatibility. \n" + "Original implementation: https://github.com/Physical-Intelligence/openpi" + ) + if pretrained_name_or_path is None: + raise ValueError("pretrained_name_or_path is required") + + # Use provided config if available, otherwise create default config + if config is None: + config = PreTrainedConfig.from_pretrained( + pretrained_name_or_path=pretrained_name_or_path, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + token=token, + cache_dir=cache_dir, + local_files_only=local_files_only, + revision=revision, + **kwargs, + ) + + # Initialize model without loading weights + # Check if dataset_stats were provided in kwargs + model = cls(config, **kwargs) + + # Now manually load and remap the state dict + try: + # Try to load the pytorch_model.bin or model.safetensors file + print(f"Loading model from: {pretrained_name_or_path}") + try: + from transformers.utils import cached_file + + # Try safetensors first + resolved_file = cached_file( + pretrained_name_or_path, + "model.safetensors", + cache_dir=kwargs.get("cache_dir"), + force_download=kwargs.get("force_download", False), + resume_download=kwargs.get("resume_download"), + proxies=kwargs.get("proxies"), + use_auth_token=kwargs.get("use_auth_token"), + revision=kwargs.get("revision"), + local_files_only=kwargs.get("local_files_only", False), + ) + from safetensors.torch import load_file + + original_state_dict = load_file(resolved_file) + print("✓ Loaded state dict from model.safetensors") + except Exception as e: + print(f"Could not load state dict from remote files: {e}") + print("Returning model without loading pretrained weights") + return model + + # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys` + fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config) + + # Then add "model." prefix for all keys that don't already have it + remapped_state_dict = {} + remap_count = 0 + + for key, value in fixed_state_dict.items(): + if not key.startswith("model."): + new_key = f"model.{key}" + remapped_state_dict[new_key] = value + remap_count += 1 + if remap_count <= 10: # Only print first 10 to avoid spam + print(f"Remapped: {key} -> {new_key}") + else: + remapped_state_dict[key] = value + + if remap_count > 0: + print(f"Remapped {remap_count} state dict keys") + + # Load the remapped state dict into the model + missing_keys, unexpected_keys = model.load_state_dict(remapped_state_dict, strict=strict) + + if missing_keys: + print(f"Missing keys when loading state dict: {len(missing_keys)} keys") + if len(missing_keys) <= 5: + for key in missing_keys: + print(f" - {key}") + else: + for key in missing_keys[:5]: + print(f" - {key}") + print(f" ... and {len(missing_keys) - 5} more") + + if unexpected_keys: + print(f"Unexpected keys when loading state dict: {len(unexpected_keys)} keys") + if len(unexpected_keys) <= 5: + for key in unexpected_keys: + print(f" - {key}") + else: + for key in unexpected_keys[:5]: + print(f" - {key}") + print(f" ... and {len(unexpected_keys) - 5} more") + + if not missing_keys and not unexpected_keys: + print("All keys loaded successfully!") + + except Exception as e: + print(f"Warning: Could not remap state dict keys: {e}") + + return model + + def _fix_pytorch_state_dict_keys( + self, state_dict, model_config + ): # see openpi `BaseModelConfig, _fix_pytorch_state_dict_keys` + """Fix state dict keys to match current model architecture.""" + import re + + fixed_state_dict = {} + + for key, value in state_dict.items(): + new_key = key + + # Handle layer norm structure changes: .weight -> .dense.weight + .dense.bias + # For gemma expert layers + if re.match( + r"paligemma_with_expert\.gemma_expert\.model\.layers\.\d+\.(input_layernorm|post_attention_layernorm)\.weight", + key, + ): + # Check if the model actually has adaRMS enabled for the expert + expert_uses_adarms = getattr( + self.model.paligemma_with_expert.gemma_expert.config, "use_adarms", False + ) + if expert_uses_adarms: + logging.warning(f"Skipping layer norm key (adaRMS mismatch): {key}") + continue + + if re.match(r"paligemma_with_expert\.gemma_expert\.model\.norm\.weight", key): + # Check if the model actually has adaRMS enabled for the expert + expert_uses_adarms = getattr( + self.model.paligemma_with_expert.gemma_expert.config, "use_adarms", False + ) + if expert_uses_adarms: + logging.warning(f"Skipping norm key (adaRMS mismatch): {key}") + continue + + # Handle MLP naming changes for pi05 + # pi05 model expects time_mlp_*, but checkpoint might have action_time_mlp_* + if key.startswith("action_time_mlp_in."): + new_key = key.replace("action_time_mlp_in.", "time_mlp_in.") + elif key.startswith("action_time_mlp_out."): + new_key = key.replace("action_time_mlp_out.", "time_mlp_out.") + # Also handle state_proj which shouldn't exist in pi05 + if key.startswith("state_proj."): + logging.warning(f"Skipping state_proj key in pi05 mode: {key}") + continue + + # Handle vision tower embedding layer potential differences + if "patch_embedding" in key: + # Some checkpoints might have this, but current model expects different structure + logging.warning(f"Vision embedding key might need handling: {key}") + + fixed_state_dict[new_key] = value + + return fixed_state_dict + + def get_optim_params(self) -> dict: + return self.parameters() + + def reset(self): + """Reset internal state - called when environment resets.""" + self._action_queue = deque(maxlen=self.config.n_action_steps) + self._queues = { + ACTION: deque(maxlen=self.config.n_action_steps), + } + + def _preprocess_images(self, batch: dict[str, Tensor]) -> tuple[list[Tensor], list[Tensor]]: + """Preprocess images for the model. + + Images from LeRobot are typically in [B, C, H, W] format and normalized to [0, 1]. + PaliGemma expects images in [B, C, H, W] format and normalized to [-1, 1]. + """ + images = [] + img_masks = [] + + # Get device from model parameters + device = next(self.parameters()).device + + present_img_keys = [key for key in self.config.image_features if key in batch] + missing_img_keys = [key for key in self.config.image_features if key not in batch] + + if len(present_img_keys) == 0: + raise ValueError( + f"All image features are missing from the batch. At least one expected. " + f"(batch: {batch.keys()}) (image_features: {self.config.image_features})" + ) + + # Preprocess image features present in the batch + for key in present_img_keys: + img = batch[key] + + # Ensure tensor is on the same device as the model + if img.device != device: + img = img.to(device) + + # Ensure float32 dtype for consistency + if img.dtype != torch.float32: + img = img.to(torch.float32) + + # from openpi preprocess_observation_pytorch: Handle both [B, C, H, W] and [B, H, W, C] formats + is_channels_first = img.shape[1] == 3 # Check if channels are in dimension 1 + + if is_channels_first: + # Convert [B, C, H, W] to [B, H, W, C] for processing + img = img.permute(0, 2, 3, 1) + + # from openpi preprocess_observation_pytorch: Resize with padding if needed + if img.shape[1:3] != self.config.image_resolution: + img = resize_with_pad_torch(img, *self.config.image_resolution) + + # Normalize from [0,1] to [-1,1] as expected by siglip + img = img * 2.0 - 1.0 + + # from openpi preprocess_observation_pytorch: Convert back to [B, C, H, W] format if it was originally channels-first + if is_channels_first: + img = img.permute(0, 3, 1, 2) # [B, H, W, C] -> [B, C, H, W] + + images.append(img) + # Create mask (all ones for real images) + bsize = img.shape[0] + mask = torch.ones(bsize, dtype=torch.bool, device=device) + img_masks.append(mask) + + # Create image features not present in the batch as fully 0 padded images + for _num_empty_cameras in range(len(missing_img_keys)): + img = torch.ones_like(img) * -1 # Padded with -1 for SigLIP + mask = torch.zeros_like(mask) # Mask is zero for empty cameras + images.append(img) + img_masks.append(mask) + + return images, img_masks + + def prepare_action(self, batch): + """Pad action""" + actions = pad_vector(batch[ACTION], self.config.max_action_dim) + return actions + + @torch.no_grad() + def select_action(self, batch: dict[str, Tensor]) -> Tensor: + """Select a single action given environment observations.""" + self.eval() + + # Action queue logic for n_action_steps > 1 + if len(self._action_queue) == 0: + actions = self.predict_action_chunk(batch)[:, : self.config.n_action_steps] + # Transpose to get shape (n_action_steps, batch_size, action_dim) + self._action_queue.extend(actions.transpose(0, 1)) + + return self._action_queue.popleft() + + @torch.no_grad() + def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: + """Predict a chunk of actions given environment observations.""" + self.eval() + + # Prepare inputs + images, img_masks = self._preprocess_images(batch) + tokens, masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"] + + # Sample actions using the model (no separate state needed for PI05) + actions = self.model.sample_actions(images, img_masks, tokens, masks) + + # Unpad actions to actual action dimension + original_action_dim = self.config.output_features[ACTION].shape[0] + actions = actions[:, :, :original_action_dim] + + return actions + + def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: + """Run the batch through the model and compute the loss for training.""" + + # Prepare inputs + images, img_masks = self._preprocess_images(batch) + tokens, masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"] + + actions = self.prepare_action(batch) + + # Compute loss (no separate state needed for PI05) + losses = self.model.forward(images, img_masks, tokens, masks, actions) + + # Truncate losses to actual action dimensions + original_action_dim = self.config.output_features[ACTION].shape[0] + losses = losses[:, :, :original_action_dim] + + loss = losses.mean() + + loss_dict = { + "loss": loss.item(), + "loss_per_dim": losses.mean(dim=[0, 1]).detach().cpu().numpy().tolist(), + } + + return loss, loss_dict diff --git a/src/lerobot/policies/pi05/processor_pi05.py b/src/lerobot/policies/pi05/processor_pi05.py new file mode 100644 index 00000000000..e29bc4c23c8 --- /dev/null +++ b/src/lerobot/policies/pi05/processor_pi05.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from copy import deepcopy +from dataclasses import dataclass +from typing import Any + +import numpy as np +import torch + +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.policies.pi05.configuration_pi05 import PI05Config +from lerobot.policies.pi05.modeling_pi05 import pad_vector +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + ProcessorStep, + ProcessorStepRegistry, + RenameObservationsProcessorStep, + TokenizerProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.processor.core import EnvTransition, TransitionKey +from lerobot.utils.constants import ( + OBS_STATE, + POLICY_POSTPROCESSOR_DEFAULT_NAME, + POLICY_PREPROCESSOR_DEFAULT_NAME, +) + + +@ProcessorStepRegistry.register(name="pi05_prepare_state_tokenizer_processor_step") +@dataclass +class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep): + """ + Processor step to prepare the state and tokenize the language input. + """ + + max_state_dim: int = 32 + task_key: str = "task" + + def __call__(self, transition: EnvTransition) -> EnvTransition: + transition = transition.copy() + + state = transition.get(TransitionKey.OBSERVATION, {}).get(OBS_STATE) + if state is None: + raise ValueError("State is required for PI05") + tasks = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.task_key) + if tasks is None: + raise ValueError("No task found in complementary data") + + # TODO: check if this necessary + state = deepcopy(state) + + # Prepare state (pad to max_state_dim) + state = pad_vector(state, self.max_state_dim) + + # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step + # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`) + state_np = state.cpu().numpy() + discretized_states = np.digitize(state_np, bins=np.linspace(-1, 1, 256 + 1)[:-1]) - 1 + + full_prompts = [] + for i, task in enumerate(tasks): + cleaned_text = task.strip().replace("_", " ").replace("\n", " ") + state_str = " ".join(map(str, discretized_states[i])) + full_prompt = f"Task: {cleaned_text}, State: {state_str};\nAction: " + full_prompts.append(full_prompt) + + transition[TransitionKey.COMPLEMENTARY_DATA][self.task_key] = full_prompts + # Normalize state to [-1, 1] range if needed (assuming it's already normalized by normalizer processor step!!) + # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`) + return transition + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + This step does not alter the feature definitions. + """ + return features + + +def make_pi05_pre_post_processors( + config: PI05Config, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the PI0 policy. + + The pre-processing pipeline prepares input data for the model by: + 1. Renaming features to match pretrained configurations. + 2. Normalizing input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Appending a newline character to the task description for tokenizer compatibility. + 5. Tokenizing the text prompt using the PaliGemma tokenizer. + 6. Moving all data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving data to the CPU. + 2. Unnormalizing the output features to their original scale. + + Args: + config: The configuration object for the PI0 policy. + dataset_stats: A dictionary of statistics for normalization. + preprocessor_kwargs: Additional arguments for the pre-processor pipeline. + postprocessor_kwargs: Additional arguments for the post-processor pipeline. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + # Add remaining processors + input_steps: list[ProcessorStep] = [ + RenameObservationsProcessorStep(rename_map={}), # To mimic the same processor as pretrained one + AddBatchDimensionProcessorStep(), + # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateTokenizerProcessorStep + # because the tokenizer step expects normalized state in [-1, 1] range for discretization + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + Pi05PrepareStateTokenizerProcessorStep(max_state_dim=config.max_state_dim), + TokenizerProcessorStep( + tokenizer_name="google/paligemma-3b-pt-224", + max_length=config.tokenizer_max_length, + padding_side="right", + padding="max_length", + ), + DeviceProcessorStep(device=config.device), + ] + + output_steps: list[ProcessorStep] = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/pi0fast/configuration_pi0fast.py b/src/lerobot/policies/pi0fast/configuration_pi0fast.py index b72bcd73598..cefd4e688db 100644 --- a/src/lerobot/policies/pi0fast/configuration_pi0fast.py +++ b/src/lerobot/policies/pi0fast/configuration_pi0fast.py @@ -1,3 +1,19 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from dataclasses import dataclass, field from lerobot.configs.policies import PreTrainedConfig @@ -6,6 +22,7 @@ from lerobot.optim.schedulers import ( CosineDecayWithWarmupSchedulerConfig, ) +from lerobot.utils.constants import OBS_IMAGES @PreTrainedConfig.register_subclass("pi0fast") @@ -99,7 +116,7 @@ def __post_init__(self): def validate_features(self) -> None: for i in range(self.empty_cameras): - key = f"observation.images.empty_camera_{i}" + key = f"{OBS_IMAGES}.empty_camera_{i}" empty_camera = PolicyFeature( type=FeatureType.VISUAL, shape=(3, 480, 640), diff --git a/src/lerobot/policies/pi0fast/modeling_pi0fast.py b/src/lerobot/policies/pi0fast/modeling_pi0fast.py index 88727b581aa..102cfb8fa5d 100644 --- a/src/lerobot/policies/pi0fast/modeling_pi0fast.py +++ b/src/lerobot/policies/pi0fast/modeling_pi0fast.py @@ -57,10 +57,9 @@ from transformers.cache_utils import HybridCache, StaticCache from transformers.models.auto import CONFIG_MAPPING -from lerobot.constants import ACTION, OBS_STATE -from lerobot.policies.normalize import Normalize, Unnormalize from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig from lerobot.policies.pretrained import PreTrainedPolicy +from lerobot.utils.constants import ACTION, OBS_STATE PRECISION = { "float16": torch.float16, @@ -146,14 +145,6 @@ def __init__( config.validate_features() self.config = config - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.language_tokenizer = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224") self.model = PI0FAST(config) @@ -221,8 +212,6 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: if self.config.adapt_to_pi_aloha: batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) - batch = self.normalize_inputs(batch) - # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by # querying the policy. if len(self._action_queue) == 0: @@ -235,8 +224,6 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: ] # self.config.max_action_dim # self.config.action_feature.shape[0] actions = actions[:, :, :original_action_dim] - actions = self.unnormalize_outputs({"action": actions})["action"] - if self.config.adapt_to_pi_aloha: actions = self._pi_aloha_encode_actions(actions) @@ -249,8 +236,6 @@ def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: if self.config.adapt_to_pi_aloha: batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION]) - batch = self.normalize_inputs(batch) - batch = self.normalize_targets(batch) loss_dict = self.model.forward(batch) return loss_dict["loss"], loss_dict diff --git a/src/lerobot/policies/pi0fast/processor_pi0fast.py b/src/lerobot/policies/pi0fast/processor_pi0fast.py new file mode 100644 index 00000000000..95b5e541b1b --- /dev/null +++ b/src/lerobot/policies/pi0fast/processor_pi0fast.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python + +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any + +import torch + +from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + RenameObservationsProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def make_pi0fast_pre_post_processors( + config: PI0FASTConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the PI0Fast policy. + + The pre-processing pipeline prepares input data for the model by: + 1. Renaming features to match pretrained configurations. + 2. Normalizing input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Moving all data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving data to the CPU. + 2. Unnormalizing the output features to their original scale. + + Args: + config: The configuration object for the PI0Fast policy. + dataset_stats: A dictionary of statistics for normalization. + preprocessor_kwargs: Additional arguments for the pre-processor pipeline. + postprocessor_kwargs: Additional arguments for the post-processor pipeline. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + input_steps = [ + RenameObservationsProcessorStep(rename_map={}), # To mimic the same processor as pretrained one + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + ] + output_steps = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/pretrained.py b/src/lerobot/policies/pretrained.py index 2f69309c167..3f5d89ec54a 100644 --- a/src/lerobot/policies/pretrained.py +++ b/src/lerobot/policies/pretrained.py @@ -18,7 +18,7 @@ from importlib.resources import files from pathlib import Path from tempfile import TemporaryDirectory -from typing import TypeVar +from typing import TypedDict, TypeVar import packaging import safetensors @@ -27,6 +27,7 @@ from huggingface_hub.errors import HfHubHTTPError from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor from torch import Tensor, nn +from typing_extensions import Unpack from lerobot.configs.policies import PreTrainedConfig from lerobot.configs.train import TrainPipelineConfig @@ -36,6 +37,10 @@ T = TypeVar("T", bound="PreTrainedPolicy") +class ActionSelectKwargs(TypedDict, total=False): + noise: Tensor | None + + class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC): """ Base class for policy models. @@ -181,7 +186,7 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict | None]: raise NotImplementedError @abc.abstractmethod - def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: + def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs: Unpack[ActionSelectKwargs]) -> Tensor: """Returns the action chunk (for action chunking policies) for a given observation, potentially in batch mode. Child classes using action chunking should use this method within `select_action` to form the action chunk @@ -190,7 +195,7 @@ def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: raise NotImplementedError @abc.abstractmethod - def select_action(self, batch: dict[str, Tensor]) -> Tensor: + def select_action(self, batch: dict[str, Tensor], **kwargs: Unpack[ActionSelectKwargs]) -> Tensor: """Return one action to run in the environment (potentially in batch mode). When the model uses a history of observations, or outputs a sequence of actions, this method deals @@ -246,7 +251,9 @@ def generate_model_card( base_model=base_model, ) - template_card = files("lerobot.templates").joinpath("lerobot_modelcard_template.md").read_text() + template_card = ( + files("lerobot.templates").joinpath("lerobot_modelcard_template.md").read_text(encoding="utf-8") + ) card = ModelCard.from_template(card_data, template_str=template_card) card.validate() return card diff --git a/src/lerobot/policies/sac/configuration_sac.py b/src/lerobot/policies/sac/configuration_sac.py index c57eeeb728f..ada12330c94 100644 --- a/src/lerobot/policies/sac/configuration_sac.py +++ b/src/lerobot/policies/sac/configuration_sac.py @@ -19,8 +19,8 @@ from lerobot.configs.policies import PreTrainedConfig from lerobot.configs.types import NormalizationMode -from lerobot.constants import ACTION, OBS_IMAGE, OBS_STATE from lerobot.optim.optimizers import MultiAdamConfig +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE def is_image_feature(key: str) -> bool: @@ -139,8 +139,6 @@ class SACConfig(PreTrainedConfig): # Training parameter # Number of steps for online training online_steps: int = 1000000 - # Seed for the online environment - online_env_seed: int = 10000 # Capacity of the online replay buffer online_buffer_capacity: int = 100000 # Capacity of the offline replay buffer @@ -225,7 +223,7 @@ def validate_features(self) -> None: "You must provide either 'observation.state' or an image observation (key starting with 'observation.image') in the input features" ) - if "action" not in self.output_features: + if ACTION not in self.output_features: raise ValueError("You must provide 'action' in the output features") @property diff --git a/src/lerobot/policies/sac/modeling_sac.py b/src/lerobot/policies/sac/modeling_sac.py index 878f3cdd859..c7c6798ed1f 100644 --- a/src/lerobot/policies/sac/modeling_sac.py +++ b/src/lerobot/policies/sac/modeling_sac.py @@ -28,10 +28,10 @@ from torch import Tensor from torch.distributions import MultivariateNormal, TanhTransform, Transform, TransformedDistribution -from lerobot.policies.normalize import NormalizeBuffer from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.sac.configuration_sac import SACConfig, is_image_feature from lerobot.policies.utils import get_device_from_parameters +from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_STATE DISCRETE_DIMENSION_INDEX = -1 # Gripper is always the last dimension @@ -45,15 +45,13 @@ class SACPolicy( def __init__( self, config: SACConfig | None = None, - dataset_stats: dict[str, dict[str, Tensor]] | None = None, ): super().__init__(config) config.validate_features() self.config = config # Determine action dimension and initialize all components - continuous_action_dim = config.output_features["action"].shape[0] - self._init_normalization(dataset_stats) + continuous_action_dim = config.output_features[ACTION].shape[0] self._init_encoders() self._init_critics(continuous_action_dim) self._init_actor(continuous_action_dim) @@ -88,8 +86,7 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: observations_features = None if self.shared_encoder and self.actor.encoder.has_images: - # Cache and normalize image features - observations_features = self.actor.encoder.get_cached_image_features(batch, normalize=True) + observations_features = self.actor.encoder.get_cached_image_features(batch) actions, _, _ = self.actor(batch, observations_features) @@ -161,7 +158,7 @@ def forward( The computed loss tensor """ # Extract common components from batch - actions: Tensor = batch["action"] + actions: Tensor = batch[ACTION] observations: dict[str, Tensor] = batch["state"] observation_features: Tensor = batch.get("observation_feature") @@ -391,28 +388,12 @@ def compute_loss_actor( actor_loss = ((self.temperature * log_probs) - min_q_preds).mean() return actor_loss - def _init_normalization(self, dataset_stats): - """Initialize input/output normalization modules.""" - self.normalize_inputs = nn.Identity() - self.normalize_targets = nn.Identity() - if self.config.dataset_stats is not None: - params = _convert_normalization_params_to_tensor(self.config.dataset_stats) - self.normalize_inputs = NormalizeBuffer( - self.config.input_features, self.config.normalization_mapping, params - ) - stats = dataset_stats or params - self.normalize_targets = NormalizeBuffer( - self.config.output_features, self.config.normalization_mapping, stats - ) - def _init_encoders(self): """Initialize shared or separate encoders for actor and critic.""" self.shared_encoder = self.config.shared_encoder - self.encoder_critic = SACObservationEncoder(self.config, self.normalize_inputs) + self.encoder_critic = SACObservationEncoder(self.config) self.encoder_actor = ( - self.encoder_critic - if self.shared_encoder - else SACObservationEncoder(self.config, self.normalize_inputs) + self.encoder_critic if self.shared_encoder else SACObservationEncoder(self.config) ) def _init_critics(self, continuous_action_dim): @@ -424,9 +405,7 @@ def _init_critics(self, continuous_action_dim): ) for _ in range(self.config.num_critics) ] - self.critic_ensemble = CriticEnsemble( - encoder=self.encoder_critic, ensemble=heads, output_normalization=self.normalize_targets - ) + self.critic_ensemble = CriticEnsemble(encoder=self.encoder_critic, ensemble=heads) target_heads = [ CriticHead( input_dim=self.encoder_critic.output_dim + continuous_action_dim, @@ -434,9 +413,7 @@ def _init_critics(self, continuous_action_dim): ) for _ in range(self.config.num_critics) ] - self.critic_target = CriticEnsemble( - encoder=self.encoder_critic, ensemble=target_heads, output_normalization=self.normalize_targets - ) + self.critic_target = CriticEnsemble(encoder=self.encoder_critic, ensemble=target_heads) self.critic_target.load_state_dict(self.critic_ensemble.state_dict()) if self.config.use_torch_compile: @@ -490,10 +467,9 @@ def _init_temperature(self): class SACObservationEncoder(nn.Module): """Encode image and/or state vector observations.""" - def __init__(self, config: SACConfig, input_normalizer: nn.Module) -> None: + def __init__(self, config: SACConfig) -> None: super().__init__() self.config = config - self.input_normalization = input_normalizer self._init_image_layers() self._init_state_layers() self._compute_output_dim() @@ -538,17 +514,17 @@ def _init_image_layers(self) -> None: ) def _init_state_layers(self) -> None: - self.has_env = "observation.environment_state" in self.config.input_features - self.has_state = "observation.state" in self.config.input_features + self.has_env = OBS_ENV_STATE in self.config.input_features + self.has_state = OBS_STATE in self.config.input_features if self.has_env: - dim = self.config.input_features["observation.environment_state"].shape[0] + dim = self.config.input_features[OBS_ENV_STATE].shape[0] self.env_encoder = nn.Sequential( nn.Linear(dim, self.config.latent_dim), nn.LayerNorm(self.config.latent_dim), nn.Tanh(), ) if self.has_state: - dim = self.config.input_features["observation.state"].shape[0] + dim = self.config.input_features[OBS_STATE].shape[0] self.state_encoder = nn.Sequential( nn.Linear(dim, self.config.latent_dim), nn.LayerNorm(self.config.latent_dim), @@ -568,16 +544,15 @@ def _compute_output_dim(self) -> None: def forward( self, obs: dict[str, Tensor], cache: dict[str, Tensor] | None = None, detach: bool = False ) -> Tensor: - obs = self.input_normalization(obs) parts = [] if self.has_images: if cache is None: - cache = self.get_cached_image_features(obs, normalize=False) + cache = self.get_cached_image_features(obs) parts.append(self._encode_images(cache, detach)) if self.has_env: - parts.append(self.env_encoder(obs["observation.environment_state"])) + parts.append(self.env_encoder(obs[OBS_ENV_STATE])) if self.has_state: - parts.append(self.state_encoder(obs["observation.state"])) + parts.append(self.state_encoder(obs[OBS_STATE])) if parts: return torch.cat(parts, dim=-1) @@ -585,7 +560,7 @@ def forward( "No parts to concatenate, you should have at least one image or environment state or state" ) - def get_cached_image_features(self, obs: dict[str, Tensor], normalize: bool = False) -> dict[str, Tensor]: + def get_cached_image_features(self, obs: dict[str, Tensor]) -> dict[str, Tensor]: """Extract and optionally cache image features from observations. This function processes image observations through the vision encoder once and returns @@ -597,26 +572,17 @@ def get_cached_image_features(self, obs: dict[str, Tensor], normalize: bool = Fa - The vision encoder forward pass is typically the main computational bottleneck during training and inference - Caching these features can provide 2-4x speedup in training and inference - Normalization behavior: - - When called from inside forward(): set normalize=False since inputs are already normalized - - When called from outside forward(): set normalize=True to ensure proper input normalization - Usage patterns: - - Called in select_action() with normalize=True + - Called in select_action() - Called in learner.py's get_observation_features() to pre-compute features for all policy components - - Called internally by forward() with normalize=False + - Called internally by forward() Args: obs: Dictionary of observation tensors containing image keys - normalize: Whether to normalize observations before encoding - Set to True when calling directly from outside the encoder's forward method - Set to False when calling from within forward() where inputs are already normalized Returns: Dictionary mapping image keys to their corresponding encoded features """ - if normalize: - obs = self.input_normalization(obs) batched = torch.cat([obs[k] for k in self.image_keys], dim=0) out = self.image_encoder(batched) chunks = torch.chunk(out, len(self.image_keys), dim=0) @@ -747,7 +713,6 @@ class CriticEnsemble(nn.Module): Args: encoder (SACObservationEncoder): encoder for observations. ensemble (List[CriticHead]): list of critic heads. - output_normalization (nn.Module): normalization layer for actions. init_final (float | None): optional initializer scale for final layers. Forward returns a tensor of shape (num_critics, batch_size) containing Q-values. @@ -757,13 +722,11 @@ def __init__( self, encoder: SACObservationEncoder, ensemble: list[CriticHead], - output_normalization: nn.Module, init_final: float | None = None, ): super().__init__() self.encoder = encoder self.init_final = init_final - self.output_normalization = output_normalization self.critics = nn.ModuleList(ensemble) def forward( @@ -775,11 +738,6 @@ def forward( device = get_device_from_parameters(self) # Move each tensor in observations to device observations = {k: v.to(device) for k, v in observations.items()} - # NOTE: We normalize actions it helps for sample efficiency - actions: dict[str, torch.tensor] = {"action": actions} - # NOTE: Normalization layer took dict in input and outputs a dict that why - actions = self.output_normalization(actions)["action"] - actions = actions.to(device) obs_enc = self.encoder(observations, cache=observation_features) @@ -1103,15 +1061,3 @@ def stddev(self): x = transform(x) return x - - -def _convert_normalization_params_to_tensor(normalization_params: dict) -> dict: - converted_params = {} - for outer_key, inner_dict in normalization_params.items(): - converted_params[outer_key] = {} - for key, value in inner_dict.items(): - converted_params[outer_key][key] = torch.tensor(value) - if "image" in outer_key: - converted_params[outer_key][key] = converted_params[outer_key][key].view(3, 1, 1) - - return converted_params diff --git a/src/lerobot/policies/sac/processor_sac.py b/src/lerobot/policies/sac/processor_sac.py new file mode 100644 index 00000000000..cf90e3cb4a0 --- /dev/null +++ b/src/lerobot/policies/sac/processor_sac.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any + +import torch + +from lerobot.policies.sac.configuration_sac import SACConfig +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + RenameObservationsProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def make_sac_pre_post_processors( + config: SACConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the SAC policy. + + The pre-processing pipeline prepares input data for the model by: + 1. Renaming features to match pretrained configurations. + 2. Normalizing input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Moving all data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving data to the CPU. + 2. Unnormalizing the output features to their original scale. + + Args: + config: The configuration object for the SAC policy. + dataset_stats: A dictionary of statistics for normalization. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + # Add remaining processors + input_steps = [ + RenameObservationsProcessorStep(rename_map={}), + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + ] + output_steps = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/sac/reward_model/configuration_classifier.py b/src/lerobot/policies/sac/reward_model/configuration_classifier.py index fc53283b309..9b76b803722 100644 --- a/src/lerobot/policies/sac/reward_model/configuration_classifier.py +++ b/src/lerobot/policies/sac/reward_model/configuration_classifier.py @@ -19,6 +19,7 @@ from lerobot.configs.types import NormalizationMode from lerobot.optim.optimizers import AdamWConfig, OptimizerConfig from lerobot.optim.schedulers import LRSchedulerConfig +from lerobot.utils.constants import OBS_IMAGE @PreTrainedConfig.register_subclass(name="reward_classifier") @@ -69,7 +70,7 @@ def get_scheduler_preset(self) -> LRSchedulerConfig | None: def validate_features(self) -> None: """Validate feature configurations.""" - has_image = any(key.startswith("observation.image") for key in self.input_features) + has_image = any(key.startswith(OBS_IMAGE) for key in self.input_features) if not has_image: raise ValueError( "You must provide an image observation (key starting with 'observation.image') in the input features" diff --git a/src/lerobot/policies/sac/reward_model/modeling_classifier.py b/src/lerobot/policies/sac/reward_model/modeling_classifier.py index cadd1c9f28b..dba6a174b4b 100644 --- a/src/lerobot/policies/sac/reward_model/modeling_classifier.py +++ b/src/lerobot/policies/sac/reward_model/modeling_classifier.py @@ -19,10 +19,9 @@ import torch from torch import Tensor, nn -from lerobot.constants import OBS_IMAGE, REWARD -from lerobot.policies.normalize import Normalize, Unnormalize from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig +from lerobot.utils.constants import OBS_IMAGE, REWARD class ClassifierOutput: @@ -108,22 +107,12 @@ class Classifier(PreTrainedPolicy): def __init__( self, config: RewardClassifierConfig, - dataset_stats: dict[str, dict[str, Tensor]] | None = None, ): from transformers import AutoModel super().__init__(config) self.config = config - # Initialize normalization (standardized with the policy framework) - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - # Set up encoder encoder = AutoModel.from_pretrained(self.config.model_name, trust_remote_code=True) # Extract vision model if we're given a multimodal model @@ -247,10 +236,6 @@ def predict(self, xs: list) -> ClassifierOutput: def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Tensor]]: """Standard forward pass for training compatible with train.py.""" - # Normalize inputs if needed - batch = self.normalize_inputs(batch) - batch = self.normalize_targets(batch) - # Extract images and labels images, labels = self.extract_images_and_labels(batch) diff --git a/src/lerobot/policies/sac/reward_model/processor_classifier.py b/src/lerobot/policies/sac/reward_model/processor_classifier.py new file mode 100644 index 00000000000..c2a34eab26a --- /dev/null +++ b/src/lerobot/policies/sac/reward_model/processor_classifier.py @@ -0,0 +1,82 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any + +import torch + +from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig +from lerobot.processor import ( + DeviceProcessorStep, + IdentityProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action + + +def make_classifier_processor( + config: RewardClassifierConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the reward classifier. + + The pre-processing pipeline prepares input data for the classifier by: + 1. Normalizing both input and output features based on dataset statistics. + 2. Moving the data to the specified device. + + The post-processing pipeline handles the classifier's output by: + 1. Moving the data to the CPU. + 2. Applying an identity step, as no unnormalization is needed for the output logits. + + Args: + config: The configuration object for the RewardClassifier. + dataset_stats: A dictionary of statistics for normalization. + preprocessor_kwargs: Additional arguments for the pre-processor pipeline. + postprocessor_kwargs: Additional arguments for the post-processor pipeline. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + input_steps = [ + NormalizerProcessorStep( + features=config.input_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + NormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device=config.device), + ] + output_steps = [DeviceProcessorStep(device="cpu"), IdentityProcessorStep()] + + return ( + PolicyProcessorPipeline( + steps=input_steps, + name="classifier_preprocessor", + ), + PolicyProcessorPipeline( + steps=output_steps, + name="classifier_postprocessor", + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/smolvla/configuration_smolvla.py b/src/lerobot/policies/smolvla/configuration_smolvla.py index 571900c4a62..eedf477a54d 100644 --- a/src/lerobot/policies/smolvla/configuration_smolvla.py +++ b/src/lerobot/policies/smolvla/configuration_smolvla.py @@ -20,6 +20,7 @@ from lerobot.optim.schedulers import ( CosineDecayWithWarmupSchedulerConfig, ) +from lerobot.utils.constants import OBS_IMAGES @PreTrainedConfig.register_subclass("smolvla") @@ -117,7 +118,7 @@ def __post_init__(self): def validate_features(self) -> None: for i in range(self.empty_cameras): - key = f"observation.images.empty_camera_{i}" + key = f"{OBS_IMAGES}.empty_camera_{i}" empty_camera = PolicyFeature( type=FeatureType.VISUAL, shape=(3, 480, 640), diff --git a/src/lerobot/policies/smolvla/modeling_smolvla.py b/src/lerobot/policies/smolvla/modeling_smolvla.py index 18f2fc58a57..23fc3ca4f72 100644 --- a/src/lerobot/policies/smolvla/modeling_smolvla.py +++ b/src/lerobot/policies/smolvla/modeling_smolvla.py @@ -53,125 +53,21 @@ """ import math -import os -import re from collections import deque -import safetensors import torch import torch.nn.functional as F # noqa: N812 from torch import Tensor, nn -from transformers import AutoProcessor -from lerobot.constants import ACTION, OBS_STATE -from lerobot.policies.normalize import ( - Normalize, - Unnormalize, -) from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig from lerobot.policies.smolvla.smolvlm_with_expert import SmolVLMWithExpertModel from lerobot.policies.utils import ( populate_queues, ) +from lerobot.utils.constants import ACTION, OBS_LANGUAGE_ATTENTION_MASK, OBS_LANGUAGE_TOKENS, OBS_STATE from lerobot.utils.utils import get_safe_dtype -# Matches ".soNNN", optionally followed by "-something", up to the "_buffer_" marker -_VARIANT_RE = re.compile(r"\.so\d+(?:-[\w]+)?_buffer_") - - -def canonicalise(k: str) -> str: - """ - Remove dataset-variant markers like '.so100-blue_' or '.so100_' from a - normalisation-buffer key. - """ - return _VARIANT_RE.sub(".buffer_", k) - - -def standardise_state_dict( - checkpoint: dict[str, torch.Tensor], ref_keys: set[str], *, verbose: bool = True -) -> tuple[dict[str, torch.Tensor], list[str]]: - """ - • Re-keys `checkpoint ` so that every entry matches the *reference* key set. - • If several variant keys collapse to the same canonical name we keep the - first one and log the collision. - • Returns the new dict + a list of entries that could not be matched. - """ - out, collisions, unmatched = {}, {}, [] - - for k, v in checkpoint.items(): - canon = canonicalise(k) - if canon in ref_keys: - if canon in out: # duplicate after collapsing - collisions.setdefault(canon, []).append(k) - else: - out[canon] = v - else: - unmatched.append(k) - - if verbose: - for canon, variants in collisions.items(): - print(f"[standardise_state_dict] '{canon}' ← {variants}") - if unmatched: - print(f"[standardise_state_dict] kept {len(unmatched)} unmatched keys") - - out.update({k: checkpoint[k] for k in unmatched}) - return out, unmatched - - -def rename_checkpoint_keys(checkpoint: dict, rename_str: str): - """ - Renames keys in a checkpoint dictionary based on the given rename string. - - Args: - checkpoint (dict): The checkpoint dictionary. - rename_str (str): A string specifying key mappings in the format "old1//new1,old2//new2". - - Returns: - dict: The modified checkpoint with renamed keys. - """ - - rename_dict = dict(pair.split("//") for pair in rename_str.split(",")) - - new_checkpoint = {} - for k, v in checkpoint.items(): - for old_key, new_key in rename_dict.items(): - if old_key in k: - k = k.replace(old_key, new_key) - new_checkpoint[k] = v - return new_checkpoint - - -def load_smolvla( - model: torch.nn.Module, - filename: str | os.PathLike, - *, - device: str = "cpu", - checkpoint_keys_mapping: str = "", -) -> torch.nn.Module: - state_dict = safetensors.torch.load_file(filename, device=device) - - # Optional user-supplied renames (e.g. "model._orig_mod.//model.") - if checkpoint_keys_mapping and "//" in checkpoint_keys_mapping: - state_dict = rename_checkpoint_keys(state_dict, checkpoint_keys_mapping) - - state_dict, _ = standardise_state_dict(state_dict, set(model.state_dict().keys())) - - # HACK(aliberts): to not overwrite normalization parameters as they should come from the dataset - norm_keys = ("normalize_inputs", "normalize_targets", "unnormalize_outputs") - state_dict = {k: v for k, v in state_dict.items() if not k.startswith(norm_keys)} - - missing, unexpected = model.load_state_dict(state_dict, strict=False) - - if not all(key.startswith(norm_keys) for key in missing) or unexpected: - raise RuntimeError( - "SmolVLA %d missing / %d unexpected keys", - len(missing), - len(unexpected), - ) - - return model - def create_sinusoidal_pos_embedding( time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu" @@ -326,28 +222,17 @@ class SmolVLAPolicy(PreTrainedPolicy): def __init__( self, config: SmolVLAConfig, - dataset_stats: dict[str, dict[str, Tensor]] | None = None, ): """ Args: config: Policy configuration class instance or None, in which case the default instantiation of the configuration class is used. - dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected - that they will be passed with a call to `load_state_dict` before the policy is used. """ super().__init__(config) config.validate_features() self.config = config - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.language_tokenizer = AutoProcessor.from_pretrained(self.config.vlm_model_name).tokenizer self.model = VLAFlowMatching(config) self.reset() @@ -357,23 +242,6 @@ def reset(self): ACTION: deque(maxlen=self.config.n_action_steps), } - # HACK(aliberts, danaaubakirova): we overwrite this classmethod here to fix smolVLA-specific issues - @classmethod - def _load_as_safetensor( - cls, - model: "SmolVLAPolicy", - model_file: str, - map_location: str, - strict: bool, - ): - safetensors.torch.load_model(model, model_file, strict=strict, device=map_location) - return load_smolvla( - model, - model_file, - device=map_location, - checkpoint_keys_mapping="model._orig_mod.//model.", - ) - def get_optim_params(self) -> dict: return self.parameters() @@ -389,7 +257,8 @@ def _get_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = Non images, img_masks = self.prepare_images(batch) state = self.prepare_state(batch) - lang_tokens, lang_masks = self.prepare_language(batch) + lang_tokens = batch[f"{OBS_LANGUAGE_TOKENS}"] + lang_masks = batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"] actions = self.model.sample_actions(images, img_masks, lang_tokens, lang_masks, state, noise=noise) @@ -397,8 +266,6 @@ def _get_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = Non original_action_dim = self.config.action_feature.shape[0] actions = actions[:, :, :original_action_dim] - actions = self.unnormalize_outputs({ACTION: actions})[ACTION] - if self.config.adapt_to_pi_aloha: actions = self._pi_aloha_encode_actions(actions) @@ -408,8 +275,6 @@ def _prepare_batch(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: if self.config.adapt_to_pi_aloha: batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) - batch = self.normalize_inputs(batch) - return batch @torch.no_grad() @@ -450,11 +315,11 @@ def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> dict[str, if self.config.adapt_to_pi_aloha: batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE]) batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION]) - batch = self.normalize_inputs(batch) - batch = self.normalize_targets(batch) + images, img_masks = self.prepare_images(batch) state = self.prepare_state(batch) - lang_tokens, lang_masks = self.prepare_language(batch) + lang_tokens = batch[f"{OBS_LANGUAGE_TOKENS}"] + lang_masks = batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"] actions = self.prepare_action(batch) actions_is_pad = batch.get("actions_id_pad") loss_dict = {} @@ -518,30 +383,6 @@ def prepare_images(self, batch): img_masks.append(mask) return images, img_masks - def prepare_language(self, batch) -> tuple[Tensor, Tensor]: - """Tokenize the text input""" - device = batch[OBS_STATE].device - tasks = batch["task"] - if isinstance(tasks, str): - tasks = [tasks] - - if len(tasks) == 1: - tasks = [tasks[0] for _ in range(batch[OBS_STATE].shape[0])] - - tasks = [task if task.endswith("\n") else f"{task}\n" for task in tasks] - - tokenized_prompt = self.language_tokenizer.__call__( - tasks, - padding=self.config.pad_language_to, - padding_side="right", - max_length=self.config.tokenizer_max_length, - return_tensors="pt", - ) - lang_tokens = tokenized_prompt["input_ids"].to(device=device) - lang_masks = tokenized_prompt["attention_mask"].to(device=device, dtype=torch.bool) - - return lang_tokens, lang_masks - def _pi_aloha_decode_state(self, state): # Flip the joints. for motor_idx in [1, 2, 8, 9]: diff --git a/src/lerobot/policies/smolvla/processor_smolvla.py b/src/lerobot/policies/smolvla/processor_smolvla.py new file mode 100644 index 00000000000..3fc130aa1d0 --- /dev/null +++ b/src/lerobot/policies/smolvla/processor_smolvla.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python + +# Copyright 2025 HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any + +import torch + +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + ComplementaryDataProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + ProcessorStepRegistry, + RenameObservationsProcessorStep, + TokenizerProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def make_smolvla_pre_post_processors( + config: SmolVLAConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the SmolVLA policy. + + The pre-processing pipeline prepares input data for the model by: + 1. Renaming features to match pretrained configurations. + 2. Normalizing input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Ensuring the language task description ends with a newline character. + 5. Tokenizing the language task description. + 6. Moving all data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving data to the CPU. + 2. Unnormalizing the output actions to their original scale. + + Args: + config: The configuration object for the SmolVLA policy. + dataset_stats: A dictionary of statistics for normalization. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + input_steps = [ + RenameObservationsProcessorStep(rename_map={}), # To mimic the same processor as pretrained one + AddBatchDimensionProcessorStep(), + SmolVLANewLineProcessor(), + TokenizerProcessorStep( + tokenizer_name=config.vlm_model_name, + padding=config.pad_language_to, + padding_side="right", + max_length=config.tokenizer_max_length, + ), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + ] + output_steps = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) + + +@ProcessorStepRegistry.register(name="smolvla_new_line_processor") +class SmolVLANewLineProcessor(ComplementaryDataProcessorStep): + """ + A processor step that ensures the 'task' description ends with a newline character. + + This step is necessary for certain tokenizers (e.g., PaliGemma) that expect a + newline at the end of the prompt. It handles both single string tasks and lists + of string tasks. + """ + + def complementary_data(self, complementary_data): + if "task" not in complementary_data: + return complementary_data + + task = complementary_data["task"] + if task is None: + return complementary_data + + new_complementary_data = dict(complementary_data) + + # Handle both string and list of strings + if isinstance(task, str): + # Single string: add newline if not present + if not task.endswith("\n"): + new_complementary_data["task"] = f"{task}\n" + elif isinstance(task, list) and all(isinstance(t, str) for t in task): + # List of strings: add newline to each if not present + new_complementary_data["task"] = [t if t.endswith("\n") else f"{t}\n" for t in task] + # If task is neither string nor list of strings, leave unchanged + + return new_complementary_data + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features diff --git a/src/lerobot/policies/tdmpc/modeling_tdmpc.py b/src/lerobot/policies/tdmpc/modeling_tdmpc.py index 7ba88e5e63b..195cf61547e 100644 --- a/src/lerobot/policies/tdmpc/modeling_tdmpc.py +++ b/src/lerobot/policies/tdmpc/modeling_tdmpc.py @@ -35,11 +35,10 @@ import torch.nn.functional as F # noqa: N812 from torch import Tensor -from lerobot.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_STATE, REWARD -from lerobot.policies.normalize import Normalize, Unnormalize from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig from lerobot.policies.utils import get_device_from_parameters, get_output_shape, populate_queues +from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_PREFIX, OBS_STATE, OBS_STR, REWARD class TDMPCPolicy(PreTrainedPolicy): @@ -63,26 +62,19 @@ class TDMPCPolicy(PreTrainedPolicy): config_class = TDMPCConfig name = "tdmpc" - def __init__(self, config: TDMPCConfig, dataset_stats: dict[str, dict[str, Tensor]] | None = None): + def __init__( + self, + config: TDMPCConfig, + ): """ Args: config: Policy configuration class instance or None, in which case the default instantiation of the configuration class is used. - dataset_stats: Dataset statistics to be used for normalization. If not passed here, it is expected - that they will be passed with a call to `load_state_dict` before the policy is used. """ super().__init__(config) config.validate_features() self.config = config - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.model = TDMPCTOLD(config) self.model_target = deepcopy(self.model) for param in self.model_target.parameters(): @@ -99,13 +91,13 @@ def reset(self): called on `env.reset()` """ self._queues = { - "observation.state": deque(maxlen=1), - "action": deque(maxlen=max(self.config.n_action_steps, self.config.n_action_repeats)), + OBS_STATE: deque(maxlen=1), + ACTION: deque(maxlen=max(self.config.n_action_steps, self.config.n_action_repeats)), } if self.config.image_features: - self._queues["observation.image"] = deque(maxlen=1) + self._queues[OBS_IMAGE] = deque(maxlen=1) if self.config.env_state_feature: - self._queues["observation.environment_state"] = deque(maxlen=1) + self._queues[OBS_ENV_STATE] = deque(maxlen=1) # Previous mean obtained from the cross-entropy method (CEM) used during MPC. It is used to warm start # CEM for the next step. self._prev_mean: torch.Tensor | None = None @@ -137,7 +129,6 @@ def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: actions = torch.clamp(actions, -1, +1) - actions = self.unnormalize_outputs({ACTION: actions})[ACTION] return actions @torch.no_grad() @@ -147,11 +138,12 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: if ACTION in batch: batch.pop(ACTION) - batch = self.normalize_inputs(batch) - if self.config.image_features: batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch[OBS_IMAGE] = batch[next(iter(self.config.image_features))] + # NOTE: for offline evaluation, we have action in the batch, so we need to pop it out + if ACTION in batch: + batch.pop(ACTION) self._queues = populate_queues(self._queues, batch) @@ -320,11 +312,9 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: """ device = get_device_from_parameters(self) - batch = self.normalize_inputs(batch) if self.config.image_features: batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch[OBS_IMAGE] = batch[next(iter(self.config.image_features))] - batch = self.normalize_targets(batch) info = {} @@ -335,7 +325,7 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: action = batch[ACTION] # (t, b, action_dim) reward = batch[REWARD] # (t, b) - observations = {k: v for k, v in batch.items() if k.startswith("observation.")} + observations = {k: v for k, v in batch.items() if k.startswith(OBS_PREFIX)} # Apply random image augmentations. if self.config.image_features and self.config.max_random_shift_ratio > 0: @@ -397,10 +387,10 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: temporal_loss_coeffs * F.mse_loss(z_preds[1:], z_targets, reduction="none").mean(dim=-1) # `z_preds` depends on the current observation and the actions. - * ~batch["observation.state_is_pad"][0] + * ~batch[f"{OBS_STR}.state_is_pad"][0] * ~batch["action_is_pad"] # `z_targets` depends on the next observation. - * ~batch["observation.state_is_pad"][1:] + * ~batch[f"{OBS_STR}.state_is_pad"][1:] ) .sum(0) .mean() @@ -413,7 +403,7 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: * F.mse_loss(reward_preds, reward, reduction="none") * ~batch["next.reward_is_pad"] # `reward_preds` depends on the current observation and the actions. - * ~batch["observation.state_is_pad"][0] + * ~batch[f"{OBS_STR}.state_is_pad"][0] * ~batch["action_is_pad"] ) .sum(0) @@ -429,11 +419,11 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: reduction="none", ).sum(0) # sum over ensemble # `q_preds_ensemble` depends on the first observation and the actions. - * ~batch["observation.state_is_pad"][0] + * ~batch[f"{OBS_STR}.state_is_pad"][0] * ~batch["action_is_pad"] # q_targets depends on the reward and the next observations. * ~batch["next.reward_is_pad"] - * ~batch["observation.state_is_pad"][1:] + * ~batch[f"{OBS_STR}.state_is_pad"][1:] ) .sum(0) .mean() @@ -451,7 +441,7 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: temporal_loss_coeffs * raw_v_value_loss # `v_targets` depends on the first observation and the actions, as does `v_preds`. - * ~batch["observation.state_is_pad"][0] + * ~batch[f"{OBS_STR}.state_is_pad"][0] * ~batch["action_is_pad"] ) .sum(0) @@ -487,7 +477,7 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: * mse * temporal_loss_coeffs # `action_preds` depends on the first observation and the actions. - * ~batch["observation.state_is_pad"][0] + * ~batch[f"{OBS_STR}.state_is_pad"][0] * ~batch["action_is_pad"] ).mean() diff --git a/src/lerobot/policies/tdmpc/processor_tdmpc.py b/src/lerobot/policies/tdmpc/processor_tdmpc.py new file mode 100644 index 00000000000..9b6f97e5002 --- /dev/null +++ b/src/lerobot/policies/tdmpc/processor_tdmpc.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python + +# Copyright 2024 Nicklas Hansen, Xiaolong Wang, Hao Su, +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any + +import torch + +from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + RenameObservationsProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def make_tdmpc_pre_post_processors( + config: TDMPCConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the TDMPC policy. + + The pre-processing pipeline prepares input data for the model by: + 1. Renaming features to match pretrained configurations. + 2. Normalizing input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Moving all data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving data to the CPU. + 2. Unnormalizing the output features to their original scale. + + Args: + config: The configuration object for the TDMPC policy. + dataset_stats: A dictionary of statistics for normalization. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + input_steps = [ + RenameObservationsProcessorStep(rename_map={}), + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + ] + output_steps = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/vqbet/configuration_vqbet.py b/src/lerobot/policies/vqbet/configuration_vqbet.py index d7a79f1891e..44ada9f17b9 100644 --- a/src/lerobot/policies/vqbet/configuration_vqbet.py +++ b/src/lerobot/policies/vqbet/configuration_vqbet.py @@ -82,7 +82,6 @@ class VQBeTConfig(PreTrainedConfig): gpt_n_head: Number of headers of GPT gpt_hidden_dim: Size of hidden dimensions of GPT dropout: Dropout rate for GPT - mlp_hidden_dim: Size of hidden dimensions of offset header / bin prediction headers parts of VQ-BeT offset_loss_weight: A constant that is multiplied to the offset loss primary_code_loss_weight: A constant that is multiplied to the primary code prediction loss secondary_code_loss_weight: A constant that is multiplied to the secondary code prediction loss @@ -125,7 +124,6 @@ class VQBeTConfig(PreTrainedConfig): gpt_n_head: int = 8 gpt_hidden_dim: int = 512 dropout: float = 0.1 - mlp_hidden_dim: int = 1024 offset_loss_weight: float = 10000.0 primary_code_loss_weight: float = 5.0 secondary_code_loss_weight: float = 0.5 diff --git a/src/lerobot/policies/vqbet/modeling_vqbet.py b/src/lerobot/policies/vqbet/modeling_vqbet.py index feb65bb4c6a..91d60970188 100644 --- a/src/lerobot/policies/vqbet/modeling_vqbet.py +++ b/src/lerobot/policies/vqbet/modeling_vqbet.py @@ -27,12 +27,11 @@ import torchvision from torch import Tensor, nn -from lerobot.constants import ACTION, OBS_IMAGES, OBS_STATE -from lerobot.policies.normalize import Normalize, Unnormalize from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.utils import get_device_from_parameters, get_output_shape, populate_queues from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig from lerobot.policies.vqbet.vqbet_utils import GPT, ResidualVQ +from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE # ruff: noqa: N806 @@ -48,7 +47,6 @@ class VQBeTPolicy(PreTrainedPolicy): def __init__( self, config: VQBeTConfig | None = None, - dataset_stats: dict[str, dict[str, Tensor]] | None = None, ): """ Args: @@ -61,14 +59,6 @@ def __init__( config.validate_features() self.config = config - self.normalize_inputs = Normalize(config.input_features, config.normalization_mapping, dataset_stats) - self.normalize_targets = Normalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.unnormalize_outputs = Unnormalize( - config.output_features, config.normalization_mapping, dataset_stats - ) - self.vqbet = VQBeTModel(config) self.reset() @@ -128,7 +118,6 @@ def reset(self): def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor: batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues} actions = self.vqbet(batch, rollout=True)[:, : self.config.action_chunk_size] - actions = self.unnormalize_outputs({ACTION: actions})[ACTION] return actions @torch.no_grad() @@ -142,10 +131,12 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: # NOTE: for offline evaluation, we have action in the batch, so we need to pop it out if ACTION in batch: batch.pop(ACTION) - batch = self.normalize_inputs(batch) batch = dict(batch) # shallow copy so that adding a key doesn't modify the original # NOTE: It's important that this happens after stacking the images into a single key. - batch["observation.images"] = torch.stack([batch[key] for key in self.config.image_features], dim=-4) + batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4) + # NOTE: for offline evaluation, we have action in the batch, so we need to pop it out + if ACTION in batch: + batch.pop(ACTION) self._queues = populate_queues(self._queues, batch) @@ -165,10 +156,8 @@ def select_action(self, batch: dict[str, Tensor]) -> Tensor: def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]: """Run the batch through the model and compute the loss for training or validation.""" - batch = self.normalize_inputs(batch) batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4) - batch = self.normalize_targets(batch) # VQ-BeT discretizes action using VQ-VAE before training BeT (please refer to section 3.2 in the VQ-BeT paper https://huggingface.co/papers/2403.03181) if not self.vqbet.action_head.vqvae_model.discretized.item(): # loss: total loss of training RVQ @@ -351,14 +340,12 @@ def __init__(self, config: VQBeTConfig): def forward(self, batch: dict[str, Tensor], rollout: bool) -> tuple[dict, dict]: # Input validation. - assert set(batch).issuperset({"observation.state", "observation.images"}) - batch_size, n_obs_steps = batch["observation.state"].shape[:2] + assert set(batch).issuperset({OBS_STATE, OBS_IMAGES}) + batch_size, n_obs_steps = batch[OBS_STATE].shape[:2] assert n_obs_steps == self.config.n_obs_steps # Extract image feature (first combine batch and sequence dims). - img_features = self.rgb_encoder( - einops.rearrange(batch["observation.images"], "b s n ... -> (b s n) ...") - ) + img_features = self.rgb_encoder(einops.rearrange(batch[OBS_IMAGES], "b s n ... -> (b s n) ...")) # Separate batch and sequence dims. img_features = einops.rearrange( img_features, "(b s n) ... -> b s n ...", b=batch_size, s=n_obs_steps, n=self.num_images @@ -370,9 +357,7 @@ def forward(self, batch: dict[str, Tensor], rollout: bool) -> tuple[dict, dict]: img_features ) # (batch, obs_step, number of different cameras, projection dims) input_tokens = [rgb_tokens[:, :, i] for i in range(rgb_tokens.size(2))] - input_tokens.append( - self.state_projector(batch["observation.state"]) - ) # (batch, obs_step, projection dims) + input_tokens.append(self.state_projector(batch[OBS_STATE])) # (batch, obs_step, projection dims) input_tokens.append(einops.repeat(self.action_token, "1 1 d -> b n d", b=batch_size, n=n_obs_steps)) # Interleave tokens by stacking and rearranging. input_tokens = torch.stack(input_tokens, dim=2) diff --git a/src/lerobot/policies/vqbet/processor_vqbet.py b/src/lerobot/policies/vqbet/processor_vqbet.py new file mode 100644 index 00000000000..1e19ff779cb --- /dev/null +++ b/src/lerobot/policies/vqbet/processor_vqbet.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python + +# Copyright 2024 Seungjae Lee and Yibin Wang and Haritheja Etukuru +# and H. Jin Kim and Nur Muhammad Mahi Shafiullah and Lerrel Pinto +# and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any + +import torch + +from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + NormalizerProcessorStep, + PolicyAction, + PolicyProcessorPipeline, + RenameObservationsProcessorStep, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action +from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME + + +def make_vqbet_pre_post_processors( + config: VQBeTConfig, + dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None, +) -> tuple[ + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + """ + Constructs pre-processor and post-processor pipelines for the VQ-BeT policy. + + The pre-processing pipeline prepares input data for the model by: + 1. Renaming features, allowing customization to match pretrained configurations. + 2. Normalizing input and output features based on dataset statistics. + 3. Adding a batch dimension. + 4. Moving all data to the specified device. + + The post-processing pipeline handles the model's output by: + 1. Moving data to the CPU. + 2. Unnormalizing the output features to their original scale. + + Args: + config: The configuration object for the VQ-BeT policy. + dataset_stats: A dictionary of statistics for normalization. + + Returns: + A tuple containing the configured pre-processor and post-processor pipelines. + """ + + input_steps = [ + RenameObservationsProcessorStep(rename_map={}), # Let the possibility to the user to rename the keys + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device=config.device), + NormalizerProcessorStep( + features={**config.input_features, **config.output_features}, + norm_map=config.normalization_mapping, + stats=dataset_stats, + ), + ] + output_steps = [ + UnnormalizerProcessorStep( + features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats + ), + DeviceProcessorStep(device="cpu"), + ] + return ( + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]]( + steps=input_steps, + name=POLICY_PREPROCESSOR_DEFAULT_NAME, + ), + PolicyProcessorPipeline[PolicyAction, PolicyAction]( + steps=output_steps, + name=POLICY_POSTPROCESSOR_DEFAULT_NAME, + to_transition=policy_action_to_transition, + to_output=transition_to_policy_action, + ), + ) diff --git a/src/lerobot/policies/vqbet/vqbet_utils.py b/src/lerobot/policies/vqbet/vqbet_utils.py index e0afe558526..7b13577f629 100644 --- a/src/lerobot/policies/vqbet/vqbet_utils.py +++ b/src/lerobot/policies/vqbet/vqbet_utils.py @@ -231,16 +231,6 @@ def _init_weights(self, module): torch.nn.init.zeros_(module.bias) torch.nn.init.ones_(module.weight) - def crop_block_size(self, gpt_block_size): - # model surgery to decrease the block size if necessary - # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024) - # but want to use a smaller block size for some smaller, simpler model - assert gpt_block_size <= self.config.gpt_block_size - self.config.gpt_block_size = gpt_block_size - self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:gpt_block_size]) - for block in self.transformer.h: - block.attn.bias = block.attn.bias[:, :, :gpt_block_size, :gpt_block_size] - def configure_parameters(self): """ This long function is unfortunately doing something very simple and is being very defensive: @@ -270,13 +260,11 @@ def configure_parameters(self): param_dict = dict(self.named_parameters()) inter_params = decay & no_decay union_params = decay | no_decay - assert len(inter_params) == 0, "parameters {} made it into both decay/no_decay sets!".format( - str(inter_params) + assert len(inter_params) == 0, ( + f"parameters {str(inter_params)} made it into both decay/no_decay sets!" ) assert len(param_dict.keys() - union_params) == 0, ( - "parameters {} were not separated into either decay/no_decay set!".format( - str(param_dict.keys() - union_params), - ) + f"parameters {str(param_dict.keys() - union_params)} were not separated into either decay/no_decay set!" ) decay = [param_dict[pn] for pn in sorted(decay)] diff --git a/src/lerobot/processor/__init__.py b/src/lerobot/processor/__init__.py index 8dd244c27f8..be11ac1af41 100644 --- a/src/lerobot/processor/__init__.py +++ b/src/lerobot/processor/__init__.py @@ -14,41 +14,120 @@ # See the License for the specific language governing permissions and # limitations under the License. -from .device_processor import DeviceProcessor -from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor -from .observation_processor import VanillaObservationProcessor -from .pipeline import ( - ActionProcessor, - DoneProcessor, +from .batch_processor import AddBatchDimensionProcessorStep +from .converters import ( + batch_to_transition, + create_transition, + transition_to_batch, +) +from .core import ( + EnvAction, EnvTransition, - IdentityProcessor, - InfoProcessor, - ObservationProcessor, + PolicyAction, + RobotAction, + RobotObservation, + TransitionKey, +) +from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep +from .device_processor import DeviceProcessorStep +from .factory import ( + make_default_processors, + make_default_robot_action_processor, + make_default_robot_observation_processor, + make_default_teleop_action_processor, +) +from .gym_action_processor import ( + Numpy2TorchActionProcessorStep, + Torch2NumpyActionProcessorStep, +) +from .hil_processor import ( + AddTeleopActionAsComplimentaryDataStep, + AddTeleopEventsAsInfoStep, + GripperPenaltyProcessorStep, + ImageCropResizeProcessorStep, + InterventionActionProcessorStep, + RewardClassifierProcessorStep, + TimeLimitProcessorStep, +) +from .joint_observations_processor import JointVelocityProcessorStep, MotorCurrentProcessorStep +from .normalize_processor import NormalizerProcessorStep, UnnormalizerProcessorStep, hotswap_stats +from .observation_processor import VanillaObservationProcessorStep +from .pipeline import ( + ActionProcessorStep, + ComplementaryDataProcessorStep, + DataProcessorPipeline, + DoneProcessorStep, + IdentityProcessorStep, + InfoProcessorStep, + ObservationProcessorStep, + PolicyActionProcessorStep, + PolicyProcessorPipeline, + ProcessorKwargs, ProcessorStep, ProcessorStepRegistry, - RewardProcessor, - RobotProcessor, - TransitionKey, - TruncatedProcessor, + RewardProcessorStep, + RobotActionProcessorStep, + RobotProcessorPipeline, + TruncatedProcessorStep, +) +from .policy_robot_bridge import ( + PolicyActionToRobotActionProcessorStep, + RobotActionToPolicyActionProcessorStep, ) -from .rename_processor import RenameProcessor +from .rename_processor import RenameObservationsProcessorStep +from .tokenizer_processor import TokenizerProcessorStep __all__ = [ - "ActionProcessor", - "DeviceProcessor", - "DoneProcessor", + "ActionProcessorStep", + "AddTeleopActionAsComplimentaryDataStep", + "AddTeleopEventsAsInfoStep", + "ComplementaryDataProcessorStep", + "batch_to_transition", + "create_transition", + "DeviceProcessorStep", + "DoneProcessorStep", + "EnvAction", "EnvTransition", - "IdentityProcessor", - "InfoProcessor", - "NormalizerProcessor", - "UnnormalizerProcessor", - "ObservationProcessor", + "GripperPenaltyProcessorStep", + "hotswap_stats", + "IdentityProcessorStep", + "ImageCropResizeProcessorStep", + "InfoProcessorStep", + "InterventionActionProcessorStep", + "JointVelocityProcessorStep", + "make_default_processors", + "make_default_teleop_action_processor", + "make_default_robot_action_processor", + "make_default_robot_observation_processor", + "MapDeltaActionToRobotActionStep", + "MapTensorToDeltaActionDictStep", + "MotorCurrentProcessorStep", + "NormalizerProcessorStep", + "Numpy2TorchActionProcessorStep", + "ObservationProcessorStep", + "PolicyAction", + "PolicyActionProcessorStep", + "PolicyProcessorPipeline", + "ProcessorKwargs", "ProcessorStep", "ProcessorStepRegistry", - "RenameProcessor", - "RewardProcessor", - "RobotProcessor", + "RobotAction", + "RobotActionProcessorStep", + "RobotObservation", + "RenameObservationsProcessorStep", + "RewardClassifierProcessorStep", + "RewardProcessorStep", + "DataProcessorPipeline", + "TimeLimitProcessorStep", + "AddBatchDimensionProcessorStep", + "RobotProcessorPipeline", + "TokenizerProcessorStep", + "Torch2NumpyActionProcessorStep", + "RobotActionToPolicyActionProcessorStep", + "PolicyActionToRobotActionProcessorStep", + "transition_to_batch", "TransitionKey", - "TruncatedProcessor", - "VanillaObservationProcessor", + "TruncatedProcessorStep", + "UnnormalizerProcessorStep", + "VanillaObservationProcessorStep", ] diff --git a/src/lerobot/processor/batch_processor.py b/src/lerobot/processor/batch_processor.py new file mode 100644 index 00000000000..e1a90421f01 --- /dev/null +++ b/src/lerobot/processor/batch_processor.py @@ -0,0 +1,254 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script defines processor steps for adding a batch dimension to various components of an environment transition. + +These steps are designed to process actions, observations, and complementary data, making them suitable for batch processing by adding a leading dimension. This is a common requirement before feeding data into a neural network model. +""" + +from dataclasses import dataclass, field + +from torch import Tensor + +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE + +from .core import EnvTransition, PolicyAction +from .pipeline import ( + ComplementaryDataProcessorStep, + ObservationProcessorStep, + PolicyActionProcessorStep, + ProcessorStep, + ProcessorStepRegistry, + TransitionKey, +) + + +@dataclass +@ProcessorStepRegistry.register(name="to_batch_processor_action") +class AddBatchDimensionActionStep(PolicyActionProcessorStep): + """ + Processor step to add a batch dimension to a 1D tensor action. + + This is useful for creating a batch of size 1 from a single action sample. + """ + + def action(self, action: PolicyAction) -> PolicyAction: + """ + Adds a batch dimension to the action if it's a 1D tensor. + + Args: + action: The action tensor. + + Returns: + The action tensor with an added batch dimension. + """ + if action.dim() != 1: + return action + return action.unsqueeze(0) + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Returns the input features unchanged. + + Adding a batch dimension does not alter the feature definition. + + Args: + features: A dictionary of policy features. + + Returns: + The original dictionary of policy features. + """ + return features + + +@dataclass +@ProcessorStepRegistry.register(name="to_batch_processor_observation") +class AddBatchDimensionObservationStep(ObservationProcessorStep): + """ + Processor step to add a batch dimension to observations. + + It handles different types of observations: + - State vectors (1D tensors). + - Single images (3D tensors). + - Dictionaries of multiple images (3D tensors). + """ + + def observation(self, observation: dict[str, Tensor]) -> dict[str, Tensor]: + """ + Adds a batch dimension to tensor-based observations in the observation dictionary. + + Args: + observation: The observation dictionary. + + Returns: + The observation dictionary with batch dimensions added to tensors. + """ + # Process state observations - add batch dim if 1D + for state_key in [OBS_STATE, OBS_ENV_STATE]: + if state_key in observation: + state_value = observation[state_key] + if isinstance(state_value, Tensor) and state_value.dim() == 1: + observation[state_key] = state_value.unsqueeze(0) + + # Process single image observation - add batch dim if 3D + if OBS_IMAGE in observation: + image_value = observation[OBS_IMAGE] + if isinstance(image_value, Tensor) and image_value.dim() == 3: + observation[OBS_IMAGE] = image_value.unsqueeze(0) + + # Process multiple image observations - add batch dim if 3D + for key, value in observation.items(): + if key.startswith(f"{OBS_IMAGES}.") and isinstance(value, Tensor) and value.dim() == 3: + observation[key] = value.unsqueeze(0) + return observation + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Returns the input features unchanged. + + Adding a batch dimension does not alter the feature definition. + + Args: + features: A dictionary of policy features. + + Returns: + The original dictionary of policy features. + """ + return features + + +@dataclass +@ProcessorStepRegistry.register(name="to_batch_processor_complementary_data") +class AddBatchDimensionComplementaryDataStep(ComplementaryDataProcessorStep): + """ + Processor step to add a batch dimension to complementary data fields. + + Handles specific keys like 'task', 'index', and 'task_index' to make them batched. + - 'task' (str) is wrapped in a list. + - 'index' and 'task_index' (0D tensors) get a batch dimension. + """ + + def complementary_data(self, complementary_data: dict) -> dict: + """ + Adds a batch dimension to specific fields in the complementary data dictionary. + + Args: + complementary_data: The complementary data dictionary. + + Returns: + The complementary data dictionary with batch dimensions added. + """ + # Process task field - wrap string in list to add batch dimension + if "task" in complementary_data: + task_value = complementary_data["task"] + if isinstance(task_value, str): + complementary_data["task"] = [task_value] + + # Process index field - add batch dim if 0D + if "index" in complementary_data: + index_value = complementary_data["index"] + if isinstance(index_value, Tensor) and index_value.dim() == 0: + complementary_data["index"] = index_value.unsqueeze(0) + + # Process task_index field - add batch dim if 0D + if "task_index" in complementary_data: + task_index_value = complementary_data["task_index"] + if isinstance(task_index_value, Tensor) and task_index_value.dim() == 0: + complementary_data["task_index"] = task_index_value.unsqueeze(0) + return complementary_data + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Returns the input features unchanged. + + Adding a batch dimension does not alter the feature definition. + + Args: + features: A dictionary of policy features. + + Returns: + The original dictionary of policy features. + """ + return features + + +@dataclass +@ProcessorStepRegistry.register(name="to_batch_processor") +class AddBatchDimensionProcessorStep(ProcessorStep): + """ + A composite processor step that adds a batch dimension to the entire environment transition. + + This step combines individual processors for actions, observations, and complementary data + to create a batched transition (batch size 1) from a single-instance transition. + + Attributes: + to_batch_action_processor: Processor for the action component. + to_batch_observation_processor: Processor for the observation component. + to_batch_complementary_data_processor: Processor for the complementary data component. + """ + + to_batch_action_processor: AddBatchDimensionActionStep = field( + default_factory=AddBatchDimensionActionStep + ) + to_batch_observation_processor: AddBatchDimensionObservationStep = field( + default_factory=AddBatchDimensionObservationStep + ) + to_batch_complementary_data_processor: AddBatchDimensionComplementaryDataStep = field( + default_factory=AddBatchDimensionComplementaryDataStep + ) + + def __call__(self, transition: EnvTransition) -> EnvTransition: + """ + Applies the batching process to all relevant parts of an environment transition. + + Args: + transition: The environment transition to process. + + Returns: + The environment transition with a batch dimension added. + """ + if transition[TransitionKey.ACTION] is not None: + transition = self.to_batch_action_processor(transition) + if transition[TransitionKey.OBSERVATION] is not None: + transition = self.to_batch_observation_processor(transition) + if transition[TransitionKey.COMPLEMENTARY_DATA] is not None: + transition = self.to_batch_complementary_data_processor(transition) + return transition + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Returns the input features unchanged. + + Adding a batch dimension does not alter the feature definition. + + Args: + features: A dictionary of policy features. + + Returns: + The original dictionary of policy features. + """ + # NOTE: We ignore the batch dimension when transforming features + return features diff --git a/src/lerobot/processor/converters.py b/src/lerobot/processor/converters.py new file mode 100644 index 00000000000..6b0b67598db --- /dev/null +++ b/src/lerobot/processor/converters.py @@ -0,0 +1,414 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from collections.abc import Sequence +from functools import singledispatch +from typing import Any + +import numpy as np +import torch + +from lerobot.utils.constants import ACTION, DONE, OBS_PREFIX, REWARD, TRUNCATED + +from .core import EnvTransition, PolicyAction, RobotAction, RobotObservation, TransitionKey + + +@singledispatch +def to_tensor( + value: Any, + *, + dtype: torch.dtype | None = torch.float32, + device: torch.device | str | None = None, +) -> torch.Tensor: + """ + Convert various data types to PyTorch tensors with configurable options. + + This is a unified tensor conversion function using single dispatch to handle + different input types appropriately. + + Args: + value: Input value to convert (tensor, array, scalar, sequence, etc.). + dtype: Target tensor dtype. If None, preserves original dtype. + device: Target device for the tensor. + + Returns: + A PyTorch tensor. + + Raises: + TypeError: If the input type is not supported. + """ + raise TypeError(f"Unsupported type for tensor conversion: {type(value)}") + + +@to_tensor.register(torch.Tensor) +def _(value: torch.Tensor, *, dtype=torch.float32, device=None, **kwargs) -> torch.Tensor: + """Handle conversion for existing PyTorch tensors.""" + if dtype is not None: + value = value.to(dtype=dtype) + if device is not None: + value = value.to(device=device) + return value + + +@to_tensor.register(np.ndarray) +def _( + value: np.ndarray, + *, + dtype=torch.float32, + device=None, + **kwargs, +) -> torch.Tensor: + """Handle conversion for numpy arrays.""" + # Check for numpy scalars (0-dimensional arrays) and treat them as scalars. + if value.ndim == 0: + # Numpy scalars should be converted to 0-dimensional tensors. + scalar_value = value.item() + return torch.tensor(scalar_value, dtype=dtype, device=device) + + # Create tensor from numpy array. + tensor = torch.from_numpy(value) + + # Apply dtype and device conversion if specified. + if dtype is not None: + tensor = tensor.to(dtype=dtype) + if device is not None: + tensor = tensor.to(device=device) + + return tensor + + +@to_tensor.register(int) +@to_tensor.register(float) +@to_tensor.register(np.integer) +@to_tensor.register(np.floating) +def _(value, *, dtype=torch.float32, device=None, **kwargs) -> torch.Tensor: + """Handle conversion for scalar values including numpy scalars.""" + return torch.tensor(value, dtype=dtype, device=device) + + +@to_tensor.register(list) +@to_tensor.register(tuple) +def _(value: Sequence, *, dtype=torch.float32, device=None, **kwargs) -> torch.Tensor: + """Handle conversion for sequences (lists, tuples).""" + return torch.tensor(value, dtype=dtype, device=device) + + +@to_tensor.register(dict) +def _(value: dict, *, device=None, **kwargs) -> dict: + """Handle conversion for dictionaries by recursively converting their values to tensors.""" + if not value: + return {} + + result = {} + for key, sub_value in value.items(): + if sub_value is None: + continue + + if isinstance(sub_value, dict): + # Recursively process nested dictionaries. + result[key] = to_tensor( + sub_value, + device=device, + **kwargs, + ) + continue + + # Convert individual values to tensors. + result[key] = to_tensor( + sub_value, + device=device, + **kwargs, + ) + return result + + +def from_tensor_to_numpy(x: torch.Tensor | Any) -> np.ndarray | float | int | Any: + """ + Convert a PyTorch tensor to a numpy array or scalar if applicable. + + If the input is not a tensor, it is returned unchanged. + + Args: + x: The input, which can be a tensor or any other type. + + Returns: + A numpy array, a scalar, or the original input. + """ + if isinstance(x, torch.Tensor): + return x.item() if x.numel() == 1 else x.detach().cpu().numpy() + return x + + +def _extract_complementary_data(batch: dict[str, Any]) -> dict[str, Any]: + """ + Extract complementary data from a batch dictionary. + + This includes padding flags, task description, and indices. + + Args: + batch: The batch dictionary. + + Returns: + A dictionary with the extracted complementary data. + """ + pad_keys = {k: v for k, v in batch.items() if "_is_pad" in k} + task_key = {"task": batch["task"]} if "task" in batch else {} + index_key = {"index": batch["index"]} if "index" in batch else {} + task_index_key = {"task_index": batch["task_index"]} if "task_index" in batch else {} + + return {**pad_keys, **task_key, **index_key, **task_index_key} + + +def create_transition( + observation: dict[str, Any] | None = None, + action: PolicyAction | RobotAction | None = None, + reward: float = 0.0, + done: bool = False, + truncated: bool = False, + info: dict[str, Any] | None = None, + complementary_data: dict[str, Any] | None = None, +) -> EnvTransition: + """ + Create an `EnvTransition` dictionary with sensible defaults. + + Args: + observation: Observation dictionary. + action: Action dictionary. + reward: Scalar reward value. + done: Episode termination flag. + truncated: Episode truncation flag. + info: Additional info dictionary. + complementary_data: Complementary data dictionary. + + Returns: + A complete `EnvTransition` dictionary. + """ + return { + TransitionKey.OBSERVATION: observation, + TransitionKey.ACTION: action, + TransitionKey.REWARD: reward, + TransitionKey.DONE: done, + TransitionKey.TRUNCATED: truncated, + TransitionKey.INFO: info if info is not None else {}, + TransitionKey.COMPLEMENTARY_DATA: complementary_data if complementary_data is not None else {}, + } + + +def robot_action_observation_to_transition( + action_observation: tuple[RobotAction, RobotObservation], +) -> EnvTransition: + """ + Convert a raw robot action and observation dictionary into a standardized `EnvTransition`. + + Args: + action: The raw action dictionary from a teleoperation device or controller. + observation: The raw observation dictionary from the environment. + + Returns: + An `EnvTransition` containing the formatted observation. + """ + if not isinstance(action_observation, tuple): + raise ValueError("action_observation should be a tuple type with an action and observation") + + action, observation = action_observation + + if action is not None and not isinstance(action, dict): + raise ValueError(f"Action should be a RobotAction type got {type(action)}") + + if observation is not None and not isinstance(observation, dict): + raise ValueError(f"Observation should be a RobotObservation type got {type(observation)}") + + return create_transition(action=action, observation=observation) + + +def robot_action_to_transition(action: RobotAction) -> EnvTransition: + """ + Convert a raw robot action dictionary into a standardized `EnvTransition`. + + Args: + action: The raw action dictionary from a teleoperation device or controller. + + Returns: + An `EnvTransition` containing the formatted action. + """ + if not isinstance(action, dict): + raise ValueError(f"Action should be a RobotAction type got {type(action)}") + return create_transition(action=action) + + +def observation_to_transition(observation: RobotObservation) -> EnvTransition: + """ + Convert a raw robot observation dictionary into a standardized `EnvTransition`. + + Args: + observation: The raw observation dictionary from the environment. + + Returns: + An `EnvTransition` containing the formatted observation. + """ + if not isinstance(observation, dict): + raise ValueError(f"Observation should be a RobotObservation type got {type(observation)}") + return create_transition(observation=observation) + + +def transition_to_robot_action(transition: EnvTransition) -> RobotAction: + """ + Extract a raw robot action dictionary for a robot from an `EnvTransition`. + + This function searches for keys in the format "action.*.pos" or "action.*.vel" + and converts them into a flat dictionary suitable for sending to a robot controller. + + Args: + transition: The `EnvTransition` containing the action. + + Returns: + A dictionary representing the raw robot action. + """ + if not isinstance(transition, dict): + raise ValueError(f"Transition should be a EnvTransition type (dict) got {type(transition)}") + + action = transition.get(TransitionKey.ACTION) + if not isinstance(action, dict): + raise ValueError(f"Action should be a RobotAction type (dict) got {type(action)}") + return transition.get(TransitionKey.ACTION) + + +def transition_to_policy_action(transition: EnvTransition) -> PolicyAction: + """ + Convert an `EnvTransition` to a `PolicyAction`. + """ + if not isinstance(transition, dict): + raise ValueError(f"Transition should be a EnvTransition type (dict) got {type(transition)}") + + action = transition.get(TransitionKey.ACTION) + if not isinstance(action, PolicyAction): + raise ValueError(f"Action should be a PolicyAction type got {type(action)}") + return action + + +def transition_to_observation(transition: EnvTransition) -> RobotObservation: + """ + Convert an `EnvTransition` to a `RobotObservation`. + """ + if not isinstance(transition, dict): + raise ValueError(f"Transition should be a EnvTransition type (dict) got {type(transition)}") + + observation = transition.get(TransitionKey.OBSERVATION) + if not isinstance(observation, dict): + raise ValueError(f"Observation should be a RobotObservation (dict) type got {type(observation)}") + return observation + + +def policy_action_to_transition(action: PolicyAction) -> EnvTransition: + """ + Convert a `PolicyAction` to an `EnvTransition`. + """ + if not isinstance(action, PolicyAction): + raise ValueError(f"Action should be a PolicyAction type got {type(action)}") + return create_transition(action=action) + + +def batch_to_transition(batch: dict[str, Any]) -> EnvTransition: + """ + Convert a batch dictionary from a dataset/dataloader into an `EnvTransition`. + + This function maps recognized keys from a batch to the `EnvTransition` structure, + filling in missing keys with sensible defaults. + + Args: + batch: A batch dictionary. + + Returns: + An `EnvTransition` dictionary. + + Raises: + ValueError: If the input is not a dictionary. + """ + + # Validate input type. + if not isinstance(batch, dict): + raise ValueError(f"EnvTransition must be a dictionary. Got {type(batch).__name__}") + + action = batch.get(ACTION) + if action is not None and not isinstance(action, PolicyAction): + raise ValueError(f"Action should be a PolicyAction type got {type(action)}") + + # Extract observation and complementary data keys. + observation_keys = {k: v for k, v in batch.items() if k.startswith(OBS_PREFIX)} + complementary_data = _extract_complementary_data(batch) + + return create_transition( + observation=observation_keys if observation_keys else None, + action=batch.get(ACTION), + reward=batch.get(REWARD, 0.0), + done=batch.get(DONE, False), + truncated=batch.get(TRUNCATED, False), + info=batch.get("info", {}), + complementary_data=complementary_data if complementary_data else None, + ) + + +def transition_to_batch(transition: EnvTransition) -> dict[str, Any]: + """ + Convert an `EnvTransition` back to the canonical batch format used in LeRobot. + + This is the inverse of `batch_to_transition`. + + Args: + transition: The `EnvTransition` to convert. + + Returns: + A batch dictionary with canonical LeRobot field names. + """ + if not isinstance(transition, dict): + raise ValueError(f"Transition should be a EnvTransition type (dict) got {type(transition)}") + + batch = { + ACTION: transition.get(TransitionKey.ACTION), + REWARD: transition.get(TransitionKey.REWARD, 0.0), + DONE: transition.get(TransitionKey.DONE, False), + TRUNCATED: transition.get(TransitionKey.TRUNCATED, False), + "info": transition.get(TransitionKey.INFO, {}), + } + + # Add complementary data. + comp_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) + if comp_data: + batch.update(comp_data) + + # Flatten observation dictionary. + observation = transition.get(TransitionKey.OBSERVATION) + if isinstance(observation, dict): + batch.update(observation) + + return batch + + +def identity_transition(transition: EnvTransition) -> EnvTransition: + """ + An identity function for transitions, returning the input unchanged. + + Useful as a default or placeholder in processing pipelines. + + Args: + tr: An `EnvTransition`. + + Returns: + The same `EnvTransition`. + """ + return transition diff --git a/src/lerobot/processor/core.py b/src/lerobot/processor/core.py new file mode 100644 index 00000000000..679ba8c5462 --- /dev/null +++ b/src/lerobot/processor/core.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from enum import Enum +from typing import Any, TypeAlias, TypedDict + +import numpy as np +import torch + + +class TransitionKey(str, Enum): + """Keys for accessing EnvTransition dictionary components.""" + + # TODO(Steven): Use consts + OBSERVATION = "observation" + ACTION = "action" + REWARD = "reward" + DONE = "done" + TRUNCATED = "truncated" + INFO = "info" + COMPLEMENTARY_DATA = "complementary_data" + + +PolicyAction: TypeAlias = torch.Tensor +RobotAction: TypeAlias = dict[str, Any] +EnvAction: TypeAlias = np.ndarray +RobotObservation: TypeAlias = dict[str, Any] + + +EnvTransition = TypedDict( + "EnvTransition", + { + TransitionKey.OBSERVATION.value: dict[str, Any] | None, + TransitionKey.ACTION.value: PolicyAction | RobotAction | EnvAction | None, + TransitionKey.REWARD.value: float | torch.Tensor | None, + TransitionKey.DONE.value: bool | torch.Tensor | None, + TransitionKey.TRUNCATED.value: bool | torch.Tensor | None, + TransitionKey.INFO.value: dict[str, Any] | None, + TransitionKey.COMPLEMENTARY_DATA.value: dict[str, Any] | None, + }, +) diff --git a/src/lerobot/processor/delta_action_processor.py b/src/lerobot/processor/delta_action_processor.py new file mode 100644 index 00000000000..a8395637ca5 --- /dev/null +++ b/src/lerobot/processor/delta_action_processor.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature + +from .core import PolicyAction, RobotAction +from .pipeline import ActionProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep + + +@ProcessorStepRegistry.register("map_tensor_to_delta_action_dict") +@dataclass +class MapTensorToDeltaActionDictStep(ActionProcessorStep): + """ + Maps a flat action tensor from a policy to a structured delta action dictionary. + + This step is typically used after a policy outputs a continuous action vector. + It decomposes the vector into named components for delta movements of the + end-effector (x, y, z) and optionally the gripper. + + Attributes: + use_gripper: If True, assumes the 4th element of the tensor is the + gripper action. + """ + + use_gripper: bool = True + + def action(self, action: PolicyAction) -> RobotAction: + if not isinstance(action, PolicyAction): + raise ValueError("Only PolicyAction is supported for this processor") + + if action.dim() > 1: + action = action.squeeze(0) + + # TODO (maractingi): add rotation + delta_action = { + "delta_x": action[0].item(), + "delta_y": action[1].item(), + "delta_z": action[2].item(), + } + if self.use_gripper: + delta_action["gripper"] = action[3].item() + return delta_action + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + for axis in ["x", "y", "z"]: + features[PipelineFeatureType.ACTION][f"delta_{axis}"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + + if self.use_gripper: + features[PipelineFeatureType.ACTION]["gripper"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + return features + + +@ProcessorStepRegistry.register("map_delta_action_to_robot_action") +@dataclass +class MapDeltaActionToRobotActionStep(RobotActionProcessorStep): + """ + Maps delta actions from teleoperators to robot target actions for inverse kinematics. + + This step converts a dictionary of delta movements (e.g., from a gamepad) + into a target action format that includes an "enabled" flag and target + end-effector positions. It also handles scaling and noise filtering. + + Attributes: + position_scale: A factor to scale the delta position inputs. + noise_threshold: The magnitude below which delta inputs are considered noise + and do not trigger an "enabled" state. + """ + + # Scale factors for delta movements + position_scale: float = 1.0 + noise_threshold: float = 1e-3 # 1 mm threshold to filter out noise + + def action(self, action: RobotAction) -> RobotAction: + # NOTE (maractingi): Action can be a dict from the teleop_devices or a tensor from the policy + # TODO (maractingi): changing this target_xyz naming convention from the teleop_devices + delta_x = action.pop("delta_x") + delta_y = action.pop("delta_y") + delta_z = action.pop("delta_z") + gripper = action.pop("gripper") + + # Determine if the teleoperator is actively providing input + # Consider enabled if any significant movement delta is detected + position_magnitude = (delta_x**2 + delta_y**2 + delta_z**2) ** 0.5 # Use Euclidean norm for position + enabled = position_magnitude > self.noise_threshold # Small threshold to avoid noise + + # Scale the deltas appropriately + scaled_delta_x = delta_x * self.position_scale + scaled_delta_y = delta_y * self.position_scale + scaled_delta_z = delta_z * self.position_scale + + # For gamepad/keyboard, we don't have rotation input, so set to 0 + # These could be extended in the future for more sophisticated teleoperators + target_wx = 0.0 + target_wy = 0.0 + target_wz = 0.0 + + # Update action with robot target format + action = { + "enabled": enabled, + "target_x": scaled_delta_x, + "target_y": scaled_delta_y, + "target_z": scaled_delta_z, + "target_wx": target_wx, + "target_wy": target_wy, + "target_wz": target_wz, + "gripper_vel": float(gripper), + } + + return action + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + for axis in ["x", "y", "z", "gripper"]: + features[PipelineFeatureType.ACTION].pop(f"delta_{axis}", None) + + for feat in ["enabled", "target_x", "target_y", "target_z", "target_wx", "target_wy", "target_wz"]: + features[PipelineFeatureType.ACTION][f"{feat}"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + + return features diff --git a/src/lerobot/processor/device_processor.py b/src/lerobot/processor/device_processor.py index 0f00bb47028..2d0dd088066 100644 --- a/src/lerobot/processor/device_processor.py +++ b/src/lerobot/processor/device_processor.py @@ -13,70 +13,182 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +""" +This script defines a processor step for moving environment transition data to a specific torch device and casting +its floating-point precision. +""" + from dataclasses import dataclass from typing import Any import torch -from lerobot.configs.types import PolicyFeature -from lerobot.processor.pipeline import EnvTransition, TransitionKey +from lerobot.configs.types import PipelineFeatureType, PolicyFeature from lerobot.utils.utils import get_safe_torch_device +from .core import EnvTransition, PolicyAction, TransitionKey +from .pipeline import ProcessorStep, ProcessorStepRegistry + +@ProcessorStepRegistry.register("device_processor") @dataclass -class DeviceProcessor: - """Processes transitions by moving tensors to the specified device. +class DeviceProcessorStep(ProcessorStep): + """ + Processor step to move all tensors within an `EnvTransition` to a specified device and optionally cast their + floating-point data type. - This processor ensures that all tensors in the transition are moved to the - specified device (CPU or GPU) before they are returned. + This is crucial for preparing data for model training or inference on hardware like GPUs. + + Attributes: + device: The target device for tensors (e.g., "cpu", "cuda", "cuda:0"). + float_dtype: The target floating-point dtype as a string (e.g., "float32", "float16", "bfloat16"). + If None, the dtype is not changed. """ - device: torch.device = "cpu" + device: str = "cpu" + float_dtype: str | None = None + + DTYPE_MAPPING = { + "float16": torch.float16, + "float32": torch.float32, + "float64": torch.float64, + "bfloat16": torch.bfloat16, + "half": torch.float16, + "float": torch.float32, + "double": torch.float64, + } def __post_init__(self): - self.device = get_safe_torch_device(self.device) + """ + Initializes the processor by converting string configurations to torch objects. + + This method sets up the `torch.device`, determines if transfers can be non-blocking, and validates the + `float_dtype` string, converting it to a `torch.dtype` object. + """ + self.tensor_device: torch.device = get_safe_torch_device(self.device) + # Update device string in case a specific GPU was selected (e.g., "cuda" -> "cuda:0") + self.device = self.tensor_device.type self.non_blocking = "cuda" in str(self.device) + # Validate and convert float_dtype string to torch dtype + if self.float_dtype is not None: + if self.float_dtype not in self.DTYPE_MAPPING: + raise ValueError( + f"Invalid float_dtype '{self.float_dtype}'. Available options: {list(self.DTYPE_MAPPING.keys())}" + ) + self._target_float_dtype = self.DTYPE_MAPPING[self.float_dtype] + else: + self._target_float_dtype = None + + def _process_tensor(self, tensor: torch.Tensor) -> torch.Tensor: + """ + Moves a single tensor to the target device and casts its dtype. + + Handles multi-GPU scenarios by not moving a tensor if it's already on a different CUDA device than + the target, which is useful when using frameworks like Accelerate. + + Args: + tensor: The input torch.Tensor. + + Returns: + The processed tensor on the correct device and with the correct dtype. + """ + # Determine target device + if tensor.is_cuda and self.tensor_device.type == "cuda": + # Both tensor and target are on GPU - preserve tensor's GPU placement. + # This handles multi-GPU scenarios where Accelerate has already placed + # tensors on the correct GPU for each process. + target_device = tensor.device + else: + # Either tensor is on CPU, or we're configured for CPU. + # In both cases, use the configured device. + target_device = self.tensor_device + + # MPS workaround: Convert float64 to float32 since MPS doesn't support float64 + if target_device.type == "mps" and tensor.dtype == torch.float64: + tensor = tensor.to(dtype=torch.float32) + + # Only move if necessary + if tensor.device != target_device: + tensor = tensor.to(target_device, non_blocking=self.non_blocking) + + # Convert float dtype if specified and tensor is floating point + if self._target_float_dtype is not None and tensor.is_floating_point(): + tensor = tensor.to(dtype=self._target_float_dtype) + + return tensor + def __call__(self, transition: EnvTransition) -> EnvTransition: - # Create a copy of the transition - new_transition = transition.copy() + """ + Applies device and dtype conversion to all tensors in an environment transition. - # Process observation tensors - observation = transition.get(TransitionKey.OBSERVATION) - if observation is not None: - new_observation = { - k: v.to(self.device, non_blocking=self.non_blocking) if isinstance(v, torch.Tensor) else v - for k, v in observation.items() - } - new_transition[TransitionKey.OBSERVATION] = new_observation - - # Process action tensor - action = transition.get(TransitionKey.ACTION) - if action is not None and isinstance(action, torch.Tensor): - new_transition[TransitionKey.ACTION] = action.to(self.device, non_blocking=self.non_blocking) - - # Process reward tensor - reward = transition.get(TransitionKey.REWARD) - if reward is not None and isinstance(reward, torch.Tensor): - new_transition[TransitionKey.REWARD] = reward.to(self.device, non_blocking=self.non_blocking) - - # Process done tensor - done = transition.get(TransitionKey.DONE) - if done is not None and isinstance(done, torch.Tensor): - new_transition[TransitionKey.DONE] = done.to(self.device, non_blocking=self.non_blocking) - - # Process truncated tensor - truncated = transition.get(TransitionKey.TRUNCATED) - if truncated is not None and isinstance(truncated, torch.Tensor): - new_transition[TransitionKey.TRUNCATED] = truncated.to( - self.device, non_blocking=self.non_blocking - ) + It iterates through the transition, finds all `torch.Tensor` objects (including those nested in + dictionaries like `observation`), and processes them. + + Args: + transition: The input `EnvTransition` object. + + Returns: + A new `EnvTransition` object with all tensors moved to the target device and dtype. + """ + new_transition = transition.copy() + action = new_transition.get(TransitionKey.ACTION) + + if action is not None and not isinstance(action, PolicyAction): + raise ValueError(f"If action is not None should be a PolicyAction type got {type(action)}") + + simple_tensor_keys = [ + TransitionKey.ACTION, + TransitionKey.REWARD, + TransitionKey.DONE, + TransitionKey.TRUNCATED, + ] + + dict_tensor_keys = [ + TransitionKey.OBSERVATION, + TransitionKey.COMPLEMENTARY_DATA, + ] + + # Process simple, top-level tensors + for key in simple_tensor_keys: + value = transition.get(key) + if isinstance(value, torch.Tensor): + new_transition[key] = self._process_tensor(value) + + # Process tensors nested within dictionaries + for key in dict_tensor_keys: + data_dict = transition.get(key) + if data_dict is not None: + new_data_dict = { + k: self._process_tensor(v) if isinstance(v, torch.Tensor) else v + for k, v in data_dict.items() + } + new_transition[key] = new_data_dict return new_transition def get_config(self) -> dict[str, Any]: - """Return configuration for serialization.""" - return {"device": self.device} + """ + Returns the serializable configuration of the processor. + + Returns: + A dictionary containing the device and float_dtype settings. + """ + return {"device": self.device, "float_dtype": self.float_dtype} + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Returns the input features unchanged. + + Device and dtype transformations do not alter the fundamental definition of the features (e.g., shape). + + Args: + features: A dictionary of policy features. - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + Returns: + The original dictionary of policy features. + """ return features diff --git a/src/lerobot/processor/factory.py b/src/lerobot/processor/factory.py new file mode 100644 index 00000000000..5a0c4107260 --- /dev/null +++ b/src/lerobot/processor/factory.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .converters import ( + observation_to_transition, + robot_action_observation_to_transition, + transition_to_observation, + transition_to_robot_action, +) +from .core import RobotAction, RobotObservation +from .pipeline import IdentityProcessorStep, RobotProcessorPipeline + + +def make_default_teleop_action_processor() -> RobotProcessorPipeline[ + tuple[RobotAction, RobotObservation], RobotAction +]: + teleop_action_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[IdentityProcessorStep()], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, + ) + return teleop_action_processor + + +def make_default_robot_action_processor() -> RobotProcessorPipeline[ + tuple[RobotAction, RobotObservation], RobotAction +]: + robot_action_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]( + steps=[IdentityProcessorStep()], + to_transition=robot_action_observation_to_transition, + to_output=transition_to_robot_action, + ) + return robot_action_processor + + +def make_default_robot_observation_processor() -> RobotProcessorPipeline[RobotObservation, RobotObservation]: + robot_observation_processor = RobotProcessorPipeline[RobotObservation, RobotObservation]( + steps=[IdentityProcessorStep()], + to_transition=observation_to_transition, + to_output=transition_to_observation, + ) + return robot_observation_processor + + +def make_default_processors(): + teleop_action_processor = make_default_teleop_action_processor() + robot_action_processor = make_default_robot_action_processor() + robot_observation_processor = make_default_robot_observation_processor() + return (teleop_action_processor, robot_action_processor, robot_observation_processor) diff --git a/src/lerobot/processor/gym_action_processor.py b/src/lerobot/processor/gym_action_processor.py new file mode 100644 index 00000000000..8fa8cfd869d --- /dev/null +++ b/src/lerobot/processor/gym_action_processor.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from lerobot.configs.types import PipelineFeatureType, PolicyFeature + +from .converters import to_tensor +from .core import EnvAction, EnvTransition, PolicyAction +from .pipeline import ActionProcessorStep, ProcessorStep, ProcessorStepRegistry + + +@ProcessorStepRegistry.register("torch2numpy_action_processor") +@dataclass +class Torch2NumpyActionProcessorStep(ActionProcessorStep): + """ + Converts a PyTorch tensor action to a NumPy array. + + This step is useful when the output of a policy (typically a torch.Tensor) + needs to be passed to an environment or component that expects a NumPy array. + + Attributes: + squeeze_batch_dim: If True, removes the first dimension of the array + if it is of size 1. This is useful for converting a + batched action of size (1, D) to a single action of size (D,). + """ + + squeeze_batch_dim: bool = True + + def action(self, action: PolicyAction) -> EnvAction: + if not isinstance(action, PolicyAction): + raise TypeError( + f"Expected PolicyAction or None, got {type(action).__name__}. " + "Use appropriate processor for non-tensor actions." + ) + + numpy_action = action.detach().cpu().numpy() + + # Remove batch dimensions but preserve action dimensions. + # Only squeeze if there's a batch dimension (first dim == 1). + if ( + self.squeeze_batch_dim + and numpy_action.shape + and len(numpy_action.shape) > 1 + and numpy_action.shape[0] == 1 + ): + numpy_action = numpy_action.squeeze(0) + + return numpy_action + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features + + +@ProcessorStepRegistry.register("numpy2torch_action_processor") +@dataclass +class Numpy2TorchActionProcessorStep(ProcessorStep): + """Converts a NumPy array action to a PyTorch tensor when action is present.""" + + def __call__(self, transition: EnvTransition) -> EnvTransition: + """Converts numpy action to torch tensor if action exists, otherwise passes through.""" + from .core import TransitionKey + + self._current_transition = transition.copy() + new_transition = self._current_transition + + action = new_transition.get(TransitionKey.ACTION) + if action is not None: + if not isinstance(action, EnvAction): + raise TypeError( + f"Expected np.ndarray or None, got {type(action).__name__}. " + "Use appropriate processor for non-tensor actions." + ) + torch_action = to_tensor(action, dtype=None) # Preserve original dtype + new_transition[TransitionKey.ACTION] = torch_action + + return new_transition + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features diff --git a/src/lerobot/processor/hil_processor.py b/src/lerobot/processor/hil_processor.py new file mode 100644 index 00000000000..f0dbac9c3c8 --- /dev/null +++ b/src/lerobot/processor/hil_processor.py @@ -0,0 +1,596 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import time +from dataclasses import dataclass +from typing import Any, Protocol, TypeVar, runtime_checkable + +import numpy as np +import torch +import torchvision.transforms.functional as F # noqa: N812 + +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.teleoperators.teleoperator import Teleoperator +from lerobot.teleoperators.utils import TeleopEvents + +from .core import EnvTransition, PolicyAction, TransitionKey +from .pipeline import ( + ComplementaryDataProcessorStep, + InfoProcessorStep, + ObservationProcessorStep, + ProcessorStep, + ProcessorStepRegistry, + TruncatedProcessorStep, +) + +GRIPPER_KEY = "gripper" +DISCRETE_PENALTY_KEY = "discrete_penalty" +TELEOP_ACTION_KEY = "teleop_action" + + +@runtime_checkable +class HasTeleopEvents(Protocol): + """ + Minimal protocol for objects that provide teleoperation events. + + This protocol defines the `get_teleop_events()` method, allowing processor + steps to interact with teleoperators that support event-based controls + (like episode termination or success flagging) without needing to know the + teleoperator's specific class. + """ + + def get_teleop_events(self) -> dict[str, Any]: + """ + Get extra control events from the teleoperator. + + Returns: + A dictionary containing control events such as: + - `is_intervention`: bool - Whether the human is currently intervening. + - `terminate_episode`: bool - Whether to terminate the current episode. + - `success`: bool - Whether the episode was successful. + - `rerecord_episode`: bool - Whether to rerecord the episode. + """ + ... + + +# Type variable constrained to Teleoperator subclasses that also implement events +TeleopWithEvents = TypeVar("TeleopWithEvents", bound=Teleoperator) + + +def _check_teleop_with_events(teleop: Teleoperator) -> None: + """ + Runtime check that a teleoperator implements the `HasTeleopEvents` protocol. + + Args: + teleop: The teleoperator instance to check. + + Raises: + TypeError: If the teleoperator does not have a `get_teleop_events` method. + """ + if not isinstance(teleop, HasTeleopEvents): + raise TypeError( + f"Teleoperator {type(teleop).__name__} must implement get_teleop_events() method. " + f"Compatible teleoperators: GamepadTeleop, KeyboardEndEffectorTeleop" + ) + + +@ProcessorStepRegistry.register("add_teleop_action_as_complementary_data") +@dataclass +class AddTeleopActionAsComplimentaryDataStep(ComplementaryDataProcessorStep): + """ + Adds the raw action from a teleoperator to the transition's complementary data. + + This is useful for human-in-the-loop scenarios where the human's input needs to + be available to downstream processors, for example, to override a policy's action + during an intervention. + + Attributes: + teleop_device: The teleoperator instance to get the action from. + """ + + teleop_device: Teleoperator + + def complementary_data(self, complementary_data: dict) -> dict: + """ + Retrieves the teleoperator's action and adds it to the complementary data. + + Args: + complementary_data: The incoming complementary data dictionary. + + Returns: + A new dictionary with the teleoperator action added under the + `teleop_action` key. + """ + new_complementary_data = dict(complementary_data) + new_complementary_data[TELEOP_ACTION_KEY] = self.teleop_device.get_action() + return new_complementary_data + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features + + +@ProcessorStepRegistry.register("add_teleop_action_as_info") +@dataclass +class AddTeleopEventsAsInfoStep(InfoProcessorStep): + """ + Adds teleoperator control events (e.g., terminate, success) to the transition's info. + + This step extracts control events from teleoperators that support event-based + interaction, making these signals available to other parts of the system. + + Attributes: + teleop_device: An instance of a teleoperator that implements the + `HasTeleopEvents` protocol. + """ + + teleop_device: TeleopWithEvents + + def __post_init__(self): + """Validates that the provided teleoperator supports events after initialization.""" + _check_teleop_with_events(self.teleop_device) + + def info(self, info: dict) -> dict: + """ + Retrieves teleoperator events and updates the info dictionary. + + Args: + info: The incoming info dictionary. + + Returns: + A new dictionary including the teleoperator events. + """ + new_info = dict(info) + + teleop_events = self.teleop_device.get_teleop_events() + new_info.update(teleop_events) + return new_info + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features + + +@ProcessorStepRegistry.register("image_crop_resize_processor") +@dataclass +class ImageCropResizeProcessorStep(ObservationProcessorStep): + """ + Crops and/or resizes image observations. + + This step iterates through all image keys in an observation dictionary and applies + the specified transformations. It handles device placement, moving tensors to the + CPU if necessary for operations not supported on certain accelerators like MPS. + + Attributes: + crop_params_dict: A dictionary mapping image keys to cropping parameters + (top, left, height, width). + resize_size: A tuple (height, width) to resize all images to. + """ + + crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None + resize_size: tuple[int, int] | None = None + + def observation(self, observation: dict) -> dict: + """ + Applies cropping and resizing to all images in the observation dictionary. + + Args: + observation: The observation dictionary, potentially containing image tensors. + + Returns: + A new observation dictionary with transformed images. + """ + if self.resize_size is None and not self.crop_params_dict: + return observation + + new_observation = dict(observation) + + # Process all image keys in the observation + for key in observation: + if "image" not in key: + continue + + image = observation[key] + device = image.device + # NOTE (maractingi): No mps kernel for crop and resize, so we need to move to cpu + if device.type == "mps": + image = image.cpu() + # Crop if crop params are provided for this key + if self.crop_params_dict is not None and key in self.crop_params_dict: + crop_params = self.crop_params_dict[key] + image = F.crop(image, *crop_params) + if self.resize_size is not None: + image = F.resize(image, self.resize_size) + image = image.clamp(0.0, 1.0) + new_observation[key] = image.to(device) + + return new_observation + + def get_config(self) -> dict[str, Any]: + """ + Returns the configuration of the step for serialization. + + Returns: + A dictionary with the crop parameters and resize dimensions. + """ + return { + "crop_params_dict": self.crop_params_dict, + "resize_size": self.resize_size, + } + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Updates the image feature shapes in the policy features dictionary if resizing is applied. + + Args: + features: The policy features dictionary. + + Returns: + The updated policy features dictionary with new image shapes. + """ + if self.resize_size is None: + return features + for key in features[PipelineFeatureType.OBSERVATION]: + if "image" in key: + nb_channel = features[PipelineFeatureType.OBSERVATION][key].shape[0] + features[PipelineFeatureType.OBSERVATION][key] = PolicyFeature( + type=features[PipelineFeatureType.OBSERVATION][key].type, + shape=(nb_channel, *self.resize_size), + ) + return features + + +@dataclass +@ProcessorStepRegistry.register("time_limit_processor") +class TimeLimitProcessorStep(TruncatedProcessorStep): + """ + Tracks episode steps and enforces a time limit by truncating the episode. + + Attributes: + max_episode_steps: The maximum number of steps allowed per episode. + current_step: The current step count for the active episode. + """ + + max_episode_steps: int + current_step: int = 0 + + def truncated(self, truncated: bool) -> bool: + """ + Increments the step counter and sets the truncated flag if the time limit is reached. + + Args: + truncated: The incoming truncated flag. + + Returns: + True if the episode step limit is reached, otherwise the incoming value. + """ + self.current_step += 1 + if self.current_step >= self.max_episode_steps: + truncated = True + # TODO (steven): missing an else truncated = False? + return truncated + + def get_config(self) -> dict[str, Any]: + """ + Returns the configuration of the step for serialization. + + Returns: + A dictionary containing the `max_episode_steps`. + """ + return { + "max_episode_steps": self.max_episode_steps, + } + + def reset(self) -> None: + """Resets the step counter, typically called at the start of a new episode.""" + self.current_step = 0 + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features + + +@dataclass +@ProcessorStepRegistry.register("gripper_penalty_processor") +class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep): + """ + Applies a penalty for inefficient gripper usage. + + This step penalizes actions that attempt to close an already closed gripper or + open an already open one, based on position thresholds. + + Attributes: + penalty: The negative reward value to apply. + max_gripper_pos: The maximum position value for the gripper, used for normalization. + """ + + penalty: float = -0.01 + max_gripper_pos: float = 30.0 + + def complementary_data(self, complementary_data: dict) -> dict: + """ + Calculates the gripper penalty and adds it to the complementary data. + + Args: + complementary_data: The incoming complementary data, which should contain + raw joint positions. + + Returns: + A new complementary data dictionary with the `discrete_penalty` key added. + """ + action = self.transition.get(TransitionKey.ACTION) + + raw_joint_positions = complementary_data.get("raw_joint_positions") + if raw_joint_positions is None: + return complementary_data + + current_gripper_pos = raw_joint_positions.get(GRIPPER_KEY, None) + if current_gripper_pos is None: + return complementary_data + + # Gripper action is a PolicyAction at this stage + gripper_action = action[-1].item() + gripper_action_normalized = gripper_action / self.max_gripper_pos + + # Normalize gripper state and action + gripper_state_normalized = current_gripper_pos / self.max_gripper_pos + + # Calculate penalty boolean as in original + gripper_penalty_bool = (gripper_state_normalized < 0.5 and gripper_action_normalized > 0.5) or ( + gripper_state_normalized > 0.75 and gripper_action_normalized < 0.5 + ) + + gripper_penalty = self.penalty * int(gripper_penalty_bool) + + # Create new complementary data with penalty info + new_complementary_data = dict(complementary_data) + new_complementary_data[DISCRETE_PENALTY_KEY] = gripper_penalty + + return new_complementary_data + + def get_config(self) -> dict[str, Any]: + """ + Returns the configuration of the step for serialization. + + Returns: + A dictionary containing the penalty value and max gripper position. + """ + return { + "penalty": self.penalty, + "max_gripper_pos": self.max_gripper_pos, + } + + def reset(self) -> None: + """Resets the processor's internal state.""" + pass + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features + + +@dataclass +@ProcessorStepRegistry.register("intervention_action_processor") +class InterventionActionProcessorStep(ProcessorStep): + """ + Handles human intervention, overriding policy actions and managing episode termination. + + When an intervention is detected (via teleoperator events in the `info` dict), + this step replaces the policy's action with the human's teleoperated action. + It also processes signals to terminate the episode or flag success. + + Attributes: + use_gripper: Whether to include the gripper in the teleoperated action. + terminate_on_success: If True, automatically sets the `done` flag when a + `success` event is received. + """ + + use_gripper: bool = False + terminate_on_success: bool = True + + def __call__(self, transition: EnvTransition) -> EnvTransition: + """ + Processes the transition to handle interventions. + + Args: + transition: The incoming environment transition. + + Returns: + The modified transition, potentially with an overridden action, updated + reward, and termination status. + """ + action = transition.get(TransitionKey.ACTION) + if not isinstance(action, PolicyAction): + raise ValueError(f"Action should be a PolicyAction type got {type(action)}") + + # Get intervention signals from complementary data + info = transition.get(TransitionKey.INFO, {}) + complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) + teleop_action = complementary_data.get(TELEOP_ACTION_KEY, {}) + is_intervention = info.get(TeleopEvents.IS_INTERVENTION, False) + terminate_episode = info.get(TeleopEvents.TERMINATE_EPISODE, False) + success = info.get(TeleopEvents.SUCCESS, False) + rerecord_episode = info.get(TeleopEvents.RERECORD_EPISODE, False) + + new_transition = transition.copy() + + # Override action if intervention is active + if is_intervention and teleop_action is not None: + if isinstance(teleop_action, dict): + # Convert teleop_action dict to tensor format + action_list = [ + teleop_action.get("delta_x", 0.0), + teleop_action.get("delta_y", 0.0), + teleop_action.get("delta_z", 0.0), + ] + if self.use_gripper: + action_list.append(teleop_action.get(GRIPPER_KEY, 1.0)) + elif isinstance(teleop_action, np.ndarray): + action_list = teleop_action.tolist() + else: + action_list = teleop_action + + teleop_action_tensor = torch.tensor(action_list, dtype=action.dtype, device=action.device) + new_transition[TransitionKey.ACTION] = teleop_action_tensor + + # Handle episode termination + new_transition[TransitionKey.DONE] = bool(terminate_episode) or ( + self.terminate_on_success and success + ) + new_transition[TransitionKey.REWARD] = float(success) + + # Update info with intervention metadata + info = new_transition.get(TransitionKey.INFO, {}) + info[TeleopEvents.IS_INTERVENTION] = is_intervention + info[TeleopEvents.RERECORD_EPISODE] = rerecord_episode + info[TeleopEvents.SUCCESS] = success + new_transition[TransitionKey.INFO] = info + + # Update complementary data with teleop action + complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) + complementary_data[TELEOP_ACTION_KEY] = new_transition.get(TransitionKey.ACTION) + new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data + + return new_transition + + def get_config(self) -> dict[str, Any]: + """ + Returns the configuration of the step for serialization. + + Returns: + A dictionary containing the step's configuration attributes. + """ + return { + "use_gripper": self.use_gripper, + "terminate_on_success": self.terminate_on_success, + } + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features + + +@dataclass +@ProcessorStepRegistry.register("reward_classifier_processor") +class RewardClassifierProcessorStep(ProcessorStep): + """ + Applies a pretrained reward classifier to image observations to predict success. + + This step uses a model to determine if the current state is successful, updating + the reward and potentially terminating the episode. + + Attributes: + pretrained_path: Path to the pretrained reward classifier model. + device: The device to run the classifier on. + success_threshold: The probability threshold to consider a prediction as successful. + success_reward: The reward value to assign on success. + terminate_on_success: If True, terminates the episode upon successful classification. + reward_classifier: The loaded classifier model instance. + """ + + pretrained_path: str | None = None + device: str = "cpu" + success_threshold: float = 0.5 + success_reward: float = 1.0 + terminate_on_success: bool = True + + reward_classifier: Any = None + + def __post_init__(self): + """Initializes the reward classifier model after the dataclass is created.""" + if self.pretrained_path is not None: + from lerobot.policies.sac.reward_model.modeling_classifier import Classifier + + self.reward_classifier = Classifier.from_pretrained(self.pretrained_path) + self.reward_classifier.to(self.device) + self.reward_classifier.eval() + + def __call__(self, transition: EnvTransition) -> EnvTransition: + """ + Processes a transition, applying the reward classifier to its image observations. + + Args: + transition: The incoming environment transition. + + Returns: + The modified transition with an updated reward and done flag based on the + classifier's prediction. + """ + new_transition = transition.copy() + observation = new_transition.get(TransitionKey.OBSERVATION) + if observation is None or self.reward_classifier is None: + return new_transition + + # Extract images from observation + images = {key: value for key, value in observation.items() if "image" in key} + + if not images: + return new_transition + + # Run reward classifier + start_time = time.perf_counter() + with torch.inference_mode(): + success = self.reward_classifier.predict_reward(images, threshold=self.success_threshold) + + classifier_frequency = 1 / (time.perf_counter() - start_time) + + # Calculate reward and termination + reward = new_transition.get(TransitionKey.REWARD, 0.0) + terminated = new_transition.get(TransitionKey.DONE, False) + + if math.isclose(success, 1, abs_tol=1e-2): + reward = self.success_reward + if self.terminate_on_success: + terminated = True + + # Update transition + new_transition[TransitionKey.REWARD] = reward + new_transition[TransitionKey.DONE] = terminated + + # Update info with classifier frequency + info = new_transition.get(TransitionKey.INFO, {}) + info["reward_classifier_frequency"] = classifier_frequency + new_transition[TransitionKey.INFO] = info + + return new_transition + + def get_config(self) -> dict[str, Any]: + """ + Returns the configuration of the step for serialization. + + Returns: + A dictionary containing the step's configuration attributes. + """ + return { + "device": self.device, + "success_threshold": self.success_threshold, + "success_reward": self.success_reward, + "terminate_on_success": self.terminate_on_success, + } + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features diff --git a/src/lerobot/processor/joint_observations_processor.py b/src/lerobot/processor/joint_observations_processor.py new file mode 100644 index 00000000000..2fbcc7c4682 --- /dev/null +++ b/src/lerobot/processor/joint_observations_processor.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Any + +import torch + +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.processor.pipeline import ( + ObservationProcessorStep, + ProcessorStepRegistry, +) +from lerobot.robots import Robot +from lerobot.utils.constants import OBS_STATE + + +@dataclass +@ProcessorStepRegistry.register("joint_velocity_processor") +class JointVelocityProcessorStep(ObservationProcessorStep): + """ + Calculates and appends joint velocity information to the observation state. + + This step computes the velocity of each joint by calculating the finite + difference between the current and the last observed joint positions. The + resulting velocity vector is then concatenated to the original state vector. + + Attributes: + dt: The time step (delta time) in seconds between observations, used for + calculating velocity. + last_joint_positions: Stores the joint positions from the previous step + to enable velocity calculation. + """ + + dt: float = 0.1 + + last_joint_positions: torch.Tensor | None = None + + def observation(self, observation: dict) -> dict: + """ + Computes joint velocities and adds them to the observation state. + + Args: + observation: The input observation dictionary, expected to contain + an `observation.state` key with joint positions. + + Returns: + A new observation dictionary with the `observation.state` tensor + extended to include joint velocities. + + Raises: + ValueError: If `observation.state` is not found in the observation. + """ + # Get current joint positions (assuming they're in observation.state) + current_positions = observation.get(OBS_STATE) + if current_positions is None: + raise ValueError(f"{OBS_STATE} is not in observation") + + # Initialize last joint positions if not already set + if self.last_joint_positions is None: + self.last_joint_positions = current_positions.clone() + joint_velocities = torch.zeros_like(current_positions) + else: + # Compute velocities + joint_velocities = (current_positions - self.last_joint_positions) / self.dt + + self.last_joint_positions = current_positions.clone() + + # Extend observation with velocities + extended_state = torch.cat([current_positions, joint_velocities], dim=-1) + + # Create new observation dict + new_observation = dict(observation) + new_observation[OBS_STATE] = extended_state + + return new_observation + + def get_config(self) -> dict[str, Any]: + """ + Returns the configuration of the step for serialization. + + Returns: + A dictionary containing the time step `dt`. + """ + return { + "dt": self.dt, + } + + def reset(self) -> None: + """Resets the internal state, clearing the last known joint positions.""" + self.last_joint_positions = None + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Updates the `observation.state` feature to reflect the added velocities. + + This method doubles the size of the first dimension of the `observation.state` + shape to account for the concatenation of position and velocity vectors. + + Args: + features: The policy features dictionary. + + Returns: + The updated policy features dictionary. + """ + if OBS_STATE in features[PipelineFeatureType.OBSERVATION]: + original_feature = features[PipelineFeatureType.OBSERVATION][OBS_STATE] + # Double the shape to account for positions + velocities + new_shape = (original_feature.shape[0] * 2,) + original_feature.shape[1:] + + features[PipelineFeatureType.OBSERVATION][OBS_STATE] = PolicyFeature( + type=original_feature.type, shape=new_shape + ) + return features + + +@dataclass +@ProcessorStepRegistry.register("current_processor") +class MotorCurrentProcessorStep(ObservationProcessorStep): + """ + Reads motor currents from a robot and appends them to the observation state. + + This step queries the robot's hardware interface to get the present current + for each motor and concatenates this information to the existing state vector. + + Attributes: + robot: An instance of a `lerobot` Robot class that provides access to + the hardware bus. + """ + + robot: Robot | None = None + + def observation(self, observation: dict) -> dict: + """ + Fetches motor currents and adds them to the observation state. + + Args: + observation: The input observation dictionary. + + Returns: + A new observation dictionary with the `observation.state` tensor + extended to include motor currents. + + Raises: + ValueError: If the `robot` attribute has not been set. + """ + # Get current values from robot state + if self.robot is None: + raise ValueError("Robot is not set") + + present_current_dict = self.robot.bus.sync_read("Present_Current") # type: ignore[attr-defined] + motor_currents = torch.tensor( + [present_current_dict[name] for name in self.robot.bus.motors], # type: ignore[attr-defined] + dtype=torch.float32, + ).unsqueeze(0) + + current_state = observation.get(OBS_STATE) + if current_state is None: + return observation + + extended_state = torch.cat([current_state, motor_currents], dim=-1) + + # Create new observation dict + new_observation = dict(observation) + new_observation[OBS_STATE] = extended_state + + return new_observation + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Updates the `observation.state` feature to reflect the added motor currents. + + This method increases the size of the first dimension of the `observation.state` + shape by the number of motors in the robot. + + Args: + features: The policy features dictionary. + + Returns: + The updated policy features dictionary. + """ + if OBS_STATE in features[PipelineFeatureType.OBSERVATION] and self.robot is not None: + original_feature = features[PipelineFeatureType.OBSERVATION][OBS_STATE] + # Add motor current dimensions to the original state shape + num_motors = 0 + if hasattr(self.robot, "bus") and hasattr(self.robot.bus, "motors"): # type: ignore[attr-defined] + num_motors = len(self.robot.bus.motors) # type: ignore[attr-defined] + + if num_motors > 0: + new_shape = (original_feature.shape[0] + num_motors,) + original_feature.shape[1:] + features[PipelineFeatureType.OBSERVATION][OBS_STATE] = PolicyFeature( + type=original_feature.type, shape=new_shape + ) + return features diff --git a/src/lerobot/processor/migrate_policy_normalization.py b/src/lerobot/processor/migrate_policy_normalization.py new file mode 100644 index 00000000000..525b7431c94 --- /dev/null +++ b/src/lerobot/processor/migrate_policy_normalization.py @@ -0,0 +1,769 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +A generic script to migrate LeRobot policies with built-in normalization layers to the new +pipeline-based processor system. + +This script performs the following steps: +1. Loads a pretrained policy model and its configuration from a local path or the + Hugging Face Hub. +2. Scans the model's state dictionary to extract normalization statistics (e.g., mean, + std, min, max) for all features. +3. Creates two new processor pipelines: + - A preprocessor that normalizes inputs (observations) and outputs (actions). + - A postprocessor that unnormalizes outputs (actions) for inference. +4. Removes the original normalization layers from the model's state dictionary, + creating a "clean" model. +5. Saves the new clean model, the preprocessor, the postprocessor, and a generated + model card to a new directory. +6. Optionally pushes all the new artifacts to the Hugging Face Hub. + +Usage: + python src/lerobot/processor/migrate_policy_normalization.py \ + --pretrained-path lerobot/act_aloha_sim_transfer_cube_human \ + --push-to-hub \ + --branch main + +Note: This script now uses the modern `make_pre_post_processors` and `make_policy_config` +factory functions from `lerobot.policies.factory` to create processors and configurations, +ensuring consistency with the current codebase. + +The script extracts normalization statistics from the old model's state_dict, creates clean +processor pipelines using the factory functions, and saves a migrated model that is compatible +with the new PolicyProcessorPipeline architecture. +""" + +import argparse +import json +import os +from pathlib import Path +from typing import Any + +import torch +from huggingface_hub import HfApi, hf_hub_download +from safetensors.torch import load_file as load_safetensors + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.policies.factory import get_policy_class, make_policy_config, make_pre_post_processors +from lerobot.utils.constants import ACTION + + +def extract_normalization_stats(state_dict: dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]: + """ + Scans a model's state_dict to find and extract normalization statistics. + + This function identifies keys corresponding to normalization layers (e.g., those + for mean, std, min, max) based on a set of predefined patterns and organizes + them into a nested dictionary. + + Args: + state_dict: The state dictionary of a pretrained policy model. + + Returns: + A nested dictionary where outer keys are feature names (e.g., + 'observation.state') and inner keys are statistic types ('mean', 'std'), + mapping to their corresponding tensor values. + """ + stats = {} + + # Define patterns to match and their prefixes to remove + normalization_patterns = [ + "normalize_inputs.buffer_", + "unnormalize_outputs.buffer_", + "normalize_targets.buffer_", + "normalize.", # Must come after normalize_* patterns + "unnormalize.", # Must come after unnormalize_* patterns + "input_normalizer.", + "output_normalizer.", + "normalalize_inputs.", + "unnormalize_outputs.", + "normalize_targets.", + "unnormalize_targets.", + ] + + # Process each key in state_dict + for key, tensor in state_dict.items(): + # Try each pattern + for pattern in normalization_patterns: + if key.startswith(pattern): + # Extract the remaining part after the pattern + remaining = key[len(pattern) :] + parts = remaining.split(".") + + # Need at least feature name and stat type + if len(parts) >= 2: + # Last part is the stat type (mean, std, min, max, etc.) + stat_type = parts[-1] + # Everything else is the feature name + feature_name = ".".join(parts[:-1]).replace("_", ".") + + # Add to stats + if feature_name not in stats: + stats[feature_name] = {} + stats[feature_name][stat_type] = tensor.clone() + + # Only process the first matching pattern + break + + return stats + + +def detect_features_and_norm_modes( + config: dict[str, Any], stats: dict[str, dict[str, torch.Tensor]] +) -> tuple[dict[str, PolicyFeature], dict[FeatureType, NormalizationMode]]: + """ + Infers policy features and normalization modes from the model config and stats. + + This function first attempts to find feature definitions and normalization + mappings directly from the policy's configuration file. If this information is + not present, it infers it from the extracted normalization statistics, using + tensor shapes to determine feature shapes and the presence of specific stat + keys (e.g., 'mean'/'std' vs 'min'/'max') to determine the normalization mode. + It applies sensible defaults if inference is not possible. + + Args: + config: The policy's configuration dictionary from `config.json`. + stats: The normalization statistics extracted from the model's state_dict. + + Returns: + A tuple containing: + - A dictionary mapping feature names to `PolicyFeature` objects. + - A dictionary mapping `FeatureType` enums to `NormalizationMode` enums. + """ + features = {} + norm_modes = {} + + # First, check if there's a normalization_mapping in the config + if "normalization_mapping" in config: + print(f"Found normalization_mapping in config: {config['normalization_mapping']}") + # Extract normalization modes from config + for feature_type_str, mode_str in config["normalization_mapping"].items(): + # Convert string to FeatureType enum + try: + if feature_type_str == "VISUAL": + feature_type = FeatureType.VISUAL + elif feature_type_str == "STATE": + feature_type = FeatureType.STATE + elif feature_type_str == "ACTION": + feature_type = FeatureType.ACTION + else: + print(f"Warning: Unknown feature type '{feature_type_str}', skipping") + continue + except (AttributeError, ValueError): + print(f"Warning: Could not parse feature type '{feature_type_str}', skipping") + continue + + # Convert string to NormalizationMode enum + try: + if mode_str == "MEAN_STD": + mode = NormalizationMode.MEAN_STD + elif mode_str == "MIN_MAX": + mode = NormalizationMode.MIN_MAX + elif mode_str == "IDENTITY": + mode = NormalizationMode.IDENTITY + else: + print( + f"Warning: Unknown normalization mode '{mode_str}' for feature type '{feature_type_str}'" + ) + continue + except (AttributeError, ValueError): + print(f"Warning: Could not parse normalization mode '{mode_str}', skipping") + continue + + norm_modes[feature_type] = mode + + # Try to extract from config + if "features" in config: + for key, feature_config in config["features"].items(): + shape = feature_config.get("shape", feature_config.get("dim")) + shape = (shape,) if isinstance(shape, int) else tuple(shape) + + # Determine feature type + if "image" in key or "visual" in key: + feature_type = FeatureType.VISUAL + elif "state" in key: + feature_type = FeatureType.STATE + elif ACTION in key: + feature_type = FeatureType.ACTION + else: + feature_type = FeatureType.STATE # Default + + features[key] = PolicyFeature(feature_type, shape) + + # If no features in config, infer from stats + if not features: + for key, stat_dict in stats.items(): + # Get shape from any stat tensor + tensor = next(iter(stat_dict.values())) + shape = tuple(tensor.shape) + + # Determine feature type based on key + if "image" in key or "visual" in key or "pixels" in key: + feature_type = FeatureType.VISUAL + elif "state" in key or "joint" in key or "position" in key: + feature_type = FeatureType.STATE + elif ACTION in key: + feature_type = FeatureType.ACTION + else: + feature_type = FeatureType.STATE + + features[key] = PolicyFeature(feature_type, shape) + + # If normalization modes weren't in config, determine based on available stats + if not norm_modes: + for key, stat_dict in stats.items(): + if key in features: + if "mean" in stat_dict and "std" in stat_dict: + feature_type = features[key].type + if feature_type not in norm_modes: + norm_modes[feature_type] = NormalizationMode.MEAN_STD + elif "min" in stat_dict and "max" in stat_dict: + feature_type = features[key].type + if feature_type not in norm_modes: + norm_modes[feature_type] = NormalizationMode.MIN_MAX + + # Default normalization modes if not detected + if FeatureType.VISUAL not in norm_modes: + norm_modes[FeatureType.VISUAL] = NormalizationMode.MEAN_STD + if FeatureType.STATE not in norm_modes: + norm_modes[FeatureType.STATE] = NormalizationMode.MIN_MAX + if FeatureType.ACTION not in norm_modes: + norm_modes[FeatureType.ACTION] = NormalizationMode.MEAN_STD + + return features, norm_modes + + +def remove_normalization_layers(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + """ + Creates a new state_dict with all normalization-related layers removed. + + This function filters the original state dictionary, excluding any keys that + match a set of predefined patterns associated with normalization modules. + + Args: + state_dict: The original model state dictionary. + + Returns: + A new state dictionary containing only the core model weights, without + any normalization parameters. + """ + new_state_dict = {} + + # Patterns to remove + remove_patterns = [ + "normalize_inputs.", + "unnormalize_outputs.", + "normalize_targets.", # Added pattern for target normalization + "normalize.", + "unnormalize.", + "input_normalizer.", + "output_normalizer.", + "normalizer.", + ] + + for key, tensor in state_dict.items(): + should_remove = any(pattern in key for pattern in remove_patterns) + if not should_remove: + new_state_dict[key] = tensor + + return new_state_dict + + +def clean_state_dict( + state_dict: dict[str, torch.Tensor], remove_str: str = "._orig_mod" +) -> dict[str, torch.Tensor]: + """ + Remove a substring (e.g. '._orig_mod') from all keys in a state dict. + + Args: + state_dict (dict): The original state dict. + remove_str (str): The substring to remove from the keys. + + Returns: + dict: A new state dict with cleaned keys. + """ + new_state_dict = {} + for k, v in state_dict.items(): + new_k = k.replace(remove_str, "") + new_state_dict[new_k] = v + return new_state_dict + + +def load_state_dict_with_missing_key_handling( + policy: torch.nn.Module, + state_dict: dict[str, torch.Tensor], + policy_type: str, + known_missing_keys_whitelist: dict[str, list[str]], +) -> list[str]: + """ + Load state dict into policy with graceful handling of missing keys. + + This function loads the state dict with strict=False, filters out whitelisted + missing keys, and provides detailed reporting about any issues found. + + Args: + policy: The policy model to load the state dict into. + state_dict: The cleaned state dictionary to load. + policy_type: The type of policy (used for whitelist lookup). + known_missing_keys_whitelist: Dictionary mapping policy types to lists of + known acceptable missing keys. + + Returns: + List of problematic missing keys that weren't in the whitelist. + """ + # Load the cleaned state dict with strict=False to capture missing/unexpected keys + load_result = policy.load_state_dict(state_dict, strict=False) + + # Check for missing keys + missing_keys = load_result.missing_keys + unexpected_keys = load_result.unexpected_keys + + # Filter out whitelisted missing keys + policy_type_lower = policy_type.lower() + whitelisted_keys = known_missing_keys_whitelist.get(policy_type_lower, []) + problematic_missing_keys = [key for key in missing_keys if key not in whitelisted_keys] + + if missing_keys: + if problematic_missing_keys: + print(f"WARNING: Found {len(problematic_missing_keys)} unexpected missing keys:") + for key in problematic_missing_keys: + print(f" - {key}") + + if len(missing_keys) > len(problematic_missing_keys): + whitelisted_missing = [key for key in missing_keys if key in whitelisted_keys] + print(f"INFO: Found {len(whitelisted_missing)} expected missing keys (whitelisted):") + for key in whitelisted_missing: + print(f" - {key}") + + if unexpected_keys: + print(f"WARNING: Found {len(unexpected_keys)} unexpected keys:") + for key in unexpected_keys: + print(f" - {key}") + + if not missing_keys and not unexpected_keys: + print("Successfully loaded cleaned state dict into policy model (all keys matched)") + else: + print("State dict loaded with some missing/unexpected keys (see details above)") + + return problematic_missing_keys + + +def convert_features_to_policy_features(features_dict: dict[str, dict]) -> dict[str, PolicyFeature]: + """ + Converts a feature dictionary from the old config format to the new `PolicyFeature` format. + + Args: + features_dict: The feature dictionary in the old format, where values are + simple dictionaries (e.g., `{"shape": [7]}`). + + Returns: + A dictionary mapping feature names to `PolicyFeature` dataclass objects. + """ + converted_features = {} + + for key, feature_dict in features_dict.items(): + # Determine feature type based on key + if "image" in key or "visual" in key: + feature_type = FeatureType.VISUAL + elif "state" in key: + feature_type = FeatureType.STATE + elif ACTION in key: + feature_type = FeatureType.ACTION + else: + feature_type = FeatureType.STATE + + # Get shape from feature dict + shape = feature_dict.get("shape", feature_dict.get("dim")) + shape = (shape,) if isinstance(shape, int) else tuple(shape) if shape is not None else () + + converted_features[key] = PolicyFeature(feature_type, shape) + + return converted_features + + +def display_migration_summary_with_warnings(problematic_missing_keys: list[str]) -> None: + """ + Display final migration summary with warnings about problematic missing keys. + + Args: + problematic_missing_keys: List of missing keys that weren't in the whitelist. + """ + if not problematic_missing_keys: + return + + print("\n" + "=" * 60) + print("IMPORTANT: MIGRATION COMPLETED WITH WARNINGS") + print("=" * 60) + print( + f"The migration was successful, but {len(problematic_missing_keys)} unexpected missing keys were found:" + ) + print() + for key in problematic_missing_keys: + print(f" - {key}") + print() + print("These missing keys may indicate:") + print(" • The model architecture has changed") + print(" • Some components were not properly saved in the original model") + print(" • The migration script needs to be updated for this policy type") + print() + print("What to do next:") + print(" 1. Test your migrated model carefully to ensure it works as expected") + print(" 2. If you encounter issues, please open an issue at:") + print(" https://github.com/huggingface/lerobot/issues") + print(" 3. Include this migration log and the missing keys listed above") + print() + print("If the model works correctly despite these warnings, the missing keys") + print("might be expected for your policy type and can be added to the whitelist.") + print("=" * 60) + + +def load_model_from_hub( + repo_id: str, revision: str | None = None +) -> tuple[dict[str, torch.Tensor], dict[str, Any], dict[str, Any] | None]: + """ + Downloads and loads a model's state_dict and configs from the Hugging Face Hub. + + Args: + repo_id: The repository ID on the Hub (e.g., 'lerobot/aloha'). + revision: The specific git revision (branch, tag, or commit hash) to use. + + Returns: + A tuple containing the model's state dictionary, the policy configuration, + and the training configuration (None if train_config.json is not found). + """ + # Download files. + safetensors_path = hf_hub_download(repo_id=repo_id, filename="model.safetensors", revision=revision) + + config_path = hf_hub_download(repo_id=repo_id, filename="config.json", revision=revision) + + # Load state_dict + state_dict = load_safetensors(safetensors_path) + + # Load config + with open(config_path) as f: + config = json.load(f) + + # Try to load train_config (optional) + train_config = None + try: + train_config_path = hf_hub_download(repo_id=repo_id, filename="train_config.json", revision=revision) + with open(train_config_path) as f: + train_config = json.load(f) + except FileNotFoundError: + print("train_config.json not found - continuing without training configuration") + + return state_dict, config, train_config + + +def main(): + parser = argparse.ArgumentParser( + description="Migrate policy models with normalization layers to new pipeline system" + ) + parser.add_argument( + "--pretrained-path", + type=str, + required=True, + help="Path to pretrained model (hub repo or local directory)", + ) + parser.add_argument( + "--output-dir", + type=str, + default=None, + help="Output directory for migrated model (default: same as pretrained-path)", + ) + parser.add_argument("--push-to-hub", action="store_true", help="Push migrated model to hub") + parser.add_argument( + "--hub-repo-id", + type=str, + default=None, + help="Hub repository ID for pushing (default: same as pretrained-path)", + ) + parser.add_argument("--revision", type=str, default=None, help="Revision of the model to load") + parser.add_argument("--private", action="store_true", help="Make the hub repository private") + parser.add_argument( + "--branch", + type=str, + default=None, + help="Git branch to use when pushing to hub. If specified, a PR will be created automatically (default: push directly to main)", + ) + + args = parser.parse_args() + + # Load model and config + print(f"Loading model from {args.pretrained_path}...") + if os.path.isdir(args.pretrained_path): + # Local directory + state_dict = load_safetensors(os.path.join(args.pretrained_path, "model.safetensors")) + with open(os.path.join(args.pretrained_path, "config.json")) as f: + config = json.load(f) + + # Try to load train_config (optional) + train_config = None + train_config_path = os.path.join(args.pretrained_path, "train_config.json") + if os.path.exists(train_config_path): + with open(train_config_path) as f: + train_config = json.load(f) + else: + print("train_config.json not found - continuing without training configuration") + else: + # Hub repository + state_dict, config, train_config = load_model_from_hub(args.pretrained_path, args.revision) + + # Extract normalization statistics + print("Extracting normalization statistics...") + stats = extract_normalization_stats(state_dict) + + print(f"Found normalization statistics for: {list(stats.keys())}") + + # Detect input features and normalization modes + print("Detecting features and normalization modes...") + features, norm_map = detect_features_and_norm_modes(config, stats) + + print(f"Detected features: {list(features.keys())}") + print(f"Normalization modes: {norm_map}") + + # Remove normalization layers from state_dict + print("Removing normalization layers from model...") + new_state_dict = remove_normalization_layers(state_dict) + new_state_dict = clean_state_dict(new_state_dict, remove_str="._orig_mod") + + removed_keys = set(state_dict.keys()) - set(new_state_dict.keys()) + if removed_keys: + print(f"Removed {len(removed_keys)} normalization layer keys") + + # Determine output path + if args.output_dir: + output_dir = Path(args.output_dir) + else: + if os.path.isdir(args.pretrained_path): + output_dir = Path(args.pretrained_path).parent / f"{Path(args.pretrained_path).name}_migrated" + else: + output_dir = Path(f"./{args.pretrained_path.replace('/', '_')}_migrated") + + output_dir.mkdir(parents=True, exist_ok=True) + + # Extract policy type from config + if "type" not in config: + raise ValueError("Policy type not found in config.json. The config must contain a 'type' field.") + + policy_type = config["type"] + print(f"Detected policy type: {policy_type}") + + # Clean up config - remove fields that shouldn't be passed to config constructor + cleaned_config = dict(config) + + # Remove fields that are not part of the config class constructors + fields_to_remove = ["normalization_mapping", "type"] + for field in fields_to_remove: + if field in cleaned_config: + print(f"Removing '{field}' field from config") + del cleaned_config[field] + + # Convert input_features and output_features to PolicyFeature objects if they exist + if "input_features" in cleaned_config: + cleaned_config["input_features"] = convert_features_to_policy_features( + cleaned_config["input_features"] + ) + if "output_features" in cleaned_config: + cleaned_config["output_features"] = convert_features_to_policy_features( + cleaned_config["output_features"] + ) + + # Add normalization mapping to config + cleaned_config["normalization_mapping"] = norm_map + + # Create policy configuration using the factory + print(f"Creating {policy_type} policy configuration...") + policy_config = make_policy_config(policy_type, **cleaned_config) + + # Create policy instance using the factory + print(f"Instantiating {policy_type} policy...") + policy_class = get_policy_class(policy_type) + policy = policy_class(policy_config) + + # Define whitelist of known missing keys that are acceptable (for example weight tie) for certain policy types + known_missing_keys_whitelist = { + "pi0": ["model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"], + # Add other policy types and their known missing keys here as needed + } + + # Load state dict with graceful missing key handling + problematic_missing_keys = load_state_dict_with_missing_key_handling( + policy=policy, + state_dict=new_state_dict, + policy_type=policy_type, + known_missing_keys_whitelist=known_missing_keys_whitelist, + ) + policy.to(torch.float32) + # Create preprocessor and postprocessor using the factory + print("Creating preprocessor and postprocessor using make_pre_post_processors...") + preprocessor, postprocessor = make_pre_post_processors(policy_cfg=policy_config, dataset_stats=stats) + + # Determine hub repo ID if pushing to hub + hub_repo_id = None + if args.push_to_hub: + if args.hub_repo_id: + hub_repo_id = args.hub_repo_id + else: + if not os.path.isdir(args.pretrained_path): + # Use same repo with "_migrated" suffix + hub_repo_id = f"{args.pretrained_path}_migrated" + else: + raise ValueError("--hub-repo-id must be specified when pushing local model to hub") + + # Save all components to local directory first + print(f"Saving preprocessor to {output_dir}...") + preprocessor.save_pretrained(output_dir) + + print(f"Saving postprocessor to {output_dir}...") + postprocessor.save_pretrained(output_dir) + + print(f"Saving model to {output_dir}...") + policy.save_pretrained(output_dir) + + # Generate and save model card + print("Generating model card...") + # Get metadata from original config + dataset_repo_id = "unknown" + if train_config is not None: + dataset_repo_id = train_config.get("repo_id", "unknown") + license = config.get("license", "apache-2.0") + + tags = config.get("tags", ["robotics", "lerobot", policy_type]) or ["robotics", "lerobot", policy_type] + tags = set(tags).union({"robotics", "lerobot", policy_type}) + tags = list(tags) + + # Generate model card + card = policy.generate_model_card( + dataset_repo_id=dataset_repo_id, model_type=policy_type, license=license, tags=tags + ) + + # Save model card locally + card.save(str(output_dir / "README.md")) + print(f"Model card saved to {output_dir / 'README.md'}") + # Push all files to hub in a single operation if requested + if args.push_to_hub and hub_repo_id: + api = HfApi() + + # Determine if we should create a PR (automatically if branch is specified) + create_pr = args.branch is not None + target_location = f"branch '{args.branch}'" if args.branch else "main branch" + + print(f"Pushing all migrated files to {hub_repo_id} on {target_location}...") + + # Upload all files in a single commit with automatic PR creation if branch specified + commit_message = "Migrate policy to PolicyProcessorPipeline system" + commit_description = None + + if create_pr: + # Separate commit description for PR body + commit_description = """**Automated Policy Migration to PolicyProcessorPipeline** + +This PR migrates your model to the new LeRobot policy format using the modern PolicyProcessorPipeline architecture. + +## What Changed + +### **New Architecture - PolicyProcessorPipeline** +Your model now uses external PolicyProcessorPipeline components for data processing instead of built-in normalization layers. This provides: +- **Modularity**: Separate preprocessing and postprocessing pipelines +- **Flexibility**: Easy to swap, configure, and debug processing steps +- **Compatibility**: Works with the latest LeRobot ecosystem + +### **Normalization Extraction** +We've extracted normalization statistics from your model's state_dict and removed the built-in normalization layers: +- **Extracted patterns**: `normalize_inputs.*`, `unnormalize_outputs.*`, `normalize.*`, `unnormalize.*`, `input_normalizer.*`, `output_normalizer.*` +- **Statistics preserved**: Mean, std, min, max values for all features +- **Clean model**: State dict now contains only core model weights + +### **Files Added** +- **preprocessor_config.json**: Configuration for input preprocessing pipeline +- **postprocessor_config.json**: Configuration for output postprocessing pipeline +- **model.safetensors**: Clean model weights without normalization layers +- **config.json**: Updated model configuration +- **train_config.json**: Training configuration +- **README.md**: Updated model card with migration information + +### **Benefits** +- **Backward Compatible**: Your model behavior remains identical +- **Future Ready**: Compatible with latest LeRobot features and updates +- **Debuggable**: Easy to inspect and modify processing steps +- **Portable**: Processors can be shared and reused across models + +### **Usage** +```python +# Load your migrated model +from lerobot.policies import get_policy_class +from lerobot.processor import PolicyProcessorPipeline + +# The preprocessor and postprocessor are now external +preprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="preprocessor_config.json") +postprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="postprocessor_config.json") +policy = get_policy_class("your-policy-type").from_pretrained("your-model-repo") + +# Process data through the pipeline +processed_batch = preprocessor(raw_batch) +action = policy(processed_batch) +final_action = postprocessor(action) +``` + +*Generated automatically by the LeRobot policy migration script*""" + + upload_kwargs = { + "repo_id": hub_repo_id, + "folder_path": output_dir, + "repo_type": "model", + "commit_message": commit_message, + "revision": args.branch, + "create_pr": create_pr, + "allow_patterns": ["*.json", "*.safetensors", "*.md"], + "ignore_patterns": ["*.tmp", "*.log"], + } + + # Add commit_description for PR body if creating PR + if create_pr and commit_description: + upload_kwargs["commit_description"] = commit_description + + api.upload_folder(**upload_kwargs) + + if create_pr: + print("All files pushed and pull request created successfully!") + else: + print("All files pushed to main branch successfully!") + + print("\nMigration complete!") + print(f"Migrated model saved to: {output_dir}") + if args.push_to_hub and hub_repo_id: + if args.branch: + print( + f"Successfully pushed all files to branch '{args.branch}' and created PR on https://huggingface.co/{hub_repo_id}" + ) + else: + print(f"Successfully pushed to https://huggingface.co/{hub_repo_id}") + if args.branch: + print(f"\nView the branch at: https://huggingface.co/{hub_repo_id}/tree/{args.branch}") + print( + f"View the PR at: https://huggingface.co/{hub_repo_id}/discussions (look for the most recent PR)" + ) + else: + print(f"\nView the changes at: https://huggingface.co/{hub_repo_id}") + + # Display final summary about any problematic missing keys + display_migration_summary_with_warnings(problematic_missing_keys) + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/processor/normalize_processor.py b/src/lerobot/processor/normalize_processor.py index 14628727f08..368c9b2700a 100644 --- a/src/lerobot/processor/normalize_processor.py +++ b/src/lerobot/processor/normalize_processor.py @@ -1,67 +1,412 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from __future__ import annotations -from collections.abc import Mapping +from copy import deepcopy from dataclasses import dataclass, field from typing import Any -import numpy as np import torch from torch import Tensor -from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.configs.types import FeatureType, NormalizationMode, PipelineFeatureType, PolicyFeature from lerobot.datasets.lerobot_dataset import LeRobotDataset -from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionKey - - -def _convert_stats_to_tensors(stats: dict[str, dict[str, Any]]) -> dict[str, dict[str, Tensor]]: - """Convert numpy arrays and other types to torch tensors.""" - tensor_stats: dict[str, dict[str, Tensor]] = {} - for key, sub in stats.items(): - tensor_stats[key] = {} - for stat_name, value in sub.items(): - if isinstance(value, np.ndarray): - tensor_val = torch.from_numpy(value.astype(np.float32)) - elif isinstance(value, torch.Tensor): - tensor_val = value.to(dtype=torch.float32) - elif isinstance(value, (int, float, list, tuple)): - tensor_val = torch.tensor(value, dtype=torch.float32) - else: - raise TypeError(f"Unsupported type for stats['{key}']['{stat_name}']: {type(value)}") - tensor_stats[key][stat_name] = tensor_val - return tensor_stats - +from lerobot.utils.constants import ACTION -@dataclass -@ProcessorStepRegistry.register(name="normalizer_processor") -class NormalizerProcessor: - """Normalizes observations and actions in a single processor step. +from .converters import from_tensor_to_numpy, to_tensor +from .core import EnvTransition, PolicyAction, TransitionKey +from .pipeline import PolicyProcessorPipeline, ProcessorStep, ProcessorStepRegistry - This processor handles normalization of both observation and action tensors - using either mean/std normalization or min/max scaling to a [-1, 1] range. - For each tensor key in the stats dictionary, the processor will: - - Use mean/std normalization if those statistics are provided: (x - mean) / std - - Use min/max scaling if those statistics are provided: 2 * (x - min) / (max - min) - 1 +@dataclass +class _NormalizationMixin: + """ + A mixin class providing core functionality for normalization and unnormalization. + + This class manages normalization statistics (`stats`), converts them to tensors for + efficient computation, handles device placement, and implements the logic for + applying normalization transformations (mean/std and min/max). It is designed to + be inherited by concrete `ProcessorStep` implementations and should not be used + directly. + + **Stats Override Preservation:** + When stats are explicitly provided during construction (e.g., via overrides in + `DataProcessorPipeline.from_pretrained()`), they are preserved even when + `load_state_dict()` is called. This allows users to override normalization + statistics from saved models while keeping the rest of the model state intact. + + Examples: + ```python + # Common use case: Override with dataset stats + from lerobot.datasets import LeRobotDataset + + dataset = LeRobotDataset("my_dataset") + pipeline = DataProcessorPipeline.from_pretrained( + "model_path", overrides={"normalizer_processor": {"stats": dataset.meta.stats}} + ) + # dataset.meta.stats will be used, not the stats from the saved model - The processor can be configured to normalize only specific keys by setting - the normalize_keys parameter. + # Custom stats override + custom_stats = {"action": {"mean": [0.0], "std": [1.0]}} + pipeline = DataProcessorPipeline.from_pretrained( + "model_path", overrides={"normalizer_processor": {"stats": custom_stats}} + ) + ``` + + Attributes: + features: A dictionary mapping feature names to `PolicyFeature` objects, defining + the data structure to be processed. + norm_map: A dictionary mapping `FeatureType` to `NormalizationMode`, specifying + which normalization method to use for each type of feature. + stats: A dictionary containing the normalization statistics (e.g., mean, std, + min, max) for each feature. + device: The PyTorch device on which to store and perform tensor operations. + eps: A small epsilon value to prevent division by zero in normalization + calculations. + normalize_observation_keys: An optional set of keys to selectively apply + normalization to specific observation features. + _tensor_stats: An internal dictionary holding the normalization statistics as + PyTorch tensors. + _stats_explicitly_provided: Internal flag tracking whether stats were explicitly + provided during construction (used for override preservation). """ - # Features and normalisation map are mandatory to match the design of normalize.py features: dict[str, PolicyFeature] norm_map: dict[FeatureType, NormalizationMode] - - # Pre-computed statistics coming from dataset.meta.stats for instance. stats: dict[str, dict[str, Any]] | None = None - - # Explicit subset of keys to normalise. If ``None`` every key (except - # "action") found in ``stats`` will be normalised. Using a ``set`` makes - # membership checks O(1). - normalize_keys: set[str] | None = None - + device: torch.device | str | None = None + dtype: torch.dtype | None = None eps: float = 1e-8 + normalize_observation_keys: set[str] | None = None _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False) + _stats_explicitly_provided: bool = field(default=False, init=False, repr=False) + + def __post_init__(self): + """ + Initializes the mixin after dataclass construction. + + This method handles the robust deserialization of `features` and `norm_map` + from JSON-compatible formats (where enums become strings and tuples become + lists) and converts the provided `stats` dictionary into a dictionary of + tensors (`_tensor_stats`) on the specified device. + """ + # Track if stats were explicitly provided (not None and not empty) + self._stats_explicitly_provided = self.stats is not None and bool(self.stats) + # Robust JSON deserialization handling (guard empty maps). + if self.features: + first_val = next(iter(self.features.values())) + if isinstance(first_val, dict): + reconstructed = {} + for key, ft_dict in self.features.items(): + reconstructed[key] = PolicyFeature( + type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"]) + ) + self.features = reconstructed + + # if keys are strings (JSON), rebuild enum map + if self.norm_map and all(isinstance(k, str) for k in self.norm_map): + reconstructed = {} + for ft_type_str, norm_mode_str in self.norm_map.items(): + reconstructed[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str) + self.norm_map = reconstructed + + # Convert stats to tensors and move to the target device once during initialization. + self.stats = self.stats or {} + if self.dtype is None: + self.dtype = torch.float32 + self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype) + + def to( + self, device: torch.device | str | None = None, dtype: torch.dtype | None = None + ) -> _NormalizationMixin: + """ + Moves the processor's normalization stats to the specified device. + + Args: + device: The target PyTorch device. + + Returns: + The instance of the class, allowing for method chaining. + """ + if device is not None: + self.device = device + if dtype is not None: + self.dtype = dtype + self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype) + return self + + def state_dict(self) -> dict[str, Tensor]: + """ + Returns the normalization statistics as a flat state dictionary. + + All tensors are moved to the CPU before being returned, which is standard practice + for saving state dictionaries. + + Returns: + A flat dictionary mapping from `'feature_name.stat_name'` to the + corresponding statistics tensor on the CPU. + """ + flat: dict[str, Tensor] = {} + for key, sub in self._tensor_stats.items(): + for stat_name, tensor in sub.items(): + flat[f"{key}.{stat_name}"] = tensor.cpu() # Always save to CPU + return flat + + def load_state_dict(self, state: dict[str, Tensor]) -> None: + """ + Loads normalization statistics from a state dictionary. + + The loaded tensors are moved to the processor's configured device. + + **Stats Override Preservation:** + If stats were explicitly provided during construction (e.g., via overrides in + `DataProcessorPipeline.from_pretrained()`), they are preserved and the state + dictionary is ignored. This allows users to override normalization statistics + while still loading the rest of the model state. + + This behavior is crucial for scenarios where users want to adapt a pretrained + model to a new dataset with different statistics without retraining the entire + model. + + Args: + state: A flat state dictionary with keys in the format + `'feature_name.stat_name'`. + + Note: + When stats are preserved due to explicit provision, only the tensor + representation is updated to ensure consistency with the current device + and dtype settings. + """ + # If stats were explicitly provided during construction, preserve them + if self._stats_explicitly_provided and self.stats is not None: + # Don't load from state_dict, keep the explicitly provided stats + # But ensure _tensor_stats is properly initialized + self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype) # type: ignore[assignment] + return + + # Normal behavior: load stats from state_dict + self._tensor_stats.clear() + for flat_key, tensor in state.items(): + key, stat_name = flat_key.rsplit(".", 1) + # Load to the processor's configured device. + self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to( + dtype=torch.float32, device=self.device + ) + + # Reconstruct the original stats dict from tensor stats for compatibility with to() method + # and other functions that rely on self.stats + self.stats = {} + for key, tensor_dict in self._tensor_stats.items(): + self.stats[key] = {} + for stat_name, tensor in tensor_dict.items(): + # Convert tensor back to python/numpy format + self.stats[key][stat_name] = from_tensor_to_numpy(tensor) + + def get_config(self) -> dict[str, Any]: + """ + Returns a serializable dictionary of the processor's configuration. + + This method is used when saving the processor to disk, ensuring that its + configuration can be reconstructed later. + + Returns: + A JSON-serializable dictionary containing the configuration. + """ + config = { + "eps": self.eps, + "features": { + key: {"type": ft.type.value, "shape": ft.shape} for key, ft in self.features.items() + }, + "norm_map": {ft_type.value: norm_mode.value for ft_type, norm_mode in self.norm_map.items()}, + } + if self.normalize_observation_keys is not None: + config["normalize_observation_keys"] = sorted(self.normalize_observation_keys) + return config + + def _normalize_observation(self, observation: dict[str, Any], inverse: bool) -> dict[str, Tensor]: + """ + Applies (un)normalization to all relevant features in an observation dictionary. + + Args: + observation: The observation dictionary to process. + inverse: If `True`, applies unnormalization; otherwise, applies normalization. + + Returns: + A new observation dictionary with the transformed tensor values. + """ + new_observation = dict(observation) + for key, feature in self.features.items(): + if self.normalize_observation_keys is not None and key not in self.normalize_observation_keys: + continue + if feature.type != FeatureType.ACTION and key in new_observation: + # Convert to tensor but preserve original dtype for adaptation logic + tensor = torch.as_tensor(new_observation[key]) + new_observation[key] = self._apply_transform(tensor, key, feature.type, inverse=inverse) + return new_observation + + def _normalize_action(self, action: Tensor, inverse: bool) -> Tensor: + # Convert to tensor but preserve original dtype for adaptation logic + """ + Applies (un)normalization to an action tensor. + + Args: + action: The action tensor to process. + inverse: If `True`, applies unnormalization; otherwise, applies normalization. + + Returns: + The transformed action tensor. + """ + processed_action = self._apply_transform(action, ACTION, FeatureType.ACTION, inverse=inverse) + return processed_action + + def _apply_transform( + self, tensor: Tensor, key: str, feature_type: FeatureType, *, inverse: bool = False + ) -> Tensor: + """ + Core logic to apply a normalization or unnormalization transformation to a tensor. + + This method selects the appropriate normalization mode based on the feature type + and applies the corresponding mathematical operation. + + Normalization Modes: + - MEAN_STD: Centers data around zero with unit variance. + - MIN_MAX: Scales data to [-1, 1] range using actual min/max values. + - QUANTILES: Scales data to [-1, 1] range using 1st and 99th percentiles (q01/q99). + - QUANTILE10: Scales data to [-1, 1] range using 10th and 90th percentiles (q10/q90). + + Args: + tensor: The input tensor to transform. + key: The feature key corresponding to the tensor. + feature_type: The `FeatureType` of the tensor. + inverse: If `True`, applies the inverse transformation (unnormalization). + + Returns: + The transformed tensor. + + Raises: + ValueError: If an unsupported normalization mode is encountered. + """ + norm_mode = self.norm_map.get(feature_type, NormalizationMode.IDENTITY) + if norm_mode == NormalizationMode.IDENTITY or key not in self._tensor_stats: + return tensor + + if norm_mode not in ( + NormalizationMode.MEAN_STD, + NormalizationMode.MIN_MAX, + NormalizationMode.QUANTILES, + NormalizationMode.QUANTILE10, + ): + raise ValueError(f"Unsupported normalization mode: {norm_mode}") + + # For Accelerate compatibility: Ensure stats are on the same device and dtype as the input tensor + if self._tensor_stats and key in self._tensor_stats: + first_stat = next(iter(self._tensor_stats[key].values())) + if first_stat.device != tensor.device or first_stat.dtype != tensor.dtype: + self.to(device=tensor.device, dtype=tensor.dtype) + + stats = self._tensor_stats[key] + + if norm_mode == NormalizationMode.MEAN_STD: + mean = stats.get("mean", None) + std = stats.get("std", None) + if mean is None or std is None: + raise ValueError( + "MEAN_STD normalization mode requires mean and std stats, please update the dataset with the correct stats" + ) + + mean, std = stats["mean"], stats["std"] + # Avoid division by zero by adding a small epsilon. + denom = std + self.eps + if inverse: + return tensor * std + mean + return (tensor - mean) / denom + + if norm_mode == NormalizationMode.MIN_MAX: + min_val = stats.get("min", None) + max_val = stats.get("max", None) + if min_val is None or max_val is None: + raise ValueError( + "MIN_MAX normalization mode requires min and max stats, please update the dataset with the correct stats" + ) + + min_val, max_val = stats["min"], stats["max"] + denom = max_val - min_val + # When min_val == max_val, substitute the denominator with a small epsilon + # to prevent division by zero. This consistently maps an input equal to + # min_val to -1, ensuring a stable transformation. + denom = torch.where( + denom == 0, torch.tensor(self.eps, device=tensor.device, dtype=tensor.dtype), denom + ) + if inverse: + # Map from [-1, 1] back to [min, max] + return (tensor + 1) / 2 * denom + min_val + # Map from [min, max] to [-1, 1] + return 2 * (tensor - min_val) / denom - 1 + + if norm_mode == NormalizationMode.QUANTILES: + q01 = stats.get("q01", None) + q99 = stats.get("q99", None) + if q01 is None or q99 is None: + raise ValueError( + "QUANTILES normalization mode requires q01 and q99 stats, please update the dataset with the correct stats using the `augment_dataset_quantile_stats.py` script" + ) + + denom = q99 - q01 + # Avoid division by zero by adding epsilon when quantiles are identical + denom = torch.where( + denom == 0, torch.tensor(self.eps, device=tensor.device, dtype=tensor.dtype), denom + ) + if inverse: + return (tensor + 1.0) * denom / 2.0 + q01 + return 2.0 * (tensor - q01) / denom - 1.0 + + if norm_mode == NormalizationMode.QUANTILE10: + q10 = stats.get("q10", None) + q90 = stats.get("q90", None) + if q10 is None or q90 is None: + raise ValueError( + "QUANTILE10 normalization mode requires q10 and q90 stats, please update the dataset with the correct stats using the `augment_dataset_quantile_stats.py` script" + ) + + denom = q90 - q10 + # Avoid division by zero by adding epsilon when quantiles are identical + denom = torch.where( + denom == 0, torch.tensor(self.eps, device=tensor.device, dtype=tensor.dtype), denom + ) + if inverse: + return (tensor + 1.0) * denom / 2.0 + q10 + return 2.0 * (tensor - q10) / denom - 1.0 + + # If necessary stats are missing, return input unchanged. + return tensor + + +@dataclass +@ProcessorStepRegistry.register(name="normalizer_processor") +class NormalizerProcessorStep(_NormalizationMixin, ProcessorStep): + """ + A processor step that applies normalization to observations and actions in a transition. + + This class uses the logic from `_NormalizationMixin` to perform forward normalization + (e.g., scaling data to have zero mean and unit variance, or to the range [-1, 1]). + It is typically used in the pre-processing pipeline before feeding data to a policy. + """ @classmethod def from_lerobot_dataset( @@ -70,158 +415,73 @@ def from_lerobot_dataset( features: dict[str, PolicyFeature], norm_map: dict[FeatureType, NormalizationMode], *, - normalize_keys: set[str] | None = None, + normalize_observation_keys: set[str] | None = None, eps: float = 1e-8, - ) -> NormalizerProcessor: - """Factory helper that pulls statistics from a :class:`LeRobotDataset`. - - The features and norm_map parameters are mandatory to match the design - pattern used in normalize.py. + device: torch.device | str | None = None, + ) -> NormalizerProcessorStep: + """ + Creates a `NormalizerProcessorStep` instance using statistics from a `LeRobotDataset`. + + Args: + dataset: The dataset from which to extract normalization statistics. + features: The feature definition for the processor. + norm_map: The mapping from feature types to normalization modes. + normalize_observation_keys: An optional set of observation keys to normalize. + eps: A small epsilon value for numerical stability. + device: The target device for the processor. + + Returns: + A new instance of `NormalizerProcessorStep`. """ - return cls( features=features, norm_map=norm_map, stats=dataset.meta.stats, - normalize_keys=normalize_keys, + normalize_observation_keys=normalize_observation_keys, eps=eps, + device=device, ) - def __post_init__(self): - # Handle deserialization from JSON config - if self.features and isinstance(list(self.features.values())[0], dict): - # Features came from JSON - need to reconstruct PolicyFeature objects - reconstructed_features = {} - for key, ft_dict in self.features.items(): - reconstructed_features[key] = PolicyFeature( - type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"]) - ) - self.features = reconstructed_features - - if self.norm_map and isinstance(list(self.norm_map.keys())[0], str): - # norm_map came from JSON - need to reconstruct enum keys and values - reconstructed_norm_map = {} - for ft_type_str, norm_mode_str in self.norm_map.items(): - reconstructed_norm_map[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str) - self.norm_map = reconstructed_norm_map - - # Convert statistics once so we avoid repeated numpy→Tensor conversions - # during runtime. - self.stats = self.stats or {} - self._tensor_stats = _convert_stats_to_tensors(self.stats) - - # Ensure *normalize_keys* is a set for fast look-ups and compare by - # value later when returning the configuration. - if self.normalize_keys is not None and not isinstance(self.normalize_keys, set): - self.normalize_keys = set(self.normalize_keys) - - def _normalize_obs(self, observation): - if observation is None: - return None - - # Decide which keys should be normalised for this call. - if self.normalize_keys is not None: - keys_to_norm = self.normalize_keys - else: - # Use feature map to skip action keys. - keys_to_norm = {k for k, ft in self.features.items() if ft.type is not FeatureType.ACTION} - - processed = dict(observation) - for key in keys_to_norm: - if key not in processed or key not in self._tensor_stats: - continue + def __call__(self, transition: EnvTransition) -> EnvTransition: + new_transition = transition.copy() - orig_val = processed[key] - tensor = ( - orig_val.to(dtype=torch.float32) - if isinstance(orig_val, torch.Tensor) - else torch.as_tensor(orig_val, dtype=torch.float32) + # Handle observation normalization. + observation = new_transition.get(TransitionKey.OBSERVATION) + if observation is not None: + new_transition[TransitionKey.OBSERVATION] = self._normalize_observation( + observation, inverse=False ) - stats = {k: v.to(tensor.device) for k, v in self._tensor_stats[key].items()} - - if "mean" in stats and "std" in stats: - mean, std = stats["mean"], stats["std"] - processed[key] = (tensor - mean) / (std + self.eps) - elif "min" in stats and "max" in stats: - min_val, max_val = stats["min"], stats["max"] - processed[key] = 2 * (tensor - min_val) / (max_val - min_val + self.eps) - 1 - return processed - - def _normalize_action(self, action): - if action is None or "action" not in self._tensor_stats: - return action - - tensor = ( - action.to(dtype=torch.float32) - if isinstance(action, torch.Tensor) - else torch.as_tensor(action, dtype=torch.float32) - ) - stats = {k: v.to(tensor.device) for k, v in self._tensor_stats["action"].items()} - if "mean" in stats and "std" in stats: - mean, std = stats["mean"], stats["std"] - return (tensor - mean) / (std + self.eps) - if "min" in stats and "max" in stats: - min_val, max_val = stats["min"], stats["max"] - return 2 * (tensor - min_val) / (max_val - min_val + self.eps) - 1 - raise ValueError("Action stats must contain either ('mean','std') or ('min','max')") - - def __call__(self, transition: EnvTransition) -> EnvTransition: - observation = self._normalize_obs(transition.get(TransitionKey.OBSERVATION)) - action = self._normalize_action(transition.get(TransitionKey.ACTION)) - # Create a new transition with normalized values - new_transition = transition.copy() - new_transition[TransitionKey.OBSERVATION] = observation - new_transition[TransitionKey.ACTION] = action - return new_transition + # Handle action normalization. + action = new_transition.get(TransitionKey.ACTION) - def get_config(self) -> dict[str, Any]: - config = { - "eps": self.eps, - "features": { - key: {"type": ft.type.value, "shape": ft.shape} for key, ft in self.features.items() - }, - "norm_map": {ft_type.value: norm_mode.value for ft_type, norm_mode in self.norm_map.items()}, - } - if self.normalize_keys is not None: - # Serialise as a list for YAML / JSON friendliness - config["normalize_keys"] = sorted(self.normalize_keys) - return config + if action is None: + return new_transition - def state_dict(self) -> dict[str, Tensor]: - flat = {} - for key, sub in self._tensor_stats.items(): - for stat_name, tensor in sub.items(): - flat[f"{key}.{stat_name}"] = tensor - return flat + if not isinstance(action, PolicyAction): + raise ValueError(f"Action should be a PolicyAction type got {type(action)}") - def load_state_dict(self, state: Mapping[str, Tensor]) -> None: - self._tensor_stats.clear() - for flat_key, tensor in state.items(): - key, stat_name = flat_key.rsplit(".", 1) - self._tensor_stats.setdefault(key, {})[stat_name] = tensor + new_transition[TransitionKey.ACTION] = self._normalize_action(action, inverse=False) - def reset(self): - pass + return new_transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: return features @dataclass @ProcessorStepRegistry.register(name="unnormalizer_processor") -class UnnormalizerProcessor: - """Inverse normalisation for observations and actions. - - Exactly mirrors :class:`NormalizerProcessor` but applies the inverse - transform. +class UnnormalizerProcessorStep(_NormalizationMixin, ProcessorStep): """ + A processor step that applies unnormalization to observations and actions. - features: dict[str, PolicyFeature] - norm_map: dict[FeatureType, NormalizationMode] - stats: dict[str, dict[str, Any]] | None = None - - _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False) + This class inverts the normalization process, scaling data back to its original + range. It is typically used in the post-processing pipeline to convert a policy's + normalized action output into a format that can be executed by a robot or + environment. + """ @classmethod def from_lerobot_dataset( @@ -229,103 +489,72 @@ def from_lerobot_dataset( dataset: LeRobotDataset, features: dict[str, PolicyFeature], norm_map: dict[FeatureType, NormalizationMode], - ) -> UnnormalizerProcessor: - return cls(features=features, norm_map=norm_map, stats=dataset.meta.stats) - - def __post_init__(self): - # Handle deserialization from JSON config - if self.features and isinstance(list(self.features.values())[0], dict): - # Features came from JSON - need to reconstruct PolicyFeature objects - reconstructed_features = {} - for key, ft_dict in self.features.items(): - reconstructed_features[key] = PolicyFeature( - type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"]) - ) - self.features = reconstructed_features + *, + device: torch.device | str | None = None, + ) -> UnnormalizerProcessorStep: + """ + Creates an `UnnormalizerProcessorStep` using statistics from a `LeRobotDataset`. - if self.norm_map and isinstance(list(self.norm_map.keys())[0], str): - # norm_map came from JSON - need to reconstruct enum keys and values - reconstructed_norm_map = {} - for ft_type_str, norm_mode_str in self.norm_map.items(): - reconstructed_norm_map[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str) - self.norm_map = reconstructed_norm_map + Args: + dataset: The dataset from which to extract normalization statistics. + features: The feature definition for the processor. + norm_map: The mapping from feature types to normalization modes. + device: The target device for the processor. - self.stats = self.stats or {} - self._tensor_stats = _convert_stats_to_tensors(self.stats) - - def _unnormalize_obs(self, observation): - if observation is None: - return None - keys = [k for k, ft in self.features.items() if ft.type is not FeatureType.ACTION] - processed = dict(observation) - for key in keys: - if key not in processed or key not in self._tensor_stats: - continue - orig_val = processed[key] - tensor = ( - orig_val.to(dtype=torch.float32) - if isinstance(orig_val, torch.Tensor) - else torch.as_tensor(orig_val, dtype=torch.float32) - ) - stats = {k: v.to(tensor.device) for k, v in self._tensor_stats[key].items()} - if "mean" in stats and "std" in stats: - mean, std = stats["mean"], stats["std"] - processed[key] = tensor * std + mean - elif "min" in stats and "max" in stats: - min_val, max_val = stats["min"], stats["max"] - processed[key] = (tensor + 1) / 2 * (max_val - min_val) + min_val - return processed - - def _unnormalize_action(self, action): - if action is None or "action" not in self._tensor_stats: - return action - tensor = ( - action.to(dtype=torch.float32) - if isinstance(action, torch.Tensor) - else torch.as_tensor(action, dtype=torch.float32) - ) - stats = {k: v.to(tensor.device) for k, v in self._tensor_stats["action"].items()} - if "mean" in stats and "std" in stats: - mean, std = stats["mean"], stats["std"] - return tensor * std + mean - if "min" in stats and "max" in stats: - min_val, max_val = stats["min"], stats["max"] - return (tensor + 1) / 2 * (max_val - min_val) + min_val - raise ValueError("Action stats must contain either ('mean','std') or ('min','max')") + Returns: + A new instance of `UnnormalizerProcessorStep`. + """ + return cls(features=features, norm_map=norm_map, stats=dataset.meta.stats, device=device) def __call__(self, transition: EnvTransition) -> EnvTransition: - observation = self._unnormalize_obs(transition.get(TransitionKey.OBSERVATION)) - action = self._unnormalize_action(transition.get(TransitionKey.ACTION)) - - # Create a new transition with unnormalized values new_transition = transition.copy() - new_transition[TransitionKey.OBSERVATION] = observation - new_transition[TransitionKey.ACTION] = action - return new_transition - def get_config(self) -> dict[str, Any]: - return { - "features": { - key: {"type": ft.type.value, "shape": ft.shape} for key, ft in self.features.items() - }, - "norm_map": {ft_type.value: norm_mode.value for ft_type, norm_mode in self.norm_map.items()}, - } + # Handle observation unnormalization. + observation = new_transition.get(TransitionKey.OBSERVATION) + if observation is not None: + new_transition[TransitionKey.OBSERVATION] = self._normalize_observation(observation, inverse=True) - def state_dict(self) -> dict[str, Tensor]: - flat = {} - for key, sub in self._tensor_stats.items(): - for stat_name, tensor in sub.items(): - flat[f"{key}.{stat_name}"] = tensor - return flat + # Handle action unnormalization. + action = new_transition.get(TransitionKey.ACTION) - def load_state_dict(self, state: Mapping[str, Tensor]) -> None: - self._tensor_stats.clear() - for flat_key, tensor in state.items(): - key, stat_name = flat_key.rsplit(".", 1) - self._tensor_stats.setdefault(key, {})[stat_name] = tensor + if action is None: + return new_transition + if not isinstance(action, PolicyAction): + raise ValueError(f"Action should be a PolicyAction type got {type(action)}") + + new_transition[TransitionKey.ACTION] = self._normalize_action(action, inverse=True) - def reset(self): - pass + return new_transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: return features + + +def hotswap_stats( + policy_processor: PolicyProcessorPipeline, stats: dict[str, dict[str, Any]] +) -> PolicyProcessorPipeline: + """ + Replaces normalization statistics in an existing `PolicyProcessorPipeline` instance. + + This function creates a deep copy of the provided pipeline and updates the + statistics of any `NormalizerProcessorStep` or `UnnormalizerProcessorStep` it + contains. This is useful for adapting a trained policy to a new environment or + dataset with different data distributions without having to reconstruct the entire + pipeline. + + Args: + policy_processor: The policy processor pipeline to modify. + stats: The new dictionary of normalization statistics to apply. + + Returns: + A new `PolicyProcessorPipeline` instance with the updated statistics. + """ + rp = deepcopy(policy_processor) + for step in rp.steps: + if isinstance(step, _NormalizationMixin): + step.stats = stats + # Re-initialize tensor_stats on the correct device. + step._tensor_stats = to_tensor(stats, device=step.device, dtype=step.dtype) # type: ignore[assignment] + return rp diff --git a/src/lerobot/processor/observation_processor.py b/src/lerobot/processor/observation_processor.py index 7d63db23835..d22d8fb96ec 100644 --- a/src/lerobot/processor/observation_processor.py +++ b/src/lerobot/processor/observation_processor.py @@ -20,32 +20,54 @@ import torch from torch import Tensor -from lerobot.configs.types import PolicyFeature -from lerobot.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE -from lerobot.processor.pipeline import ObservationProcessor, ProcessorStepRegistry +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE, OBS_STR + +from .pipeline import ObservationProcessorStep, ProcessorStepRegistry @dataclass @ProcessorStepRegistry.register(name="observation_processor") -class VanillaObservationProcessor(ObservationProcessor): +class VanillaObservationProcessorStep(ObservationProcessorStep): """ - Processes environment observations into the LeRobot format by handling both images and states. - - Image processing: - - Converts channel-last (H, W, C) images to channel-first (C, H, W) - - Normalizes uint8 images ([0, 255]) to float32 ([0, 1]) - - Adds a batch dimension if missing - - Supports single images and image dictionaries - - State processing: - - Maps 'environment_state' to observation.environment_state - - Maps 'agent_pos' to observation.state - - Converts numpy arrays to tensors - - Adds a batch dimension if missing + Processes standard Gymnasium observations into the LeRobot format. + + This step handles both image and state data from a typical observation dictionary, + preparing it for use in a LeRobot policy. + + **Image Processing:** + - Converts channel-last (H, W, C), `uint8` images to channel-first (C, H, W), + `float32` tensors. + - Normalizes pixel values from the [0, 255] range to [0, 1]. + - Adds a batch dimension if one is not already present. + - Recognizes a single image under the key `"pixels"` and maps it to + `"observation.image"`. + - Recognizes a dictionary of images under the key `"pixels"` and maps them + to `"observation.images.{camera_name}"`. + + **State Processing:** + - Maps the `"environment_state"` key to `"observation.environment_state"`. + - Maps the `"agent_pos"` key to `"observation.state"`. + - Converts NumPy arrays to PyTorch tensors. + - Adds a batch dimension if one is not already present. """ def _process_single_image(self, img: np.ndarray) -> Tensor: - """Process a single image array.""" + """ + Processes a single NumPy image array into a channel-first, normalized tensor. + + Args: + img: A NumPy array representing the image, expected to be in channel-last + (H, W, C) format with a `uint8` dtype. + + Returns: + A `float32` PyTorch tensor in channel-first (B, C, H, W) format, with + pixel values normalized to the [0, 1] range. + + Raises: + ValueError: If the input image does not appear to be in channel-last + format or is not of `uint8` dtype. + """ # Convert to tensor img_tensor = torch.from_numpy(img) @@ -106,19 +128,32 @@ def _process_observation(self, observation): def observation(self, observation): return self._process_observation(observation) - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - """Transforms feature keys to a standardized contract. - - This method handles several renaming patterns: - - Exact matches (e.g., 'pixels' -> 'OBS_IMAGE'). - - Prefixed exact matches (e.g., 'observation.pixels' -> 'OBS_IMAGE'). - - Prefix matches (e.g., 'pixels.cam1' -> 'OBS_IMAGES.cam1'). - - Prefixed prefix matches (e.g., 'observation.pixels.cam1' -> 'OBS_IMAGES.cam1'). - - environment_state -> OBS_ENV_STATE, - - agent_pos -> OBS_STATE, - - observation.environment_state -> OBS_ENV_STATE, - - observation.agent_pos -> OBS_STATE + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: """ + Transforms feature keys from the Gym standard to the LeRobot standard. + + This method standardizes the feature dictionary by renaming keys according + to LeRobot's conventions, ensuring that policies can be constructed correctly. + It handles various raw key formats, including those with an "observation." prefix. + + **Renaming Rules:** + - `pixels` or `observation.pixels` -> `observation.image` + - `pixels.{cam}` or `observation.pixels.{cam}` -> `observation.images.{cam}` + - `environment_state` or `observation.environment_state` -> `observation.environment_state` + - `agent_pos` or `observation.agent_pos` -> `observation.state` + + Args: + features: The policy features dictionary with Gym-style keys. + + Returns: + The policy features dictionary with standardized LeRobot keys. + """ + # Build a new features mapping keyed by the same FeatureType buckets + # We assume callers already placed features in the correct FeatureType. + new_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = {ft: {} for ft in features} + exact_pairs = { "pixels": OBS_IMAGE, "environment_state": OBS_ENV_STATE, @@ -129,29 +164,43 @@ def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, Poli "pixels.": f"{OBS_IMAGES}.", } - for key in list(features.keys()): - matched_prefix = False - for old_prefix, new_prefix in prefix_pairs.items(): - prefixed_old = f"observation.{old_prefix}" - if key.startswith(prefixed_old): - suffix = key[len(prefixed_old) :] - features[f"{new_prefix}{suffix}"] = features.pop(key) - matched_prefix = True - break - - if key.startswith(old_prefix): - suffix = key[len(old_prefix) :] - features[f"{new_prefix}{suffix}"] = features.pop(key) - matched_prefix = True - break - - if matched_prefix: - continue - - for old, new in exact_pairs.items(): - if key == old or key == f"observation.{old}": - if key in features: - features[new] = features.pop(key) + # Iterate over all incoming feature buckets and normalize/move each entry + for src_ft, bucket in features.items(): + for key, feat in list(bucket.items()): + handled = False + + # Prefix-based rules (e.g. pixels.cam1 -> OBS_IMAGES.cam1) + for old_prefix, new_prefix in prefix_pairs.items(): + prefixed_old = f"{OBS_STR}.{old_prefix}" + if key.startswith(prefixed_old): + suffix = key[len(prefixed_old) :] + new_key = f"{new_prefix}{suffix}" + new_features[src_ft][new_key] = feat + handled = True break - return features + if key.startswith(old_prefix): + suffix = key[len(old_prefix) :] + new_key = f"{new_prefix}{suffix}" + new_features[src_ft][new_key] = feat + handled = True + break + + if handled: + continue + + # Exact-name rules (pixels, environment_state, agent_pos) + for old, new in exact_pairs.items(): + if key == old or key == f"{OBS_STR}.{old}": + new_key = new + new_features[src_ft][new_key] = feat + handled = True + break + + if handled: + continue + + # Default: keep key in the same source FeatureType bucket + new_features[src_ft][key] = feat + + return new_features diff --git a/src/lerobot/processor/pipeline.py b/src/lerobot/processor/pipeline.py index 6e1b2a2cb10..e14d8b0b9c1 100644 --- a/src/lerobot/processor/pipeline.py +++ b/src/lerobot/processor/pipeline.py @@ -13,72 +13,76 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +""" +This module defines a generic, sequential data processing pipeline framework, primarily designed for +transforming robotics data (observations, actions, rewards, etc.). + +The core components are: +- ProcessorStep: An abstract base class for a single data transformation operation. +- ProcessorStepRegistry: A mechanism to register and retrieve ProcessorStep classes by name. +- DataProcessorPipeline: A class that chains multiple ProcessorStep instances together to form a complete + data processing workflow. It integrates with the Hugging Face Hub for easy sharing and versioning of + pipelines, including their configuration and state. +- Specialized abstract ProcessorStep subclasses (e.g., ObservationProcessorStep, ActionProcessorStep) + to simplify the creation of steps that target specific parts of a data transition. +""" + from __future__ import annotations import importlib import json import os +import re +from abc import ABC, abstractmethod from collections.abc import Callable, Iterable, Sequence from copy import deepcopy from dataclasses import dataclass, field -from enum import Enum from pathlib import Path -from typing import Any, Protocol, TypedDict +from typing import Any, Generic, TypeAlias, TypedDict, TypeVar, cast import torch -from huggingface_hub import ModelHubMixin, hf_hub_download -from huggingface_hub.errors import HfHubHTTPError +from huggingface_hub import hf_hub_download from safetensors.torch import load_file, save_file -from lerobot.configs.types import PolicyFeature - +from lerobot.configs.types import PipelineFeatureType, PolicyFeature +from lerobot.utils.hub import HubMixin -class TransitionKey(str, Enum): - """Keys for accessing EnvTransition dictionary components.""" +from .converters import batch_to_transition, create_transition, transition_to_batch +from .core import EnvAction, EnvTransition, PolicyAction, RobotAction, TransitionKey - # TODO(Steven): Use consts - OBSERVATION = "observation" - ACTION = "action" - REWARD = "reward" - DONE = "done" - TRUNCATED = "truncated" - INFO = "info" - COMPLEMENTARY_DATA = "complementary_data" +# Generic type variables for pipeline input and output. +TInput = TypeVar("TInput") +TOutput = TypeVar("TOutput") -EnvTransition = TypedDict( - "EnvTransition", - { - TransitionKey.OBSERVATION.value: dict[str, Any] | None, - TransitionKey.ACTION.value: Any | torch.Tensor | None, - TransitionKey.REWARD.value: float | torch.Tensor | None, - TransitionKey.DONE.value: bool | torch.Tensor | None, - TransitionKey.TRUNCATED.value: bool | torch.Tensor | None, - TransitionKey.INFO.value: dict[str, Any] | None, - TransitionKey.COMPLEMENTARY_DATA.value: dict[str, Any] | None, - }, -) +class ProcessorStepRegistry: + """A registry for ProcessorStep classes to allow instantiation from a string name. + This class provides a way to map string identifiers to `ProcessorStep` classes, + which is useful for deserializing pipelines from configuration files without -class ProcessorStepRegistry: - """Registry for processor steps that enables saving/loading by name instead of module path.""" + hardcoding class imports. + """ _registry: dict[str, type] = {} @classmethod - def register(cls, name: str = None): - """Decorator to register a processor step class. + def register(cls, name: str | None = None): + """A class decorator to register a ProcessorStep. Args: - name: Optional registration name. If not provided, uses class name. + name: The name to register the class under. If None, the class's `__name__` is used. - Example: - @ProcessorStepRegistry.register("adaptive_normalizer") - class AdaptiveObservationNormalizer: - ... + Returns: + A decorator function that registers the class and returns it. + + Raises: + ValueError: If a step with the same name is already registered. """ def decorator(step_class: type) -> type: + """The actual decorator that performs the registration.""" registration_name = name if name is not None else step_class.__name__ if registration_name in cls._registry: @@ -88,7 +92,7 @@ def decorator(step_class: type) -> type: ) cls._registry[registration_name] = step_class - # Store the registration name on the class for later reference + # Store the registration name on the class for easy lookup during serialization. step_class._registry_name = registration_name return step_class @@ -96,16 +100,16 @@ def decorator(step_class: type) -> type: @classmethod def get(cls, name: str) -> type: - """Get a registered processor step class by name. + """Retrieves a processor step class from the registry by its name. Args: - name: The registration name of the step. + name: The name of the step to retrieve. Returns: - The registered step class. + The processor step class corresponding to the given name. Raises: - KeyError: If the step is not registered. + KeyError: If the name is not found in the registry. """ if name not in cls._registry: available = list(cls._registry.keys()) @@ -118,310 +122,231 @@ def get(cls, name: str) -> type: @classmethod def unregister(cls, name: str) -> None: - """Remove a step from the registry.""" + """Removes a processor step from the registry. + + Args: + name: The name of the step to unregister. + """ cls._registry.pop(name, None) @classmethod def list(cls) -> list[str]: - """List all registered step names.""" + """Returns a list of all registered processor step names.""" return list(cls._registry.keys()) @classmethod def clear(cls) -> None: - """Clear all registrations.""" + """Clears all processor steps from the registry.""" cls._registry.clear() -class ProcessorStep(Protocol): - """Structural typing interface for a single processor step. - - A step is any callable accepting a full `EnvTransition` dict and - returning a (possibly modified) dict of the same structure. Implementers - are encouraged—but not required—to expose the optional helper methods - listed below. When present, these hooks let `RobotProcessor` - automatically serialise the step's configuration and learnable state using - a safe-to-share JSON + SafeTensors format. - - - **Required**: - - ``__call__(transition: EnvTransition) -> EnvTransition`` - - ``feature_contract(features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]`` - - Optional helper protocol: - * ``get_config() -> dict[str, Any]`` – User-defined JSON-serializable - configuration and state. YOU decide what to save here. This is where all - non-tensor state goes (e.g., name, counter, threshold, window_size). - The config dict will be passed to your class constructor when loading. - * ``state_dict() -> dict[str, torch.Tensor]`` – PyTorch tensor state ONLY. - This is exclusively for torch.Tensor objects (e.g., learned weights, - running statistics as tensors). Never put simple Python types here. - * ``load_state_dict(state)`` – Inverse of ``state_dict``. Receives a dict - containing torch tensors only. - * ``reset()`` – Clear internal buffers at episode boundaries. - - Example separation: - - get_config(): {"name": "my_step", "learning_rate": 0.01, "window_size": 10} - - state_dict(): {"weights": torch.tensor(...), "running_mean": torch.tensor(...)} +class ProcessorStep(ABC): + """Abstract base class for a single step in a data processing pipeline. + + Each step must implement the `__call__` method to perform its transformation + on a data transition and the `transform_features` method to describe how it + alters the shape or type of data features. + + Subclasses can optionally be stateful by implementing `state_dict` and `load_state_dict`. """ - def __call__(self, transition: EnvTransition) -> EnvTransition: ... + _current_transition: EnvTransition | None = None + + @property + def transition(self) -> EnvTransition: + """Provides access to the most recent transition being processed. + + This is useful for steps that need to access other parts of the transition + data beyond their primary target (e.g., an action processing step that + needs to look at the observation). + + Raises: + ValueError: If accessed before the step has been called with a transition. + """ + if self._current_transition is None: + raise ValueError("Transition is not set. Make sure to call the step with a transition first.") + return self._current_transition + + @abstractmethod + def __call__(self, transition: EnvTransition) -> EnvTransition: + """Processes an environment transition. + + This method should contain the core logic of the processing step. + + Args: + transition: The input data transition to be processed. + + Returns: + The processed transition. + """ + return transition - def get_config(self) -> dict[str, Any]: ... + def get_config(self) -> dict[str, Any]: + """Returns the configuration of the step for serialization. - def state_dict(self) -> dict[str, torch.Tensor]: ... + Returns: + A JSON-serializable dictionary of configuration parameters. + """ + return {} - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: ... + def state_dict(self) -> dict[str, torch.Tensor]: + """Returns the state of the step (e.g., learned parameters, running means). - def reset(self) -> None: ... + Returns: + A dictionary mapping state names to tensors. + """ + return {} - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: ... + def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: + """Loads the step's state from a state dictionary. + Args: + state: A dictionary of state tensors. + """ + return None -def _default_batch_to_transition(batch: dict[str, Any]) -> EnvTransition: # noqa: D401 - """Convert a *batch* dict coming from Learobot replay/dataset code into an - ``EnvTransition`` dictionary. + def reset(self) -> None: + """Resets the internal state of the processor step, if any.""" + return None - The function maps well known keys to the EnvTransition structure. Missing keys are - filled with sane defaults (``None`` or ``0.0``/``False``). + @abstractmethod + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """Defines how this step modifies the description of pipeline features. - Keys recognised (case-sensitive): + This method is used to track changes in data shapes, dtypes, or modalities + as data flows through the pipeline, without needing to process actual data. - * "observation.*" (keys starting with "observation." are grouped into observation dict) - * "action" - * "next.reward" - * "next.done" - * "next.truncated" - * "info" + Args: + features: A dictionary describing the input features for observations, actions, etc. - Additional keys are ignored so that existing dataloaders can carry extra - metadata without breaking the processor. - """ + Returns: + A dictionary describing the output features after this step's transformation. + """ + return features - # Extract observation keys - observation_keys = {k: v for k, v in batch.items() if k.startswith("observation.")} - observation = observation_keys if observation_keys else None - - # Extract padding and task keys for complementary data - pad_keys = {k: v for k, v in batch.items() if "_is_pad" in k} - task_key = {"task": batch["task"]} if "task" in batch else {} - complementary_data = {**pad_keys, **task_key} if pad_keys or task_key else {} - - transition: EnvTransition = { - TransitionKey.OBSERVATION: observation, - TransitionKey.ACTION: batch.get("action"), - TransitionKey.REWARD: batch.get("next.reward", 0.0), - TransitionKey.DONE: batch.get("next.done", False), - TransitionKey.TRUNCATED: batch.get("next.truncated", False), - TransitionKey.INFO: batch.get("info", {}), - TransitionKey.COMPLEMENTARY_DATA: complementary_data, - } - return transition - - -def _default_transition_to_batch(transition: EnvTransition) -> dict[str, Any]: # noqa: D401 - """Inverse of :pyfunc:`_default_batch_to_transition`. Returns a dict with - the canonical field names used throughout *LeRobot*. - """ - batch = { - "action": transition.get(TransitionKey.ACTION), - "next.reward": transition.get(TransitionKey.REWARD, 0.0), - "next.done": transition.get(TransitionKey.DONE, False), - "next.truncated": transition.get(TransitionKey.TRUNCATED, False), - "info": transition.get(TransitionKey.INFO, {}), - } +class ProcessorKwargs(TypedDict, total=False): + """A TypedDict for optional keyword arguments used in pipeline construction.""" - # Add padding and task data from complementary_data - complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA) - if complementary_data: - pad_data = {k: v for k, v in complementary_data.items() if "_is_pad" in k} - batch.update(pad_data) + to_transition: Callable[[dict[str, Any]], EnvTransition] | None + to_output: Callable[[EnvTransition], Any] | None + name: str | None + before_step_hooks: list[Callable[[int, EnvTransition], None]] | None + after_step_hooks: list[Callable[[int, EnvTransition], None]] | None - if "task" in complementary_data: - batch["task"] = complementary_data["task"] - # Handle observation - flatten dict to observation.* keys if it's a dict - observation = transition.get(TransitionKey.OBSERVATION) - if isinstance(observation, dict): - batch.update(observation) +class ProcessorMigrationError(Exception): + """Raised when a model needs migration to the processor format""" - return batch + def __init__(self, model_path: str | Path, migration_command: str, original_error: str): + self.model_path = model_path + self.migration_command = migration_command + self.original_error = original_error + super().__init__( + f"Model '{model_path}' requires migration to processor format. " + f"Run: {migration_command}\n\nOriginal error: {original_error}" + ) @dataclass -class RobotProcessor(ModelHubMixin): - """ - Composable, debuggable post-processing processor for robot transitions. - - The class orchestrates an ordered collection of small, functional transforms—steps—executed - left-to-right on each incoming `EnvTransition`. It can process both `EnvTransition` dicts - and batch dictionaries, automatically converting between formats as needed. - - Args: - steps: Ordered list of processing steps executed on every call. Defaults to empty list. - name: Human-readable identifier that is persisted inside the JSON config. - Defaults to "RobotProcessor". - to_transition: Function to convert batch dict to EnvTransition dict. - Defaults to _default_batch_to_transition. - to_output: Function to convert EnvTransition dict to the desired output format. - Usually it is a batch dict or EnvTransition dict. - Defaults to _default_transition_to_batch. - before_step_hooks: List of hooks called before each step. Each hook receives the step - index and transition, and can optionally return a modified transition. - after_step_hooks: List of hooks called after each step. Each hook receives the step - index and transition, and can optionally return a modified transition. - - Hook Semantics: - - Hooks are executed sequentially in the order they were registered. There is no way to - reorder hooks after registration without creating a new pipeline. - - Hooks are for observation/monitoring only and DO NOT modify transitions. They are called - with the step index and current transition for logging, debugging, or monitoring purposes. - - All hooks for a given type (before/after) are executed for every step, or none at all if - an error occurs. There is no partial execution of hooks. - - Hooks should generally be stateless to maintain predictable behavior. If you need stateful - processing, consider implementing a proper ProcessorStep instead. - - To remove hooks, use the unregister methods. To remove steps, you must create a new pipeline. - - Hooks ALWAYS receive transitions in EnvTransition format, regardless of the input format - passed to __call__. This ensures consistent hook behavior whether processing batch dicts - or EnvTransition objects. +class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]): + """A sequential pipeline for processing data, integrated with the Hugging Face Hub. + + This class chains together multiple `ProcessorStep` instances to form a complete + data processing workflow. It's generic, allowing for custom input and output types, + which are handled by the `to_transition` and `to_output` converters. + + Attributes: + steps: A sequence of `ProcessorStep` objects that make up the pipeline. + name: A descriptive name for the pipeline. + to_transition: A function to convert raw input data into the standardized `EnvTransition` format. + to_output: A function to convert the final `EnvTransition` into the desired output format. + before_step_hooks: A list of functions to be called before each step is executed. + after_step_hooks: A list of functions to be called after each step is executed. """ steps: Sequence[ProcessorStep] = field(default_factory=list) - name: str = "RobotProcessor" + name: str = "DataProcessorPipeline" - to_transition: Callable[[dict[str, Any]], EnvTransition] = field( - default_factory=lambda: _default_batch_to_transition, repr=False + to_transition: Callable[[TInput], EnvTransition] = field( + default_factory=lambda: cast(Callable[[TInput], EnvTransition], batch_to_transition), repr=False ) - to_output: Callable[[EnvTransition], dict[str, Any] | EnvTransition] = field( - default_factory=lambda: _default_transition_to_batch, repr=False + to_output: Callable[[EnvTransition], TOutput] = field( + default_factory=lambda: cast(Callable[[EnvTransition], TOutput], transition_to_batch), + repr=False, ) - # Processor-level hooks for observation/monitoring - # Hooks do not modify transitions - they are called for logging, debugging, or monitoring purposes before_step_hooks: list[Callable[[int, EnvTransition], None]] = field(default_factory=list, repr=False) after_step_hooks: list[Callable[[int, EnvTransition], None]] = field(default_factory=list, repr=False) - def __call__(self, data: EnvTransition | dict[str, Any]): - """Process data through all steps. - - The method accepts either the classic EnvTransition dict or a batch dictionary - (like the ones returned by ReplayBuffer or LeRobotDataset). If a dict is supplied - it is first converted to the internal dict format using to_transition; after all - steps are executed the dict is transformed back into a batch dict with to_batch and the - result is returned – thereby preserving the caller's original data type. + def __call__(self, data: TInput) -> TOutput: + """Processes input data through the full pipeline. Args: - data: Either an EnvTransition dict or a batch dictionary to process. + data: The input data to process. Returns: - The processed data in the same format as the input (EnvTransition or batch dict). - - Raises: - ValueError: If the transition is not a valid EnvTransition format. + The processed data in the specified output format. """ - # Check if we need to convert back to batch format at the end - _, called_with_batch = self._prepare_transition(data) - - # Use step_through to get the iterator - step_iterator = self.step_through(data) + transition = self.to_transition(data) + transformed_transition = self._forward(transition) + return self.to_output(transformed_transition) - # Get initial state (before any steps) - current_transition = next(step_iterator) - - # Process each step with hooks - for idx, next_transition in enumerate(step_iterator): - # Apply before hooks with current state (before step execution) - for hook in self.before_step_hooks: - hook(idx, current_transition) - - # Move to next state (after step execution) - current_transition = next_transition - - # Apply after hooks with updated state - for hook in self.after_step_hooks: - hook(idx, current_transition) - - # Convert back to original format if needed - return self.to_output(current_transition) if called_with_batch else current_transition - - def _prepare_transition(self, data: EnvTransition | dict[str, Any]) -> tuple[EnvTransition, bool]: - """Prepare and validate transition data for processing. + def _forward(self, transition: EnvTransition) -> EnvTransition: + """Executes all processing steps and hooks in sequence. Args: - data: Either an EnvTransition dict or a batch dictionary to process. + transition: The initial `EnvTransition` object. Returns: - A tuple of (prepared_transition, called_with_batch_flag) - - Raises: - ValueError: If the transition is not a valid EnvTransition format. + The final `EnvTransition` after all steps have been applied. """ - # Check if data is already an EnvTransition or needs conversion - if isinstance(data, dict) and not all(isinstance(k, TransitionKey) for k in data.keys()): - # It's a batch dict, convert it - called_with_batch = True - transition = self.to_transition(data) - else: - # It's already an EnvTransition - called_with_batch = False - transition = data - - # Basic validation - if not isinstance(transition, dict): - raise ValueError(f"EnvTransition must be a dictionary. Got {type(transition).__name__}") + for idx, processor_step in enumerate(self.steps): + # Execute pre-hooks + for hook in self.before_step_hooks: + hook(idx, transition) - return transition, called_with_batch + transition = processor_step(transition) - def step_through(self, data: EnvTransition | dict[str, Any]) -> Iterable[EnvTransition]: - """Yield the intermediate results after each processor step. + # Execute post-hooks + for hook in self.after_step_hooks: + hook(idx, transition) + return transition - This is a low-level method that does NOT apply hooks. It simply executes each step - and yields the intermediate results. This allows users to debug the pipeline or - apply custom logic between steps if needed. + def step_through(self, data: TInput) -> Iterable[EnvTransition]: + """Processes data step-by-step, yielding the transition at each stage. - Note: This method always yields EnvTransition objects regardless of input format. - If you need the results in the original input format, you'll need to convert them - using `to_output()`. + This is a generator method useful for debugging and inspecting the intermediate + state of the data as it passes through the pipeline. Args: - data: Either an EnvTransition dict or a batch dictionary to process. + data: The input data. Yields: - The intermediate EnvTransition results after each step. + The `EnvTransition` object, starting with the initial state and then after + each processing step. """ - transition, _ = self._prepare_transition(data) + transition = self.to_transition(data) - # Yield initial state + # Yield the initial state before any processing. yield transition - # Process each step WITHOUT hooks (low-level method) for processor_step in self.steps: transition = processor_step(transition) yield transition def _save_pretrained(self, save_directory: Path, **kwargs): - """Internal save method for ModelHubMixin compatibility.""" - # Extract config_filename from kwargs if provided - config_filename = kwargs.pop("config_filename", None) - self.save_pretrained(save_directory, config_filename=config_filename) - - def save_pretrained(self, save_directory: str | Path, config_filename: str | None = None, **kwargs): - """Serialize the processor definition and parameters to *save_directory*. + """Internal method to comply with `HubMixin`'s saving mechanism. - Args: - save_directory: Directory where the processor will be saved. - config_filename: Optional custom config filename. If not provided, defaults to - "{self.name}.json" where self.name is sanitized for filesystem compatibility. + This method does the actual saving work and is called by HubMixin.save_pretrained. """ - os.makedirs(str(save_directory), exist_ok=True) - - # Sanitize processor name for use in filenames - import re + config_filename = kwargs.pop("config_filename", None) - # The huggingface hub does not allow special characters in the repo name, so we sanitize the name + # Sanitize the pipeline name to create a valid filename prefix. sanitized_name = re.sub(r"[^a-zA-Z0-9_]", "_", self.name.lower()) - # Use sanitized name for config if not provided if config_filename is None: config_filename = f"{sanitized_name}.json" @@ -430,40 +355,31 @@ def save_pretrained(self, save_directory: str | Path, config_filename: str | Non "steps": [], } + # Iterate through each step to build its configuration entry. for step_index, processor_step in enumerate(self.steps): - # Check if step was registered registry_name = getattr(processor_step.__class__, "_registry_name", None) step_entry: dict[str, Any] = {} + # Prefer registry name for portability, otherwise fall back to full class path. if registry_name: - # Use registry name for registered steps step_entry["registry_name"] = registry_name else: - # Fall back to full module path for unregistered steps step_entry["class"] = ( f"{processor_step.__class__.__module__}.{processor_step.__class__.__name__}" ) + # Save step configuration if `get_config` is implemented. if hasattr(processor_step, "get_config"): step_entry["config"] = processor_step.get_config() + # Save step state if `state_dict` is implemented and returns a non-empty dict. if hasattr(processor_step, "state_dict"): state = processor_step.state_dict() if state: - # Clone tensors to avoid shared memory issues - # This ensures each tensor has its own memory allocation - # The reason is to avoid the following error: - # RuntimeError: Some tensors share memory, this will lead to duplicate memory on disk - # and potential differences when loading them again - # ------------------------------------------------------------------------------ - # Since the state_dict of processor will be light, we can just clone the tensors - # and save them to the disk. - cloned_state = {} - for key, tensor in state.items(): - cloned_state[key] = tensor.clone() - - # Include pipeline name and step index to ensure unique filenames - # This prevents conflicts when multiple processors are saved in the same directory + # Clone tensors to avoid modifying the original state. + cloned_state = {key: tensor.clone() for key, tensor in state.items()} + + # Create a unique filename for the state file. if registry_name: state_filename = f"{sanitized_name}_step_{step_index}_{registry_name}.safetensors" else: @@ -474,13 +390,69 @@ def save_pretrained(self, save_directory: str | Path, config_filename: str | Non config["steps"].append(step_entry) + # Write the main configuration JSON file. with open(os.path.join(str(save_directory), config_filename), "w") as file_pointer: json.dump(config, file_pointer, indent=2) + def save_pretrained( + self, + save_directory: str | Path | None = None, + *, + repo_id: str | None = None, + push_to_hub: bool = False, + card_kwargs: dict[str, Any] | None = None, + config_filename: str | None = None, + **push_to_hub_kwargs, + ): + """Saves the pipeline's configuration and state to a directory. + + This method creates a JSON configuration file that defines the pipeline's structure + (name and steps). For each stateful step, it also saves a `.safetensors` file + containing its state dictionary. + + Args: + save_directory: The directory where the pipeline will be saved. If None, saves to + HF_LEROBOT_HOME/processors/{sanitized_pipeline_name}. + repo_id: ID of your repository on the Hub. Used only if `push_to_hub=True`. + push_to_hub: Whether or not to push your object to the Hugging Face Hub after saving it. + card_kwargs: Additional arguments passed to the card template to customize the card. + config_filename: The name of the JSON configuration file. If None, a name is + generated from the pipeline's `name` attribute. + **push_to_hub_kwargs: Additional key word arguments passed along to the push_to_hub method. + """ + if save_directory is None: + # Use default directory in HF_LEROBOT_HOME + from lerobot.utils.constants import HF_LEROBOT_HOME + + sanitized_name = re.sub(r"[^a-zA-Z0-9_]", "_", self.name.lower()) + save_directory = HF_LEROBOT_HOME / "processors" / sanitized_name + + # For direct saves (not through hub), handle config_filename + if not push_to_hub and config_filename is not None: + # Call _save_pretrained directly with config_filename + save_directory = Path(save_directory) + save_directory.mkdir(parents=True, exist_ok=True) + self._save_pretrained(save_directory, config_filename=config_filename) + return None + + # Pass config_filename through kwargs for _save_pretrained when using hub + if config_filename is not None: + push_to_hub_kwargs["config_filename"] = config_filename + + # Call parent's save_pretrained which will call our _save_pretrained + return super().save_pretrained( + save_directory=save_directory, + repo_id=repo_id, + push_to_hub=push_to_hub, + card_kwargs=card_kwargs, + **push_to_hub_kwargs, + ) + @classmethod def from_pretrained( cls, pretrained_model_name_or_path: str | Path, + config_filename: str, *, force_download: bool = False, resume_download: bool | None = None, @@ -489,267 +461,798 @@ def from_pretrained( cache_dir: str | Path | None = None, local_files_only: bool = False, revision: str | None = None, - config_filename: str | None = None, overrides: dict[str, Any] | None = None, + to_transition: Callable[[TInput], EnvTransition] | None = None, + to_output: Callable[[EnvTransition], TOutput] | None = None, **kwargs, - ) -> RobotProcessor: - """Load a serialized processor from source (local path or Hugging Face Hub identifier). + ) -> DataProcessorPipeline[TInput, TOutput]: + """Loads a pipeline from a local directory, single file, or Hugging Face Hub repository. + + This method implements a simplified loading pipeline with intelligent migration detection: + + **Simplified Loading Strategy**: + 1. **Config Loading** (_load_config): + - **Directory**: Load specified config_filename from directory + - **Single file**: Load file directly (config_filename ignored) + - **Hub repository**: Download specified config_filename from Hub + + 2. **Config Validation** (_validate_loaded_config): + - Format validation: Ensure config is valid processor format + - Migration detection: Guide users to migrate old LeRobot models + - Clear errors: Provide actionable error messages + + 3. **Step Construction** (_build_steps_with_overrides): + - Class resolution: Registry lookup or dynamic imports + - Override merging: User parameters override saved config + - State loading: Load .safetensors files for stateful steps + + 4. **Override Validation** (_validate_overrides_used): + - Ensure all user overrides were applied (catch typos) + - Provide helpful error messages with available keys + + **Migration Detection**: + - **Smart detection**: Analyzes JSON files to detect old LeRobot models + - **Precise targeting**: Avoids false positives on other HuggingFace models + - **Clear guidance**: Provides exact migration command to run + - **Error mode**: Always raises ProcessorMigrationError for clear user action + + **Loading Examples**: + ```python + # Directory loading + pipeline = DataProcessorPipeline.from_pretrained("/models/my_model", config_filename="processor.json") + + # Single file loading + pipeline = DataProcessorPipeline.from_pretrained( + "/models/my_model/processor.json", config_filename="processor.json" + ) + + # Hub loading + pipeline = DataProcessorPipeline.from_pretrained("user/repo", config_filename="processor.json") + + # Multiple configs (preprocessor/postprocessor) + preprocessor = DataProcessorPipeline.from_pretrained( + "model", config_filename="policy_preprocessor.json" + ) + postprocessor = DataProcessorPipeline.from_pretrained( + "model", config_filename="policy_postprocessor.json" + ) + ``` + + **Override System**: + - **Key matching**: Use registry names or class names as override keys + - **Config merging**: User overrides take precedence over saved config + - **Validation**: Ensure all override keys match actual steps (catch typos) + - **Example**: overrides={"NormalizeStep": {"device": "cuda"}} Args: - pretrained_model_name_or_path: Local path to a saved processor directory or Hugging Face Hub identifier - (e.g., "username/processor-name"). - config_filename: Optional specific config filename to load. If not provided, will: - - For local paths: look for any .json file in the directory (error if multiple found) - - For HF Hub: try common names ("processor.json", "preprocessor.json", "postprocessor.json") - overrides: Optional dictionary mapping step names to configuration overrides. - Keys must match exact step class names (for unregistered steps) or registry names - (for registered steps). Values are dictionaries containing parameter overrides - that will be merged with the saved configuration. This is useful for providing - non-serializable objects like environment instances. + pretrained_model_name_or_path: The identifier of the repository on the Hugging Face Hub, + a path to a local directory, or a path to a single config file. + config_filename: The name of the pipeline's JSON configuration file. Always required + to prevent ambiguity when multiple configs exist (e.g., preprocessor vs postprocessor). + force_download: Whether to force (re)downloading the files. + resume_download: Whether to resume a previously interrupted download. + proxies: A dictionary of proxy servers to use. + token: The token to use as HTTP bearer authorization for private Hub repositories. + cache_dir: The path to a specific cache folder to store downloaded files. + local_files_only: If True, avoid downloading files from the Hub. + revision: The specific model version to use (e.g., a branch name, tag name, or commit id). + overrides: A dictionary to override the configuration of specific steps. Keys should + match the step's class name or registry name. + to_transition: A custom function to convert input data to `EnvTransition`. + to_output: A custom function to convert the final `EnvTransition` to the output format. + **kwargs: Additional arguments (not used). Returns: - A RobotProcessor instance loaded from the saved configuration. + An instance of `DataProcessorPipeline` loaded with the specified configuration and state. Raises: - ImportError: If a processor step class cannot be loaded or imported. - ValueError: If a step cannot be instantiated with the provided configuration. - KeyError: If an override key doesn't match any step in the saved configuration. - - Examples: - Basic loading: - ```python - processor = RobotProcessor.from_pretrained("path/to/processor") - ``` - - Loading specific config file: - ```python - processor = RobotProcessor.from_pretrained( - "username/multi-processor-repo", config_filename="preprocessor.json" - ) - ``` + FileNotFoundError: If the config file cannot be found. + ValueError: If configuration is ambiguous or instantiation fails. + ImportError: If a step's class cannot be imported. + KeyError: If an override key doesn't match any step in the pipeline. + ProcessorMigrationError: If the model requires migration to processor format. + """ + model_id = str(pretrained_model_name_or_path) + hub_download_kwargs = { + "force_download": force_download, + "resume_download": resume_download, + "proxies": proxies, + "token": token, + "cache_dir": cache_dir, + "local_files_only": local_files_only, + "revision": revision, + } - Loading with overrides for non-serializable objects: - ```python - import gym + # 1. Load configuration using simplified 3-way logic + loaded_config, base_path = cls._load_config(model_id, config_filename, hub_download_kwargs) - env = gym.make("CartPole-v1") - processor = RobotProcessor.from_pretrained( - "username/cartpole-processor", overrides={"ActionRepeatStep": {"env": env}} - ) - ``` - - Multiple overrides: - ```python - processor = RobotProcessor.from_pretrained( - "path/to/processor", - overrides={ - "CustomStep": {"param1": "new_value"}, - "device_processor": {"device": "cuda:1"}, # For registered steps - }, - ) - ``` + # 2. Validate configuration and handle migration + cls._validate_loaded_config(model_id, loaded_config, config_filename) + + # 3. Build steps with overrides + steps, validated_overrides = cls._build_steps_with_overrides( + loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs + ) + + # 4. Validate that all overrides were used + cls._validate_overrides_used(validated_overrides, loaded_config) + + # 5. Construct and return the final pipeline instance + return cls( + steps=steps, + name=loaded_config.get("name", "DataProcessorPipeline"), + to_transition=to_transition or cast(Callable[[TInput], EnvTransition], batch_to_transition), + to_output=to_output or cast(Callable[[EnvTransition], TOutput], transition_to_batch), + ) + + @classmethod + def _load_config( + cls, + model_id: str, + config_filename: str, + hub_download_kwargs: dict[str, Any], + ) -> tuple[dict[str, Any], Path]: + """Load configuration from local file or Hugging Face Hub. + + This method implements a super-simplified 3-way loading strategy: + + 1. **Local directory**: Load config_filename from directory + - Example: model_id="/models/my_model", config_filename="processor.json" + - Loads: "/models/my_model/processor.json" + + 2. **Single file**: Load file directly (ignore config_filename) + - Example: model_id="/models/my_model/processor.json" + - Loads: "/models/my_model/processor.json" (config_filename ignored) + + 3. **Hub repository**: Download config_filename from Hub + - Example: model_id="user/repo", config_filename="processor.json" + - Downloads and loads: config_filename from Hub repo + + **Benefits of Explicit config_filename**: + - No auto-detection complexity or edge cases + - No risk of loading wrong config (preprocessor vs postprocessor) + - Consistent behavior across local and Hub usage + - Clear, predictable errors + + Args: + model_id: The model identifier (Hub repo ID, local directory, or file path) + config_filename: The explicit config filename to load (always required) + hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.) + + Returns: + Tuple of (loaded_config, base_path) + - loaded_config: Parsed JSON config dict (always loaded, never None) + - base_path: Directory containing config file (for state file resolution) + + Raises: + FileNotFoundError: If config file cannot be found locally or on Hub """ - # Use the local variable name 'source' for clarity - source = str(pretrained_model_name_or_path) - - if Path(source).is_dir(): - # Local path - use it directly - base_path = Path(source) - - if config_filename is None: - # Look for any .json file in the directory - json_files = list(base_path.glob("*.json")) - if len(json_files) == 0: - raise FileNotFoundError(f"No .json configuration files found in {source}") - elif len(json_files) > 1: - raise ValueError( - f"Multiple .json files found in {source}: {[f.name for f in json_files]}. " - f"Please specify which one to load using the config_filename parameter." - ) - config_filename = json_files[0].name - - with open(base_path / config_filename) as file_pointer: - loaded_config: dict[str, Any] = json.load(file_pointer) + model_path = Path(model_id) + + if model_path.is_dir(): + # Directory: load specified config from directory + config_path = model_path / config_filename + if not config_path.exists(): + # Check for migration before giving clear error + if cls._should_suggest_migration(model_path): + cls._suggest_processor_migration(model_id, f"Config file '{config_filename}' not found") + raise FileNotFoundError( + f"Config file '{config_filename}' not found in directory '{model_id}'" + ) + + with open(config_path) as f: + return json.load(f), model_path + + elif model_path.is_file(): + # File: load file directly (config_filename is ignored for single files) + with open(model_path) as f: + return json.load(f), model_path.parent + else: - # Hugging Face Hub - download all required files - if config_filename is None: - # Try common config names - common_names = [ - "processor.json", - "preprocessor.json", - "postprocessor.json", - "robotprocessor.json", - ] - config_path = None - for name in common_names: - try: - config_path = hf_hub_download( - source, - name, - repo_type="model", - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - token=token, - cache_dir=cache_dir, - local_files_only=local_files_only, - revision=revision, - ) - config_filename = name - break - except (FileNotFoundError, OSError, HfHubHTTPError): - # FileNotFoundError: local file issues - # OSError: network/system errors - # HfHubHTTPError: file not found on Hub (404) or other HTTP errors - continue - - if config_path is None: - raise FileNotFoundError( - f"No processor configuration file found in {source}. " - f"Tried: {common_names}. Please specify the config_filename parameter." - ) - else: - # Download specific config file + # Hub: download specified config + try: config_path = hf_hub_download( - source, - config_filename, + repo_id=model_id, + filename=config_filename, repo_type="model", - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - token=token, - cache_dir=cache_dir, - local_files_only=local_files_only, - revision=revision, + **hub_download_kwargs, ) - with open(config_path) as file_pointer: - loaded_config = json.load(file_pointer) + with open(config_path) as f: + return json.load(f), Path(config_path).parent - # Store downloaded files in the same directory as the config - base_path = Path(config_path).parent + except Exception as e: + raise FileNotFoundError( + f"Could not find '{config_filename}' on the HuggingFace Hub at '{model_id}'" + ) from e - # Handle None overrides - if overrides is None: - overrides = {} + @classmethod + def _validate_loaded_config( + cls, model_id: str, loaded_config: dict[str, Any], config_filename: str + ) -> None: + """Validate that a config was loaded and is a valid processor config. + + This method validates processor config format with intelligent migration detection: + + **Config Format Validation**: + - Use _is_processor_config() to validate structure + - Must have "steps" field with list of step configurations + - Each step needs "class" or "registry_name" + - If validation fails AND local directory: Check for migration need + - If migration needed: Raise ProcessorMigrationError with command + - If no migration: Raise ValueError with helpful error message + + **Migration Detection Logic**: + - Only triggered for local directories (not Hub repos) + - Analyzes all JSON files in directory to detect old LeRobot models + - Provides exact migration command with model path - # Validate that all override keys will be matched - override_keys = set(overrides.keys()) + Args: + model_id: The model identifier (used for migration detection) + loaded_config: The loaded config dictionary (guaranteed non-None) + config_filename: The config filename that was loaded (for error messages) + + Raises: + ValueError: If config format is invalid + ProcessorMigrationError: If model needs migration to processor format + """ + # Validate that this is actually a processor config + if not cls._is_processor_config(loaded_config): + if Path(model_id).is_dir() and cls._should_suggest_migration(Path(model_id)): + cls._suggest_processor_migration( + model_id, + f"Config file '{config_filename}' is not a valid processor configuration", + ) + raise ValueError( + f"Config file '{config_filename}' is not a valid processor configuration. " + f"Expected a config with 'steps' field, but got: {list(loaded_config.keys())}" + ) + + @classmethod + def _build_steps_with_overrides( + cls, + loaded_config: dict[str, Any], + overrides: dict[str, Any], + model_id: str, + base_path: Path | None, + hub_download_kwargs: dict[str, Any], + ) -> tuple[list[ProcessorStep], set[str]]: + """Build all processor steps with overrides and state loading. + + This method orchestrates the complete step construction pipeline: + + **For each step in loaded_config["steps"]**: + + 1. **Class Resolution** (via _resolve_step_class): + - **If "registry_name" exists**: Look up in ProcessorStepRegistry + Example: {"registry_name": "normalize_step"} -> Get registered class + - **Else use "class" field**: Dynamic import from full module path + Example: {"class": "lerobot.processor.normalize.NormalizeStep"} + - **Result**: (step_class, step_key) where step_key is used for overrides + + 2. **Step Instantiation** (via _instantiate_step): + - **Merge configs**: saved_config + user_overrides + - **Override priority**: User overrides take precedence over saved config + - **Example**: saved={"mean": 0.0}, override={"mean": 1.0} -> final={"mean": 1.0} + - **Result**: Instantiated ProcessorStep object + + 3. **State Loading** (via _load_step_state): + - **If step has "state_file"**: Load tensor state from .safetensors + - **Local first**: Check base_path/state_file.safetensors + - **Hub fallback**: Download state file if not found locally + - **Optional**: Only load if step has load_state_dict method + + 4. **Override Tracking**: + - **Track used overrides**: Remove step_key from remaining set + - **Purpose**: Validate all user overrides were applied (detect typos) + + **Error Handling**: + - Class resolution errors -> ImportError with helpful message + - Instantiation errors -> ValueError with config details + - State loading errors -> Propagated from load_state_dict + + Args: + loaded_config: The loaded processor configuration (must have "steps" field) + overrides: User-provided parameter overrides (keyed by class/registry name) + model_id: The model identifier (needed for Hub state file downloads) + base_path: Local directory path for finding state files + hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.) + Returns: + Tuple of (instantiated_steps_list, unused_override_keys) + - instantiated_steps_list: List of ready-to-use ProcessorStep instances + - unused_override_keys: Override keys that didn't match any step (for validation) + + Raises: + ImportError: If a step class cannot be imported or found in registry + ValueError: If a step cannot be instantiated with its configuration + """ steps: list[ProcessorStep] = [] + override_keys = set(overrides.keys()) + for step_entry in loaded_config["steps"]: - # Check if step uses registry name or module path - if "registry_name" in step_entry: - # Load from registry - try: - step_class = ProcessorStepRegistry.get(step_entry["registry_name"]) - step_key = step_entry["registry_name"] - except KeyError as e: - raise ImportError(f"Failed to load processor step from registry. {str(e)}") from e - else: - # Fall back to module path loading for backward compatibility - full_class_path = step_entry["class"] - module_path, class_name = full_class_path.rsplit(".", 1) - - # Import the module containing the step class - try: - module = importlib.import_module(module_path) - step_class = getattr(module, class_name) - step_key = class_name - except (ImportError, AttributeError) as e: - raise ImportError( - f"Failed to load processor step '{full_class_path}'. " - f"Make sure the module '{module_path}' is installed and contains class '{class_name}'. " - f"Consider registering the step using @ProcessorStepRegistry.register() for better portability. " - f"Error: {str(e)}" - ) from e - - # Instantiate the step with its config - try: - saved_cfg = step_entry.get("config", {}) - step_overrides = overrides.get(step_key, {}) - merged_cfg = {**saved_cfg, **step_overrides} - step_instance: ProcessorStep = step_class(**merged_cfg) + # 1. Get step class and key + step_class, step_key = cls._resolve_step_class(step_entry) - # Track which override keys were used - if step_key in override_keys: - override_keys.discard(step_key) + # 2. Instantiate step with overrides + step_instance = cls._instantiate_step(step_entry, step_class, step_key, overrides) - except Exception as e: - step_name = step_entry.get("registry_name", step_entry.get("class", "Unknown")) - raise ValueError( - f"Failed to instantiate processor step '{step_name}' with config: {step_entry.get('config', {})}. " + # 3. Load step state if available + cls._load_step_state(step_instance, step_entry, model_id, base_path, hub_download_kwargs) + + # 4. Track used overrides + if step_key in override_keys: + override_keys.discard(step_key) + + steps.append(step_instance) + + return steps, override_keys + + @classmethod + def _resolve_step_class(cls, step_entry: dict[str, Any]) -> tuple[type[ProcessorStep], str]: + """Resolve step class from registry or import path. + + This method implements a two-tier resolution strategy: + + **Tier 1: Registry-based resolution** (preferred): + - **If "registry_name" in step_entry**: Look up in ProcessorStepRegistry + - **Advantage**: Faster, no imports needed, guaranteed compatibility + - **Example**: {"registry_name": "normalize_step"} -> Get pre-registered class + - **Error**: KeyError if registry_name not found -> Convert to ImportError + + **Tier 2: Dynamic import fallback**: + - **Else use "class" field**: Full module.ClassName import path + - **Process**: Split "module.path.ClassName" into module + class parts + - **Import**: Use importlib.import_module() + getattr() + - **Example**: "lerobot.processor.normalize.NormalizeStep" + a. Import module: "lerobot.processor.normalize" + b. Get class: getattr(module, "NormalizeStep") + - **step_key**: Use class_name ("NormalizeStep") for overrides + + **Override Key Strategy**: + - Registry steps: Use registry_name ("normalize_step") + - Import steps: Use class_name ("NormalizeStep") + - This allows users to override with: {"normalize_step": {...}} or {"NormalizeStep": {...}} + + **Error Handling**: + - Registry KeyError -> ImportError with registry context + - Import/Attribute errors -> ImportError with helpful suggestions + - All errors include troubleshooting guidance + + Args: + step_entry: The step configuration dictionary (must have "registry_name" or "class") + + Returns: + Tuple of (step_class, step_key) + - step_class: The resolved ProcessorStep class (ready for instantiation) + - step_key: The key used for user overrides (registry_name or class_name) + + Raises: + ImportError: If step class cannot be loaded from registry or import path + """ + if "registry_name" in step_entry: + try: + step_class = ProcessorStepRegistry.get(step_entry["registry_name"]) + return step_class, step_entry["registry_name"] + except KeyError as e: + raise ImportError(f"Failed to load processor step from registry. {str(e)}") from e + else: + # Fallback to dynamic import using the full class path + full_class_path = step_entry["class"] + module_path, class_name = full_class_path.rsplit(".", 1) + + try: + module = importlib.import_module(module_path) + step_class = getattr(module, class_name) + return step_class, class_name + except (ImportError, AttributeError) as e: + raise ImportError( + f"Failed to load processor step '{full_class_path}'. " + f"Make sure the module '{module_path}' is installed and contains class '{class_name}'. " + f"Consider registering the step using @ProcessorStepRegistry.register() for better portability. " f"Error: {str(e)}" ) from e - # Load state if available - if "state_file" in step_entry and hasattr(step_instance, "load_state_dict"): - if Path(source).is_dir(): - # Local path - read directly - state_path = str(base_path / step_entry["state_file"]) - else: - # Hugging Face Hub - download the state file - state_path = hf_hub_download( - source, - step_entry["state_file"], - repo_type="model", - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - token=token, - cache_dir=cache_dir, - local_files_only=local_files_only, - revision=revision, - ) - - step_instance.load_state_dict(load_file(state_path)) + @classmethod + def _instantiate_step( + cls, + step_entry: dict[str, Any], + step_class: type[ProcessorStep], + step_key: str, + overrides: dict[str, Any], + ) -> ProcessorStep: + """Instantiate a single processor step with config overrides. + + This method handles the configuration merging and instantiation logic: + + **Configuration Merging Strategy**: + 1. **Extract saved config**: Get step_entry.get("config", {}) from saved pipeline + - Example: {"config": {"mean": 0.0, "std": 1.0}} + 2. **Extract user overrides**: Get overrides.get(step_key, {}) for this step + - Example: overrides = {"NormalizeStep": {"mean": 2.0, "device": "cuda"}} + 3. **Merge with priority**: {**saved_cfg, **step_overrides} + - **Override priority**: User values override saved values + - **Result**: {"mean": 2.0, "std": 1.0, "device": "cuda"} + + **Instantiation Process**: + - **Call constructor**: step_class(**merged_cfg) + - **Example**: NormalizeStep(mean=2.0, std=1.0, device="cuda") + + **Error Handling**: + - **Any exception during instantiation**: Convert to ValueError + - **Include context**: step name, attempted config, original error + - **Purpose**: Help users debug configuration issues + - **Common causes**: + a. Invalid parameter types (str instead of float) + b. Missing required parameters + c. Incompatible parameter combinations - steps.append(step_instance) + Args: + step_entry: The step configuration from saved config (contains "config" dict) + step_class: The step class to instantiate (already resolved) + step_key: The key used for overrides ("registry_name" or class name) + overrides: User-provided parameter overrides (keyed by step_key) - # Check for unused override keys - if override_keys: - available_keys = [] - for step_entry in loaded_config["steps"]: - if "registry_name" in step_entry: - available_keys.append(step_entry["registry_name"]) - else: - full_class_path = step_entry["class"] - class_name = full_class_path.rsplit(".", 1)[1] - available_keys.append(class_name) + Returns: + The instantiated processor step (ready for use) - raise KeyError( - f"Override keys {list(override_keys)} do not match any step in the saved configuration. " - f"Available step keys: {available_keys}. " - f"Make sure override keys match exact step class names or registry names." + Raises: + ValueError: If step cannot be instantiated, with detailed error context + """ + try: + saved_cfg = step_entry.get("config", {}) + step_overrides = overrides.get(step_key, {}) + merged_cfg = {**saved_cfg, **step_overrides} + return step_class(**merged_cfg) + except Exception as e: + step_name = step_entry.get("registry_name", step_entry.get("class", "Unknown")) + raise ValueError( + f"Failed to instantiate processor step '{step_name}' with config: {step_entry.get('config', {})}. " + f"Error: {str(e)}" + ) from e + + @classmethod + def _load_step_state( + cls, + step_instance: ProcessorStep, + step_entry: dict[str, Any], + model_id: str, + base_path: Path | None, + hub_download_kwargs: dict[str, Any], + ) -> None: + """Load state dictionary for a processor step if available. + + This method implements conditional state loading with local/Hub fallback: + + **Precondition Checks** (early return if not met): + 1. **"state_file" in step_entry**: Step config specifies a state file + - **If missing**: Step has no saved state (e.g., stateless transforms) + 2. **hasattr(step_instance, "load_state_dict")**: Step supports state loading + - **If missing**: Step doesn't implement state loading (rare) + + **State File Resolution Strategy**: + 1. **Local file priority**: Check base_path/state_filename exists + - **Advantage**: Faster, no network calls + - **Example**: "/models/my_model/normalize_step_0.safetensors" + - **Use case**: Loading from local saved model directory + + 2. **Hub download fallback**: Download state file from repository + - **When triggered**: Local file not found or base_path is None + - **Process**: Use hf_hub_download with same parameters as config + - **Example**: Download "normalize_step_0.safetensors" from "user/repo" + - **Result**: Downloaded to local cache, path returned + + **State Loading Process**: + - **Load tensors**: Use safetensors.torch.load_file() + - **Apply to step**: Call step_instance.load_state_dict(tensor_dict) + - **In-place modification**: Updates step's internal tensor state + + **Common state file examples**: + - "normalize_step_0.safetensors" - normalization statistics + - "custom_step_1.safetensors" - learned parameters + - "tokenizer_step_2.safetensors" - vocabulary embeddings + + Args: + step_instance: The step instance to load state into (must have load_state_dict) + step_entry: The step configuration dictionary (may contain "state_file") + model_id: The model identifier (used for Hub downloads if needed) + base_path: Local directory path for finding state files (None for Hub-only) + hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.) + + Note: + This method modifies step_instance in-place and returns None. + If state loading fails, exceptions from load_state_dict propagate. + """ + if "state_file" not in step_entry or not hasattr(step_instance, "load_state_dict"): + return + + state_filename = step_entry["state_file"] + + # Try local file first + if base_path and (base_path / state_filename).exists(): + state_path = str(base_path / state_filename) + else: + # Download from Hub + state_path = hf_hub_download( + repo_id=model_id, + filename=state_filename, + repo_type="model", + **hub_download_kwargs, ) - return cls(steps, loaded_config.get("name", "RobotProcessor")) + step_instance.load_state_dict(load_file(state_path)) + + @classmethod + def _validate_overrides_used( + cls, remaining_override_keys: set[str], loaded_config: dict[str, Any] + ) -> None: + """Validate that all provided overrides were used. + + This method ensures user overrides are valid to catch typos and configuration errors: + + **Validation Logic**: + 1. **If remaining_override_keys is empty**: All overrides were used -> Success + - **Early return**: No validation needed + - **Normal case**: User provided correct override keys + + 2. **If remaining_override_keys has entries**: Some overrides unused -> Error + - **Root cause**: User provided keys that don't match any step + - **Common issues**: + a. Typos in step names ("NormalizStep" vs "NormalizeStep") + b. Using wrong key type (class name vs registry name) + c. Step doesn't exist in saved pipeline + + **Helpful Error Generation**: + - **Extract available keys**: Build list of valid override keys from config + a. **Registry steps**: Use "registry_name" directly + b. **Import steps**: Extract class name from "class" field + - Example: "lerobot.processor.normalize.NormalizeStep" -> "NormalizeStep" + - **Error message includes**: + a. Invalid keys provided by user + b. List of valid keys they can use + c. Guidance about registry vs class names + + **Override Key Resolution Rules**: + - Steps with "registry_name": Use registry_name for overrides + - Steps with "class": Use final class name for overrides + - Users must match these exact keys in their overrides dict + + Args: + remaining_override_keys: Override keys that weren't matched to any step + loaded_config: The loaded processor configuration (contains "steps" list) + + Raises: + KeyError: If any override keys were not used, with helpful error message + """ + if not remaining_override_keys: + return + + available_keys = [ + step.get("registry_name") or step["class"].rsplit(".", 1)[1] for step in loaded_config["steps"] + ] + + raise KeyError( + f"Override keys {list(remaining_override_keys)} do not match any step in the saved configuration. " + f"Available step keys: {available_keys}. " + f"Make sure override keys match exact step class names or registry names." + ) + + @classmethod + def _should_suggest_migration(cls, model_path: Path) -> bool: + """Check if directory has JSON files but no processor configs. + + This method implements smart migration detection to avoid false positives: + + **Decision Logic**: + 1. **No JSON files found**: Return False + - **Reason**: Empty directory or only non-config files + - **Example**: Directory with only .safetensors, .md files + - **Action**: No migration needed + + 2. **JSON files exist**: Analyze each file + - **Goal**: Determine if ANY file is a valid processor config + - **Process**: + a. Try to parse each .json file + b. Skip files with JSON parse errors (malformed) + c. Check if parsed config passes _is_processor_config() + - **If ANY valid processor found**: Return False (no migration) + - **If NO valid processors found**: Return True (migration needed) + + **Examples**: + - **No migration**: ["processor.json", "config.json"] where processor.json is valid + - **Migration needed**: ["config.json", "train.json"] where both are model configs + - **No migration**: [] (empty directory) + - **Migration needed**: ["old_model_config.json"] with old LeRobot format + + **Why this works**: + - **Precise detection**: Only suggests migration for actual old LeRobot models + - **Avoids false positives**: Won't trigger on other HuggingFace model types + - **Graceful handling**: Ignores malformed JSON files + + Args: + model_path: Path to local directory to analyze + + Returns: + True if directory has JSON configs but none are processor configs (migration needed) + False if no JSON files or at least one valid processor config exists + """ + json_files = list(model_path.glob("*.json")) + if len(json_files) == 0: + return False + + # Check if any JSON file is a processor config + for json_file in json_files: + try: + with open(json_file) as f: + config = json.load(f) + + if cls._is_processor_config(config): + return False # Found at least one processor config, no migration needed + + except (json.JSONDecodeError, OSError): + # Skip files that can't be parsed as JSON + continue + + # Have JSON files but no processor configs - suggest migration + return True + + @classmethod + def _is_processor_config(cls, config: dict) -> bool: + """Check if config follows DataProcessorPipeline format. + + This method validates the processor configuration structure: + + **Required Structure Validation**: + 1. **"steps" field existence**: Must have top-level "steps" key + - **If missing**: Not a processor config (e.g., model config, train config) + - **Example invalid**: {"type": "act", "hidden_dim": 256} + + 2. **"steps" field type**: Must be a list, not other types + - **If not list**: Invalid format + - **Example invalid**: {"steps": "some_string"} or {"steps": {"key": "value"}} + + 3. **Empty steps validation**: Empty list is valid + - **If len(steps) == 0**: Return True immediately + - **Use case**: Empty processor pipeline (no-op) + - **Example valid**: {"name": "EmptyProcessor", "steps": []} + + **Individual Step Validation** (for non-empty steps): + For each step in the steps list: + 1. **Step type**: Must be a dictionary + - **If not dict**: Invalid step format + - **Example invalid**: ["string_step", 123, true] + + 2. **Step identifier**: Must have either "class" OR "registry_name" + - **"registry_name"**: Registered step (preferred) + Example: {"registry_name": "normalize_step", "config": {...}} + - **"class"**: Full import path + Example: {"class": "lerobot.processor.normalize.NormalizeStep"} + - **If neither**: Invalid step (can't resolve class) + - **If both**: Also valid (registry_name takes precedence) + + **Valid Processor Config Examples**: + - {"steps": []} - Empty processor + - {"steps": [{"registry_name": "normalize"}]} - Registry step + - {"steps": [{"class": "my.module.Step"}]} - Import step + - {"name": "MyProcessor", "steps": [...]} - With name + + **Invalid Config Examples**: + - {"type": "act"} - Missing "steps" + - {"steps": "normalize"} - Steps not a list + - {"steps": [{}]} - Step missing class/registry_name + - {"steps": ["string"]} - Step not a dict + + Args: + config: The configuration dictionary to validate + + Returns: + True if config follows valid DataProcessorPipeline format, False otherwise + """ + # Must have a "steps" field with a list of step configurations + if not isinstance(config.get("steps"), list): + return False + + steps = config["steps"] + if len(steps) == 0: + return True # Empty processor is valid + + # Each step must be a dict with either "class" or "registry_name" + for step in steps: + if not isinstance(step, dict): + return False + if not ("class" in step or "registry_name" in step): + return False + + return True + + @classmethod + def _suggest_processor_migration(cls, model_path: str | Path, original_error: str) -> None: + """Raise migration error when we detect JSON files but no processor configs. + + This method is called when migration detection determines that a model + directory contains configuration files but none are valid processor configs. + This typically indicates an old LeRobot model that needs migration. + + **When this is called**: + - User tries to load DataProcessorPipeline from local directory + - Directory contains JSON configuration files + - None of the JSON files follow processor config format + - _should_suggest_migration() returned True + + **Migration Command Generation**: + - Constructs exact command user needs to run + - Uses the migration script: migrate_policy_normalization.py + - Includes the model path automatically + - Example: "python src/lerobot/processor/migrate_policy_normalization.py --pretrained-path /models/old_model" + + **Error Structure**: + - **Always raises**: ProcessorMigrationError (never returns) + - **Includes**: model_path, migration_command, original_error + - **Purpose**: Force user attention to migration need + - **User experience**: Clear actionable error with exact command to run + + **Migration Process**: + The suggested command will: + 1. Extract normalization stats from old model + 2. Create new processor configs (preprocessor + postprocessor) + 3. Remove normalization layers from model + 4. Save migrated model with processor pipeline + + Args: + model_path: Path to the model directory needing migration + original_error: The error that triggered migration detection (for context) + + Raises: + ProcessorMigrationError: Always raised (this method never returns normally) + """ + migration_command = ( + f"python src/lerobot/processor/migrate_policy_normalization.py --pretrained-path {model_path}" + ) + + raise ProcessorMigrationError(model_path, migration_command, original_error) def __len__(self) -> int: - """Return the number of steps in the processor.""" + """Returns the number of steps in the pipeline.""" return len(self.steps) - def __getitem__(self, idx: int | slice) -> ProcessorStep | RobotProcessor: - """Indexing helper exposing underlying steps. - * ``int`` – returns the idx-th ProcessorStep. - * ``slice`` – returns a new RobotProcessor with the sliced steps. + def __getitem__(self, idx: int | slice) -> ProcessorStep | DataProcessorPipeline[TInput, TOutput]: + """Retrieves a step or a sub-pipeline by index or slice. + + Args: + idx: An integer index or a slice object. + + Returns: + A `ProcessorStep` if `idx` is an integer, or a new `DataProcessorPipeline` + containing the sliced steps. """ if isinstance(idx, slice): - return RobotProcessor(self.steps[idx], self.name) + # Return a new pipeline instance with the sliced steps. + return DataProcessorPipeline( + steps=self.steps[idx], + name=self.name, + to_transition=self.to_transition, + to_output=self.to_output, + before_step_hooks=self.before_step_hooks.copy(), + after_step_hooks=self.after_step_hooks.copy(), + ) return self.steps[idx] def register_before_step_hook(self, fn: Callable[[int, EnvTransition], None]): - """Attach fn to be executed before every processor step.""" + """Registers a function to be called before each step. + + Args: + fn: A callable that accepts the step index and the current transition. + """ self.before_step_hooks.append(fn) def unregister_before_step_hook(self, fn: Callable[[int, EnvTransition], None]): - """Remove a previously registered before_step hook. + """Unregisters a 'before_step' hook. Args: - fn: The exact function reference that was registered. Must be the same object. + fn: The exact function object that was previously registered. Raises: - ValueError: If the hook is not found in the registered hooks. + ValueError: If the hook is not found in the list. """ try: self.before_step_hooks.remove(fn) @@ -759,17 +1262,21 @@ def unregister_before_step_hook(self, fn: Callable[[int, EnvTransition], None]): ) from None def register_after_step_hook(self, fn: Callable[[int, EnvTransition], None]): - """Attach fn to be executed after every processor step.""" + """Registers a function to be called after each step. + + Args: + fn: A callable that accepts the step index and the current transition. + """ self.after_step_hooks.append(fn) def unregister_after_step_hook(self, fn: Callable[[int, EnvTransition], None]): - """Remove a previously registered after_step hook. + """Unregisters an 'after_step' hook. Args: - fn: The exact function reference that was registered. Must be the same object. + fn: The exact function object that was previously registered. Raises: - ValueError: If the hook is not found in the registered hooks. + ValueError: If the hook is not found in the list. """ try: self.after_step_hooks.remove(fn) @@ -779,13 +1286,13 @@ def unregister_after_step_hook(self, fn: Callable[[int, EnvTransition], None]): ) from None def reset(self): - """Clear state in every step that implements ``reset()`` and fire registered hooks.""" + """Resets the state of all stateful steps in the pipeline.""" for step in self.steps: if hasattr(step, "reset"): - step.reset() # type: ignore[attr-defined] + step.reset() def __repr__(self) -> str: - """Return a readable string representation of the processor.""" + """Provides a concise string representation of the pipeline.""" step_names = [step.__class__.__name__ for step in self.steps] if not step_names: @@ -793,472 +1300,417 @@ def __repr__(self) -> str: elif len(step_names) <= 3: steps_repr = f"steps={len(step_names)}: [{', '.join(step_names)}]" else: - # Show first 2 and last 1 with ellipsis for long lists + # For long pipelines, show the first, second, and last steps. displayed = f"{step_names[0]}, {step_names[1]}, ..., {step_names[-1]}" steps_repr = f"steps={len(step_names)}: [{displayed}]" parts = [f"name='{self.name}'", steps_repr] - return f"RobotProcessor({', '.join(parts)})" + return f"DataProcessorPipeline({', '.join(parts)})" def __post_init__(self): + """Validates that all provided steps are instances of `ProcessorStep`.""" for i, step in enumerate(self.steps): - if not callable(step): - raise TypeError( - f"Step {i} ({type(step).__name__}) must define __call__(transition) -> EnvTransition" - ) + if not isinstance(step, ProcessorStep): + raise TypeError(f"Step {i} ({type(step).__name__}) must inherit from ProcessorStep") - fc = getattr(step, "feature_contract", None) - if not callable(fc): - raise TypeError( - f"Step {i} ({type(step).__name__}) must define feature_contract(features) -> dict[str, Any]" - ) + def transform_features( + self, initial_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """Applies feature transformations from all steps sequentially. - def feature_contract(self, initial_features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - """ - Apply ALL steps in order. Each step must implement - feature_contract(features) and return a dict (full or incremental schema). + This method propagates a feature description dictionary through each step's + `transform_features` method, allowing the pipeline to statically determine + the output feature specification without processing any real data. + + Args: + initial_features: A dictionary describing the initial features. + + Returns: + The final feature description after all transformations. """ - features: dict[str, PolicyFeature] = deepcopy(initial_features) + features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = deepcopy(initial_features) for _, step in enumerate(self.steps): - out = step.feature_contract(features) - if not isinstance(out, dict): - raise TypeError(f"{step.__class__.__name__}.feature_contract must return dict[str, Any]") + out = step.transform_features(features) features = out return features + # Convenience methods for processing individual parts of a transition. + def process_observation(self, observation: dict[str, Any]) -> dict[str, Any]: + """Processes only the observation part of a transition through the pipeline. -class ObservationProcessor: - """Base class for processors that modify only the observation component of a transition. + Args: + observation: The observation dictionary. - Subclasses should override the `observation` method to implement custom observation processing. - This class handles the boilerplate of extracting and reinserting the processed observation - into the transition dict, eliminating the need to implement the `__call__` method in subclasses. + Returns: + The processed observation dictionary. + """ + transition: EnvTransition = create_transition(observation=observation) + transformed_transition = self._forward(transition) + return transformed_transition[TransitionKey.OBSERVATION] - Example: - ```python - class MyObservationScaler(ObservationProcessor): - def __init__(self, scale_factor): - self.scale_factor = scale_factor + def process_action( + self, action: PolicyAction | RobotAction | EnvAction + ) -> PolicyAction | RobotAction | EnvAction: + """Processes only the action part of a transition through the pipeline. - def observation(self, observation): - return observation * self.scale_factor - ``` + Args: + action: The action data. - By inheriting from this class, you avoid writing repetitive code to handle transition dict - manipulation, focusing only on the specific observation processing logic. - """ + Returns: + The processed action. + """ + transition: EnvTransition = create_transition(action=action) + transformed_transition = self._forward(transition) + return transformed_transition[TransitionKey.ACTION] - def observation(self, observation): - """Process the observation component. + def process_reward(self, reward: float | torch.Tensor) -> float | torch.Tensor: + """Processes only the reward part of a transition through the pipeline. Args: - observation: The observation to process + reward: The reward value. Returns: - The processed observation + The processed reward. """ - return observation + transition: EnvTransition = create_transition(reward=reward) + transformed_transition = self._forward(transition) + return transformed_transition[TransitionKey.REWARD] - def __call__(self, transition: EnvTransition) -> EnvTransition: - observation = transition.get(TransitionKey.OBSERVATION) - if observation is None: - return transition + def process_done(self, done: bool | torch.Tensor) -> bool | torch.Tensor: + """Processes only the done flag of a transition through the pipeline. - processed_observation = self.observation(observation) - # Create a new transition dict with the processed observation - new_transition = transition.copy() - new_transition[TransitionKey.OBSERVATION] = processed_observation - return new_transition + Args: + done: The done flag. - def get_config(self) -> dict[str, Any]: - return {} + Returns: + The processed done flag. + """ + transition: EnvTransition = create_transition(done=done) + transformed_transition = self._forward(transition) + return transformed_transition[TransitionKey.DONE] - def state_dict(self) -> dict[str, torch.Tensor]: - return {} + def process_truncated(self, truncated: bool | torch.Tensor) -> bool | torch.Tensor: + """Processes only the truncated flag of a transition through the pipeline. - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass + Args: + truncated: The truncated flag. - def reset(self) -> None: - pass + Returns: + The processed truncated flag. + """ + transition: EnvTransition = create_transition(truncated=truncated) + transformed_transition = self._forward(transition) + return transformed_transition[TransitionKey.TRUNCATED] - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - return features + def process_info(self, info: dict[str, Any]) -> dict[str, Any]: + """Processes only the info dictionary of a transition through the pipeline. + Args: + info: The info dictionary. -class ActionProcessor: - """Base class for processors that modify only the action component of a transition. + Returns: + The processed info dictionary. + """ + transition: EnvTransition = create_transition(info=info) + transformed_transition = self._forward(transition) + return transformed_transition[TransitionKey.INFO] - Subclasses should override the `action` method to implement custom action processing. - This class handles the boilerplate of extracting and reinserting the processed action - into the transition dict, eliminating the need to implement the `__call__` method in subclasses. + def process_complementary_data(self, complementary_data: dict[str, Any]) -> dict[str, Any]: + """Processes only the complementary data part of a transition through the pipeline. - Example: - ```python - class ActionClipping(ActionProcessor): - def __init__(self, min_val, max_val): - self.min_val = min_val - self.max_val = max_val + Args: + complementary_data: The complementary data dictionary. - def action(self, action): - return np.clip(action, self.min_val, self.max_val) - ``` + Returns: + The processed complementary data dictionary. + """ + transition: EnvTransition = create_transition(complementary_data=complementary_data) + transformed_transition = self._forward(transition) + return transformed_transition[TransitionKey.COMPLEMENTARY_DATA] - By inheriting from this class, you avoid writing repetitive code to handle transition dict - manipulation, focusing only on the specific action processing logic. - """ - def action(self, action): - """Process the action component. +# Type aliases for semantic clarity. +RobotProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput] +PolicyProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput] + + +class ObservationProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` that specifically targets the observation in a transition.""" + + @abstractmethod + def observation(self, observation: dict[str, Any]) -> dict[str, Any]: + """Processes an observation dictionary. Subclasses must implement this method. Args: - action: The action to process + observation: The input observation dictionary from the transition. Returns: - The processed action + The processed observation dictionary. """ - return action + ... def __call__(self, transition: EnvTransition) -> EnvTransition: - action = transition.get(TransitionKey.ACTION) - if action is None: - return transition + """Applies the `observation` method to the transition's observation.""" + self._current_transition = transition.copy() + new_transition = self._current_transition - processed_action = self.action(action) - # Create a new transition dict with the processed action - new_transition = transition.copy() - new_transition[TransitionKey.ACTION] = processed_action - return new_transition + observation = new_transition.get(TransitionKey.OBSERVATION) + if observation is None or not isinstance(observation, dict): + raise ValueError("ObservationProcessorStep requires an observation in the transition.") - def get_config(self) -> dict[str, Any]: - return {} + processed_observation = self.observation(observation.copy()) + new_transition[TransitionKey.OBSERVATION] = processed_observation + return new_transition - def state_dict(self) -> dict[str, torch.Tensor]: - return {} - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass +class ActionProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` that specifically targets the action in a transition.""" - def reset(self) -> None: - pass + @abstractmethod + def action( + self, action: PolicyAction | RobotAction | EnvAction + ) -> PolicyAction | RobotAction | EnvAction: + """Processes an action. Subclasses must implement this method. - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - return features + Args: + action: The input action from the transition. + Returns: + The processed action. + """ + ... -class RewardProcessor: - """Base class for processors that modify only the reward component of a transition. + def __call__(self, transition: EnvTransition) -> EnvTransition: + """Applies the `action` method to the transition's action.""" + self._current_transition = transition.copy() + new_transition = self._current_transition - Subclasses should override the `reward` method to implement custom reward processing. - This class handles the boilerplate of extracting and reinserting the processed reward - into the transition dict, eliminating the need to implement the `__call__` method in subclasses. + action = new_transition.get(TransitionKey.ACTION) + if action is None: + raise ValueError("ActionProcessorStep requires an action in the transition.") - Example: - ```python - class RewardScaler(RewardProcessor): - def __init__(self, scale_factor): - self.scale_factor = scale_factor + processed_action = self.action(action) + new_transition[TransitionKey.ACTION] = processed_action + return new_transition - def reward(self, reward): - return reward * self.scale_factor - ``` - By inheriting from this class, you avoid writing repetitive code to handle transition dict - manipulation, focusing only on the specific reward processing logic. - """ +class RobotActionProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` for processing a `RobotAction` (a dictionary).""" - def reward(self, reward): - """Process the reward component. + @abstractmethod + def action(self, action: RobotAction) -> RobotAction: + """Processes a `RobotAction`. Subclasses must implement this method. Args: - reward: The reward to process + action: The input `RobotAction` dictionary. Returns: - The processed reward + The processed `RobotAction`. """ - return reward + ... def __call__(self, transition: EnvTransition) -> EnvTransition: - reward = transition.get(TransitionKey.REWARD) - if reward is None: - return transition - - processed_reward = self.reward(reward) - # Create a new transition dict with the processed reward - new_transition = transition.copy() - new_transition[TransitionKey.REWARD] = processed_reward - return new_transition + """Applies the `action` method to the transition's action, ensuring it's a `RobotAction`.""" + self._current_transition = transition.copy() + new_transition = self._current_transition - def get_config(self) -> dict[str, Any]: - return {} + action = new_transition.get(TransitionKey.ACTION) + if action is None or not isinstance(action, dict): + raise ValueError(f"Action should be a RobotAction type (dict), but got {type(action)}") - def state_dict(self) -> dict[str, torch.Tensor]: - return {} + processed_action = self.action(action.copy()) + new_transition[TransitionKey.ACTION] = processed_action + return new_transition - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass - def reset(self) -> None: - pass +class PolicyActionProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` for processing a `PolicyAction` (a tensor or dict of tensors).""" - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - return features + @abstractmethod + def action(self, action: PolicyAction) -> PolicyAction: + """Processes a `PolicyAction`. Subclasses must implement this method. + Args: + action: The input `PolicyAction`. -class DoneProcessor: - """Base class for processors that modify only the done flag of a transition. + Returns: + The processed `PolicyAction`. + """ + ... - Subclasses should override the `done` method to implement custom done flag processing. - This class handles the boilerplate of extracting and reinserting the processed done flag - into the transition dict, eliminating the need to implement the `__call__` method in subclasses. + def __call__(self, transition: EnvTransition) -> EnvTransition: + """Applies the `action` method to the transition's action, ensuring it's a `PolicyAction`.""" + self._current_transition = transition.copy() + new_transition = self._current_transition - Example: - ```python - class TimeoutDone(DoneProcessor): - def __init__(self, max_steps): - self.steps = 0 - self.max_steps = max_steps + action = new_transition.get(TransitionKey.ACTION) + if not isinstance(action, PolicyAction): + raise ValueError(f"Action should be a PolicyAction type (tensor), but got {type(action)}") - def done(self, done): - self.steps += 1 - return done or self.steps >= self.max_steps + processed_action = self.action(action) + new_transition[TransitionKey.ACTION] = processed_action + return new_transition - def reset(self): - self.steps = 0 - ``` - By inheriting from this class, you avoid writing repetitive code to handle transition dict - manipulation, focusing only on the specific done flag processing logic. - """ +class RewardProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` that specifically targets the reward in a transition.""" - def done(self, done): - """Process the done flag. + @abstractmethod + def reward(self, reward) -> float | torch.Tensor: + """Processes a reward. Subclasses must implement this method. Args: - done: The done flag to process + reward: The input reward from the transition. Returns: - The processed done flag + The processed reward. """ - return done + ... def __call__(self, transition: EnvTransition) -> EnvTransition: - done = transition.get(TransitionKey.DONE) - if done is None: - return transition + """Applies the `reward` method to the transition's reward.""" + self._current_transition = transition.copy() + new_transition = self._current_transition - processed_done = self.done(done) - # Create a new transition dict with the processed done flag - new_transition = transition.copy() - new_transition[TransitionKey.DONE] = processed_done - return new_transition + reward = new_transition.get(TransitionKey.REWARD) + if reward is None: + raise ValueError("RewardProcessorStep requires a reward in the transition.") - def get_config(self) -> dict[str, Any]: - return {} + processed_reward = self.reward(reward) + new_transition[TransitionKey.REWARD] = processed_reward + return new_transition - def state_dict(self) -> dict[str, torch.Tensor]: - return {} - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass +class DoneProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` that specifically targets the 'done' flag in a transition.""" - def reset(self) -> None: - pass + @abstractmethod + def done(self, done) -> bool | torch.Tensor: + """Processes a 'done' flag. Subclasses must implement this method. - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - return features + Args: + done: The input 'done' flag from the transition. + Returns: + The processed 'done' flag. + """ + ... -class TruncatedProcessor: - """Base class for processors that modify only the truncated flag of a transition. + def __call__(self, transition: EnvTransition) -> EnvTransition: + """Applies the `done` method to the transition's 'done' flag.""" + self._current_transition = transition.copy() + new_transition = self._current_transition - Subclasses should override the `truncated` method to implement custom truncated flag processing. - This class handles the boilerplate of extracting and reinserting the processed truncated flag - into the transition dict, eliminating the need to implement the `__call__` method in subclasses. + done = new_transition.get(TransitionKey.DONE) + if done is None: + raise ValueError("DoneProcessorStep requires a done flag in the transition.") - Example: - ```python - class EarlyTruncation(TruncatedProcessor): - def __init__(self, threshold): - self.threshold = threshold + processed_done = self.done(done) + new_transition[TransitionKey.DONE] = processed_done + return new_transition - def truncated(self, truncated): - # Additional truncation condition - return truncated or some_condition > self.threshold - ``` - By inheriting from this class, you avoid writing repetitive code to handle transition dict - manipulation, focusing only on the specific truncated flag processing logic. - """ +class TruncatedProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` that specifically targets the 'truncated' flag in a transition.""" - def truncated(self, truncated): - """Process the truncated flag. + @abstractmethod + def truncated(self, truncated) -> bool | torch.Tensor: + """Processes a 'truncated' flag. Subclasses must implement this method. Args: - truncated: The truncated flag to process + truncated: The input 'truncated' flag from the transition. Returns: - The processed truncated flag + The processed 'truncated' flag. """ - return truncated + ... def __call__(self, transition: EnvTransition) -> EnvTransition: - truncated = transition.get(TransitionKey.TRUNCATED) + """Applies the `truncated` method to the transition's 'truncated' flag.""" + self._current_transition = transition.copy() + new_transition = self._current_transition + + truncated = new_transition.get(TransitionKey.TRUNCATED) if truncated is None: - return transition + raise ValueError("TruncatedProcessorStep requires a truncated flag in the transition.") processed_truncated = self.truncated(truncated) - # Create a new transition dict with the processed truncated flag - new_transition = transition.copy() new_transition[TransitionKey.TRUNCATED] = processed_truncated return new_transition - def get_config(self) -> dict[str, Any]: - return {} - - def state_dict(self) -> dict[str, torch.Tensor]: - return {} - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass +class InfoProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` that specifically targets the 'info' dictionary in a transition.""" - def reset(self) -> None: - pass - - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - return features - - -class InfoProcessor: - """Base class for processors that modify only the info dictionary of a transition. - - Subclasses should override the `info` method to implement custom info processing. - This class handles the boilerplate of extracting and reinserting the processed info - into the transition dict, eliminating the need to implement the `__call__` method in subclasses. - - Example: - ```python - class InfoAugmenter(InfoProcessor): - def __init__(self): - self.step_count = 0 - - def info(self, info): - info = info.copy() # Create a copy to avoid modifying the original - info["steps"] = self.step_count - self.step_count += 1 - return info - - def reset(self): - self.step_count = 0 - ``` - - By inheriting from this class, you avoid writing repetitive code to handle transition dict - manipulation, focusing only on the specific info dictionary processing logic. - """ - - def info(self, info): - """Process the info dictionary. + @abstractmethod + def info(self, info) -> dict[str, Any]: + """Processes an 'info' dictionary. Subclasses must implement this method. Args: - info: The info dictionary to process + info: The input 'info' dictionary from the transition. Returns: - The processed info dictionary + The processed 'info' dictionary. """ - return info + ... def __call__(self, transition: EnvTransition) -> EnvTransition: - info = transition.get(TransitionKey.INFO) - if info is None: - return transition + """Applies the `info` method to the transition's 'info' dictionary.""" + self._current_transition = transition.copy() + new_transition = self._current_transition + + info = new_transition.get(TransitionKey.INFO) + if info is None or not isinstance(info, dict): + raise ValueError("InfoProcessorStep requires an info dictionary in the transition.") - processed_info = self.info(info) - # Create a new transition dict with the processed info - new_transition = transition.copy() + processed_info = self.info(info.copy()) new_transition[TransitionKey.INFO] = processed_info return new_transition - def get_config(self) -> dict[str, Any]: - return {} - - def state_dict(self) -> dict[str, torch.Tensor]: - return {} - - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass - def reset(self) -> None: - pass - - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - return features +class ComplementaryDataProcessorStep(ProcessorStep, ABC): + """An abstract `ProcessorStep` that targets the 'complementary_data' in a transition.""" - -class ComplementaryDataProcessor: - """Base class for processors that modify only the complementary data of a transition. - - Subclasses should override the `complementary_data` method to implement custom complementary data processing. - This class handles the boilerplate of extracting and reinserting the processed complementary data - into the transition dict, eliminating the need to implement the `__call__` method in subclasses. - """ - - def complementary_data(self, complementary_data): - """Process the complementary data. + @abstractmethod + def complementary_data(self, complementary_data) -> dict[str, Any]: + """Processes a 'complementary_data' dictionary. Subclasses must implement this method. Args: - complementary_data: The complementary data to process + complementary_data: The input 'complementary_data' from the transition. Returns: - The processed complementary data + The processed 'complementary_data' dictionary. """ - return complementary_data + ... def __call__(self, transition: EnvTransition) -> EnvTransition: - complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA) - if complementary_data is None: - return transition + """Applies the `complementary_data` method to the transition's data.""" + self._current_transition = transition.copy() + new_transition = self._current_transition + + complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA) + if complementary_data is None or not isinstance(complementary_data, dict): + raise ValueError("ComplementaryDataProcessorStep requires complementary data in the transition.") - processed_complementary_data = self.complementary_data(complementary_data) - # Create a new transition dict with the processed complementary data - new_transition = transition.copy() + processed_complementary_data = self.complementary_data(complementary_data.copy()) new_transition[TransitionKey.COMPLEMENTARY_DATA] = processed_complementary_data return new_transition - def get_config(self) -> dict[str, Any]: - return {} - - def state_dict(self) -> dict[str, torch.Tensor]: - return {} - - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass - - def reset(self) -> None: - pass - - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - return features +class IdentityProcessorStep(ProcessorStep): + """A no-op processor step that returns the input transition and features unchanged. -class IdentityProcessor: - """Identity processor that does nothing.""" + This can be useful as a placeholder or for debugging purposes. + """ def __call__(self, transition: EnvTransition) -> EnvTransition: + """Returns the transition without modification.""" return transition - def get_config(self) -> dict[str, Any]: - return {} - - def state_dict(self) -> dict[str, torch.Tensor]: - return {} - - def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: - pass - - def reset(self) -> None: - pass - - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """Returns the features without modification.""" return features diff --git a/src/lerobot/processor/policy_robot_bridge.py b/src/lerobot/processor/policy_robot_bridge.py new file mode 100644 index 00000000000..25887d414ee --- /dev/null +++ b/src/lerobot/processor/policy_robot_bridge.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import asdict, dataclass +from typing import Any + +import torch + +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature +from lerobot.processor import ActionProcessorStep, PolicyAction, ProcessorStepRegistry, RobotAction +from lerobot.utils.constants import ACTION + + +@dataclass +@ProcessorStepRegistry.register("robot_action_to_policy_action_processor") +class RobotActionToPolicyActionProcessorStep(ActionProcessorStep): + """Processor step to map a dictionary to a tensor action.""" + + motor_names: list[str] + + def action(self, action: RobotAction) -> PolicyAction: + if len(self.motor_names) != len(action): + raise ValueError(f"Action must have {len(self.motor_names)} elements, got {len(action)}") + return torch.tensor([action[f"{name}.pos"] for name in self.motor_names]) + + def get_config(self) -> dict[str, Any]: + return asdict(self) + + def transform_features(self, features): + features[PipelineFeatureType.ACTION][ACTION] = PolicyFeature( + type=FeatureType.ACTION, shape=(len(self.motor_names),) + ) + return features + + +@dataclass +@ProcessorStepRegistry.register("policy_action_to_robot_action_processor") +class PolicyActionToRobotActionProcessorStep(ActionProcessorStep): + """Processor step to map a policy action to a robot action.""" + + motor_names: list[str] + + def action(self, action: PolicyAction) -> RobotAction: + if len(self.motor_names) != len(action): + raise ValueError(f"Action must have {len(self.motor_names)} elements, got {len(action)}") + return {f"{name}.pos": action[i] for i, name in enumerate(self.motor_names)} + + def get_config(self) -> dict[str, Any]: + return asdict(self) + + def transform_features(self, features): + for name in self.motor_names: + features[PipelineFeatureType.ACTION][f"{name}.pos"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + return features diff --git a/src/lerobot/processor/rename_processor.py b/src/lerobot/processor/rename_processor.py index 4fe4105a5a3..6cae5921ff3 100644 --- a/src/lerobot/processor/rename_processor.py +++ b/src/lerobot/processor/rename_processor.py @@ -13,20 +13,30 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from copy import deepcopy from dataclasses import dataclass, field from typing import Any -from lerobot.configs.types import PolicyFeature -from lerobot.processor.pipeline import ( - ObservationProcessor, - ProcessorStepRegistry, -) +from lerobot.configs.types import PipelineFeatureType, PolicyFeature + +from .pipeline import ObservationProcessorStep, ProcessorStepRegistry @dataclass -@ProcessorStepRegistry.register(name="rename_processor") -class RenameProcessor(ObservationProcessor): - """Rename processor that renames keys in the observation.""" +@ProcessorStepRegistry.register(name="rename_observations_processor") +class RenameObservationsProcessorStep(ObservationProcessorStep): + """ + A processor step that renames keys in an observation dictionary. + + This step is useful for creating a standardized data interface by mapping keys + from an environment's format to the format expected by a LeRobot policy or + other downstream components. + + Attributes: + rename_map: A dictionary mapping from old key names to new key names. + Keys present in an observation that are not in this map will + be kept with their original names. + """ rename_map: dict[str, str] = field(default_factory=dict) @@ -43,9 +53,41 @@ def observation(self, observation): def get_config(self) -> dict[str, Any]: return {"rename_map": self.rename_map} - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: """Transforms: - Each key in the observation that appears in `rename_map` is renamed to its value. - Keys not in `rename_map` remain unchanged. """ - return {self.rename_map.get(k, k): v for k, v in features.items()} + new_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = features.copy() + new_features[PipelineFeatureType.OBSERVATION] = { + self.rename_map.get(k, k): v for k, v in features[PipelineFeatureType.OBSERVATION].items() + } + return new_features + + +def rename_stats(stats: dict[str, dict[str, Any]], rename_map: dict[str, str]) -> dict[str, dict[str, Any]]: + """ + Renames the top-level keys in a statistics dictionary using a provided mapping. + + This is a helper function typically used to keep normalization statistics + consistent with renamed observation or action features. It performs a defensive + deep copy to avoid modifying the original `stats` dictionary. + + Args: + stats: A nested dictionary of statistics, where top-level keys are + feature names (e.g., `{"observation.state": {"mean": 0.5}}`). + rename_map: A dictionary mapping old feature names to new feature names. + + Returns: + A new statistics dictionary with its top-level keys renamed. Returns an + empty dictionary if the input `stats` is empty. + """ + if not stats: + return {} + renamed: dict[str, dict[str, Any]] = {} + for old_key, sub_stats in stats.items(): + new_key = rename_map.get(old_key, old_key) + renamed[new_key] = deepcopy(sub_stats) if sub_stats is not None else {} + return renamed diff --git a/src/lerobot/processor/tokenizer_processor.py b/src/lerobot/processor/tokenizer_processor.py new file mode 100644 index 00000000000..2ef89c10711 --- /dev/null +++ b/src/lerobot/processor/tokenizer_processor.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This script defines a processor for tokenizing natural language instructions from an environment transition. + +It uses a tokenizer from the Hugging Face `transformers` library to convert task descriptions (text) into +token IDs and attention masks, which are then added to the observation dictionary. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any + +import torch + +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature +from lerobot.utils.constants import OBS_LANGUAGE_ATTENTION_MASK, OBS_LANGUAGE_TOKENS +from lerobot.utils.import_utils import _transformers_available + +from .core import EnvTransition, TransitionKey +from .pipeline import ObservationProcessorStep, ProcessorStepRegistry + +# Conditional import for type checking and lazy loading +if TYPE_CHECKING or _transformers_available: + from transformers import AutoTokenizer +else: + AutoTokenizer = None + + +@dataclass +@ProcessorStepRegistry.register(name="tokenizer_processor") +class TokenizerProcessorStep(ObservationProcessorStep): + """ + Processor step to tokenize a natural language task description. + + This step extracts a task string from the `complementary_data` of an `EnvTransition`, + tokenizes it using a Hugging Face `transformers` tokenizer, and adds the resulting + token IDs and attention mask to the `observation` dictionary. + + Requires the `transformers` library to be installed. + + Attributes: + tokenizer_name: The name of a pretrained tokenizer from the Hugging Face Hub (e.g., "bert-base-uncased"). + tokenizer: A pre-initialized tokenizer object. If provided, `tokenizer_name` is ignored. + max_length: The maximum length to pad or truncate sequences to. + task_key: The key in `complementary_data` where the task string is stored. + padding_side: The side to pad on ('left' or 'right'). + padding: The padding strategy ('max_length', 'longest', etc.). + truncation: Whether to truncate sequences longer than `max_length`. + input_tokenizer: The internal tokenizer instance, loaded during initialization. + """ + + tokenizer_name: str | None = None + tokenizer: Any | None = None # Use `Any` for compatibility without a hard dependency + max_length: int = 512 + task_key: str = "task" + padding_side: str = "right" + padding: str = "max_length" + truncation: bool = True + + # Internal tokenizer instance (not part of the config) + input_tokenizer: Any = field(default=None, init=False, repr=False) + + def __post_init__(self): + """ + Initializes the tokenizer after the dataclass is created. + + It checks for the availability of the `transformers` library and loads the tokenizer + either from a provided object or by name from the Hugging Face Hub. + + Raises: + ImportError: If the `transformers` library is not installed. + ValueError: If neither `tokenizer` nor `tokenizer_name` is provided. + """ + if not _transformers_available: + raise ImportError( + "The 'transformers' library is not installed. " + "Please install it with `pip install 'lerobot[transformers-dep]'` to use TokenizerProcessorStep." + ) + + if self.tokenizer is not None: + # Use provided tokenizer object directly + self.input_tokenizer = self.tokenizer + elif self.tokenizer_name is not None: + if AutoTokenizer is None: + raise ImportError("AutoTokenizer is not available") + self.input_tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name) + else: + raise ValueError( + "Either 'tokenizer' or 'tokenizer_name' must be provided. " + "Pass a tokenizer object directly or a tokenizer name to auto-load." + ) + + def get_task(self, transition: EnvTransition) -> list[str] | None: + """ + Extracts the task description(s) from the transition's complementary data. + + Args: + transition: The environment transition. + + Returns: + A list of task strings, or None if the task key is not found or the value is None. + """ + complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA) + if complementary_data is None: + raise ValueError("Complementary data is None so no task can be extracted from it") + + task = complementary_data[self.task_key] + if task is None: + raise ValueError("Task extracted from Complementary data is None") + + # Standardize to a list of strings for the tokenizer + if isinstance(task, str): + return [task] + elif isinstance(task, list) and all(isinstance(t, str) for t in task): + return task + + return None + + def observation(self, observation: dict[str, Any]) -> dict[str, Any]: + """ + Tokenizes the task description and adds it to the observation dictionary. + + This method retrieves the task, tokenizes it, moves the resulting tensors to the + same device as other data in the transition, and updates the observation. + + Args: + observation: The original observation dictionary. + + Returns: + The updated observation dictionary including token IDs and an attention mask. + """ + task = self.get_task(self.transition) + if task is None: + raise ValueError("Task cannot be None") + + # Tokenize the task (this will create CPU tensors) + tokenized_prompt = self._tokenize_text(task) + + # Detect the device from existing tensors in the transition to ensure consistency + target_device = self._detect_device(self.transition) + + # Move new tokenized tensors to the detected device + if target_device is not None: + tokenized_prompt = { + k: v.to(target_device) if isinstance(v, torch.Tensor) else v + for k, v in tokenized_prompt.items() + } + + # Create a new observation dict to avoid modifying the original in place + new_observation = dict(observation) + + # Add tokenized data to the observation + new_observation[OBS_LANGUAGE_TOKENS] = tokenized_prompt["input_ids"] + new_observation[OBS_LANGUAGE_ATTENTION_MASK] = tokenized_prompt["attention_mask"].to(dtype=torch.bool) + + return new_observation + + def _detect_device(self, transition: EnvTransition) -> torch.device | None: + """ + Detects the torch.device from existing tensors in the transition. + + It checks tensors in the observation dictionary first, then the action tensor. + + Args: + transition: The environment transition. + + Returns: + The detected `torch.device`, or None if no tensors are found. + """ + # Check observation tensors first (most likely place to find tensors) + observation = transition.get(TransitionKey.OBSERVATION) + if observation: + for value in observation.values(): + if isinstance(value, torch.Tensor): + return value.device + + # Fallback to checking the action tensor + action = transition.get(TransitionKey.ACTION) + if isinstance(action, torch.Tensor): + return action.device + + return None # No tensors found, default will be CPU + + def _tokenize_text(self, text: str | list[str]) -> dict[str, torch.Tensor]: + """ + A wrapper around the tokenizer call. + + Args: + text: A string or list of strings to tokenize. + + Returns: + A dictionary containing tokenized 'input_ids' and 'attention_mask' as PyTorch tensors. + """ + return self.input_tokenizer( + text, + max_length=self.max_length, + truncation=self.truncation, + padding=self.padding, + padding_side=self.padding_side, + return_tensors="pt", + ) + + def get_config(self) -> dict[str, Any]: + """ + Returns the serializable configuration of the processor. + + Note: The tokenizer object itself is not serialized. If the processor was initialized + with a tokenizer name, that name will be included in the config. + + Returns: + A dictionary with the processor's configuration parameters. + """ + config = { + "max_length": self.max_length, + "task_key": self.task_key, + "padding_side": self.padding_side, + "padding": self.padding, + "truncation": self.truncation, + } + + # Only save tokenizer_name if it was used to create the tokenizer + if self.tokenizer_name is not None and self.tokenizer is None: + config["tokenizer_name"] = self.tokenizer_name + + return config + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + """ + Adds feature definitions for the language tokens and attention mask. + + This updates the policy features dictionary to include the new data added to the + observation, ensuring downstream components are aware of their shape and type. + + Args: + features: The dictionary of existing policy features. + + Returns: + The updated dictionary of policy features. + """ + # Add a feature for the token IDs if it doesn't already exist + if OBS_LANGUAGE_TOKENS not in features[PipelineFeatureType.OBSERVATION]: + features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_TOKENS] = PolicyFeature( + type=FeatureType.LANGUAGE, shape=(self.max_length,) + ) + + # Add a feature for the attention mask if it doesn't already exist + if OBS_LANGUAGE_ATTENTION_MASK not in features[PipelineFeatureType.OBSERVATION]: + features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_ATTENTION_MASK] = PolicyFeature( + type=FeatureType.LANGUAGE, shape=(self.max_length,) + ) + + return features diff --git a/src/lerobot/scripts/rl/actor.py b/src/lerobot/rl/actor.py similarity index 86% rename from src/lerobot/scripts/rl/actor.py rename to src/lerobot/rl/actor.py index 1c8f9286bfa..54d0fba69fa 100644 --- a/src/lerobot/scripts/rl/actor.py +++ b/src/lerobot/rl/actor.py @@ -24,7 +24,7 @@ - Start an actor server for real robot training with human-in-the-loop intervention: ```bash -python -m lerobot.scripts.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json +python -m lerobot.rl.actor --config_path src/lerobot/configs/train_config_hilserl_so100.json ``` **NOTE**: The actor server requires a running learner server to connect to. Ensure the learner @@ -35,7 +35,7 @@ reduce interventions as the policy improves. **WORKFLOW**: -1. Determine robot workspace bounds using `find_joint_limits.py` +1. Determine robot workspace bounds using `lerobot-find-joint-limits` 2. Record demonstrations with `gym_manipulator.py` in record mode 3. Process the dataset and determine camera crops with `crop_dataset_roi.py` 4. Start the learner server with the training configuration @@ -62,9 +62,12 @@ from lerobot.configs.train import TrainRLServerPipelineConfig from lerobot.policies.factory import make_policy from lerobot.policies.sac.modeling_sac import SACPolicy +from lerobot.processor import TransitionKey +from lerobot.rl.process import ProcessSignalHandler +from lerobot.rl.queue import get_last_item_from_queue from lerobot.robots import so100_follower # noqa: F401 -from lerobot.scripts.rl.gym_manipulator import make_robot_env from lerobot.teleoperators import gamepad, so101_leader # noqa: F401 +from lerobot.teleoperators.utils import TeleopEvents from lerobot.transport import services_pb2, services_pb2_grpc from lerobot.transport.utils import ( bytes_to_state_dict, @@ -74,8 +77,6 @@ send_bytes_in_chunks, transitions_to_bytes, ) -from lerobot.utils.process import ProcessSignalHandler -from lerobot.utils.queue import get_last_item_from_queue from lerobot.utils.random_utils import set_seed from lerobot.utils.robot_utils import busy_wait from lerobot.utils.transition import ( @@ -89,12 +90,14 @@ init_logging, ) -ACTOR_SHUTDOWN_TIMEOUT = 30 - +from .gym_manipulator import ( + create_transition, + make_processors, + make_robot_env, + step_env_and_process_transition, +) -################################################# -# Main entry point # -################################################# +# Main entry point @parser.wrap() @@ -201,9 +204,7 @@ def actor_cli(cfg: TrainRLServerPipelineConfig): logging.info("[ACTOR] queues closed") -################################################# -# Core algorithm functions # -################################################# +# Core algorithm functions def act_with_policy( @@ -236,7 +237,8 @@ def act_with_policy( logging.info("make_env online") - online_env = make_robot_env(cfg=cfg.env) + online_env, teleop_device = make_robot_env(cfg=cfg.env) + env_processor, action_processor = make_processors(online_env, teleop_device, cfg.env, cfg.policy.device) set_seed(cfg.seed) device = get_safe_torch_device(cfg.policy.device, log=True) @@ -257,6 +259,12 @@ def act_with_policy( assert isinstance(policy, nn.Module) obs, info = online_env.reset() + env_processor.reset() + action_processor.reset() + + # Process initial observation + transition = create_transition(observation=obs, info=info) + transition = env_processor(transition) # NOTE: For the moment we will solely handle the case of a single environment sum_reward_episode = 0 @@ -274,45 +282,71 @@ def act_with_policy( logging.info("[ACTOR] Shutting down act_with_policy") return - if interaction_step >= cfg.policy.online_step_before_learning: - # Time policy inference and check if it meets FPS requirement - with policy_timer: - action = policy.select_action(batch=obs) - policy_fps = policy_timer.fps_last + observation = { + k: v for k, v in transition[TransitionKey.OBSERVATION].items() if k in cfg.policy.input_features + } - log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step) + # Time policy inference and check if it meets FPS requirement + with policy_timer: + # Extract observation from transition for policy + action = policy.select_action(batch=observation) + policy_fps = policy_timer.fps_last + + log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step) + + # Use the new step function + new_transition = step_env_and_process_transition( + env=online_env, + transition=transition, + action=action, + env_processor=env_processor, + action_processor=action_processor, + ) - else: - action = online_env.action_space.sample() + # Extract values from processed transition + next_observation = { + k: v + for k, v in new_transition[TransitionKey.OBSERVATION].items() + if k in cfg.policy.input_features + } - next_obs, reward, done, truncated, info = online_env.step(action) + # Teleop action is the action that was executed in the environment + # It is either the action from the teleop device or the action from the policy + executed_action = new_transition[TransitionKey.COMPLEMENTARY_DATA]["teleop_action"] + + reward = new_transition[TransitionKey.REWARD] + done = new_transition.get(TransitionKey.DONE, False) + truncated = new_transition.get(TransitionKey.TRUNCATED, False) sum_reward_episode += float(reward) - # Increment total steps counter for intervention rate episode_total_steps += 1 - # NOTE: We override the action if the intervention is True, because the action applied is the intervention action - if "is_intervention" in info and info["is_intervention"]: - # NOTE: The action space for demonstration before hand is with the full action space - # but sometimes for example we want to deactivate the gripper - action = info["action_intervention"] + # Check for intervention from transition info + intervention_info = new_transition[TransitionKey.INFO] + if intervention_info.get(TeleopEvents.IS_INTERVENTION, False): episode_intervention = True - # Increment intervention steps counter episode_intervention_steps += 1 + complementary_info = { + "discrete_penalty": torch.tensor( + [new_transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)] + ), + } + # Create transition for learner (convert to old format) list_transition_to_send_to_learner.append( Transition( - state=obs, - action=action, + state=observation, + action=executed_action, reward=reward, - next_state=next_obs, + next_state=next_observation, done=done, - truncated=truncated, # TODO: (azouitine) Handle truncation properly - complementary_info=info, + truncated=truncated, + complementary_info=complementary_info, ) ) - # assign obs to the next obs and continue the rollout - obs = next_obs + + # Update transition for next iteration + transition = new_transition if done or truncated: logging.info(f"[ACTOR] Global step {interaction_step}: Episode reward: {sum_reward_episode}") @@ -347,21 +381,27 @@ def act_with_policy( ) ) - # Reset intervention counters + # Reset intervention counters and environment sum_reward_episode = 0.0 episode_intervention = False episode_intervention_steps = 0 episode_total_steps = 0 + + # Reset environment and processors obs, info = online_env.reset() + env_processor.reset() + action_processor.reset() + + # Process initial observation + transition = create_transition(observation=obs, info=info) + transition = env_processor(transition) if cfg.env.fps is not None: dt_time = time.perf_counter() - start_time busy_wait(1 / cfg.env.fps - dt_time) -################################################# -# Communication Functions - Group all gRPC/messaging functions # -################################################# +# Communication Functions - Group all gRPC/messaging functions def establish_learner_connection( @@ -606,9 +646,7 @@ def interactions_stream( return services_pb2.Empty() -################################################# -# Policy functions # -################################################# +# Policy functions def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device): @@ -640,9 +678,7 @@ def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device) logging.info("[ACTOR] Loaded discrete critic parameters from Learner.") -################################################# -# Utilities functions # -################################################# +# Utilities functions def push_transitions_to_transport_queue(transitions: list, transitions_queue): diff --git a/src/lerobot/utils/buffer.py b/src/lerobot/rl/buffer.py similarity index 96% rename from src/lerobot/utils/buffer.py rename to src/lerobot/rl/buffer.py index c65801896b4..917e4e2cc96 100644 --- a/src/lerobot/utils/buffer.py +++ b/src/lerobot/rl/buffer.py @@ -24,6 +24,7 @@ from tqdm import tqdm from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, REWARD from lerobot.utils.transition import Transition @@ -175,7 +176,7 @@ def _initialize_storage( self.complementary_info[key] = torch.empty( (self.capacity, *value_shape), device=self.storage_device ) - elif isinstance(value, (int, float)): + elif isinstance(value, (int | float)): # Handle scalar values similar to reward self.complementary_info[key] = torch.empty((self.capacity,), device=self.storage_device) else: @@ -222,7 +223,7 @@ def add( value = complementary_info[key] if isinstance(value, torch.Tensor): self.complementary_info[key][self.position].copy_(value.squeeze(dim=0)) - elif isinstance(value, (int, float)): + elif isinstance(value, (int | float)): self.complementary_info[key][self.position] = value self.position = (self.position + 1) % self.capacity @@ -240,7 +241,7 @@ def sample(self, batch_size: int) -> BatchTransition: idx = torch.randint(low=0, high=high, size=(batch_size,), device=self.storage_device) # Identify image keys that need augmentation - image_keys = [k for k in self.states if k.startswith("observation.image")] if self.use_drq else [] + image_keys = [k for k in self.states if k.startswith(OBS_IMAGE)] if self.use_drq else [] # Create batched state and next_state batch_state = {} @@ -466,7 +467,7 @@ def from_lerobot_dataset( if list_transition: first_transition = list_transition[0] first_state = {k: v.to(device) for k, v in first_transition["state"].items()} - first_action = first_transition["action"].to(device) + first_action = first_transition[ACTION].to(device) # Get complementary info if available first_complementary_info = None @@ -491,7 +492,7 @@ def from_lerobot_dataset( elif isinstance(v, torch.Tensor): data[k] = v.to(storage_device) - action = data["action"] + action = data[ACTION] replay_buffer.add( state=data["state"], @@ -529,12 +530,12 @@ def to_lerobot_dataset( # Add "action" sample_action = self.actions[0] - act_info = guess_feature_info(t=sample_action, name="action") - features["action"] = act_info + act_info = guess_feature_info(t=sample_action, name=ACTION) + features[ACTION] = act_info # Add "reward" and "done" - features["next.reward"] = {"dtype": "float32", "shape": (1,)} - features["next.done"] = {"dtype": "bool", "shape": (1,)} + features[REWARD] = {"dtype": "float32", "shape": (1,)} + features[DONE] = {"dtype": "bool", "shape": (1,)} # Add state keys for key in self.states: @@ -576,9 +577,9 @@ def to_lerobot_dataset( frame_dict[key] = self.states[key][actual_idx].cpu() # Fill action, reward, done - frame_dict["action"] = self.actions[actual_idx].cpu() - frame_dict["next.reward"] = torch.tensor([self.rewards[actual_idx]], dtype=torch.float32).cpu() - frame_dict["next.done"] = torch.tensor([self.dones[actual_idx]], dtype=torch.bool).cpu() + frame_dict[ACTION] = self.actions[actual_idx].cpu() + frame_dict[REWARD] = torch.tensor([self.rewards[actual_idx]], dtype=torch.float32).cpu() + frame_dict[DONE] = torch.tensor([self.dones[actual_idx]], dtype=torch.bool).cpu() frame_dict["task"] = task_name # Add complementary_info if available @@ -647,7 +648,7 @@ def _lerobotdataset_to_transitions( # Check if the dataset has "next.done" key sample = dataset[0] - has_done_key = "next.done" in sample + has_done_key = DONE in sample # Check for complementary_info keys complementary_info_keys = [key for key in sample if key.startswith("complementary_info.")] @@ -667,14 +668,14 @@ def _lerobotdataset_to_transitions( current_state[key] = val.unsqueeze(0) # Add batch dimension # ----- 2) Action ----- - action = current_sample["action"].unsqueeze(0) # Add batch dimension + action = current_sample[ACTION].unsqueeze(0) # Add batch dimension # ----- 3) Reward and done ----- - reward = float(current_sample["next.reward"].item()) # ensure float + reward = float(current_sample[REWARD].item()) # ensure float # Determine done flag - use next.done if available, otherwise infer from episode boundaries if has_done_key: - done = bool(current_sample["next.done"].item()) # ensure bool + done = bool(current_sample[DONE].item()) # ensure bool else: # If this is the last frame or if next frame is in a different episode, mark as done done = False @@ -787,8 +788,8 @@ def concatenate_batch_transitions( } # Concatenate basic fields - left_batch_transitions["action"] = torch.cat( - [left_batch_transitions["action"], right_batch_transition["action"]], dim=0 + left_batch_transitions[ACTION] = torch.cat( + [left_batch_transitions[ACTION], right_batch_transition[ACTION]], dim=0 ) left_batch_transitions["reward"] = torch.cat( [left_batch_transitions["reward"], right_batch_transition["reward"]], dim=0 diff --git a/src/lerobot/scripts/rl/crop_dataset_roi.py b/src/lerobot/rl/crop_dataset_roi.py similarity index 92% rename from src/lerobot/scripts/rl/crop_dataset_roi.py rename to src/lerobot/rl/crop_dataset_roi.py index c4318c41551..4345fed3c10 100644 --- a/src/lerobot/scripts/rl/crop_dataset_roi.py +++ b/src/lerobot/rl/crop_dataset_roi.py @@ -25,6 +25,7 @@ from tqdm import tqdm # type: ignore from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.utils.constants import DONE, REWARD def select_rect_roi(img): @@ -159,7 +160,7 @@ def get_image_from_lerobot_dataset(dataset: LeRobotDataset): return image_dict -def convert_lerobot_dataset_to_cropper_lerobot_dataset( +def convert_lerobot_dataset_to_cropped_lerobot_dataset( original_dataset: LeRobotDataset, crop_params_dict: dict[str, tuple[int, int, int, int]], new_repo_id: str, @@ -189,7 +190,7 @@ def convert_lerobot_dataset_to_cropper_lerobot_dataset( # 1. Create a new (empty) LeRobotDataset for writing. new_dataset = LeRobotDataset.create( repo_id=new_repo_id, - fps=original_dataset.fps, + fps=int(original_dataset.fps), root=new_dataset_root, robot_type=original_dataset.meta.robot_type, features=original_dataset.meta.info["features"], @@ -212,7 +213,7 @@ def convert_lerobot_dataset_to_cropper_lerobot_dataset( for key, value in frame.items(): if key in ("task_index", "timestamp", "episode_index", "frame_index", "index", "task"): continue - if key in ("next.done", "next.reward"): + if key in (DONE, REWARD): # if not isinstance(value, str) and len(value.shape) == 0: value = value.unsqueeze(0) @@ -274,6 +275,12 @@ def convert_lerobot_dataset_to_cropper_lerobot_dataset( default="", help="The natural language task to describe the dataset.", ) + parser.add_argument( + "--new-repo-id", + type=str, + default=None, + help="The repository id for the new cropped and resized dataset. If not provided, it defaults to `repo_id` + '_cropped_resized'.", + ) args = parser.parse_args() dataset = LeRobotDataset(repo_id=args.repo_id, root=args.root) @@ -293,10 +300,16 @@ def convert_lerobot_dataset_to_cropper_lerobot_dataset( for key, roi in rois.items(): print(f"{key}: {roi}") - new_repo_id = args.repo_id + "_cropped_resized" - new_dataset_root = Path(str(dataset.root) + "_cropped_resized") + new_repo_id = args.new_repo_id if args.new_repo_id else args.repo_id + "_cropped_resized" + + if args.new_repo_id: + new_dataset_name = args.new_repo_id.split("/")[-1] + # Parent 1: HF user, Parent 2: HF LeRobot Home + new_dataset_root = dataset.root.parent.parent / new_dataset_name + else: + new_dataset_root = Path(str(dataset.root) + "_cropped_resized") - cropped_resized_dataset = convert_lerobot_dataset_to_cropper_lerobot_dataset( + cropped_resized_dataset = convert_lerobot_dataset_to_cropped_lerobot_dataset( original_dataset=dataset, crop_params_dict=rois, new_repo_id=new_repo_id, diff --git a/src/lerobot/scripts/rl/eval_policy.py b/src/lerobot/rl/eval_policy.py similarity index 97% rename from src/lerobot/scripts/rl/eval_policy.py rename to src/lerobot/rl/eval_policy.py index aa97483b65c..7cec66800aa 100644 --- a/src/lerobot/scripts/rl/eval_policy.py +++ b/src/lerobot/rl/eval_policy.py @@ -25,12 +25,13 @@ make_robot_from_config, so100_follower, ) -from lerobot.scripts.rl.gym_manipulator import make_robot_env from lerobot.teleoperators import ( gamepad, # noqa: F401 so101_leader, # noqa: F401 ) +from .gym_manipulator import make_robot_env + logging.basicConfig(level=logging.INFO) diff --git a/src/lerobot/rl/gym_manipulator.py b/src/lerobot/rl/gym_manipulator.py new file mode 100644 index 00000000000..ad36f1b3641 --- /dev/null +++ b/src/lerobot/rl/gym_manipulator.py @@ -0,0 +1,770 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from dataclasses import dataclass +from typing import Any + +import gymnasium as gym +import numpy as np +import torch + +from lerobot.cameras import opencv # noqa: F401 +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.envs.configs import HILSerlRobotEnvConfig +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + AddTeleopActionAsComplimentaryDataStep, + AddTeleopEventsAsInfoStep, + DataProcessorPipeline, + DeviceProcessorStep, + EnvTransition, + GripperPenaltyProcessorStep, + ImageCropResizeProcessorStep, + InterventionActionProcessorStep, + JointVelocityProcessorStep, + MapDeltaActionToRobotActionStep, + MapTensorToDeltaActionDictStep, + MotorCurrentProcessorStep, + Numpy2TorchActionProcessorStep, + RewardClassifierProcessorStep, + RobotActionToPolicyActionProcessorStep, + TimeLimitProcessorStep, + Torch2NumpyActionProcessorStep, + TransitionKey, + VanillaObservationProcessorStep, + create_transition, +) +from lerobot.processor.converters import identity_transition +from lerobot.robots import ( # noqa: F401 + RobotConfig, + make_robot_from_config, + so100_follower, +) +from lerobot.robots.robot import Robot +from lerobot.robots.so100_follower.robot_kinematic_processor import ( + EEBoundsAndSafety, + EEReferenceAndDelta, + ForwardKinematicsJointsToEEObservation, + GripperVelocityToJoint, + InverseKinematicsRLStep, +) +from lerobot.teleoperators import ( + gamepad, # noqa: F401 + keyboard, # noqa: F401 + make_teleoperator_from_config, + so101_leader, # noqa: F401 +) +from lerobot.teleoperators.teleoperator import Teleoperator +from lerobot.teleoperators.utils import TeleopEvents +from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, REWARD +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.utils import log_say + +logging.basicConfig(level=logging.INFO) + + +@dataclass +class DatasetConfig: + """Configuration for dataset creation and management.""" + + repo_id: str + task: str + root: str | None = None + num_episodes_to_record: int = 5 + replay_episode: int | None = None + push_to_hub: bool = False + + +@dataclass +class GymManipulatorConfig: + """Main configuration for gym manipulator environment.""" + + env: HILSerlRobotEnvConfig + dataset: DatasetConfig + mode: str | None = None # Either "record", "replay", None + device: str = "cpu" + + +def reset_follower_position(robot_arm: Robot, target_position: np.ndarray) -> None: + """Reset robot arm to target position using smooth trajectory.""" + current_position_dict = robot_arm.bus.sync_read("Present_Position") + current_position = np.array( + [current_position_dict[name] for name in current_position_dict], dtype=np.float32 + ) + trajectory = torch.from_numpy( + np.linspace(current_position, target_position, 50) + ) # NOTE: 30 is just an arbitrary number + for pose in trajectory: + action_dict = dict(zip(current_position_dict, pose, strict=False)) + robot_arm.bus.sync_write("Goal_Position", action_dict) + busy_wait(0.015) + + +class RobotEnv(gym.Env): + """Gym environment for robotic control with human intervention support.""" + + def __init__( + self, + robot, + use_gripper: bool = False, + display_cameras: bool = False, + reset_pose: list[float] | None = None, + reset_time_s: float = 5.0, + ) -> None: + """Initialize robot environment with configuration options. + + Args: + robot: Robot interface for hardware communication. + use_gripper: Whether to include gripper in action space. + display_cameras: Whether to show camera feeds during execution. + reset_pose: Joint positions for environment reset. + reset_time_s: Time to wait during reset. + """ + super().__init__() + + self.robot = robot + self.display_cameras = display_cameras + + # Connect to the robot if not already connected. + if not self.robot.is_connected: + self.robot.connect() + + # Episode tracking. + self.current_step = 0 + self.episode_data = None + + self._joint_names = [f"{key}.pos" for key in self.robot.bus.motors] + self._image_keys = self.robot.cameras.keys() + + self.reset_pose = reset_pose + self.reset_time_s = reset_time_s + + self.use_gripper = use_gripper + + self._joint_names = list(self.robot.bus.motors.keys()) + self._raw_joint_positions = None + + self._setup_spaces() + + def _get_observation(self) -> dict[str, Any]: + """Get current robot observation including joint positions and camera images.""" + obs_dict = self.robot.get_observation() + raw_joint_joint_position = {f"{name}.pos": obs_dict[f"{name}.pos"] for name in self._joint_names} + joint_positions = np.array([raw_joint_joint_position[f"{name}.pos"] for name in self._joint_names]) + + images = {key: obs_dict[key] for key in self._image_keys} + + return {"agent_pos": joint_positions, "pixels": images, **raw_joint_joint_position} + + def _setup_spaces(self) -> None: + """Configure observation and action spaces based on robot capabilities.""" + current_observation = self._get_observation() + + observation_spaces = {} + + # Define observation spaces for images and other states. + if current_observation is not None and "pixels" in current_observation: + prefix = OBS_IMAGES + observation_spaces = { + f"{prefix}.{key}": gym.spaces.Box( + low=0, high=255, shape=current_observation["pixels"][key].shape, dtype=np.uint8 + ) + for key in current_observation["pixels"] + } + + if current_observation is not None: + agent_pos = current_observation["agent_pos"] + observation_spaces[OBS_STATE] = gym.spaces.Box( + low=0, + high=10, + shape=agent_pos.shape, + dtype=np.float32, + ) + + self.observation_space = gym.spaces.Dict(observation_spaces) + + # Define the action space for joint positions along with setting an intervention flag. + action_dim = 3 + bounds = {} + bounds["min"] = -np.ones(action_dim) + bounds["max"] = np.ones(action_dim) + + if self.use_gripper: + action_dim += 1 + bounds["min"] = np.concatenate([bounds["min"], [0]]) + bounds["max"] = np.concatenate([bounds["max"], [2]]) + + self.action_space = gym.spaces.Box( + low=bounds["min"], + high=bounds["max"], + shape=(action_dim,), + dtype=np.float32, + ) + + def reset( + self, *, seed: int | None = None, options: dict[str, Any] | None = None + ) -> tuple[dict[str, Any], dict[str, Any]]: + """Reset environment to initial state. + + Args: + seed: Random seed for reproducibility. + options: Additional reset options. + + Returns: + Tuple of (observation, info) dictionaries. + """ + # Reset the robot + # self.robot.reset() + start_time = time.perf_counter() + if self.reset_pose is not None: + log_say("Reset the environment.", play_sounds=True) + reset_follower_position(self.robot, np.array(self.reset_pose)) + log_say("Reset the environment done.", play_sounds=True) + + busy_wait(self.reset_time_s - (time.perf_counter() - start_time)) + + super().reset(seed=seed, options=options) + + # Reset episode tracking variables. + self.current_step = 0 + self.episode_data = None + obs = self._get_observation() + self._raw_joint_positions = {f"{key}.pos": obs[f"{key}.pos"] for key in self._joint_names} + return obs, {TeleopEvents.IS_INTERVENTION: False} + + def step(self, action) -> tuple[dict[str, np.ndarray], float, bool, bool, dict[str, Any]]: + """Execute one environment step with given action.""" + joint_targets_dict = {f"{key}.pos": action[i] for i, key in enumerate(self.robot.bus.motors.keys())} + + self.robot.send_action(joint_targets_dict) + + obs = self._get_observation() + + self._raw_joint_positions = {f"{key}.pos": obs[f"{key}.pos"] for key in self._joint_names} + + if self.display_cameras: + self.render() + + self.current_step += 1 + + reward = 0.0 + terminated = False + truncated = False + + return ( + obs, + reward, + terminated, + truncated, + {TeleopEvents.IS_INTERVENTION: False}, + ) + + def render(self) -> None: + """Display robot camera feeds.""" + import cv2 + + current_observation = self._get_observation() + if current_observation is not None: + image_keys = [key for key in current_observation if "image" in key] + + for key in image_keys: + cv2.imshow(key, cv2.cvtColor(current_observation[key].numpy(), cv2.COLOR_RGB2BGR)) + cv2.waitKey(1) + + def close(self) -> None: + """Close environment and disconnect robot.""" + if self.robot.is_connected: + self.robot.disconnect() + + def get_raw_joint_positions(self) -> dict[str, float]: + """Get raw joint positions.""" + return self._raw_joint_positions + + +def make_robot_env(cfg: HILSerlRobotEnvConfig) -> tuple[gym.Env, Any]: + """Create robot environment from configuration. + + Args: + cfg: Environment configuration. + + Returns: + Tuple of (gym environment, teleoperator device). + """ + # Check if this is a GymHIL simulation environment + if cfg.name == "gym_hil": + assert cfg.robot is None and cfg.teleop is None, "GymHIL environment does not support robot or teleop" + import gym_hil # noqa: F401 + + # Extract gripper settings with defaults + use_gripper = cfg.processor.gripper.use_gripper if cfg.processor.gripper is not None else True + gripper_penalty = cfg.processor.gripper.gripper_penalty if cfg.processor.gripper is not None else 0.0 + + env = gym.make( + f"gym_hil/{cfg.task}", + image_obs=True, + render_mode="human", + use_gripper=use_gripper, + gripper_penalty=gripper_penalty, + ) + + return env, None + + # Real robot environment + assert cfg.robot is not None, "Robot config must be provided for real robot environment" + assert cfg.teleop is not None, "Teleop config must be provided for real robot environment" + + robot = make_robot_from_config(cfg.robot) + teleop_device = make_teleoperator_from_config(cfg.teleop) + teleop_device.connect() + + # Create base environment with safe defaults + use_gripper = cfg.processor.gripper.use_gripper if cfg.processor.gripper is not None else True + display_cameras = ( + cfg.processor.observation.display_cameras if cfg.processor.observation is not None else False + ) + reset_pose = cfg.processor.reset.fixed_reset_joint_positions if cfg.processor.reset is not None else None + + env = RobotEnv( + robot=robot, + use_gripper=use_gripper, + display_cameras=display_cameras, + reset_pose=reset_pose, + ) + + return env, teleop_device + + +def make_processors( + env: gym.Env, teleop_device: Teleoperator | None, cfg: HILSerlRobotEnvConfig, device: str = "cpu" +) -> tuple[ + DataProcessorPipeline[EnvTransition, EnvTransition], DataProcessorPipeline[EnvTransition, EnvTransition] +]: + """Create environment and action processors. + + Args: + env: Robot environment instance. + teleop_device: Teleoperator device for intervention. + cfg: Processor configuration. + device: Target device for computations. + + Returns: + Tuple of (environment processor, action processor). + """ + terminate_on_success = ( + cfg.processor.reset.terminate_on_success if cfg.processor.reset is not None else True + ) + + if cfg.name == "gym_hil": + action_pipeline_steps = [ + InterventionActionProcessorStep(terminate_on_success=terminate_on_success), + Torch2NumpyActionProcessorStep(), + ] + + env_pipeline_steps = [ + Numpy2TorchActionProcessorStep(), + VanillaObservationProcessorStep(), + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device=device), + ] + + return DataProcessorPipeline( + steps=env_pipeline_steps, to_transition=identity_transition, to_output=identity_transition + ), DataProcessorPipeline( + steps=action_pipeline_steps, to_transition=identity_transition, to_output=identity_transition + ) + + # Full processor pipeline for real robot environment + # Get robot and motor information for kinematics + motor_names = list(env.robot.bus.motors.keys()) + + # Set up kinematics solver if inverse kinematics is configured + kinematics_solver = None + if cfg.processor.inverse_kinematics is not None: + kinematics_solver = RobotKinematics( + urdf_path=cfg.processor.inverse_kinematics.urdf_path, + target_frame_name=cfg.processor.inverse_kinematics.target_frame_name, + joint_names=motor_names, + ) + + env_pipeline_steps = [VanillaObservationProcessorStep()] + + if cfg.processor.observation is not None: + if cfg.processor.observation.add_joint_velocity_to_observation: + env_pipeline_steps.append(JointVelocityProcessorStep(dt=1.0 / cfg.fps)) + if cfg.processor.observation.add_current_to_observation: + env_pipeline_steps.append(MotorCurrentProcessorStep(robot=env.robot)) + + if kinematics_solver is not None: + env_pipeline_steps.append( + ForwardKinematicsJointsToEEObservation( + kinematics=kinematics_solver, + motor_names=motor_names, + ) + ) + + if cfg.processor.image_preprocessing is not None: + env_pipeline_steps.append( + ImageCropResizeProcessorStep( + crop_params_dict=cfg.processor.image_preprocessing.crop_params_dict, + resize_size=cfg.processor.image_preprocessing.resize_size, + ) + ) + + # Add time limit processor if reset config exists + if cfg.processor.reset is not None: + env_pipeline_steps.append( + TimeLimitProcessorStep(max_episode_steps=int(cfg.processor.reset.control_time_s * cfg.fps)) + ) + + # Add gripper penalty processor if gripper config exists and enabled + if cfg.processor.gripper is not None and cfg.processor.gripper.use_gripper: + env_pipeline_steps.append( + GripperPenaltyProcessorStep( + penalty=cfg.processor.gripper.gripper_penalty, + max_gripper_pos=cfg.processor.max_gripper_pos, + ) + ) + + if ( + cfg.processor.reward_classifier is not None + and cfg.processor.reward_classifier.pretrained_path is not None + ): + env_pipeline_steps.append( + RewardClassifierProcessorStep( + pretrained_path=cfg.processor.reward_classifier.pretrained_path, + device=device, + success_threshold=cfg.processor.reward_classifier.success_threshold, + success_reward=cfg.processor.reward_classifier.success_reward, + terminate_on_success=terminate_on_success, + ) + ) + + env_pipeline_steps.append(AddBatchDimensionProcessorStep()) + env_pipeline_steps.append(DeviceProcessorStep(device=device)) + + action_pipeline_steps = [ + AddTeleopActionAsComplimentaryDataStep(teleop_device=teleop_device), + AddTeleopEventsAsInfoStep(teleop_device=teleop_device), + InterventionActionProcessorStep( + use_gripper=cfg.processor.gripper.use_gripper if cfg.processor.gripper is not None else False, + terminate_on_success=terminate_on_success, + ), + ] + + # Replace InverseKinematicsProcessor with new kinematic processors + if cfg.processor.inverse_kinematics is not None and kinematics_solver is not None: + # Add EE bounds and safety processor + inverse_kinematics_steps = [ + MapTensorToDeltaActionDictStep( + use_gripper=cfg.processor.gripper.use_gripper if cfg.processor.gripper is not None else False + ), + MapDeltaActionToRobotActionStep(), + EEReferenceAndDelta( + kinematics=kinematics_solver, + end_effector_step_sizes=cfg.processor.inverse_kinematics.end_effector_step_sizes, + motor_names=motor_names, + use_latched_reference=False, + use_ik_solution=True, + ), + EEBoundsAndSafety( + end_effector_bounds=cfg.processor.inverse_kinematics.end_effector_bounds, + ), + GripperVelocityToJoint( + clip_max=cfg.processor.max_gripper_pos, + speed_factor=1.0, + discrete_gripper=True, + ), + InverseKinematicsRLStep( + kinematics=kinematics_solver, motor_names=motor_names, initial_guess_current_joints=False + ), + ] + action_pipeline_steps.extend(inverse_kinematics_steps) + action_pipeline_steps.append(RobotActionToPolicyActionProcessorStep(motor_names=motor_names)) + + return DataProcessorPipeline( + steps=env_pipeline_steps, to_transition=identity_transition, to_output=identity_transition + ), DataProcessorPipeline( + steps=action_pipeline_steps, to_transition=identity_transition, to_output=identity_transition + ) + + +def step_env_and_process_transition( + env: gym.Env, + transition: EnvTransition, + action: torch.Tensor, + env_processor: DataProcessorPipeline[EnvTransition, EnvTransition], + action_processor: DataProcessorPipeline[EnvTransition, EnvTransition], +) -> EnvTransition: + """ + Execute one step with processor pipeline. + + Args: + env: The robot environment + transition: Current transition state + action: Action to execute + env_processor: Environment processor + action_processor: Action processor + + Returns: + Processed transition with updated state. + """ + + # Create action transition + transition[TransitionKey.ACTION] = action + transition[TransitionKey.OBSERVATION] = ( + env.get_raw_joint_positions() if hasattr(env, "get_raw_joint_positions") else {} + ) + processed_action_transition = action_processor(transition) + processed_action = processed_action_transition[TransitionKey.ACTION] + + obs, reward, terminated, truncated, info = env.step(processed_action) + + reward = reward + processed_action_transition[TransitionKey.REWARD] + terminated = terminated or processed_action_transition[TransitionKey.DONE] + truncated = truncated or processed_action_transition[TransitionKey.TRUNCATED] + complementary_data = processed_action_transition[TransitionKey.COMPLEMENTARY_DATA].copy() + new_info = processed_action_transition[TransitionKey.INFO].copy() + new_info.update(info) + + new_transition = create_transition( + observation=obs, + action=processed_action, + reward=reward, + done=terminated, + truncated=truncated, + info=new_info, + complementary_data=complementary_data, + ) + new_transition = env_processor(new_transition) + + return new_transition + + +def control_loop( + env: gym.Env, + env_processor: DataProcessorPipeline[EnvTransition, EnvTransition], + action_processor: DataProcessorPipeline[EnvTransition, EnvTransition], + teleop_device: Teleoperator, + cfg: GymManipulatorConfig, +) -> None: + """Main control loop for robot environment interaction. + if cfg.mode == "record": then a dataset will be created and recorded + + Args: + env: The robot environment + env_processor: Environment processor + action_processor: Action processor + teleop_device: Teleoperator device + cfg: gym_manipulator configuration + """ + dt = 1.0 / cfg.env.fps + + print(f"Starting control loop at {cfg.env.fps} FPS") + print("Controls:") + print("- Use gamepad/teleop device for intervention") + print("- When not intervening, robot will stay still") + print("- Press Ctrl+C to exit") + + # Reset environment and processors + obs, info = env.reset() + complementary_data = ( + {"raw_joint_positions": info.pop("raw_joint_positions")} if "raw_joint_positions" in info else {} + ) + env_processor.reset() + action_processor.reset() + + # Process initial observation + transition = create_transition(observation=obs, info=info, complementary_data=complementary_data) + transition = env_processor(data=transition) + + # Determine if gripper is used + use_gripper = cfg.env.processor.gripper.use_gripper if cfg.env.processor.gripper is not None else True + + dataset = None + if cfg.mode == "record": + action_features = teleop_device.action_features + features = { + ACTION: action_features, + REWARD: {"dtype": "float32", "shape": (1,), "names": None}, + DONE: {"dtype": "bool", "shape": (1,), "names": None}, + } + if use_gripper: + features["complementary_info.discrete_penalty"] = { + "dtype": "float32", + "shape": (1,), + "names": ["discrete_penalty"], + } + + for key, value in transition[TransitionKey.OBSERVATION].items(): + if key == OBS_STATE: + features[key] = { + "dtype": "float32", + "shape": value.squeeze(0).shape, + "names": None, + } + if "image" in key: + features[key] = { + "dtype": "video", + "shape": value.squeeze(0).shape, + "names": ["channels", "height", "width"], + } + + # Create dataset + dataset = LeRobotDataset.create( + cfg.dataset.repo_id, + cfg.env.fps, + root=cfg.dataset.root, + use_videos=True, + image_writer_threads=4, + image_writer_processes=0, + features=features, + ) + + episode_idx = 0 + episode_step = 0 + episode_start_time = time.perf_counter() + + while episode_idx < cfg.dataset.num_episodes_to_record: + step_start_time = time.perf_counter() + + # Create a neutral action (no movement) + neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32) + if use_gripper: + neutral_action = torch.cat([neutral_action, torch.tensor([1.0])]) # Gripper stay + + # Use the new step function + transition = step_env_and_process_transition( + env=env, + transition=transition, + action=neutral_action, + env_processor=env_processor, + action_processor=action_processor, + ) + terminated = transition.get(TransitionKey.DONE, False) + truncated = transition.get(TransitionKey.TRUNCATED, False) + + if cfg.mode == "record": + observations = { + k: v.squeeze(0).cpu() + for k, v in transition[TransitionKey.OBSERVATION].items() + if isinstance(v, torch.Tensor) + } + # Use teleop_action if available, otherwise use the action from the transition + action_to_record = transition[TransitionKey.COMPLEMENTARY_DATA].get( + "teleop_action", transition[TransitionKey.ACTION] + ) + frame = { + **observations, + ACTION: action_to_record.cpu(), + REWARD: np.array([transition[TransitionKey.REWARD]], dtype=np.float32), + DONE: np.array([terminated or truncated], dtype=bool), + } + if use_gripper: + discrete_penalty = transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0) + frame["complementary_info.discrete_penalty"] = np.array([discrete_penalty], dtype=np.float32) + + if dataset is not None: + frame["task"] = cfg.dataset.task + dataset.add_frame(frame) + + episode_step += 1 + + # Handle episode termination + if terminated or truncated: + episode_time = time.perf_counter() - episode_start_time + logging.info( + f"Episode ended after {episode_step} steps in {episode_time:.1f}s with reward {transition[TransitionKey.REWARD]}" + ) + episode_step = 0 + episode_idx += 1 + + if dataset is not None: + if transition[TransitionKey.INFO].get("rerecord_episode", False): + logging.info(f"Re-recording episode {episode_idx}") + dataset.clear_episode_buffer() + episode_idx -= 1 + else: + logging.info(f"Saving episode {episode_idx}") + dataset.save_episode() + + # Reset for new episode + obs, info = env.reset() + env_processor.reset() + action_processor.reset() + + transition = create_transition(observation=obs, info=info) + transition = env_processor(transition) + + # Maintain fps timing + busy_wait(dt - (time.perf_counter() - step_start_time)) + + if dataset is not None and cfg.dataset.push_to_hub: + logging.info("Pushing dataset to hub") + dataset.push_to_hub() + + +def replay_trajectory( + env: gym.Env, action_processor: DataProcessorPipeline, cfg: GymManipulatorConfig +) -> None: + """Replay recorded trajectory on robot environment.""" + assert cfg.dataset.replay_episode is not None, "Replay episode must be provided for replay" + + dataset = LeRobotDataset( + cfg.dataset.repo_id, + root=cfg.dataset.root, + episodes=[cfg.dataset.replay_episode], + download_videos=False, + ) + episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == cfg.dataset.replay_episode) + actions = episode_frames.select_columns(ACTION) + + _, info = env.reset() + + for action_data in actions: + start_time = time.perf_counter() + transition = create_transition( + observation=env.get_raw_joint_positions() if hasattr(env, "get_raw_joint_positions") else {}, + action=action_data[ACTION], + ) + transition = action_processor(transition) + env.step(transition[TransitionKey.ACTION]) + busy_wait(1 / cfg.env.fps - (time.perf_counter() - start_time)) + + +@parser.wrap() +def main(cfg: GymManipulatorConfig) -> None: + """Main entry point for gym manipulator script.""" + env, teleop_device = make_robot_env(cfg.env) + env_processor, action_processor = make_processors(env, teleop_device, cfg.env, cfg.device) + + print("Environment observation space:", env.observation_space) + print("Environment action space:", env.action_space) + print("Environment processor:", env_processor) + print("Action processor:", action_processor) + + if cfg.mode == "replay": + replay_trajectory(env, action_processor, cfg) + exit() + + control_loop(env, env_processor, action_processor, teleop_device, cfg) + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/scripts/rl/learner.py b/src/lerobot/rl/learner.py similarity index 96% rename from src/lerobot/scripts/rl/learner.py rename to src/lerobot/rl/learner.py index f9f3901ce7c..d9758d3a35c 100644 --- a/src/lerobot/scripts/rl/learner.py +++ b/src/lerobot/rl/learner.py @@ -25,7 +25,7 @@ - Start a learner server for training: ```bash -python -m lerobot.scripts.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json +python -m lerobot.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json ``` **NOTE**: Start the learner server before launching the actor server. The learner opens a gRPC server @@ -62,19 +62,16 @@ from lerobot.cameras import opencv # noqa: F401 from lerobot.configs import parser from lerobot.configs.train import TrainRLServerPipelineConfig -from lerobot.constants import ( - CHECKPOINTS_DIR, - LAST_CHECKPOINT_LINK, - PRETRAINED_MODEL_DIR, - TRAINING_STATE_DIR, -) from lerobot.datasets.factory import make_dataset from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.policies.factory import make_policy from lerobot.policies.sac.modeling_sac import SACPolicy +from lerobot.rl.buffer import ReplayBuffer, concatenate_batch_transitions +from lerobot.rl.process import ProcessSignalHandler +from lerobot.rl.wandb_utils import WandBLogger from lerobot.robots import so100_follower # noqa: F401 -from lerobot.scripts.rl import learner_service from lerobot.teleoperators import gamepad, so101_leader # noqa: F401 +from lerobot.teleoperators.utils import TeleopEvents from lerobot.transport import services_pb2_grpc from lerobot.transport.utils import ( MAX_MESSAGE_SIZE, @@ -82,8 +79,13 @@ bytes_to_transitions, state_to_bytes, ) -from lerobot.utils.buffer import ReplayBuffer, concatenate_batch_transitions -from lerobot.utils.process import ProcessSignalHandler +from lerobot.utils.constants import ( + ACTION, + CHECKPOINTS_DIR, + LAST_CHECKPOINT_LINK, + PRETRAINED_MODEL_DIR, + TRAINING_STATE_DIR, +) from lerobot.utils.random_utils import set_seed from lerobot.utils.train_utils import ( get_step_checkpoint_dir, @@ -97,14 +99,8 @@ get_safe_torch_device, init_logging, ) -from lerobot.utils.wandb_utils import WandBLogger -LOG_PREFIX = "[LEARNER]" - - -################################################# -# MAIN ENTRY POINTS AND CORE ALGORITHM FUNCTIONS # -################################################# +from .learner_service import MAX_WORKERS, SHUTDOWN_TIMEOUT, LearnerService @parser.wrap() @@ -156,7 +152,7 @@ def train(cfg: TrainRLServerPipelineConfig, job_name: str | None = None): # Setup WandB logging if enabled if cfg.wandb.enable and cfg.wandb.project: - from lerobot.utils.wandb_utils import WandBLogger + from lerobot.rl.wandb_utils import WandBLogger wandb_logger = WandBLogger(cfg) else: @@ -249,9 +245,7 @@ def start_learner_threads( logging.info("[LEARNER] queues closed") -################################################# -# Core algorithm functions # -################################################# +# Core algorithm functions def add_actor_information_and_train( @@ -407,7 +401,7 @@ def add_actor_information_and_train( left_batch_transitions=batch, right_batch_transition=batch_offline ) - actions = batch["action"] + actions = batch[ACTION] rewards = batch["reward"] observations = batch["state"] next_observations = batch["next_state"] @@ -420,7 +414,7 @@ def add_actor_information_and_train( # Create a batch dictionary with all required elements for the forward method forward_batch = { - "action": actions, + ACTION: actions, "reward": rewards, "state": observations, "next_state": next_observations, @@ -465,7 +459,7 @@ def add_actor_information_and_train( left_batch_transitions=batch, right_batch_transition=batch_offline ) - actions = batch["action"] + actions = batch[ACTION] rewards = batch["reward"] observations = batch["state"] next_observations = batch["next_state"] @@ -479,7 +473,7 @@ def add_actor_information_and_train( # Create a batch dictionary with all required elements for the forward method forward_batch = { - "action": actions, + ACTION: actions, "reward": rewards, "state": observations, "next_state": next_observations, @@ -645,7 +639,7 @@ def start_learner( # TODO: Check if its useful _ = ProcessSignalHandler(False, display_pid=True) - service = learner_service.LearnerService( + service = LearnerService( shutdown_event=shutdown_event, parameters_queue=parameters_queue, seconds_between_pushes=cfg.policy.actor_learner_config.policy_parameters_push_frequency, @@ -655,7 +649,7 @@ def start_learner( ) server = grpc.server( - ThreadPoolExecutor(max_workers=learner_service.MAX_WORKERS), + ThreadPoolExecutor(max_workers=MAX_WORKERS), options=[ ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), @@ -676,7 +670,7 @@ def start_learner( shutdown_event.wait() logging.info("[LEARNER] Stopping gRPC server...") - server.stop(learner_service.SHUTDOWN_TIMEOUT) + server.stop(SHUTDOWN_TIMEOUT) logging.info("[LEARNER] gRPC server stopped") @@ -819,9 +813,7 @@ def make_optimizers_and_scheduler(cfg: TrainRLServerPipelineConfig, policy: nn.M return optimizers, lr_scheduler -################################################# -# Training setup functions # -################################################# +# Training setup functions def handle_resume_logic(cfg: TrainRLServerPipelineConfig) -> TrainRLServerPipelineConfig: @@ -1022,9 +1014,7 @@ def initialize_offline_replay_buffer( return offline_replay_buffer -################################################# -# Utilities/Helpers functions # -################################################# +# Utilities/Helpers functions def get_observation_features( @@ -1048,10 +1038,8 @@ def get_observation_features( return None, None with torch.no_grad(): - observation_features = policy.actor.encoder.get_cached_image_features(observations, normalize=True) - next_observation_features = policy.actor.encoder.get_cached_image_features( - next_observations, normalize=True - ) + observation_features = policy.actor.encoder.get_cached_image_features(observations) + next_observation_features = policy.actor.encoder.get_cached_image_features(next_observations) return observation_features, next_observation_features @@ -1166,7 +1154,7 @@ def process_transitions( # Skip transitions with NaN values if check_nan_in_transition( observations=transition["state"], - actions=transition["action"], + actions=transition[ACTION], next_state=transition["next_state"], ): logging.warning("[LEARNER] NaN detected in transition, skipping") @@ -1176,7 +1164,7 @@ def process_transitions( # Add to offline buffer if it's an intervention if dataset_repo_id is not None and transition.get("complementary_info", {}).get( - "is_intervention" + TeleopEvents.IS_INTERVENTION ): offline_replay_buffer.add(**transition) diff --git a/src/lerobot/scripts/rl/learner_service.py b/src/lerobot/rl/learner_service.py similarity index 98% rename from src/lerobot/scripts/rl/learner_service.py rename to src/lerobot/rl/learner_service.py index b07c296e6e2..7ef38119b28 100644 --- a/src/lerobot/scripts/rl/learner_service.py +++ b/src/lerobot/rl/learner_service.py @@ -19,9 +19,9 @@ import time from multiprocessing import Event, Queue +from lerobot.rl.queue import get_last_item_from_queue from lerobot.transport import services_pb2, services_pb2_grpc from lerobot.transport.utils import receive_bytes_in_chunks, send_bytes_in_chunks -from lerobot.utils.queue import get_last_item_from_queue MAX_WORKERS = 3 # Stream parameters, send transitions and interactions SHUTDOWN_TIMEOUT = 10 diff --git a/src/lerobot/utils/process.py b/src/lerobot/rl/process.py similarity index 100% rename from src/lerobot/utils/process.py rename to src/lerobot/rl/process.py diff --git a/src/lerobot/utils/queue.py b/src/lerobot/rl/queue.py similarity index 100% rename from src/lerobot/utils/queue.py rename to src/lerobot/rl/queue.py diff --git a/src/lerobot/utils/wandb_utils.py b/src/lerobot/rl/wandb_utils.py similarity index 98% rename from src/lerobot/utils/wandb_utils.py rename to src/lerobot/rl/wandb_utils.py index 91b4ec95cfe..01cef9487d4 100644 --- a/src/lerobot/utils/wandb_utils.py +++ b/src/lerobot/rl/wandb_utils.py @@ -23,7 +23,7 @@ from termcolor import colored from lerobot.configs.train import TrainPipelineConfig -from lerobot.constants import PRETRAINED_MODEL_DIR +from lerobot.utils.constants import PRETRAINED_MODEL_DIR def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[str] | str: @@ -137,7 +137,7 @@ def log_dict( self._wandb.define_metric(new_custom_key, hidden=True) for k, v in d.items(): - if not isinstance(v, (int, float, str)): + if not isinstance(v, (int | float | str)): logging.warning( f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.' ) diff --git a/src/lerobot/robots/__init__.py b/src/lerobot/robots/__init__.py index d8fd0de9311..1dba0f1b089 100644 --- a/src/lerobot/robots/__init__.py +++ b/src/lerobot/robots/__init__.py @@ -1,3 +1,19 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from .config import RobotConfig from .robot import Robot from .utils import make_robot_from_config diff --git a/src/lerobot/robots/hope_jr/hope_jr_arm.py b/src/lerobot/robots/hope_jr/hope_jr_arm.py index 0e3a615a91f..220a29f8c40 100644 --- a/src/lerobot/robots/hope_jr/hope_jr_arm.py +++ b/src/lerobot/robots/hope_jr/hope_jr_arm.py @@ -20,12 +20,12 @@ from typing import Any from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorNormMode from lerobot.motors.calibration_gui import RangeFinderGUI from lerobot.motors.feetech import ( FeetechMotorsBus, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from ..utils import ensure_safe_goal_position @@ -105,7 +105,7 @@ def connect(self, calibrate: bool = True) -> None: def is_calibrated(self) -> bool: return self.bus.is_calibrated - def calibrate(self, limb_name: str = None) -> None: + def calibrate(self) -> None: groups = { "all": list(self.bus.motors.keys()), "shoulder": ["shoulder_pitch", "shoulder_yaw", "shoulder_roll"], diff --git a/src/lerobot/robots/hope_jr/hope_jr_hand.py b/src/lerobot/robots/hope_jr/hope_jr_hand.py index 8dc100e06e3..9e960642b29 100644 --- a/src/lerobot/robots/hope_jr/hope_jr_hand.py +++ b/src/lerobot/robots/hope_jr/hope_jr_hand.py @@ -20,12 +20,12 @@ from typing import Any from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorNormMode from lerobot.motors.calibration_gui import RangeFinderGUI from lerobot.motors.feetech import ( FeetechMotorsBus, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from .config_hope_jr import HopeJrHandConfig diff --git a/src/lerobot/robots/koch_follower/koch_follower.py b/src/lerobot/robots/koch_follower/koch_follower.py index 563325b881e..41a57828b96 100644 --- a/src/lerobot/robots/koch_follower/koch_follower.py +++ b/src/lerobot/robots/koch_follower/koch_follower.py @@ -20,12 +20,12 @@ from typing import Any from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.dynamixel import ( DynamixelMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from ..utils import ensure_safe_goal_position diff --git a/src/lerobot/robots/lekiwi/lekiwi.py b/src/lerobot/robots/lekiwi/lekiwi.py index 7004cc0fed5..357109cb09a 100644 --- a/src/lerobot/robots/lekiwi/lekiwi.py +++ b/src/lerobot/robots/lekiwi/lekiwi.py @@ -23,12 +23,12 @@ import numpy as np from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.feetech import ( FeetechMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from ..utils import ensure_safe_goal_position diff --git a/src/lerobot/robots/lekiwi/lekiwi_client.py b/src/lerobot/robots/lekiwi/lekiwi_client.py index 9a800140131..19744e2446f 100644 --- a/src/lerobot/robots/lekiwi/lekiwi_client.py +++ b/src/lerobot/robots/lekiwi/lekiwi_client.py @@ -23,7 +23,8 @@ import cv2 import numpy as np -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.constants import ACTION, OBS_STATE +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from .config_lekiwi import LeKiwiClientConfig @@ -203,7 +204,7 @@ def _remote_state_from_obs( state_vec = np.array([flat_state[key] for key in self._state_order], dtype=np.float32) - obs_dict: dict[str, Any] = {**flat_state, "observation.state": state_vec} + obs_dict: dict[str, Any] = {**flat_state, OBS_STATE: state_vec} # Decode images current_frames: dict[str, np.ndarray] = {} @@ -329,7 +330,7 @@ def send_action(self, action: dict[str, Any]) -> dict[str, Any]: actions = np.array([action.get(k, 0.0) for k in self._state_order], dtype=np.float32) action_sent = {key: actions[i] for i, key in enumerate(self._state_order)} - action_sent["action"] = actions + action_sent[ACTION] = actions return action_sent def disconnect(self): diff --git a/src/lerobot/robots/robot.py b/src/lerobot/robots/robot.py index 2a900438094..5e88b915b1f 100644 --- a/src/lerobot/robots/robot.py +++ b/src/lerobot/robots/robot.py @@ -19,8 +19,8 @@ import draccus -from lerobot.constants import HF_LEROBOT_CALIBRATION, ROBOTS from lerobot.motors import MotorCalibration +from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, ROBOTS from .config import RobotConfig diff --git a/src/lerobot/robots/so100_follower/__init__.py b/src/lerobot/robots/so100_follower/__init__.py index b995aab1370..5dc43ac3bca 100644 --- a/src/lerobot/robots/so100_follower/__init__.py +++ b/src/lerobot/robots/so100_follower/__init__.py @@ -14,6 +14,5 @@ # See the License for the specific language governing permissions and # limitations under the License. -from .config_so100_follower import SO100FollowerConfig, SO100FollowerEndEffectorConfig +from .config_so100_follower import SO100FollowerConfig from .so100_follower import SO100Follower -from .so100_follower_end_effector import SO100FollowerEndEffector diff --git a/src/lerobot/robots/so100_follower/config_so100_follower.py b/src/lerobot/robots/so100_follower/config_so100_follower.py index 561790e77f8..272b8c43f4d 100644 --- a/src/lerobot/robots/so100_follower/config_so100_follower.py +++ b/src/lerobot/robots/so100_follower/config_so100_follower.py @@ -39,35 +39,3 @@ class SO100FollowerConfig(RobotConfig): # Set to `True` for backward compatibility with previous policies/dataset use_degrees: bool = False - - -@RobotConfig.register_subclass("so100_follower_end_effector") -@dataclass -class SO100FollowerEndEffectorConfig(SO100FollowerConfig): - """Configuration for the SO100FollowerEndEffector robot.""" - - # Path to URDF file for kinematics - # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: - # https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf - urdf_path: str | None = None - - # End-effector frame name in the URDF - target_frame_name: str = "gripper_frame_link" - - # Default bounds for the end-effector position (in meters) - end_effector_bounds: dict[str, list[float]] = field( - default_factory=lambda: { - "min": [-1.0, -1.0, -1.0], # min x, y, z - "max": [1.0, 1.0, 1.0], # max x, y, z - } - ) - - max_gripper_pos: float = 50 - - end_effector_step_sizes: dict[str, float] = field( - default_factory=lambda: { - "x": 0.02, - "y": 0.02, - "z": 0.02, - } - ) diff --git a/src/lerobot/robots/so100_follower/robot_kinematic_processor.py b/src/lerobot/robots/so100_follower/robot_kinematic_processor.py new file mode 100644 index 00000000000..87e832db6ee --- /dev/null +++ b/src/lerobot/robots/so100_follower/robot_kinematic_processor.py @@ -0,0 +1,610 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field +from typing import Any + +import numpy as np + +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature +from lerobot.model.kinematics import RobotKinematics +from lerobot.processor import ( + EnvTransition, + ObservationProcessorStep, + ProcessorStep, + ProcessorStepRegistry, + RobotAction, + RobotActionProcessorStep, + TransitionKey, +) +from lerobot.utils.rotation import Rotation + + +@ProcessorStepRegistry.register("ee_reference_and_delta") +@dataclass +class EEReferenceAndDelta(RobotActionProcessorStep): + """ + Computes a target end-effector pose from a relative delta command. + + This step takes a desired change in position and orientation (`target_*`) and applies it to a + reference end-effector pose to calculate an absolute target pose. The reference pose is derived + from the current robot joint positions using forward kinematics. + + The processor can operate in two modes: + 1. `use_latched_reference=True`: The reference pose is "latched" or saved at the moment the action + is first enabled. Subsequent commands are relative to this fixed reference. + 2. `use_latched_reference=False`: The reference pose is updated to the robot's current pose at + every step. + + Attributes: + kinematics: The robot's kinematic model for forward kinematics. + end_effector_step_sizes: A dictionary scaling the input delta commands. + motor_names: A list of motor names required for forward kinematics. + use_latched_reference: If True, latch the reference pose on enable; otherwise, always use the + current pose as the reference. + reference_ee_pose: Internal state storing the latched reference pose. + _prev_enabled: Internal state to detect the rising edge of the enable signal. + _command_when_disabled: Internal state to hold the last command while disabled. + """ + + kinematics: RobotKinematics + end_effector_step_sizes: dict + motor_names: list[str] + use_latched_reference: bool = ( + True # If True, latch reference on enable; if False, always use current pose + ) + use_ik_solution: bool = False + + reference_ee_pose: np.ndarray | None = field(default=None, init=False, repr=False) + _prev_enabled: bool = field(default=False, init=False, repr=False) + _command_when_disabled: np.ndarray | None = field(default=None, init=False, repr=False) + + def action(self, action: RobotAction) -> RobotAction: + observation = self.transition.get(TransitionKey.OBSERVATION).copy() + + if observation is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + if self.use_ik_solution and "IK_solution" in self.transition.get(TransitionKey.COMPLEMENTARY_DATA): + q_raw = self.transition.get(TransitionKey.COMPLEMENTARY_DATA)["IK_solution"] + else: + q_raw = np.array( + [ + float(v) + for k, v in observation.items() + if isinstance(k, str) + and k.endswith(".pos") + and k.removesuffix(".pos") in self.motor_names + ], + dtype=float, + ) + + if q_raw is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + # Current pose from FK on measured joints + t_curr = self.kinematics.forward_kinematics(q_raw) + + enabled = bool(action.pop("enabled")) + tx = float(action.pop("target_x")) + ty = float(action.pop("target_y")) + tz = float(action.pop("target_z")) + wx = float(action.pop("target_wx")) + wy = float(action.pop("target_wy")) + wz = float(action.pop("target_wz")) + gripper_vel = float(action.pop("gripper_vel")) + + desired = None + + if enabled: + ref = t_curr + if self.use_latched_reference: + # Latched reference mode: latch reference at the rising edge + if not self._prev_enabled or self.reference_ee_pose is None: + self.reference_ee_pose = t_curr.copy() + ref = self.reference_ee_pose if self.reference_ee_pose is not None else t_curr + + delta_p = np.array( + [ + tx * self.end_effector_step_sizes["x"], + ty * self.end_effector_step_sizes["y"], + tz * self.end_effector_step_sizes["z"], + ], + dtype=float, + ) + r_abs = Rotation.from_rotvec([wx, wy, wz]).as_matrix() + desired = np.eye(4, dtype=float) + desired[:3, :3] = ref[:3, :3] @ r_abs + desired[:3, 3] = ref[:3, 3] + delta_p + + self._command_when_disabled = desired.copy() + else: + # While disabled, keep sending the same command to avoid drift. + if self._command_when_disabled is None: + # If we've never had an enabled command yet, freeze current FK pose once. + self._command_when_disabled = t_curr.copy() + desired = self._command_when_disabled.copy() + + # Write action fields + pos = desired[:3, 3] + tw = Rotation.from_matrix(desired[:3, :3]).as_rotvec() + action["ee.x"] = float(pos[0]) + action["ee.y"] = float(pos[1]) + action["ee.z"] = float(pos[2]) + action["ee.wx"] = float(tw[0]) + action["ee.wy"] = float(tw[1]) + action["ee.wz"] = float(tw[2]) + action["ee.gripper_vel"] = gripper_vel + + self._prev_enabled = enabled + return action + + def reset(self): + """Resets the internal state of the processor.""" + self._prev_enabled = False + self.reference_ee_pose = None + self._command_when_disabled = None + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + for feat in [ + "enabled", + "target_x", + "target_y", + "target_z", + "target_wx", + "target_wy", + "target_wz", + "gripper_vel", + ]: + features[PipelineFeatureType.ACTION].pop(f"{feat}", None) + + for feat in ["x", "y", "z", "wx", "wy", "wz", "gripper_vel"]: + features[PipelineFeatureType.ACTION][f"ee.{feat}"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + + return features + + +@ProcessorStepRegistry.register("ee_bounds_and_safety") +@dataclass +class EEBoundsAndSafety(RobotActionProcessorStep): + """ + Clips the end-effector pose to predefined bounds and checks for unsafe jumps. + + This step ensures that the target end-effector pose remains within a safe operational workspace. + It also moderates the command to prevent large, sudden movements between consecutive steps. + + Attributes: + end_effector_bounds: A dictionary with "min" and "max" keys for position clipping. + max_ee_step_m: The maximum allowed change in position (in meters) between steps. + _last_pos: Internal state storing the last commanded position. + """ + + end_effector_bounds: dict + max_ee_step_m: float = 0.05 + _last_pos: np.ndarray | None = field(default=None, init=False, repr=False) + + def action(self, action: RobotAction) -> RobotAction: + x = action["ee.x"] + y = action["ee.y"] + z = action["ee.z"] + wx = action["ee.wx"] + wy = action["ee.wy"] + wz = action["ee.wz"] + # TODO(Steven): ee.gripper_vel does not need to be bounded + + if None in (x, y, z, wx, wy, wz): + raise ValueError( + "Missing required end-effector pose components: x, y, z, wx, wy, wz must all be present in action" + ) + + pos = np.array([x, y, z], dtype=float) + twist = np.array([wx, wy, wz], dtype=float) + + # Clip position + pos = np.clip(pos, self.end_effector_bounds["min"], self.end_effector_bounds["max"]) + + # Check for jumps in position + if self._last_pos is not None: + dpos = pos - self._last_pos + n = float(np.linalg.norm(dpos)) + if n > self.max_ee_step_m and n > 0: + pos = self._last_pos + dpos * (self.max_ee_step_m / n) + raise ValueError(f"EE jump {n:.3f}m > {self.max_ee_step_m}m") + + self._last_pos = pos + + action["ee.x"] = float(pos[0]) + action["ee.y"] = float(pos[1]) + action["ee.z"] = float(pos[2]) + action["ee.wx"] = float(twist[0]) + action["ee.wy"] = float(twist[1]) + action["ee.wz"] = float(twist[2]) + return action + + def reset(self): + """Resets the last known position and orientation.""" + self._last_pos = None + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + return features + + +@ProcessorStepRegistry.register("inverse_kinematics_ee_to_joints") +@dataclass +class InverseKinematicsEEToJoints(RobotActionProcessorStep): + """ + Computes desired joint positions from a target end-effector pose using inverse kinematics (IK). + + This step translates a Cartesian command (position and orientation of the end-effector) into + the corresponding joint-space commands for each motor. + + Attributes: + kinematics: The robot's kinematic model for inverse kinematics. + motor_names: A list of motor names for which to compute joint positions. + q_curr: Internal state storing the last joint positions, used as an initial guess for the IK solver. + initial_guess_current_joints: If True, use the robot's current joint state as the IK guess. + If False, use the solution from the previous step. + """ + + kinematics: RobotKinematics + motor_names: list[str] + q_curr: np.ndarray | None = field(default=None, init=False, repr=False) + initial_guess_current_joints: bool = True + + def action(self, action: RobotAction) -> RobotAction: + x = action.pop("ee.x") + y = action.pop("ee.y") + z = action.pop("ee.z") + wx = action.pop("ee.wx") + wy = action.pop("ee.wy") + wz = action.pop("ee.wz") + gripper_pos = action.pop("ee.gripper_pos") + + if None in (x, y, z, wx, wy, wz, gripper_pos): + raise ValueError( + "Missing required end-effector pose components: ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_pos must all be present in action" + ) + + observation = self.transition.get(TransitionKey.OBSERVATION).copy() + if observation is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + q_raw = np.array( + [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")], + dtype=float, + ) + if q_raw is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + if self.initial_guess_current_joints: # Use current joints as initial guess + self.q_curr = q_raw + else: # Use previous ik solution as initial guess + if self.q_curr is None: + self.q_curr = q_raw + + # Build desired 4x4 transform from pos + rotvec (twist) + t_des = np.eye(4, dtype=float) + t_des[:3, :3] = Rotation.from_rotvec([wx, wy, wz]).as_matrix() + t_des[:3, 3] = [x, y, z] + + # Compute inverse kinematics + q_target = self.kinematics.inverse_kinematics(self.q_curr, t_des) + self.q_curr = q_target + + # TODO: This is sentitive to order of motor_names = q_target mapping + for i, name in enumerate(self.motor_names): + if name != "gripper": + action[f"{name}.pos"] = float(q_target[i]) + else: + action["gripper.pos"] = float(gripper_pos) + + return action + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + for feat in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]: + features[PipelineFeatureType.ACTION].pop(f"ee.{feat}", None) + + for name in self.motor_names: + features[PipelineFeatureType.ACTION][f"{name}.pos"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + + return features + + def reset(self): + """Resets the initial guess for the IK solver.""" + self.q_curr = None + + +@ProcessorStepRegistry.register("gripper_velocity_to_joint") +@dataclass +class GripperVelocityToJoint(RobotActionProcessorStep): + """ + Converts a gripper velocity command into a target gripper joint position. + + This step integrates a normalized velocity command over time to produce a position command, + taking the current gripper position as a starting point. It also supports a discrete mode + where integer actions map to open, close, or no-op. + + Attributes: + motor_names: A list of motor names, which must include 'gripper'. + speed_factor: A scaling factor to convert the normalized velocity command to a position change. + clip_min: The minimum allowed gripper joint position. + clip_max: The maximum allowed gripper joint position. + discrete_gripper: If True, treat the input action as discrete (0: open, 1: close, 2: stay). + """ + + speed_factor: float = 20.0 + clip_min: float = 0.0 + clip_max: float = 100.0 + discrete_gripper: bool = False + + def action(self, action: RobotAction) -> RobotAction: + observation = self.transition.get(TransitionKey.OBSERVATION).copy() + + gripper_vel = action.pop("ee.gripper_vel") + + if observation is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + q_raw = np.array( + [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")], + dtype=float, + ) + if q_raw is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + if self.discrete_gripper: + # Discrete gripper actions are in [0, 1, 2] + # 0: open, 1: close, 2: stay + # We need to shift them to [-1, 0, 1] and then scale them to clip_max + gripper_vel = (gripper_vel - 1) * self.clip_max + + # Compute desired gripper position + delta = gripper_vel * float(self.speed_factor) + # TODO: This assumes gripper is the last specified joint in the robot + gripper_pos = float(np.clip(q_raw[-1] + delta, self.clip_min, self.clip_max)) + action["ee.gripper_pos"] = gripper_pos + + return action + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + features[PipelineFeatureType.ACTION].pop("ee.gripper_vel", None) + features[PipelineFeatureType.ACTION]["ee.gripper_pos"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + + return features + + +def compute_forward_kinematics_joints_to_ee( + joints: dict[str, Any], kinematics: RobotKinematics, motor_names: list[str] +) -> dict[str, Any]: + motor_joint_values = [joints[f"{n}.pos"] for n in motor_names] + + q = np.array(motor_joint_values, dtype=float) + t = kinematics.forward_kinematics(q) + pos = t[:3, 3] + tw = Rotation.from_matrix(t[:3, :3]).as_rotvec() + gripper_pos = joints["gripper.pos"] + for n in motor_names: + joints.pop(f"{n}.pos") + joints["ee.x"] = float(pos[0]) + joints["ee.y"] = float(pos[1]) + joints["ee.z"] = float(pos[2]) + joints["ee.wx"] = float(tw[0]) + joints["ee.wy"] = float(tw[1]) + joints["ee.wz"] = float(tw[2]) + joints["ee.gripper_pos"] = float(gripper_pos) + return joints + + +@ProcessorStepRegistry.register("forward_kinematics_joints_to_ee_observation") +@dataclass +class ForwardKinematicsJointsToEEObservation(ObservationProcessorStep): + """ + Computes the end-effector pose from joint positions using forward kinematics (FK). + + This step is typically used to add the robot's Cartesian pose to the observation space, + which can be useful for visualization or as an input to a policy. + + Attributes: + kinematics: The robot's kinematic model. + """ + + kinematics: RobotKinematics + motor_names: list[str] + + def observation(self, observation: dict[str, Any]) -> dict[str, Any]: + return compute_forward_kinematics_joints_to_ee(observation, self.kinematics, self.motor_names) + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We only use the ee pose in the dataset, so we don't need the joint positions + for n in self.motor_names: + features[PipelineFeatureType.OBSERVATION].pop(f"{n}.pos", None) + # We specify the dataset features of this step that we want to be stored in the dataset + for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]: + features[PipelineFeatureType.OBSERVATION][f"ee.{k}"] = PolicyFeature( + type=FeatureType.STATE, shape=(1,) + ) + return features + + +@ProcessorStepRegistry.register("forward_kinematics_joints_to_ee_action") +@dataclass +class ForwardKinematicsJointsToEEAction(RobotActionProcessorStep): + """ + Computes the end-effector pose from joint positions using forward kinematics (FK). + + This step is typically used to add the robot's Cartesian pose to the observation space, + which can be useful for visualization or as an input to a policy. + + Attributes: + kinematics: The robot's kinematic model. + """ + + kinematics: RobotKinematics + motor_names: list[str] + + def action(self, action: RobotAction) -> RobotAction: + return compute_forward_kinematics_joints_to_ee(action, self.kinematics, self.motor_names) + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We only use the ee pose in the dataset, so we don't need the joint positions + for n in self.motor_names: + features[PipelineFeatureType.ACTION].pop(f"{n}.pos", None) + # We specify the dataset features of this step that we want to be stored in the dataset + for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]: + features[PipelineFeatureType.ACTION][f"ee.{k}"] = PolicyFeature( + type=FeatureType.STATE, shape=(1,) + ) + return features + + +@ProcessorStepRegistry.register(name="forward_kinematics_joints_to_ee") +@dataclass +class ForwardKinematicsJointsToEE(ProcessorStep): + kinematics: RobotKinematics + motor_names: list[str] + + def __post_init__(self): + self.joints_to_ee_action_processor = ForwardKinematicsJointsToEEAction( + kinematics=self.kinematics, motor_names=self.motor_names + ) + self.joints_to_ee_observation_processor = ForwardKinematicsJointsToEEObservation( + kinematics=self.kinematics, motor_names=self.motor_names + ) + + def __call__(self, transition: EnvTransition) -> EnvTransition: + if transition.get(TransitionKey.ACTION) is not None: + transition = self.joints_to_ee_action_processor(transition) + if transition.get(TransitionKey.OBSERVATION) is not None: + transition = self.joints_to_ee_observation_processor(transition) + return transition + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + if features[PipelineFeatureType.ACTION] is not None: + features = self.joints_to_ee_action_processor.transform_features(features) + if features[PipelineFeatureType.OBSERVATION] is not None: + features = self.joints_to_ee_observation_processor.transform_features(features) + return features + + +@ProcessorStepRegistry.register("inverse_kinematics_rl_step") +@dataclass +class InverseKinematicsRLStep(ProcessorStep): + """ + Computes desired joint positions from a target end-effector pose using inverse kinematics (IK). + + This is modified from the InverseKinematicsEEToJoints step to be used in the RL pipeline. + """ + + kinematics: RobotKinematics + motor_names: list[str] + q_curr: np.ndarray | None = field(default=None, init=False, repr=False) + initial_guess_current_joints: bool = True + + def __call__(self, transition: EnvTransition) -> EnvTransition: + new_transition = dict(transition) + action = new_transition.get(TransitionKey.ACTION) + if action is None: + raise ValueError("Action is required for InverseKinematicsEEToJoints") + action = dict(action) + + x = action.pop("ee.x") + y = action.pop("ee.y") + z = action.pop("ee.z") + wx = action.pop("ee.wx") + wy = action.pop("ee.wy") + wz = action.pop("ee.wz") + gripper_pos = action.pop("ee.gripper_pos") + + if None in (x, y, z, wx, wy, wz, gripper_pos): + raise ValueError( + "Missing required end-effector pose components: ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_pos must all be present in action" + ) + + observation = new_transition.get(TransitionKey.OBSERVATION).copy() + if observation is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + q_raw = np.array( + [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")], + dtype=float, + ) + if q_raw is None: + raise ValueError("Joints observation is require for computing robot kinematics") + + if self.initial_guess_current_joints: # Use current joints as initial guess + self.q_curr = q_raw + else: # Use previous ik solution as initial guess + if self.q_curr is None: + self.q_curr = q_raw + + # Build desired 4x4 transform from pos + rotvec (twist) + t_des = np.eye(4, dtype=float) + t_des[:3, :3] = Rotation.from_rotvec([wx, wy, wz]).as_matrix() + t_des[:3, 3] = [x, y, z] + + # Compute inverse kinematics + q_target = self.kinematics.inverse_kinematics(self.q_curr, t_des) + self.q_curr = q_target + + # TODO: This is sentitive to order of motor_names = q_target mapping + for i, name in enumerate(self.motor_names): + if name != "gripper": + action[f"{name}.pos"] = float(q_target[i]) + else: + action["gripper.pos"] = float(gripper_pos) + + new_transition[TransitionKey.ACTION] = action + complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) + complementary_data["IK_solution"] = q_target + new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data + return new_transition + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + for feat in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]: + features[PipelineFeatureType.ACTION].pop(f"ee.{feat}", None) + + for name in self.motor_names: + features[PipelineFeatureType.ACTION][f"{name}.pos"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + + return features + + def reset(self): + """Resets the initial guess for the IK solver.""" + self.q_curr = None diff --git a/src/lerobot/robots/so100_follower/so100_follower.py b/src/lerobot/robots/so100_follower/so100_follower.py index 1e117e80bdd..d660ebed432 100644 --- a/src/lerobot/robots/so100_follower/so100_follower.py +++ b/src/lerobot/robots/so100_follower/so100_follower.py @@ -20,12 +20,12 @@ from typing import Any from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.feetech import ( FeetechMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from ..utils import ensure_safe_goal_position diff --git a/src/lerobot/robots/so100_follower/so100_follower_end_effector.py b/src/lerobot/robots/so100_follower/so100_follower_end_effector.py deleted file mode 100644 index 5fe2993cb30..00000000000 --- a/src/lerobot/robots/so100_follower/so100_follower_end_effector.py +++ /dev/null @@ -1,200 +0,0 @@ -# !/usr/bin/env python - -# Copyright 2025 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging -import time -from typing import Any - -import numpy as np - -from lerobot.cameras import make_cameras_from_configs -from lerobot.errors import DeviceNotConnectedError -from lerobot.model.kinematics import RobotKinematics -from lerobot.motors import Motor, MotorNormMode -from lerobot.motors.feetech import FeetechMotorsBus - -from . import SO100Follower -from .config_so100_follower import SO100FollowerEndEffectorConfig - -logger = logging.getLogger(__name__) - - -class SO100FollowerEndEffector(SO100Follower): - """ - SO100Follower robot with end-effector space control. - - This robot inherits from SO100Follower but transforms actions from - end-effector space to joint space before sending them to the motors. - """ - - config_class = SO100FollowerEndEffectorConfig - name = "so100_follower_end_effector" - - def __init__(self, config: SO100FollowerEndEffectorConfig): - super().__init__(config) - self.bus = FeetechMotorsBus( - port=self.config.port, - motors={ - "shoulder_pan": Motor(1, "sts3215", MotorNormMode.DEGREES), - "shoulder_lift": Motor(2, "sts3215", MotorNormMode.DEGREES), - "elbow_flex": Motor(3, "sts3215", MotorNormMode.DEGREES), - "wrist_flex": Motor(4, "sts3215", MotorNormMode.DEGREES), - "wrist_roll": Motor(5, "sts3215", MotorNormMode.DEGREES), - "gripper": Motor(6, "sts3215", MotorNormMode.RANGE_0_100), - }, - calibration=self.calibration, - ) - - self.cameras = make_cameras_from_configs(config.cameras) - - self.config = config - - # Initialize the kinematics module for the so100 robot - if self.config.urdf_path is None: - raise ValueError( - "urdf_path must be provided in the configuration for end-effector control. " - "Please set urdf_path in your SO100FollowerEndEffectorConfig." - ) - - self.kinematics = RobotKinematics( - urdf_path=self.config.urdf_path, - target_frame_name=self.config.target_frame_name, - ) - - # Store the bounds for end-effector position - self.end_effector_bounds = self.config.end_effector_bounds - - self.current_ee_pos = None - self.current_joint_pos = None - - @property - def action_features(self) -> dict[str, Any]: - """ - Define action features for end-effector control. - Returns dictionary with dtype, shape, and names. - """ - return { - "dtype": "float32", - "shape": (4,), - "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2, "gripper": 3}, - } - - def send_action(self, action: dict[str, Any]) -> dict[str, Any]: - """ - Transform action from end-effector space to joint space and send to motors. - - Args: - action: Dictionary with keys 'delta_x', 'delta_y', 'delta_z' for end-effector control - or a numpy array with [delta_x, delta_y, delta_z] - - Returns: - The joint-space action that was sent to the motors - """ - - if not self.is_connected: - raise DeviceNotConnectedError(f"{self} is not connected.") - - # Convert action to numpy array if not already - if isinstance(action, dict): - if all(k in action for k in ["delta_x", "delta_y", "delta_z"]): - delta_ee = np.array( - [ - action["delta_x"] * self.config.end_effector_step_sizes["x"], - action["delta_y"] * self.config.end_effector_step_sizes["y"], - action["delta_z"] * self.config.end_effector_step_sizes["z"], - ], - dtype=np.float32, - ) - if "gripper" not in action: - action["gripper"] = [1.0] - action = np.append(delta_ee, action["gripper"]) - else: - logger.warning( - f"Expected action keys 'delta_x', 'delta_y', 'delta_z', got {list(action.keys())}" - ) - action = np.zeros(4, dtype=np.float32) - - if self.current_joint_pos is None: - # Read current joint positions - current_joint_pos = self.bus.sync_read("Present_Position") - self.current_joint_pos = np.array([current_joint_pos[name] for name in self.bus.motors]) - - # Calculate current end-effector position using forward kinematics - if self.current_ee_pos is None: - self.current_ee_pos = self.kinematics.forward_kinematics(self.current_joint_pos) - - # Set desired end-effector position by adding delta - desired_ee_pos = np.eye(4) - desired_ee_pos[:3, :3] = self.current_ee_pos[:3, :3] # Keep orientation - - # Add delta to position and clip to bounds - desired_ee_pos[:3, 3] = self.current_ee_pos[:3, 3] + action[:3] - if self.end_effector_bounds is not None: - desired_ee_pos[:3, 3] = np.clip( - desired_ee_pos[:3, 3], - self.end_effector_bounds["min"], - self.end_effector_bounds["max"], - ) - - # Compute inverse kinematics to get joint positions - target_joint_values_in_degrees = self.kinematics.inverse_kinematics( - self.current_joint_pos, desired_ee_pos - ) - - # Create joint space action dictionary - joint_action = { - f"{key}.pos": target_joint_values_in_degrees[i] for i, key in enumerate(self.bus.motors.keys()) - } - - # Handle gripper separately if included in action - # Gripper delta action is in the range 0 - 2, - # We need to shift the action to the range -1, 1 so that we can expand it to -Max_gripper_pos, Max_gripper_pos - joint_action["gripper.pos"] = np.clip( - self.current_joint_pos[-1] + (action[-1] - 1) * self.config.max_gripper_pos, - 5, - self.config.max_gripper_pos, - ) - - self.current_ee_pos = desired_ee_pos.copy() - self.current_joint_pos = target_joint_values_in_degrees.copy() - self.current_joint_pos[-1] = joint_action["gripper.pos"] - - # Send joint space action to parent class - return super().send_action(joint_action) - - def get_observation(self) -> dict[str, Any]: - if not self.is_connected: - raise DeviceNotConnectedError(f"{self} is not connected.") - - # Read arm position - start = time.perf_counter() - obs_dict = self.bus.sync_read("Present_Position") - obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()} - dt_ms = (time.perf_counter() - start) * 1e3 - logger.debug(f"{self} read state: {dt_ms:.1f}ms") - - # Capture images from cameras - for cam_key, cam in self.cameras.items(): - start = time.perf_counter() - obs_dict[cam_key] = cam.async_read() - dt_ms = (time.perf_counter() - start) * 1e3 - logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") - - return obs_dict - - def reset(self): - self.current_ee_pos = None - self.current_joint_pos = None diff --git a/src/lerobot/robots/so101_follower/so101_follower.py b/src/lerobot/robots/so101_follower/so101_follower.py index 31b06c2fd44..acfd4bd114b 100644 --- a/src/lerobot/robots/so101_follower/so101_follower.py +++ b/src/lerobot/robots/so101_follower/so101_follower.py @@ -20,12 +20,12 @@ from typing import Any from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.feetech import ( FeetechMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from ..utils import ensure_safe_goal_position diff --git a/src/lerobot/robots/stretch3/README.md b/src/lerobot/robots/stretch3/README.md index 72473228635..027f12d6508 100644 --- a/src/lerobot/robots/stretch3/README.md +++ b/src/lerobot/robots/stretch3/README.md @@ -170,8 +170,4 @@ python lerobot/scripts/control_robot.py \ --control.episode=0 ``` -Follow [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) to train a policy on your data and run inference on your robot. You will need to adapt the code for Stretch. - -> TODO(rcadene, aliberts): Add already setup environment and policy yaml configuration files - If you need help, please reach out on Discord in the channel `#stretch3-mobile-arm`. diff --git a/src/lerobot/robots/stretch3/configuration_stretch3.py b/src/lerobot/robots/stretch3/configuration_stretch3.py index d4e217ca0db..c1226bf9029 100644 --- a/src/lerobot/robots/stretch3/configuration_stretch3.py +++ b/src/lerobot/robots/stretch3/configuration_stretch3.py @@ -49,5 +49,3 @@ class Stretch3RobotConfig(RobotConfig): ), } ) - - mock: bool = False diff --git a/src/lerobot/robots/stretch3/robot_stretch3.py b/src/lerobot/robots/stretch3/robot_stretch3.py index b907d6a3f2b..73df360b2cd 100644 --- a/src/lerobot/robots/stretch3/robot_stretch3.py +++ b/src/lerobot/robots/stretch3/robot_stretch3.py @@ -22,8 +22,8 @@ from stretch_body.robot_params import RobotParams from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.constants import OBS_IMAGES, OBS_STATE from lerobot.datasets.utils import get_nested_item +from lerobot.utils.constants import OBS_IMAGES, OBS_STATE from ..robot import Robot from .configuration_stretch3 import Stretch3RobotConfig @@ -164,10 +164,6 @@ def send_action(self, action: np.ndarray) -> np.ndarray: # TODO(aliberts): return action_sent when motion is limited return action - def print_logs(self) -> None: - pass - # TODO(aliberts): move robot-specific logs logic here - def teleop_safety_stop(self) -> None: if self.teleop is not None: self.teleop._safety_stop(robot=self) diff --git a/src/lerobot/robots/utils.py b/src/lerobot/robots/utils.py index 261e59a32eb..aca5c871680 100644 --- a/src/lerobot/robots/utils.py +++ b/src/lerobot/robots/utils.py @@ -14,13 +14,16 @@ import logging from pprint import pformat +from typing import cast -from lerobot.robots import RobotConfig +from lerobot.utils.import_utils import make_device_from_device_class +from .config import RobotConfig from .robot import Robot def make_robot_from_config(config: RobotConfig) -> Robot: + # TODO(Steven): Consider just using the make_device_from_device_class for all types if config.type == "koch_follower": from .koch_follower import KochFollower @@ -29,10 +32,6 @@ def make_robot_from_config(config: RobotConfig) -> Robot: from .so100_follower import SO100Follower return SO100Follower(config) - elif config.type == "so100_follower_end_effector": - from .so100_follower import SO100FollowerEndEffector - - return SO100FollowerEndEffector(config) elif config.type == "so101_follower": from .so101_follower import SO101Follower @@ -70,9 +69,13 @@ def make_robot_from_config(config: RobotConfig) -> Robot: return MockRobot(config) else: - raise ValueError(config.type) + try: + return cast(Robot, make_device_from_device_class(config)) + except Exception as e: + raise ValueError(f"Error creating robot with config {config}: {e}") from e +# TODO(pepijn): Move to pipeline step to make sure we don't have to do this in the robot code and send action to robot is clean for use in dataset def ensure_safe_goal_position( goal_present_pos: dict[str, tuple[float, float]], max_relative_target: float | dict[str, float] ) -> dict[str, float]: diff --git a/src/lerobot/robots/viperx/README.md b/src/lerobot/robots/viperx/README.md index 5b57d61f52e..2e8fc72896a 100644 --- a/src/lerobot/robots/viperx/README.md +++ b/src/lerobot/robots/viperx/README.md @@ -118,7 +118,7 @@ echo ${HF_USER}/aloha_test If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with [Rerun](https://github.com/rerun-io/rerun): ```bash -python -m lerobot.scripts.visualize_dataset \ +lerobot-dataset-viz \ --repo-id ${HF_USER}/aloha_test --episode 0 ``` @@ -193,6 +193,4 @@ As you can see, it's almost the same command as previously used to record your t ## More -Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth explanation. - If you have any question or need help, please reach out on Discord in the channel `#aloha-arm`. diff --git a/src/lerobot/robots/viperx/viperx.py b/src/lerobot/robots/viperx/viperx.py index 881640cd5f7..31e99ffdbe2 100644 --- a/src/lerobot/robots/viperx/viperx.py +++ b/src/lerobot/robots/viperx/viperx.py @@ -18,13 +18,13 @@ from typing import Any from lerobot.cameras.utils import make_cameras_from_configs -from lerobot.constants import OBS_STATE -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.dynamixel import ( DynamixelMotorsBus, OperatingMode, ) +from lerobot.utils.constants import OBS_STATE +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..robot import Robot from ..utils import ensure_safe_goal_position diff --git a/src/lerobot/scripts/display_sys_info.py b/src/lerobot/scripts/display_sys_info.py deleted file mode 100644 index 4d3cc291f30..00000000000 --- a/src/lerobot/scripts/display_sys_info.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python - -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Use this script to get a quick summary of your system config. -It should be able to run without any of LeRobot's dependencies or LeRobot itself installed. -""" - -import platform - -HAS_HF_HUB = True -HAS_HF_DATASETS = True -HAS_NP = True -HAS_TORCH = True -HAS_LEROBOT = True - -try: - import huggingface_hub -except ImportError: - HAS_HF_HUB = False - -try: - import datasets -except ImportError: - HAS_HF_DATASETS = False - -try: - import numpy as np -except ImportError: - HAS_NP = False - -try: - import torch -except ImportError: - HAS_TORCH = False - -try: - import lerobot -except ImportError: - HAS_LEROBOT = False - - -lerobot_version = lerobot.__version__ if HAS_LEROBOT else "N/A" -hf_hub_version = huggingface_hub.__version__ if HAS_HF_HUB else "N/A" -hf_datasets_version = datasets.__version__ if HAS_HF_DATASETS else "N/A" -np_version = np.__version__ if HAS_NP else "N/A" - -torch_version = torch.__version__ if HAS_TORCH else "N/A" -torch_cuda_available = torch.cuda.is_available() if HAS_TORCH else "N/A" -cuda_version = torch._C._cuda_getCompiledVersion() if HAS_TORCH and torch.version.cuda is not None else "N/A" - - -# TODO(aliberts): refactor into an actual command `lerobot env` -def display_sys_info() -> dict: - """Run this to get basic system info to help for tracking issues & bugs.""" - info = { - "`lerobot` version": lerobot_version, - "Platform": platform.platform(), - "Python version": platform.python_version(), - "Huggingface_hub version": hf_hub_version, - "Dataset version": hf_datasets_version, - "Numpy version": np_version, - "PyTorch version (GPU?)": f"{torch_version} ({torch_cuda_available})", - "Cuda version": cuda_version, - "Using GPU in script?": "", - # "Using distributed or parallel set-up in script?": "", - } - print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the last point.\n") - print(format_dict(info)) - return info - - -def format_dict(d: dict) -> str: - return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n" - - -if __name__ == "__main__": - display_sys_info() diff --git a/src/lerobot/calibrate.py b/src/lerobot/scripts/lerobot_calibrate.py similarity index 95% rename from src/lerobot/calibrate.py rename to src/lerobot/scripts/lerobot_calibrate.py index 0aa61a2f92b..0f247caefef 100644 --- a/src/lerobot/calibrate.py +++ b/src/lerobot/scripts/lerobot_calibrate.py @@ -52,6 +52,7 @@ so100_leader, so101_leader, ) +from lerobot.utils.import_utils import register_third_party_devices from lerobot.utils.utils import init_logging @@ -83,6 +84,7 @@ def calibrate(cfg: CalibrateConfig): def main(): + register_third_party_devices() calibrate() diff --git a/src/lerobot/scripts/visualize_dataset.py b/src/lerobot/scripts/lerobot_dataset_viz.py similarity index 93% rename from src/lerobot/scripts/visualize_dataset.py rename to src/lerobot/scripts/lerobot_dataset_viz.py index dda12594abd..55708d9a9c4 100644 --- a/src/lerobot/scripts/visualize_dataset.py +++ b/src/lerobot/scripts/lerobot_dataset_viz.py @@ -29,14 +29,14 @@ - Visualize data stored on a local machine: ``` -local$ python -m lerobot.scripts.visualize_dataset \ +local$ lerobot-dataset-viz \ --repo-id lerobot/pusht \ --episode-index 0 ``` - Visualize data stored on a distant machine with a local viewer: ``` -distant$ python -m lerobot.scripts.visualize_dataset \ +distant$ lerobot-dataset-viz \ --repo-id lerobot/pusht \ --episode-index 0 \ --save 1 \ @@ -50,7 +50,7 @@ (You need to forward the websocket port to the distant machine, with `ssh -L 9087:localhost:9087 username@remote-host`) ``` -distant$ python -m lerobot.scripts.visualize_dataset \ +distant$ lerobot-dataset-viz \ --repo-id lerobot/pusht \ --episode-index 0 \ --mode distant \ @@ -75,6 +75,7 @@ import tqdm from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD class EpisodeSampler(torch.utils.data.Sampler): @@ -156,20 +157,20 @@ def visualize_dataset( rr.log(key, rr.Image(to_hwc_uint8_numpy(batch[key][i]))) # display each dimension of action space (e.g. actuators command) - if "action" in batch: - for dim_idx, val in enumerate(batch["action"][i]): - rr.log(f"action/{dim_idx}", rr.Scalar(val.item())) + if ACTION in batch: + for dim_idx, val in enumerate(batch[ACTION][i]): + rr.log(f"{ACTION}/{dim_idx}", rr.Scalar(val.item())) # display each dimension of observed state space (e.g. agent position in joint space) - if "observation.state" in batch: - for dim_idx, val in enumerate(batch["observation.state"][i]): + if OBS_STATE in batch: + for dim_idx, val in enumerate(batch[OBS_STATE][i]): rr.log(f"state/{dim_idx}", rr.Scalar(val.item())) - if "next.done" in batch: - rr.log("next.done", rr.Scalar(batch["next.done"][i].item())) + if DONE in batch: + rr.log(DONE, rr.Scalar(batch[DONE][i].item())) - if "next.reward" in batch: - rr.log("next.reward", rr.Scalar(batch["next.reward"][i].item())) + if REWARD in batch: + rr.log(REWARD, rr.Scalar(batch[REWARD][i].item())) if "next.success" in batch: rr.log("next.success", rr.Scalar(batch["next.success"][i].item())) diff --git a/src/lerobot/scripts/eval.py b/src/lerobot/scripts/lerobot_eval.py similarity index 62% rename from src/lerobot/scripts/eval.py rename to src/lerobot/scripts/lerobot_eval.py index 13d30c68663..d45be5c428e 100644 --- a/src/lerobot/scripts/eval.py +++ b/src/lerobot/scripts/lerobot_eval.py @@ -46,16 +46,20 @@ You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py """ +import concurrent.futures as cf import json import logging import threading import time +from collections import defaultdict from collections.abc import Callable from contextlib import nullcontext from copy import deepcopy from dataclasses import asdict +from functools import partial from pathlib import Path from pprint import pformat +from typing import Any, TypedDict import einops import gymnasium as gym @@ -68,10 +72,16 @@ from lerobot.configs import parser from lerobot.configs.eval import EvalPipelineConfig from lerobot.envs.factory import make_env -from lerobot.envs.utils import add_envs_task, check_env_attributes_and_types, preprocess_observation -from lerobot.policies.factory import make_policy +from lerobot.envs.utils import ( + add_envs_task, + check_env_attributes_and_types, + close_envs, + preprocess_observation, +) +from lerobot.policies.factory import make_policy, make_pre_post_processors from lerobot.policies.pretrained import PreTrainedPolicy -from lerobot.policies.utils import get_device_from_parameters +from lerobot.processor import PolicyAction, PolicyProcessorPipeline +from lerobot.utils.constants import ACTION, DONE, OBS_STR, REWARD from lerobot.utils.io_utils import write_video from lerobot.utils.random_utils import set_seed from lerobot.utils.utils import ( @@ -84,6 +94,8 @@ def rollout( env: gym.vector.VectorEnv, policy: PreTrainedPolicy, + preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction], seeds: list[int] | None = None, return_observations: bool = False, render_callback: Callable[[gym.vector.VectorEnv], None] | None = None, @@ -120,7 +132,6 @@ def rollout( The dictionary described above. """ assert isinstance(policy, nn.Module), "Policy must be a PyTorch nn module." - device = get_device_from_parameters(policy) # Reset the policy and environments. policy.reset() @@ -145,29 +156,26 @@ def rollout( leave=False, ) check_env_attributes_and_types(env) - while not np.all(done): + while not np.all(done) and step < max_steps: # Numpy array to tensor and changing dictionary keys to LeRobot policy format. observation = preprocess_observation(observation) if return_observations: all_observations.append(deepcopy(observation)) - observation = { - key: observation[key].to(device, non_blocking=device.type == "cuda") for key in observation - } - # Infer "task" from attributes of environments. # TODO: works with SyncVectorEnv but not AsyncVectorEnv observation = add_envs_task(env, observation) - + observation = preprocessor(observation) with torch.inference_mode(): action = policy.select_action(observation) + action = postprocessor(action) # Convert to CPU / numpy. - action = action.to("cpu").numpy() - assert action.ndim == 2, "Action dimensions should be (batch, action_dim)" + action_numpy: np.ndarray = action.to("cpu").numpy() + assert action_numpy.ndim == 2, "Action dimensions should be (batch, action_dim)" # Apply the next action. - observation, reward, terminated, truncated, info = env.step(action) + observation, reward, terminated, truncated, info = env.step(action_numpy) if render_callback is not None: render_callback(env) @@ -179,9 +187,14 @@ def rollout( successes = [False] * env.num_envs # Keep track of which environments are done so far. + # Mark the episode as done if we reach the maximum step limit. + # This ensures that the rollout always terminates cleanly at `max_steps`, + # and allows logging/saving (e.g., videos) to be triggered consistently. done = terminated | truncated | done + if step + 1 == max_steps: + done = np.ones_like(done, dtype=bool) - all_actions.append(torch.from_numpy(action)) + all_actions.append(torch.from_numpy(action_numpy)) all_rewards.append(torch.from_numpy(reward)) all_dones.append(torch.from_numpy(done)) all_successes.append(torch.tensor(successes)) @@ -200,7 +213,7 @@ def rollout( # Stack the sequence along the first dimension so that we have (batch, sequence, *) tensors. ret = { - "action": torch.stack(all_actions, dim=1), + ACTION: torch.stack(all_actions, dim=1), "reward": torch.stack(all_rewards, dim=1), "success": torch.stack(all_successes, dim=1), "done": torch.stack(all_dones, dim=1), @@ -209,7 +222,7 @@ def rollout( stacked_observations = {} for key in all_observations[0]: stacked_observations[key] = torch.stack([obs[key] for obs in all_observations], dim=1) - ret["observation"] = stacked_observations + ret[OBS_STR] = stacked_observations if hasattr(policy, "use_original_modules"): policy.use_original_modules() @@ -220,6 +233,8 @@ def rollout( def eval_policy( env: gym.vector.VectorEnv, policy: PreTrainedPolicy, + preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction], n_episodes: int, max_episodes_rendered: int = 0, videos_dir: Path | None = None, @@ -296,8 +311,10 @@ def render_frame(env: gym.vector.VectorEnv): start_seed + (batch_ix * env.num_envs), start_seed + ((batch_ix + 1) * env.num_envs) ) rollout_data = rollout( - env, - policy, + env=env, + policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, seeds=list(seeds) if seeds else None, return_observations=return_episode_data, render_callback=render_frame if max_episodes_rendered > 0 else None, @@ -423,28 +440,28 @@ def _compile_episode_data( """ ep_dicts = [] total_frames = 0 - for ep_ix in range(rollout_data["action"].shape[0]): + for ep_ix in range(rollout_data[ACTION].shape[0]): # + 2 to include the first done frame and the last observation frame. num_frames = done_indices[ep_ix].item() + 2 total_frames += num_frames # Here we do `num_frames - 1` as we don't want to include the last observation frame just yet. ep_dict = { - "action": rollout_data["action"][ep_ix, : num_frames - 1], + ACTION: rollout_data[ACTION][ep_ix, : num_frames - 1], "episode_index": torch.tensor([start_episode_index + ep_ix] * (num_frames - 1)), "frame_index": torch.arange(0, num_frames - 1, 1), "timestamp": torch.arange(0, num_frames - 1, 1) / fps, - "next.done": rollout_data["done"][ep_ix, : num_frames - 1], + DONE: rollout_data["done"][ep_ix, : num_frames - 1], "next.success": rollout_data["success"][ep_ix, : num_frames - 1], - "next.reward": rollout_data["reward"][ep_ix, : num_frames - 1].type(torch.float32), + REWARD: rollout_data["reward"][ep_ix, : num_frames - 1].type(torch.float32), } # For the last observation frame, all other keys will just be copy padded. for k in ep_dict: ep_dict[k] = torch.cat([ep_dict[k], ep_dict[k][-1:]]) - for key in rollout_data["observation"]: - ep_dict[key] = rollout_data["observation"][key][ep_ix, :num_frames] + for key in rollout_data[OBS_STR]: + ep_dict[key] = rollout_data[OBS_STR][key][ep_ix, :num_frames] ep_dicts.append(ep_dict) @@ -471,7 +488,7 @@ def eval_main(cfg: EvalPipelineConfig): logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}") logging.info("Making environment.") - env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs) + envs = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs) logging.info("Making policy.") @@ -479,28 +496,255 @@ def eval_main(cfg: EvalPipelineConfig): cfg=cfg.policy, env_cfg=cfg.env, ) - policy.eval() + policy.eval() + preprocessor, postprocessor = make_pre_post_processors( + policy_cfg=cfg.policy, + pretrained_path=cfg.policy.pretrained_path, + # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility. + preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}}, + ) with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(): - info = eval_policy( - env, - policy, - cfg.eval.n_episodes, + info = eval_policy_all( + envs=envs, + policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, + n_episodes=cfg.eval.n_episodes, max_episodes_rendered=10, videos_dir=Path(cfg.output_dir) / "videos", start_seed=cfg.seed, + max_parallel_tasks=cfg.env.max_parallel_tasks, ) - print(info["aggregated"]) + print("Overall Aggregated Metrics:") + print(info["overall"]) + + # Print per-suite stats + for task_group, task_group_info in info.items(): + print(f"\nAggregated Metrics for {task_group}:") + print(task_group_info) + # Close all vec envs + close_envs(envs) # Save info with open(Path(cfg.output_dir) / "eval_info.json", "w") as f: json.dump(info, f, indent=2) - env.close() - logging.info("End of eval") +# ---- typed payload returned by one task eval ---- +class TaskMetrics(TypedDict): + sum_rewards: list[float] + max_rewards: list[float] + successes: list[bool] + video_paths: list[str] + + +ACC_KEYS = ("sum_rewards", "max_rewards", "successes", "video_paths") + + +def eval_one( + env: gym.vector.VectorEnv, + *, + policy: PreTrainedPolicy, + preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction], + n_episodes: int, + max_episodes_rendered: int, + videos_dir: Path | None, + return_episode_data: bool, + start_seed: int | None, +) -> TaskMetrics: + """Evaluates one task_id of one suite using the provided vec env.""" + + task_videos_dir = videos_dir + + task_result = eval_policy( + env=env, + policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, + n_episodes=n_episodes, + max_episodes_rendered=max_episodes_rendered, + videos_dir=task_videos_dir, + return_episode_data=return_episode_data, + start_seed=start_seed, + ) + + per_episode = task_result["per_episode"] + return TaskMetrics( + sum_rewards=[ep["sum_reward"] for ep in per_episode], + max_rewards=[ep["max_reward"] for ep in per_episode], + successes=[ep["success"] for ep in per_episode], + video_paths=task_result.get("video_paths", []), + ) + + +def run_one( + task_group: str, + task_id: int, + env, + *, + policy, + preprocessor, + postprocessor, + n_episodes: int, + max_episodes_rendered: int, + videos_dir: Path | None, + return_episode_data: bool, + start_seed: int | None, +): + """ + Run eval_one for a single (task_group, task_id, env). + Returns (task_group, task_id, task_metrics_dict). + This function is intentionally module-level to make it easy to test. + """ + task_videos_dir = None + if videos_dir is not None: + task_videos_dir = videos_dir / f"{task_group}_{task_id}" + task_videos_dir.mkdir(parents=True, exist_ok=True) + + # Call the existing eval_one (assumed to return TaskMetrics-like dict) + metrics = eval_one( + env, + policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, + n_episodes=n_episodes, + max_episodes_rendered=max_episodes_rendered, + videos_dir=task_videos_dir, + return_episode_data=return_episode_data, + start_seed=start_seed, + ) + # ensure we always provide video_paths key to simplify accumulation + if max_episodes_rendered > 0: + metrics.setdefault("video_paths", []) + return task_group, task_id, metrics + + +def eval_policy_all( + envs: dict[str, dict[int, gym.vector.VectorEnv]], + policy, + preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction], + n_episodes: int, + *, + max_episodes_rendered: int = 0, + videos_dir: Path | None = None, + return_episode_data: bool = False, + start_seed: int | None = None, + max_parallel_tasks: int = 1, +) -> dict: + """ + Evaluate a nested `envs` dict: {task_group: {task_id: vec_env}}. + This implementation flattens tasks, runs them sequentially or via ThreadPoolExecutor, + accumulates per-group and overall statistics, and returns the same aggregate metrics + schema as the single-env evaluator (avg_sum_reward / avg_max_reward / pc_success / timings) + plus per-task infos. + """ + start_t = time.time() + + # Flatten envs into list of (task_group, task_id, env) + tasks = [(tg, tid, vec) for tg, group in envs.items() for tid, vec in group.items()] + + # accumulators: track metrics at both per-group level and across all groups + group_acc: dict[str, dict[str, list]] = defaultdict(lambda: {k: [] for k in ACC_KEYS}) + overall: dict[str, list] = {k: [] for k in ACC_KEYS} + per_task_infos: list[dict] = [] + + # small inline helper to accumulate one task's metrics into accumulators + def _accumulate_to(group: str, metrics: dict): + # metrics expected to contain 'sum_rewards', 'max_rewards', 'successes', optionally 'video_paths' + # but eval_one may store per-episode lists; we assume metrics uses scalars averaged per task as before. + # To be robust, accept scalars or lists. + def _append(key, value): + if value is None: + return + if isinstance(value, list): + group_acc[group][key].extend(value) + overall[key].extend(value) + else: + group_acc[group][key].append(value) + overall[key].append(value) + + _append("sum_rewards", metrics.get("sum_rewards")) + _append("max_rewards", metrics.get("max_rewards")) + _append("successes", metrics.get("successes")) + # video_paths is list-like + paths = metrics.get("video_paths", []) + if paths: + group_acc[group]["video_paths"].extend(paths) + overall["video_paths"].extend(paths) + + # Choose runner (sequential vs threaded) + task_runner = partial( + run_one, + policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, + n_episodes=n_episodes, + max_episodes_rendered=max_episodes_rendered, + videos_dir=videos_dir, + return_episode_data=return_episode_data, + start_seed=start_seed, + ) + + if max_parallel_tasks <= 1: + # sequential path (single accumulator path on the main thread) + # NOTE: keeping a single-threaded accumulator avoids concurrent list appends or locks + for task_group, task_id, env in tasks: + tg, tid, metrics = task_runner(task_group, task_id, env) + _accumulate_to(tg, metrics) + per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics}) + else: + # threaded path: submit all tasks, consume completions on main thread and accumulate there + with cf.ThreadPoolExecutor(max_workers=max_parallel_tasks) as executor: + fut2meta = {} + for task_group, task_id, env in tasks: + fut = executor.submit(task_runner, task_group, task_id, env) + fut2meta[fut] = (task_group, task_id) + for fut in cf.as_completed(fut2meta): + tg, tid, metrics = fut.result() + _accumulate_to(tg, metrics) + per_task_infos.append({"task_group": tg, "task_id": tid, "metrics": metrics}) + + # compute aggregated metrics helper (robust to lists/scalars) + def _agg_from_list(xs): + if not xs: + return float("nan") + arr = np.array(xs, dtype=float) + return float(np.nanmean(arr)) + + # compute per-group aggregates + groups_aggregated = {} + for group, acc in group_acc.items(): + groups_aggregated[group] = { + "avg_sum_reward": _agg_from_list(acc["sum_rewards"]), + "avg_max_reward": _agg_from_list(acc["max_rewards"]), + "pc_success": _agg_from_list(acc["successes"]) * 100 if acc["successes"] else float("nan"), + "n_episodes": len(acc["sum_rewards"]), + "video_paths": list(acc["video_paths"]), + } + + # overall aggregates + overall_agg = { + "avg_sum_reward": _agg_from_list(overall["sum_rewards"]), + "avg_max_reward": _agg_from_list(overall["max_rewards"]), + "pc_success": _agg_from_list(overall["successes"]) * 100 if overall["successes"] else float("nan"), + "n_episodes": len(overall["sum_rewards"]), + "eval_s": time.time() - start_t, + "eval_ep_s": (time.time() - start_t) / max(1, len(overall["sum_rewards"])), + "video_paths": list(overall["video_paths"]), + } + + return { + "per_task": per_task_infos, + "per_group": groups_aggregated, + "overall": overall_agg, + } + + def main(): init_logging() eval_main() diff --git a/src/lerobot/find_cameras.py b/src/lerobot/scripts/lerobot_find_cameras.py similarity index 99% rename from src/lerobot/find_cameras.py rename to src/lerobot/scripts/lerobot_find_cameras.py index ec8f5ff3052..e17dca8055f 100644 --- a/src/lerobot/find_cameras.py +++ b/src/lerobot/scripts/lerobot_find_cameras.py @@ -24,7 +24,7 @@ ``` """ -# NOTE(Steven): RealSense can also be identified/opened as OpenCV cameras. If you know the camera is a RealSense, use the `lerobot.find_cameras realsense` flag to avoid confusion. +# NOTE(Steven): RealSense can also be identified/opened as OpenCV cameras. If you know the camera is a RealSense, use the `lerobot-find-cameras realsense` flag to avoid confusion. # NOTE(Steven): macOS cameras sometimes report different FPS at init time, not an issue here as we don't specify FPS when opening the cameras, but the information displayed might not be truthful. import argparse diff --git a/src/lerobot/scripts/find_joint_limits.py b/src/lerobot/scripts/lerobot_find_joint_limits.py similarity index 93% rename from src/lerobot/scripts/find_joint_limits.py rename to src/lerobot/scripts/lerobot_find_joint_limits.py index f7e07514f59..07d57a76089 100644 --- a/src/lerobot/scripts/find_joint_limits.py +++ b/src/lerobot/scripts/lerobot_find_joint_limits.py @@ -20,13 +20,13 @@ Example: ```shell -python -m lerobot.scripts.server.find_joint_limits \ - --robot.type=so100_follower \ - --robot.port=/dev/tty.usbmodem58760431541 \ - --robot.id=black \ - --teleop.type=so100_leader \ - --teleop.port=/dev/tty.usbmodem58760431551 \ - --teleop.id=blue +lerobot-find-joint-limits \ + --robot.type=so100_follower \ + --robot.port=/dev/tty.usbmodem58760431541 \ + --robot.id=black \ + --teleop.type=so100_leader \ + --teleop.port=/dev/tty.usbmodem58760431551 \ + --teleop.id=blue ``` """ @@ -117,5 +117,9 @@ def find_joint_and_ee_bounds(cfg: FindJointLimitsConfig): busy_wait(0.01) -if __name__ == "__main__": +def main(): find_joint_and_ee_bounds() + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/find_port.py b/src/lerobot/scripts/lerobot_find_port.py similarity index 100% rename from src/lerobot/find_port.py rename to src/lerobot/scripts/lerobot_find_port.py diff --git a/src/lerobot/scripts/visualize_image_transforms.py b/src/lerobot/scripts/lerobot_imgtransform_viz.py similarity index 97% rename from src/lerobot/scripts/visualize_image_transforms.py rename to src/lerobot/scripts/lerobot_imgtransform_viz.py index 14caf89df00..bc13f05086a 100644 --- a/src/lerobot/scripts/visualize_image_transforms.py +++ b/src/lerobot/scripts/lerobot_imgtransform_viz.py @@ -20,10 +20,10 @@ Example: ```bash -python -m lerobot.scripts.visualize_image_transforms \ - --repo_id=lerobot/pusht \ - --episodes='[0]' \ - --image_transforms.enable=True +lerobot-imgtransform-viz \ + --repo_id=lerobot/pusht \ + --episodes='[0]' \ + --image_transforms.enable=True ``` """ @@ -126,5 +126,9 @@ def visualize_image_transforms(cfg: DatasetConfig, output_dir: Path = OUTPUT_DIR save_each_transform(cfg.image_transforms, original_frame, output_dir, n_examples) -if __name__ == "__main__": +def main(): visualize_image_transforms() + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/scripts/lerobot_info.py b/src/lerobot/scripts/lerobot_info.py new file mode 100644 index 00000000000..9b49cad187e --- /dev/null +++ b/src/lerobot/scripts/lerobot_info.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Use this script to get a quick summary of your system config. +It should be able to run without any of LeRobot's dependencies or LeRobot itself installed. + +Example: + +```shell +lerobot-info +``` +""" + +import importlib +import platform + + +def get_package_version(package_name: str) -> str: + """Get the version of a package if it exists, otherwise return 'N/A'.""" + try: + module = importlib.import_module(package_name) + return getattr(module, "__version__", "Installed (version not found)") + except ImportError: + return "N/A" + + +def get_sys_info() -> dict: + """Run this to get basic system info to help for tracking issues & bugs.""" + # General package versions + info = { + "lerobot version": get_package_version("lerobot"), + "Platform": platform.platform(), + "Python version": platform.python_version(), + "Huggingface Hub version": get_package_version("huggingface_hub"), + "Datasets version": get_package_version("datasets"), + "Numpy version": get_package_version("numpy"), + } + + # PyTorch and GPU specific information + torch_version = "N/A" + torch_cuda_available = "N/A" + cuda_version = "N/A" + gpu_model = "N/A" + try: + import torch + + torch_version = torch.__version__ + torch_cuda_available = torch.cuda.is_available() + if torch_cuda_available: + cuda_version = torch.version.cuda + # Gets the name of the first available GPU + gpu_model = torch.cuda.get_device_name(0) + except ImportError: + # If torch is not installed, the default "N/A" values will be used. + pass + + info.update( + { + "PyTorch version": torch_version, + "Is PyTorch built with CUDA support?": torch_cuda_available, + "Cuda version": cuda_version, + "GPU model": gpu_model, + "Using GPU in script?": "", + } + ) + + return info + + +def format_dict_for_markdown(d: dict) -> str: + """Formats a dictionary into a markdown-friendly bulleted list.""" + return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + + +def main(): + system_info = get_sys_info() + print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the last point.\n") + print(format_dict_for_markdown(system_info)) + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/record.py b/src/lerobot/scripts/lerobot_record.py similarity index 65% rename from src/lerobot/record.py rename to src/lerobot/scripts/lerobot_record.py index f39a05fb5ea..55846ff63ad 100644 --- a/src/lerobot/record.py +++ b/src/lerobot/scripts/lerobot_record.py @@ -21,11 +21,12 @@ lerobot-record \ --robot.type=so100_follower \ --robot.port=/dev/tty.usbmodem58760431541 \ - --robot.cameras="{laptop: {type: opencv, camera_index: 0, width: 640, height: 480}}" \ + --robot.cameras="{laptop: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \ --robot.id=black \ - --dataset.repo_id=aliberts/record-test \ + --dataset.repo_id=/ \ --dataset.num_episodes=2 \ --dataset.single_task="Grab the cube" \ + --display_data=true # <- Teleop optional if you want to teleoperate to record or in between episodes with a policy \ # --teleop.type=so100_leader \ # --teleop.port=/dev/tty.usbmodem58760431551 \ @@ -59,9 +60,10 @@ import logging import time -from dataclasses import asdict, dataclass +from dataclasses import asdict, dataclass, field from pathlib import Path from pprint import pformat +from typing import Any from lerobot.cameras import ( # noqa: F401 CameraConfig, # noqa: F401 @@ -72,10 +74,20 @@ from lerobot.configs.policies import PreTrainedConfig from lerobot.datasets.image_writer import safe_stop_image_writer from lerobot.datasets.lerobot_dataset import LeRobotDataset -from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features +from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features +from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts from lerobot.datasets.video_utils import VideoEncodingManager -from lerobot.policies.factory import make_policy +from lerobot.policies.factory import make_policy, make_pre_post_processors from lerobot.policies.pretrained import PreTrainedPolicy +from lerobot.processor import ( + PolicyAction, + PolicyProcessorPipeline, + RobotAction, + RobotObservation, + RobotProcessorPipeline, + make_default_processors, +) +from lerobot.processor.rename_processor import rename_stats from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, @@ -97,6 +109,7 @@ so101_leader, ) from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop +from lerobot.utils.constants import ACTION, OBS_STR from lerobot.utils.control_utils import ( init_keyboard_listener, is_headless, @@ -104,13 +117,14 @@ sanity_check_dataset_name, sanity_check_dataset_robot_compatibility, ) +from lerobot.utils.import_utils import register_third_party_devices from lerobot.utils.robot_utils import busy_wait from lerobot.utils.utils import ( get_safe_torch_device, init_logging, log_say, ) -from lerobot.utils.visualization_utils import _init_rerun, log_rerun_data +from lerobot.utils.visualization_utils import init_rerun, log_rerun_data @dataclass @@ -149,6 +163,8 @@ class DatasetRecordConfig: # Number of episodes to record before batch encoding videos # Set to 1 for immediate encoding (default behavior), or higher for batched encoding video_encoding_batch_size: int = 1 + # Rename map for the observation to override the image and state keys + rename_map: dict[str, str] = field(default_factory=dict) def __post_init__(self): if self.single_task is None: @@ -187,14 +203,55 @@ def __get_path_fields__(cls) -> list[str]: return ["policy"] +""" --------------- record_loop() data flow -------------------------- + [ Robot ] + V + [ robot.get_observation() ] ---> raw_obs + V + [ robot_observation_processor ] ---> processed_obs + V + .-----( ACTION LOGIC )------------------. + V V + [ From Teleoperator ] [ From Policy ] + | | + | [teleop.get_action] -> raw_action | [predict_action] + | | | | + | V | V + | [teleop_action_processor] | | + | | | | + '---> processed_teleop_action '---> processed_policy_action + | | + '-------------------------.-------------' + V + [ robot_action_processor ] --> robot_action_to_send + V + [ robot.send_action() ] -- (Robot Executes) + V + ( Save to Dataset ) + V + ( Rerun Log / Loop Wait ) +""" + + @safe_stop_image_writer def record_loop( robot: Robot, events: dict, fps: int, + teleop_action_processor: RobotProcessorPipeline[ + tuple[RobotAction, RobotObservation], RobotAction + ], # runs after teleop + robot_action_processor: RobotProcessorPipeline[ + tuple[RobotAction, RobotObservation], RobotAction + ], # runs before robot + robot_observation_processor: RobotProcessorPipeline[ + RobotObservation, RobotObservation + ], # runs after robot dataset: LeRobotDataset | None = None, teleop: Teleoperator | list[Teleoperator] | None = None, policy: PreTrainedPolicy | None = None, + preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]] | None = None, + postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction] | None = None, control_time_s: int | None = None, single_task: str | None = None, display_data: bool = False, @@ -211,11 +268,7 @@ def record_loop( for t in teleop if isinstance( t, - ( - so100_leader.SO100Leader, - so101_leader.SO101Leader, - koch_leader.KochLeader, - ), + (so100_leader.SO100Leader | so101_leader.SO101Leader | koch_leader.KochLeader), ) ), None, @@ -226,9 +279,11 @@ def record_loop( "For multi-teleop, the list must contain exactly one KeyboardTeleop and one arm teleoperator. Currently only supported for LeKiwi robot." ) - # if policy is given it needs cleaning up - if policy is not None: + # Reset policy and processor if they are provided + if policy is not None and preprocessor is not None and postprocessor is not None: policy.reset() + preprocessor.reset() + postprocessor.reset() timestamp = 0 start_episode_t = time.perf_counter() @@ -239,32 +294,46 @@ def record_loop( events["exit_early"] = False break - observation = robot.get_observation() + # Get robot observation + obs = robot.get_observation() + + # Applies a pipeline to the raw robot observation, default is IdentityProcessor + obs_processed = robot_observation_processor(obs) if policy is not None or dataset is not None: - observation_frame = build_dataset_frame(dataset.features, observation, prefix="observation") + observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR) - if policy is not None: + # Get action from either policy or teleop + if policy is not None and preprocessor is not None and postprocessor is not None: action_values = predict_action( - observation_frame, - policy, - get_safe_torch_device(policy.config.device), - policy.config.use_amp, + observation=observation_frame, + policy=policy, + device=get_safe_torch_device(policy.config.device), + preprocessor=preprocessor, + postprocessor=postprocessor, + use_amp=policy.config.use_amp, task=single_task, robot_type=robot.robot_type, ) - action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)} + + action_names = dataset.features[ACTION]["names"] + act_processed_policy: RobotAction = { + f"{name}": float(action_values[i]) for i, name in enumerate(action_names) + } + elif policy is None and isinstance(teleop, Teleoperator): - action = teleop.get_action() + act = teleop.get_action() + + # Applies a pipeline to the raw teleop action, default is IdentityProcessor + act_processed_teleop = teleop_action_processor((act, obs)) + elif policy is None and isinstance(teleop, list): - # TODO(pepijn, steven): clean the record loop for use of multiple robots (possibly with pipeline) arm_action = teleop_arm.get_action() arm_action = {f"arm_{k}": v for k, v in arm_action.items()} - keyboard_action = teleop_keyboard.get_action() base_action = robot._from_keyboard_to_base_action(keyboard_action) - - action = {**arm_action, **base_action} if len(base_action) > 0 else arm_action + act = {**arm_action, **base_action} if len(base_action) > 0 else arm_action + act_processed_teleop = teleop_action_processor((act, obs)) else: logging.info( "No policy or teleoperator provided, skipping action generation." @@ -273,17 +342,28 @@ def record_loop( ) continue + # Applies a pipeline to the action, default is IdentityProcessor + if policy is not None and act_processed_policy is not None: + action_values = act_processed_policy + robot_action_to_send = robot_action_processor((act_processed_policy, obs)) + else: + action_values = act_processed_teleop + robot_action_to_send = robot_action_processor((act_processed_teleop, obs)) + + # Send action to robot # Action can eventually be clipped using `max_relative_target`, - # so action actually sent is saved in the dataset. - sent_action = robot.send_action(action) + # so action actually sent is saved in the dataset. action = postprocessor.process(action) + # TODO(steven, pepijn, adil): we should use a pipeline step to clip the action, so the sent action is the action that we input to the robot. + _sent_action = robot.send_action(robot_action_to_send) + # Write to dataset if dataset is not None: - action_frame = build_dataset_frame(dataset.features, sent_action, prefix="action") + action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION) frame = {**observation_frame, **action_frame, "task": single_task} dataset.add_frame(frame) if display_data: - log_rerun_data(observation, action) + log_rerun_data(observation=obs_processed, action=action_values) dt_s = time.perf_counter() - start_loop_t busy_wait(1 / fps - dt_s) @@ -296,14 +376,27 @@ def record(cfg: RecordConfig) -> LeRobotDataset: init_logging() logging.info(pformat(asdict(cfg))) if cfg.display_data: - _init_rerun(session_name="recording") + init_rerun(session_name="recording") robot = make_robot_from_config(cfg.robot) teleop = make_teleoperator_from_config(cfg.teleop) if cfg.teleop is not None else None - action_features = hw_to_dataset_features(robot.action_features, "action", cfg.dataset.video) - obs_features = hw_to_dataset_features(robot.observation_features, "observation", cfg.dataset.video) - dataset_features = {**action_features, **obs_features} + teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors() + + dataset_features = combine_feature_dicts( + aggregate_pipeline_dataset_features( + pipeline=teleop_action_processor, + initial_features=create_initial_features( + action=robot.action_features + ), # TODO(steven, pepijn): in future this should be come from teleop or policy + use_videos=cfg.dataset.video, + ), + aggregate_pipeline_dataset_features( + pipeline=robot_observation_processor, + initial_features=create_initial_features(observation=robot.observation_features), + use_videos=cfg.dataset.video, + ), + ) if cfg.resume: dataset = LeRobotDataset( @@ -335,6 +428,18 @@ def record(cfg: RecordConfig) -> LeRobotDataset: # Load pretrained policy policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta) + preprocessor = None + postprocessor = None + if cfg.policy is not None: + preprocessor, postprocessor = make_pre_post_processors( + policy_cfg=cfg.policy, + pretrained_path=cfg.policy.pretrained_path, + dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map), + preprocessor_overrides={ + "device_processor": {"device": cfg.policy.device}, + "rename_observations_processor": {"rename_map": cfg.dataset.rename_map}, + }, + ) robot.connect() if teleop is not None: @@ -350,8 +455,13 @@ def record(cfg: RecordConfig) -> LeRobotDataset: robot=robot, events=events, fps=cfg.dataset.fps, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, teleop=teleop, policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, dataset=dataset, control_time_s=cfg.dataset.episode_time_s, single_task=cfg.dataset.single_task, @@ -368,6 +478,9 @@ def record(cfg: RecordConfig) -> LeRobotDataset: robot=robot, events=events, fps=cfg.dataset.fps, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, teleop=teleop, control_time_s=cfg.dataset.reset_time_s, single_task=cfg.dataset.single_task, @@ -401,6 +514,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset: def main(): + register_third_party_devices() record() diff --git a/src/lerobot/replay.py b/src/lerobot/scripts/lerobot_replay.py similarity index 81% rename from src/lerobot/replay.py rename to src/lerobot/scripts/lerobot_replay.py index cd76d114e4e..ffd7b2b22a1 100644 --- a/src/lerobot/replay.py +++ b/src/lerobot/scripts/lerobot_replay.py @@ -23,7 +23,7 @@ --robot.port=/dev/tty.usbmodem58760431541 \ --robot.id=black \ --dataset.repo_id=aliberts/record-test \ - --dataset.episode=2 + --dataset.episode=0 ``` Example replay with bimanual so100: @@ -45,9 +45,11 @@ from pathlib import Path from pprint import pformat -import draccus - +from lerobot.configs import parser from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.processor import ( + make_default_robot_action_processor, +) from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, @@ -55,10 +57,11 @@ hope_jr, koch_follower, make_robot_from_config, - reachy2, so100_follower, so101_follower, ) +from lerobot.utils.constants import ACTION +from lerobot.utils.import_utils import register_third_party_devices from lerobot.utils.robot_utils import busy_wait from lerobot.utils.utils import ( init_logging, @@ -86,17 +89,19 @@ class ReplayConfig: play_sounds: bool = True -@draccus.wrap() +@parser.wrap() def replay(cfg: ReplayConfig): init_logging() logging.info(pformat(asdict(cfg))) + robot_action_processor = make_default_robot_action_processor() + robot = make_robot_from_config(cfg.robot) dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode]) # Filter dataset to only include frames from the specified episode since episodes are chunked in dataset V3.0 episode_frames = dataset.hf_dataset.filter(lambda x: x["episode_index"] == cfg.dataset.episode) - actions = episode_frames.select_columns("action") + actions = episode_frames.select_columns(ACTION) robot.connect() @@ -104,12 +109,16 @@ def replay(cfg: ReplayConfig): for idx in range(len(episode_frames)): start_episode_t = time.perf_counter() - action_array = actions[idx]["action"] + action_array = actions[idx][ACTION] action = {} - for i, name in enumerate(dataset.features["action"]["names"]): + for i, name in enumerate(dataset.features[ACTION]["names"]): action[name] = action_array[i] - robot.send_action(action) + robot_obs = robot.get_observation() + + processed_action = robot_action_processor((action, robot_obs)) + + _ = robot.send_action(processed_action) dt_s = time.perf_counter() - start_episode_t busy_wait(1 / dataset.fps - dt_s) @@ -118,6 +127,7 @@ def replay(cfg: ReplayConfig): def main(): + register_third_party_devices() replay() diff --git a/src/lerobot/setup_motors.py b/src/lerobot/scripts/lerobot_setup_motors.py similarity index 100% rename from src/lerobot/setup_motors.py rename to src/lerobot/scripts/lerobot_setup_motors.py diff --git a/src/lerobot/teleoperate.py b/src/lerobot/scripts/lerobot_teleoperate.py similarity index 52% rename from src/lerobot/teleoperate.py rename to src/lerobot/scripts/lerobot_teleoperate.py index e7be6967ba1..0a418f3bcad 100644 --- a/src/lerobot/teleoperate.py +++ b/src/lerobot/scripts/lerobot_teleoperate.py @@ -56,11 +56,17 @@ from dataclasses import asdict, dataclass from pprint import pformat -import draccus import rerun as rr from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401 +from lerobot.configs import parser +from lerobot.processor import ( + RobotAction, + RobotObservation, + RobotProcessorPipeline, + make_default_processors, +) from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, @@ -82,9 +88,10 @@ so100_leader, so101_leader, ) +from lerobot.utils.import_utils import register_third_party_devices from lerobot.utils.robot_utils import busy_wait from lerobot.utils.utils import init_logging, move_cursor_up -from lerobot.utils.visualization_utils import _init_rerun, log_rerun_data +from lerobot.utils.visualization_utils import init_rerun, log_rerun_data @dataclass @@ -100,50 +107,105 @@ class TeleoperateConfig: def teleop_loop( - teleop: Teleoperator, robot: Robot, fps: int, display_data: bool = False, duration: float | None = None + teleop: Teleoperator, + robot: Robot, + fps: int, + teleop_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction], + robot_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction], + robot_observation_processor: RobotProcessorPipeline[RobotObservation, RobotObservation], + display_data: bool = False, + duration: float | None = None, ): + """ + This function continuously reads actions from a teleoperation device, processes them through optional + pipelines, sends them to a robot, and optionally displays the robot's state. The loop runs at a + specified frequency until a set duration is reached or it is manually interrupted. + + Args: + teleop: The teleoperator device instance providing control actions. + robot: The robot instance being controlled. + fps: The target frequency for the control loop in frames per second. + display_data: If True, fetches robot observations and displays them in the console and Rerun. + duration: The maximum duration of the teleoperation loop in seconds. If None, the loop runs indefinitely. + teleop_action_processor: An optional pipeline to process raw actions from the teleoperator. + robot_action_processor: An optional pipeline to process actions before they are sent to the robot. + robot_observation_processor: An optional pipeline to process raw observations from the robot. + """ + display_len = max(len(key) for key in robot.action_features) start = time.perf_counter() + while True: loop_start = time.perf_counter() - action = teleop.get_action() + + # Get robot observation + # Not really needed for now other than for visualization + # teleop_action_processor can take None as an observation + # given that it is the identity processor as default + obs = robot.get_observation() + + # Get teleop action + raw_action = teleop.get_action() + + # Process teleop action through pipeline + teleop_action = teleop_action_processor((raw_action, obs)) + + # Process action for robot through pipeline + robot_action_to_send = robot_action_processor((teleop_action, obs)) + + # Send processed action to robot (robot_action_processor.to_output should return dict[str, Any]) + _ = robot.send_action(robot_action_to_send) + if display_data: - observation = robot.get_observation() - log_rerun_data(observation, action) + # Process robot observation through pipeline + obs_transition = robot_observation_processor(obs) + + log_rerun_data( + observation=obs_transition, + action=teleop_action, + ) + + print("\n" + "-" * (display_len + 10)) + print(f"{'NAME':<{display_len}} | {'NORM':>7}") + # Display the final robot action that was sent + for motor, value in robot_action_to_send.items(): + print(f"{motor:<{display_len}} | {value:>7.2f}") + move_cursor_up(len(robot_action_to_send) + 5) - robot.send_action(action) dt_s = time.perf_counter() - loop_start busy_wait(1 / fps - dt_s) - loop_s = time.perf_counter() - loop_start - - print("\n" + "-" * (display_len + 10)) - print(f"{'NAME':<{display_len}} | {'NORM':>7}") - for motor, value in action.items(): - print(f"{motor:<{display_len}} | {value:>7.2f}") print(f"\ntime: {loop_s * 1e3:.2f}ms ({1 / loop_s:.0f} Hz)") if duration is not None and time.perf_counter() - start >= duration: return - move_cursor_up(len(action) + 5) - -@draccus.wrap() +@parser.wrap() def teleoperate(cfg: TeleoperateConfig): init_logging() logging.info(pformat(asdict(cfg))) if cfg.display_data: - _init_rerun(session_name="teleoperation") + init_rerun(session_name="teleoperation") teleop = make_teleoperator_from_config(cfg.teleop) robot = make_robot_from_config(cfg.robot) + teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors() teleop.connect() robot.connect() try: - teleop_loop(teleop, robot, cfg.fps, display_data=cfg.display_data, duration=cfg.teleop_time_s) + teleop_loop( + teleop=teleop, + robot=robot, + fps=cfg.fps, + display_data=cfg.display_data, + duration=cfg.teleop_time_s, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, + ) except KeyboardInterrupt: pass finally: @@ -154,6 +216,7 @@ def teleoperate(cfg: TeleoperateConfig): def main(): + register_third_party_devices() teleoperate() diff --git a/src/lerobot/scripts/train.py b/src/lerobot/scripts/lerobot_train.py similarity index 68% rename from src/lerobot/scripts/train.py rename to src/lerobot/scripts/lerobot_train.py index 398bea90e04..bc66618ca97 100644 --- a/src/lerobot/scripts/train.py +++ b/src/lerobot/scripts/lerobot_train.py @@ -30,11 +30,13 @@ from lerobot.datasets.sampler import EpisodeAwareSampler from lerobot.datasets.utils import cycle from lerobot.envs.factory import make_env +from lerobot.envs.utils import close_envs from lerobot.optim.factory import make_optimizer_and_scheduler -from lerobot.policies.factory import make_policy +from lerobot.policies.factory import make_policy, make_pre_post_processors from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.policies.utils import get_device_from_parameters -from lerobot.scripts.eval import eval_policy +from lerobot.rl.wandb_utils import WandBLogger +from lerobot.scripts.lerobot_eval import eval_policy_all from lerobot.utils.logging_utils import AverageMeter, MetricsTracker from lerobot.utils.random_utils import set_seed from lerobot.utils.train_utils import ( @@ -50,7 +52,6 @@ has_method, init_logging, ) -from lerobot.utils.wandb_utils import WandBLogger def update_policy( @@ -64,6 +65,28 @@ def update_policy( use_amp: bool = False, lock=None, ) -> tuple[MetricsTracker, dict]: + """ + Performs a single training step to update the policy's weights. + + This function executes the forward and backward passes, clips gradients, and steps the optimizer and + learning rate scheduler. It also handles mixed-precision training via a GradScaler. + + Args: + train_metrics: A MetricsTracker instance to record training statistics. + policy: The policy model to be trained. + batch: A batch of training data. + optimizer: The optimizer used to update the policy's parameters. + grad_clip_norm: The maximum norm for gradient clipping. + grad_scaler: The GradScaler for automatic mixed-precision training. + lr_scheduler: An optional learning rate scheduler. + use_amp: A boolean indicating whether to use automatic mixed precision. + lock: An optional lock for thread-safe optimizer updates. + + Returns: + A tuple containing: + - The updated MetricsTracker with new statistics for this step. + - A dictionary of outputs from the policy's forward pass, for logging purposes. + """ start_time = time.perf_counter() device = get_device_from_parameters(policy) policy.train() @@ -107,6 +130,20 @@ def update_policy( @parser.wrap() def train(cfg: TrainPipelineConfig): + """ + Main function to train a policy. + + This function orchestrates the entire training pipeline, including: + - Setting up logging, seeding, and device configuration. + - Creating the dataset, evaluation environment (if applicable), policy, and optimizer. + - Handling resumption from a checkpoint. + - Running the main training loop, which involves fetching data batches and calling `update_policy`. + - Periodically logging metrics, saving model checkpoints, and evaluating the policy. + - Pushing the final trained model to the Hugging Face Hub if configured. + + Args: + cfg: A `TrainPipelineConfig` object containing all training configurations. + """ cfg.validate() logging.info(pformat(cfg.to_dict())) @@ -141,6 +178,37 @@ def train(cfg: TrainPipelineConfig): ds_meta=dataset.meta, ) + # Create processors - only provide dataset_stats if not resuming from saved processors + processor_kwargs = {} + postprocessor_kwargs = {} + if (cfg.policy.pretrained_path and not cfg.resume) or not cfg.policy.pretrained_path: + # Only provide dataset_stats when not resuming from saved processor state + processor_kwargs["dataset_stats"] = dataset.meta.stats + + if cfg.policy.pretrained_path is not None: + processor_kwargs["preprocessor_overrides"] = { + "device_processor": {"device": device.type}, + "normalizer_processor": { + "stats": dataset.meta.stats, + "features": {**policy.config.input_features, **policy.config.output_features}, + "norm_map": policy.config.normalization_mapping, + }, + } + postprocessor_kwargs["postprocessor_overrides"] = { + "unnormalizer_processor": { + "stats": dataset.meta.stats, + "features": policy.config.output_features, + "norm_map": policy.config.normalization_mapping, + }, + } + + preprocessor, postprocessor = make_pre_post_processors( + policy_cfg=cfg.policy, + pretrained_path=cfg.policy.pretrained_path, + **processor_kwargs, + **postprocessor_kwargs, + ) + logging.info("Creating optimizer and scheduler") optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy) grad_scaler = GradScaler(device.type, enabled=cfg.policy.use_amp) @@ -205,15 +273,9 @@ def train(cfg: TrainPipelineConfig): for _ in range(step, cfg.steps): start_time = time.perf_counter() batch = next(dl_iter) + batch = preprocessor(batch) train_tracker.dataloading_s = time.perf_counter() - start_time - for key in batch: - if isinstance(batch[key], torch.Tensor): - if batch[key].dtype != torch.bool: - batch[key] = batch[key].type(torch.float32) if device.type == "mps" else batch[key] - - batch[key] = batch[key].to(device, non_blocking=device.type == "cuda") - train_tracker, output_dict = update_policy( train_tracker, policy, @@ -245,7 +307,9 @@ def train(cfg: TrainPipelineConfig): if cfg.save_checkpoint and is_saving_step: logging.info(f"Checkpoint policy after step {step}") checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step) - save_checkpoint(checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler) + save_checkpoint( + checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler, preprocessor, postprocessor + ) update_last_checkpoint(checkpoint_dir) if wandb_logger: wandb_logger.log_policy(checkpoint_dir) @@ -257,15 +321,25 @@ def train(cfg: TrainPipelineConfig): torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(), ): - eval_info = eval_policy( - eval_env, - policy, - cfg.eval.n_episodes, + eval_info = eval_policy_all( + envs=eval_env, # dict[suite][task_id] -> vec_env + policy=policy, + preprocessor=preprocessor, + postprocessor=postprocessor, + n_episodes=cfg.eval.n_episodes, videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}", max_episodes_rendered=4, start_seed=cfg.seed, + max_parallel_tasks=cfg.env.max_parallel_tasks, ) + # overall metrics (suite-agnostic) + aggregated = eval_info["overall"] + + # optional: per-suite logging + for suite, suite_info in eval_info.items(): + logging.info("Suite %s aggregated: %s", suite, suite_info) + # meters/tracker eval_metrics = { "avg_sum_reward": AverageMeter("∑rwrd", ":.3f"), "pc_success": AverageMeter("success", ":.1f"), @@ -274,21 +348,22 @@ def train(cfg: TrainPipelineConfig): eval_tracker = MetricsTracker( cfg.batch_size, dataset.num_frames, dataset.num_episodes, eval_metrics, initial_step=step ) - eval_tracker.eval_s = eval_info["aggregated"].pop("eval_s") - eval_tracker.avg_sum_reward = eval_info["aggregated"].pop("avg_sum_reward") - eval_tracker.pc_success = eval_info["aggregated"].pop("pc_success") - logging.info(eval_tracker) + eval_tracker.eval_s = aggregated.pop("eval_s") + eval_tracker.avg_sum_reward = aggregated.pop("avg_sum_reward") + eval_tracker.pc_success = aggregated.pop("pc_success") if wandb_logger: wandb_log_dict = {**eval_tracker.to_dict(), **eval_info} wandb_logger.log_dict(wandb_log_dict, step, mode="eval") - wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval") + wandb_logger.log_video(eval_info["overall"]["video_paths"][0], step, mode="eval") if eval_env: - eval_env.close() + close_envs(eval_env) logging.info("End of training") if cfg.policy.push_to_hub: policy.push_model_to_hub(cfg) + preprocessor.push_to_hub(cfg.policy.repo_id) + postprocessor.push_to_hub(cfg.policy.repo_id) def main(): diff --git a/src/lerobot/scripts/rl/gym_manipulator.py b/src/lerobot/scripts/rl/gym_manipulator.py deleted file mode 100644 index 046be03e868..00000000000 --- a/src/lerobot/scripts/rl/gym_manipulator.py +++ /dev/null @@ -1,2264 +0,0 @@ -# !/usr/bin/env python - -# Copyright 2025 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -Robot Environment for LeRobot Manipulation Tasks - -This module provides a comprehensive gym-compatible environment for robot manipulation -with support for: -- Multiple robot types (SO100, SO101, Koch and Moss) -- Human intervention via leader-follower control or gamepad - -- End-effector and joint space control -- Image processing (cropping and resizing) - -The environment is built using a composable wrapper pattern where each wrapper -adds specific functionality to the base RobotEnv. - -Example: - env = make_robot_env(cfg) - obs, info = env.reset() - action = policy.select_action(obs) - obs, reward, terminated, truncated, info = env.step(action) -""" - -import logging -import time -from collections import deque -from collections.abc import Sequence -from threading import Lock -from typing import Annotated, Any - -import gymnasium as gym -import numpy as np -import torch -import torchvision.transforms.functional as F # noqa: N812 - -from lerobot.cameras import opencv # noqa: F401 -from lerobot.configs import parser -from lerobot.envs.configs import EnvConfig -from lerobot.envs.utils import preprocess_observation -from lerobot.model.kinematics import RobotKinematics -from lerobot.robots import ( # noqa: F401 - RobotConfig, - make_robot_from_config, - so100_follower, -) -from lerobot.teleoperators import ( - gamepad, # noqa: F401 - keyboard, # noqa: F401 - make_teleoperator_from_config, - so101_leader, # noqa: F401 -) -from lerobot.teleoperators.gamepad.teleop_gamepad import GamepadTeleop -from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardEndEffectorTeleop -from lerobot.utils.robot_utils import busy_wait -from lerobot.utils.utils import log_say - -logging.basicConfig(level=logging.INFO) - - -def reset_follower_position(robot_arm, target_position): - current_position_dict = robot_arm.bus.sync_read("Present_Position") - current_position = np.array( - [current_position_dict[name] for name in current_position_dict], dtype=np.float32 - ) - trajectory = torch.from_numpy( - np.linspace(current_position, target_position, 50) - ) # NOTE: 30 is just an arbitrary number - for pose in trajectory: - action_dict = dict(zip(current_position_dict, pose, strict=False)) - robot_arm.bus.sync_write("Goal_Position", action_dict) - busy_wait(0.015) - - -class TorchBox(gym.spaces.Box): - """ - A version of gym.spaces.Box that handles PyTorch tensors. - - This class extends gym.spaces.Box to work with PyTorch tensors, - providing compatibility between NumPy arrays and PyTorch tensors. - """ - - def __init__( - self, - low: float | Sequence[float] | np.ndarray, - high: float | Sequence[float] | np.ndarray, - shape: Sequence[int] | None = None, - np_dtype: np.dtype | type = np.float32, - torch_dtype: torch.dtype = torch.float32, - device: str = "cpu", - seed: int | np.random.Generator | None = None, - ) -> None: - """ - Initialize the PyTorch-compatible Box space. - - Args: - low: Lower bounds of the space. - high: Upper bounds of the space. - shape: Shape of the space. If None, inferred from low and high. - np_dtype: NumPy data type for internal storage. - torch_dtype: PyTorch data type for tensor conversion. - device: PyTorch device for returned tensors. - seed: Random seed for sampling. - """ - super().__init__(low, high, shape=shape, dtype=np_dtype, seed=seed) - self.torch_dtype = torch_dtype - self.device = device - - def sample(self) -> torch.Tensor: - """ - Sample a random point from the space. - - Returns: - A PyTorch tensor within the space bounds. - """ - arr = super().sample() - return torch.as_tensor(arr, dtype=self.torch_dtype, device=self.device) - - def contains(self, x: torch.Tensor) -> bool: - """ - Check if a tensor is within the space bounds. - - Args: - x: The PyTorch tensor to check. - - Returns: - Boolean indicating whether the tensor is within bounds. - """ - # Move to CPU/numpy and cast to the internal dtype - arr = x.detach().cpu().numpy().astype(self.dtype, copy=False) - return super().contains(arr) - - def seed(self, seed: int | np.random.Generator | None = None): - """ - Set the random seed for sampling. - - Args: - seed: The random seed to use. - - Returns: - List containing the seed. - """ - super().seed(seed) - return [seed] - - def __repr__(self) -> str: - """ - Return a string representation of the space. - - Returns: - Formatted string with space details. - """ - return ( - f"TorchBox({self.low_repr}, {self.high_repr}, {self.shape}, " - f"np={self.dtype.name}, torch={self.torch_dtype}, device={self.device})" - ) - - -class TorchActionWrapper(gym.Wrapper): - """ - Wrapper that changes the action space to use PyTorch tensors. - - This wrapper modifies the action space to return PyTorch tensors when sampled - and handles converting PyTorch actions to NumPy when stepping the environment. - """ - - def __init__(self, env: gym.Env, device: str): - """ - Initialize the PyTorch action space wrapper. - - Args: - env: The environment to wrap. - device: The PyTorch device to use for tensor operations. - """ - super().__init__(env) - self.action_space = TorchBox( - low=env.action_space.low, - high=env.action_space.high, - shape=env.action_space.shape, - torch_dtype=torch.float32, - device=torch.device("cpu"), - ) - - def step(self, action: torch.Tensor): - """ - Step the environment with a PyTorch tensor action. - - This method handles conversion from PyTorch tensors to NumPy arrays - for compatibility with the underlying environment. - - Args: - action: PyTorch tensor action to take. - - Returns: - Tuple of (observation, reward, terminated, truncated, info). - """ - if action.dim() == 2: - action = action.squeeze(0) - action = action.detach().cpu().numpy() - return self.env.step(action) - - -class RobotEnv(gym.Env): - """ - Gym-compatible environment for evaluating robotic control policies with integrated human intervention. - - This environment wraps a robot interface to provide a consistent API for policy evaluation. It supports both relative (delta) - and absolute joint position commands and automatically configures its observation and action spaces based on the robot's - sensors and configuration. - """ - - def __init__( - self, - robot, - use_gripper: bool = False, - display_cameras: bool = False, - ): - """ - Initialize the RobotEnv environment. - - The environment is set up with a robot interface, which is used to capture observations and send joint commands. The setup - supports both relative (delta) adjustments and absolute joint positions for controlling the robot. - - Args: - robot: The robot interface object used to connect and interact with the physical robot. - display_cameras: If True, the robot's camera feeds will be displayed during execution. - """ - super().__init__() - - self.robot = robot - self.display_cameras = display_cameras - - # Connect to the robot if not already connected. - if not self.robot.is_connected: - self.robot.connect() - - # Episode tracking. - self.current_step = 0 - self.episode_data = None - - self._joint_names = [f"{key}.pos" for key in self.robot.bus.motors] - self._image_keys = self.robot.cameras.keys() - - self.current_observation = None - - self.use_gripper = use_gripper - - self._setup_spaces() - - def _get_observation(self) -> dict[str, np.ndarray]: - """Helper to convert a dictionary from bus.sync_read to an ordered numpy array.""" - obs_dict = self.robot.get_observation() - joint_positions = np.array([obs_dict[name] for name in self._joint_names]) - - images = {key: obs_dict[key] for key in self._image_keys} - self.current_observation = {"agent_pos": joint_positions, "pixels": images} - - def _setup_spaces(self): - """ - Dynamically configure the observation and action spaces based on the robot's capabilities. - - Observation Space: - - For keys with "image": A Box space with pixel values ranging from 0 to 255. - - For non-image keys: A nested Dict space is created under 'observation.state' with a suitable range. - - Action Space: - - The action space is defined as a Box space representing joint position commands. It is defined as relative (delta) - or absolute, based on the configuration. - """ - self._get_observation() - - observation_spaces = {} - - # Define observation spaces for images and other states. - if "pixels" in self.current_observation: - prefix = "observation.images" - observation_spaces = { - f"{prefix}.{key}": gym.spaces.Box( - low=0, high=255, shape=self.current_observation["pixels"][key].shape, dtype=np.uint8 - ) - for key in self.current_observation["pixels"] - } - - observation_spaces["observation.state"] = gym.spaces.Box( - low=0, - high=10, - shape=self.current_observation["agent_pos"].shape, - dtype=np.float32, - ) - - self.observation_space = gym.spaces.Dict(observation_spaces) - - # Define the action space for joint positions along with setting an intervention flag. - action_dim = 3 - bounds = {} - bounds["min"] = -np.ones(action_dim) - bounds["max"] = np.ones(action_dim) - - if self.use_gripper: - action_dim += 1 - bounds["min"] = np.concatenate([bounds["min"], [0]]) - bounds["max"] = np.concatenate([bounds["max"], [2]]) - - self.action_space = gym.spaces.Box( - low=bounds["min"], - high=bounds["max"], - shape=(action_dim,), - dtype=np.float32, - ) - - def reset(self, seed=None, options=None) -> tuple[dict[str, np.ndarray], dict[str, Any]]: - """ - Reset the environment to its initial state. - This method resets the step counter and clears any episodic data. - - Args: - seed: A seed for random number generation to ensure reproducibility. - options: Additional options to influence the reset behavior. - - Returns: - A tuple containing: - - observation (dict): The initial sensor observation. - - info (dict): A dictionary with supplementary information, including the key "is_intervention". - """ - super().reset(seed=seed, options=options) - - self.robot.reset() - - # Reset episode tracking variables. - self.current_step = 0 - self.episode_data = None - self.current_observation = None - self._get_observation() - return self.current_observation, {"is_intervention": False} - - def step(self, action) -> tuple[dict[str, np.ndarray], float, bool, bool, dict[str, Any]]: - """ - Execute a single step within the environment using the specified action. - - The provided action is processed and sent to the robot as joint position commands - that may be either absolute values or deltas based on the environment configuration. - - Args: - action: The commanded joint positions as a numpy array or torch tensor. - - Returns: - A tuple containing: - - observation (dict): The new sensor observation after taking the step. - - reward (float): The step reward (default is 0.0 within this wrapper). - - terminated (bool): True if the episode has reached a terminal state. - - truncated (bool): True if the episode was truncated (e.g., time constraints). - - info (dict): Additional debugging information including intervention status. - """ - action_dict = {"delta_x": action[0], "delta_y": action[1], "delta_z": action[2]} - - # 1.0 action corresponds to no-op action - action_dict["gripper"] = action[3] if self.use_gripper else 1.0 - - self.robot.send_action(action_dict) - - self._get_observation() - - if self.display_cameras: - self.render() - - self.current_step += 1 - - reward = 0.0 - terminated = False - truncated = False - - return ( - self.current_observation, - reward, - terminated, - truncated, - {"is_intervention": False}, - ) - - def render(self): - """ - Render the current state of the environment by displaying the robot's camera feeds. - """ - import cv2 - - image_keys = [key for key in self.current_observation if "image" in key] - - for key in image_keys: - cv2.imshow(key, cv2.cvtColor(self.current_observation[key].numpy(), cv2.COLOR_RGB2BGR)) - cv2.waitKey(1) - - def close(self): - """ - Close the environment and clean up resources by disconnecting the robot. - - If the robot is currently connected, this method properly terminates the connection to ensure that all - associated resources are released. - """ - if self.robot.is_connected: - self.robot.disconnect() - - -class AddJointVelocityToObservation(gym.ObservationWrapper): - """ - Wrapper that adds joint velocity information to the observation. - - This wrapper computes joint velocities by tracking changes in joint positions over time, - and extends the observation space to include these velocities. - """ - - def __init__(self, env, joint_velocity_limits=100.0, fps=30, num_dof=6): - """ - Initialize the joint velocity wrapper. - - Args: - env: The environment to wrap. - joint_velocity_limits: Maximum expected joint velocity for space bounds. - fps: Frames per second used to calculate velocity (position delta / time). - num_dof: Number of degrees of freedom (joints) in the robot. - """ - super().__init__(env) - - # Extend observation space to include joint velocities - old_low = self.observation_space["observation.state"].low - old_high = self.observation_space["observation.state"].high - old_shape = self.observation_space["observation.state"].shape - - self.last_joint_positions = np.zeros(num_dof) - - new_low = np.concatenate([old_low, np.ones(num_dof) * -joint_velocity_limits]) - new_high = np.concatenate([old_high, np.ones(num_dof) * joint_velocity_limits]) - - new_shape = (old_shape[0] + num_dof,) - - self.observation_space["observation.state"] = gym.spaces.Box( - low=new_low, - high=new_high, - shape=new_shape, - dtype=np.float32, - ) - - self.dt = 1.0 / fps - - def observation(self, observation): - """ - Add joint velocity information to the observation. - - Args: - observation: The original observation from the environment. - - Returns: - The modified observation with joint velocities. - """ - joint_velocities = (observation["agent_pos"] - self.last_joint_positions) / self.dt - self.last_joint_positions = observation["agent_pos"] - observation["agent_pos"] = np.concatenate([observation["agent_pos"], joint_velocities], axis=-1) - return observation - - -class AddCurrentToObservation(gym.ObservationWrapper): - """ - Wrapper that adds motor current information to the observation. - - This wrapper extends the observation space to include the current values - from each motor, providing information about the forces being applied. - """ - - def __init__(self, env, max_current=500, num_dof=6): - """ - Initialize the current observation wrapper. - - Args: - env: The environment to wrap. - max_current: Maximum expected current for space bounds. - num_dof: Number of degrees of freedom (joints) in the robot. - """ - super().__init__(env) - - # Extend observation space to include joint velocities - old_low = self.observation_space["observation.state"].low - old_high = self.observation_space["observation.state"].high - old_shape = self.observation_space["observation.state"].shape - - new_low = np.concatenate([old_low, np.zeros(num_dof)]) - new_high = np.concatenate([old_high, np.ones(num_dof) * max_current]) - - new_shape = (old_shape[0] + num_dof,) - - self.observation_space["observation.state"] = gym.spaces.Box( - low=new_low, - high=new_high, - shape=new_shape, - dtype=np.float32, - ) - - def observation(self, observation): - """ - Add current information to the observation. - - Args: - observation: The original observation from the environment. - - Returns: - The modified observation with current values. - """ - present_current_dict = self.env.unwrapped.robot.bus.sync_read("Present_Current") - present_current_observation = np.array( - [present_current_dict[name] for name in self.env.unwrapped.robot.bus.motors] - ) - observation["agent_pos"] = np.concatenate( - [observation["agent_pos"], present_current_observation], axis=-1 - ) - return observation - - -class RewardWrapper(gym.Wrapper): - def __init__(self, env, reward_classifier, device="cuda"): - """ - Wrapper to add reward prediction to the environment using a trained classifier. - - Args: - env: The environment to wrap. - reward_classifier: The reward classifier model. - device: The device to run the model on. - """ - self.env = env - - self.device = device - - self.reward_classifier = torch.compile(reward_classifier) - self.reward_classifier.to(self.device) - - def step(self, action): - """ - Execute a step and compute the reward using the classifier. - - Args: - action: The action to take in the environment. - - Returns: - Tuple of (observation, reward, terminated, truncated, info). - """ - observation, _, terminated, truncated, info = self.env.step(action) - - images = {} - for key in observation: - if "image" in key: - images[key] = observation[key].to(self.device, non_blocking=(self.device == "cuda")) - if images[key].dim() == 3: - images[key] = images[key].unsqueeze(0) - - start_time = time.perf_counter() - with torch.inference_mode(): - success = ( - self.reward_classifier.predict_reward(images, threshold=0.7) - if self.reward_classifier is not None - else 0.0 - ) - info["Reward classifier frequency"] = 1 / (time.perf_counter() - start_time) - - reward = 0.0 - if success == 1.0: - terminated = True - reward = 1.0 - - return observation, reward, terminated, truncated, info - - def reset(self, seed=None, options=None): - """ - Reset the environment. - - Args: - seed: Random seed for reproducibility. - options: Additional reset options. - - Returns: - The initial observation and info from the wrapped environment. - """ - return self.env.reset(seed=seed, options=options) - - -class TimeLimitWrapper(gym.Wrapper): - """ - Wrapper that adds a time limit to episodes and tracks execution time. - - This wrapper terminates episodes after a specified time has elapsed, providing - better control over episode length. - """ - - def __init__(self, env, control_time_s, fps): - """ - Initialize the time limit wrapper. - - Args: - env: The environment to wrap. - control_time_s: Maximum episode duration in seconds. - fps: Frames per second for calculating the maximum number of steps. - """ - self.env = env - self.control_time_s = control_time_s - self.fps = fps - - self.last_timestamp = 0.0 - self.episode_time_in_s = 0.0 - - self.max_episode_steps = int(self.control_time_s * self.fps) - - self.current_step = 0 - - def step(self, action): - """ - Step the environment and track time elapsed. - - Args: - action: The action to take in the environment. - - Returns: - Tuple of (observation, reward, terminated, truncated, info). - """ - obs, reward, terminated, truncated, info = self.env.step(action) - time_since_last_step = time.perf_counter() - self.last_timestamp - self.episode_time_in_s += time_since_last_step - self.last_timestamp = time.perf_counter() - self.current_step += 1 - # check if last timestep took more time than the expected fps - if 1.0 / time_since_last_step < self.fps: - logging.debug(f"Current timestep exceeded expected fps {self.fps}") - - if self.current_step >= self.max_episode_steps: - terminated = True - return obs, reward, terminated, truncated, info - - def reset(self, seed=None, options=None): - """ - Reset the environment and time tracking. - - Args: - seed: Random seed for reproducibility. - options: Additional reset options. - - Returns: - The initial observation and info from the wrapped environment. - """ - self.episode_time_in_s = 0.0 - self.last_timestamp = time.perf_counter() - self.current_step = 0 - return self.env.reset(seed=seed, options=options) - - -class ImageCropResizeWrapper(gym.Wrapper): - """ - Wrapper that crops and resizes image observations. - - This wrapper processes image observations to focus on relevant regions by - cropping and then resizing to a standard size. - """ - - def __init__( - self, - env, - crop_params_dict: dict[str, Annotated[tuple[int], 4]], - resize_size=None, - ): - """ - Initialize the image crop and resize wrapper. - - Args: - env: The environment to wrap. - crop_params_dict: Dictionary mapping image observation keys to crop parameters - (top, left, height, width). - resize_size: Target size for resized images (height, width). Defaults to (128, 128). - """ - super().__init__(env) - self.env = env - self.crop_params_dict = crop_params_dict - print(f"obs_keys , {self.env.observation_space}") - print(f"crop params dict {crop_params_dict.keys()}") - for key_crop in crop_params_dict: - if key_crop not in self.env.observation_space.keys(): # noqa: SIM118 - raise ValueError(f"Key {key_crop} not in observation space") - for key in crop_params_dict: - new_shape = (3, resize_size[0], resize_size[1]) - self.observation_space[key] = gym.spaces.Box(low=0, high=255, shape=new_shape) - - self.resize_size = resize_size - if self.resize_size is None: - self.resize_size = (128, 128) - - def step(self, action): - """ - Step the environment and process image observations. - - Args: - action: The action to take in the environment. - - Returns: - Tuple of (observation, reward, terminated, truncated, info) with processed images. - """ - obs, reward, terminated, truncated, info = self.env.step(action) - for k in self.crop_params_dict: - device = obs[k].device - if obs[k].dim() >= 3: - # Reshape to combine height and width dimensions for easier calculation - batch_size = obs[k].size(0) - channels = obs[k].size(1) - flattened_spatial_dims = obs[k].view(batch_size, channels, -1) - - # Calculate standard deviation across spatial dimensions (H, W) - # If any channel has std=0, all pixels in that channel have the same value - # This is helpful if one camera mistakenly covered or the image is black - std_per_channel = torch.std(flattened_spatial_dims, dim=2) - if (std_per_channel <= 0.02).any(): - logging.warning( - f"Potential hardware issue detected: All pixels have the same value in observation {k}" - ) - - if device == torch.device("mps:0"): - obs[k] = obs[k].cpu() - - obs[k] = F.crop(obs[k], *self.crop_params_dict[k]) - obs[k] = F.resize(obs[k], self.resize_size) - # TODO (michel-aractingi): Bug in resize, it returns values outside [0, 1] - obs[k] = obs[k].clamp(0.0, 1.0) - obs[k] = obs[k].to(device) - - return obs, reward, terminated, truncated, info - - def reset(self, seed=None, options=None): - """ - Reset the environment and process image observations. - - Args: - seed: Random seed for reproducibility. - options: Additional reset options. - - Returns: - Tuple of (observation, info) with processed images. - """ - obs, info = self.env.reset(seed=seed, options=options) - for k in self.crop_params_dict: - device = obs[k].device - if device == torch.device("mps:0"): - obs[k] = obs[k].cpu() - obs[k] = F.crop(obs[k], *self.crop_params_dict[k]) - obs[k] = F.resize(obs[k], self.resize_size) - obs[k] = obs[k].clamp(0.0, 1.0) - obs[k] = obs[k].to(device) - return obs, info - - -class ConvertToLeRobotObservation(gym.ObservationWrapper): - """ - Wrapper that converts standard observations to LeRobot format. - - This wrapper processes observations to match the expected format for LeRobot, - including normalizing image values and moving tensors to the specified device. - """ - - def __init__(self, env, device: str = "cpu"): - """ - Initialize the LeRobot observation converter. - - Args: - env: The environment to wrap. - device: Target device for the observation tensors. - """ - super().__init__(env) - - self.device = torch.device(device) - - def observation(self, observation): - """ - Convert observations to LeRobot format. - - Args: - observation: The original observation from the environment. - - Returns: - The processed observation with normalized images and proper tensor formats. - """ - observation = preprocess_observation(observation) - observation = { - key: observation[key].to(self.device, non_blocking=self.device.type == "cuda") - for key in observation - } - return observation - - -class ResetWrapper(gym.Wrapper): - """ - Wrapper that handles environment reset procedures. - - This wrapper provides additional functionality during environment reset, - including the option to reset to a fixed pose or allow manual reset. - """ - - def __init__( - self, - env: RobotEnv, - reset_pose: np.ndarray | None = None, - reset_time_s: float = 5, - ): - """ - Initialize the reset wrapper. - - Args: - env: The environment to wrap. - reset_pose: Fixed joint positions to reset to. If None, manual reset is used. - reset_time_s: Time in seconds to wait after reset or allowed for manual reset. - """ - super().__init__(env) - self.reset_time_s = reset_time_s - self.reset_pose = reset_pose - self.robot = self.unwrapped.robot - - def reset(self, *, seed=None, options=None): - """ - Reset the environment with either fixed or manual reset procedure. - - If reset_pose is provided, the robot will move to that position. - Otherwise, manual teleoperation control is allowed for reset_time_s seconds. - - Args: - seed: Random seed for reproducibility. - options: Additional reset options. - - Returns: - The initial observation and info from the wrapped environment. - """ - start_time = time.perf_counter() - if self.reset_pose is not None: - log_say("Reset the environment.", play_sounds=True) - reset_follower_position(self.unwrapped.robot, self.reset_pose) - log_say("Reset the environment done.", play_sounds=True) - - if hasattr(self.env, "robot_leader"): - self.env.robot_leader.bus.sync_write("Torque_Enable", 1) - log_say("Reset the leader robot.", play_sounds=True) - reset_follower_position(self.env.robot_leader, self.reset_pose) - log_say("Reset the leader robot done.", play_sounds=True) - else: - log_say( - f"Manually reset the environment for {self.reset_time_s} seconds.", - play_sounds=True, - ) - start_time = time.perf_counter() - while time.perf_counter() - start_time < self.reset_time_s: - action = self.env.robot_leader.get_action() - self.unwrapped.robot.send_action(action) - - log_say("Manual reset of the environment done.", play_sounds=True) - - busy_wait(self.reset_time_s - (time.perf_counter() - start_time)) - - return super().reset(seed=seed, options=options) - - -class BatchCompatibleWrapper(gym.ObservationWrapper): - """ - Wrapper that ensures observations are compatible with batch processing. - - This wrapper adds a batch dimension to observations that don't already have one, - making them compatible with models that expect batched inputs. - """ - - def __init__(self, env): - """ - Initialize the batch compatibility wrapper. - - Args: - env: The environment to wrap. - """ - super().__init__(env) - - def observation(self, observation: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: - """ - Add batch dimensions to observations if needed. - - Args: - observation: Dictionary of observation tensors. - - Returns: - Dictionary of observation tensors with batch dimensions. - """ - for key in observation: - if "image" in key and observation[key].dim() == 3: - observation[key] = observation[key].unsqueeze(0) - if "state" in key and observation[key].dim() == 1: - observation[key] = observation[key].unsqueeze(0) - if "velocity" in key and observation[key].dim() == 1: - observation[key] = observation[key].unsqueeze(0) - return observation - - -class GripperPenaltyWrapper(gym.RewardWrapper): - """ - Wrapper that adds penalties for inefficient gripper commands. - - This wrapper modifies rewards to discourage excessive gripper movement - or commands that attempt to move the gripper beyond its physical limits. - """ - - def __init__(self, env, penalty: float = -0.1): - """ - Initialize the gripper penalty wrapper. - - Args: - env: The environment to wrap. - penalty: Negative reward value to apply for inefficient gripper actions. - """ - super().__init__(env) - self.penalty = penalty - self.last_gripper_state = None - - def reward(self, reward, action): - """ - Apply penalties to reward based on gripper actions. - - Args: - reward: The original reward from the environment. - action: The action that was taken. - - Returns: - Modified reward with penalty applied if necessary. - """ - gripper_state_normalized = self.last_gripper_state / self.unwrapped.robot.config.max_gripper_pos - - action_normalized = action - 1.0 # action / MAX_GRIPPER_COMMAND - - gripper_penalty_bool = (gripper_state_normalized < 0.5 and action_normalized > 0.5) or ( - gripper_state_normalized > 0.75 and action_normalized < -0.5 - ) - - return reward + self.penalty * int(gripper_penalty_bool) - - def step(self, action): - """ - Step the environment and apply gripper penalties. - - Args: - action: The action to take in the environment. - - Returns: - Tuple of (observation, reward, terminated, truncated, info) with penalty applied. - """ - self.last_gripper_state = self.unwrapped.robot.bus.sync_read("Present_Position")["gripper"] - - gripper_action = action[-1] - obs, reward, terminated, truncated, info = self.env.step(action) - gripper_penalty = self.reward(reward, gripper_action) - - info["discrete_penalty"] = gripper_penalty - - return obs, reward, terminated, truncated, info - - def reset(self, **kwargs): - """ - Reset the environment and penalty tracking. - - Args: - **kwargs: Keyword arguments passed to the wrapped environment's reset. - - Returns: - The initial observation and info with gripper penalty initialized. - """ - self.last_gripper_state = None - obs, info = super().reset(**kwargs) - info["gripper_penalty"] = 0.0 - return obs, info - - -class GripperActionWrapper(gym.ActionWrapper): - """ - Wrapper that processes gripper control commands. - - This wrapper quantizes and processes gripper commands, adding a sleep time between - consecutive gripper actions to prevent rapid toggling. - """ - - def __init__(self, env, quantization_threshold: float = 0.2, gripper_sleep: float = 0.0): - """ - Initialize the gripper action wrapper. - - Args: - env: The environment to wrap. - quantization_threshold: Threshold below which gripper commands are quantized to zero. - gripper_sleep: Minimum time in seconds between consecutive gripper commands. - """ - super().__init__(env) - self.quantization_threshold = quantization_threshold - self.gripper_sleep = gripper_sleep - self.last_gripper_action_time = 0.0 - self.last_gripper_action = None - - def action(self, action): - """ - Process gripper commands in the action. - - Args: - action: The original action from the agent. - - Returns: - Modified action with processed gripper command. - """ - if self.gripper_sleep > 0.0: - if ( - self.last_gripper_action is not None - and time.perf_counter() - self.last_gripper_action_time < self.gripper_sleep - ): - action[-1] = self.last_gripper_action - else: - self.last_gripper_action_time = time.perf_counter() - self.last_gripper_action = action[-1] - - gripper_command = action[-1] - # Gripper actions are between 0, 2 - # we want to quantize them to -1, 0 or 1 - gripper_command = gripper_command - 1.0 - - if self.quantization_threshold is not None: - # Quantize gripper command to -1, 0 or 1 - gripper_command = ( - np.sign(gripper_command) if abs(gripper_command) > self.quantization_threshold else 0.0 - ) - gripper_command = gripper_command * self.unwrapped.robot.config.max_gripper_pos - - gripper_state = self.unwrapped.robot.bus.sync_read("Present_Position")["gripper"] - - gripper_action_value = np.clip( - gripper_state + gripper_command, 0, self.unwrapped.robot.config.max_gripper_pos - ) - action[-1] = gripper_action_value.item() - return action - - def reset(self, **kwargs): - """ - Reset the gripper action tracking. - - Args: - **kwargs: Keyword arguments passed to the wrapped environment's reset. - - Returns: - The initial observation and info. - """ - obs, info = super().reset(**kwargs) - self.last_gripper_action_time = 0.0 - self.last_gripper_action = None - return obs, info - - -class EEObservationWrapper(gym.ObservationWrapper): - """ - Wrapper that adds end-effector pose information to observations. - - This wrapper computes the end-effector pose using forward kinematics - and adds it to the observation space. - """ - - def __init__(self, env, ee_pose_limits): - """ - Initialize the end-effector observation wrapper. - - Args: - env: The environment to wrap. - ee_pose_limits: Dictionary with 'min' and 'max' keys containing limits for EE pose. - """ - super().__init__(env) - - # Extend observation space to include end effector pose - prev_space = self.observation_space["observation.state"] - - self.observation_space["observation.state"] = gym.spaces.Box( - low=np.concatenate([prev_space.low, ee_pose_limits["min"]]), - high=np.concatenate([prev_space.high, ee_pose_limits["max"]]), - shape=(prev_space.shape[0] + 3,), - dtype=np.float32, - ) - - self.kinematics = RobotKinematics( - urdf_path=env.unwrapped.robot.config.urdf_path, - target_frame_name=env.unwrapped.robot.config.target_frame_name, - ) - - def observation(self, observation): - """ - Add end-effector pose to the observation. - - Args: - observation: Original observation from the environment. - - Returns: - Enhanced observation with end-effector pose information. - """ - current_joint_pos = self.unwrapped.current_observation["agent_pos"] - - current_ee_pos = self.kinematics.forward_kinematics(current_joint_pos)[:3, 3] - observation["agent_pos"] = np.concatenate([observation["agent_pos"], current_ee_pos], -1) - return observation - - -########################################################### -# Wrappers related to human intervention and input devices -########################################################### - - -class BaseLeaderControlWrapper(gym.Wrapper): - """ - Base class for leader-follower robot control wrappers. - - This wrapper enables human intervention through a leader-follower robot setup, - where the human can control a leader robot to guide the follower robot's movements. - """ - - def __init__( - self, - env, - teleop_device, - end_effector_step_sizes, - use_geared_leader_arm: bool = False, - use_gripper=False, - ): - """ - Initialize the base leader control wrapper. - - Args: - env: The environment to wrap. - teleop_device: The teleoperation device. - use_geared_leader_arm: Whether to use a geared leader arm setup. - use_gripper: Whether to include gripper control. - """ - super().__init__(env) - self.robot_leader = teleop_device - self.robot_follower = env.unwrapped.robot - self.use_geared_leader_arm = use_geared_leader_arm - self.use_gripper: bool = use_gripper - self.end_effector_step_sizes = np.array(list(end_effector_step_sizes.values())) - - # Set up keyboard event tracking - self._init_keyboard_events() - self.event_lock = Lock() # Thread-safe access to events - - # Initialize robot control - self.kinematics = RobotKinematics( - urdf_path=env.unwrapped.robot.config.urdf_path, - target_frame_name=env.unwrapped.robot.config.target_frame_name, - ) - self.leader_torque_enabled = True - self.prev_leader_gripper = None - - # Configure leader arm - # NOTE: Lower the gains of leader arm for automatic take-over - # With lower gains we can manually move the leader arm without risk of injury to ourselves or the robot - # With higher gains, it would be dangerous and difficult to modify the leader's pose while torque is enabled - # Default value for P_coeff is 32 - self.robot_leader.bus.sync_write("Torque_Enable", 1) - for motor in self.robot_leader.bus.motors: - self.robot_leader.bus.write("P_Coefficient", motor, 16) - self.robot_leader.bus.write("I_Coefficient", motor, 0) - self.robot_leader.bus.write("D_Coefficient", motor, 16) - - self.leader_tracking_error_queue = deque(maxlen=4) - self._init_keyboard_listener() - - def _init_keyboard_events(self): - """ - Initialize the keyboard events dictionary. - - This method sets up tracking for keyboard events used for intervention control. - It should be overridden in subclasses to add additional events. - """ - self.keyboard_events = { - "episode_success": False, - "episode_end": False, - "rerecord_episode": False, - } - - def _handle_key_press(self, key, keyboard_device): - """ - Handle key press events. - - Args: - key: The key that was pressed. - keyboard: The keyboard module with key definitions. - - This method should be overridden in subclasses for additional key handling. - """ - try: - if key == keyboard_device.Key.esc: - self.keyboard_events["episode_end"] = True - return - if key == keyboard_device.Key.left: - self.keyboard_events["rerecord_episode"] = True - return - if hasattr(key, "char") and key.char == "s": - logging.info("Key 's' pressed. Episode success triggered.") - self.keyboard_events["episode_success"] = True - return - except Exception as e: - logging.error(f"Error handling key press: {e}") - - def _init_keyboard_listener(self): - """ - Initialize the keyboard listener for intervention control. - - This method sets up keyboard event handling if not in headless mode. - """ - from pynput import keyboard as keyboard_device - - def on_press(key): - with self.event_lock: - self._handle_key_press(key, keyboard_device) - - self.listener = keyboard_device.Listener(on_press=on_press) - self.listener.start() - - def _check_intervention(self): - """ - Check if human intervention is needed. - - Returns: - Boolean indicating whether intervention is needed. - - This method should be overridden in subclasses with specific intervention logic. - """ - return False - - def _handle_intervention(self, action): - """ - Process actions during intervention mode. - - Args: - action: The original action from the agent. - - Returns: - Tuple of (modified_action, intervention_action). - """ - if self.leader_torque_enabled: - self.robot_leader.bus.sync_write("Torque_Enable", 0) - self.leader_torque_enabled = False - - leader_pos_dict = self.robot_leader.bus.sync_read("Present_Position") - follower_pos_dict = self.robot_follower.bus.sync_read("Present_Position") - - leader_pos = np.array([leader_pos_dict[name] for name in leader_pos_dict]) - follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict]) - - self.leader_tracking_error_queue.append(np.linalg.norm(follower_pos[:-1] - leader_pos[:-1])) - - # [:3, 3] Last column of the transformation matrix corresponds to the xyz translation - leader_ee = self.kinematics.forward_kinematics(leader_pos)[:3, 3] - follower_ee = self.kinematics.forward_kinematics(follower_pos)[:3, 3] - - action = np.clip(leader_ee - follower_ee, -self.end_effector_step_sizes, self.end_effector_step_sizes) - # Normalize the action to the range [-1, 1] - action = action / self.end_effector_step_sizes - - if self.use_gripper: - if self.prev_leader_gripper is None: - self.prev_leader_gripper = np.clip( - leader_pos[-1], 0, self.robot_follower.config.max_gripper_pos - ) - - # Get gripper action delta based on leader pose - leader_gripper = leader_pos[-1] - gripper_delta = leader_gripper - self.prev_leader_gripper - - # Normalize by max angle and quantize to {0,1,2} - normalized_delta = gripper_delta / self.robot_follower.config.max_gripper_pos - if normalized_delta >= 0.3: - gripper_action = 2 - elif normalized_delta <= 0.1: - gripper_action = 0 - else: - gripper_action = 1 - - action = np.append(action, gripper_action) - - return action - - def _handle_leader_teleoperation(self): - """ - Handle leader teleoperation in non-intervention mode. - - This method synchronizes the leader robot position with the follower. - """ - - prev_leader_pos_dict = self.robot_leader.bus.sync_read("Present_Position") - prev_leader_pos = np.array( - [prev_leader_pos_dict[name] for name in prev_leader_pos_dict], dtype=np.float32 - ) - - if not self.leader_torque_enabled: - self.robot_leader.bus.sync_write("Torque_Enable", 1) - self.leader_torque_enabled = True - - follower_pos_dict = self.robot_follower.bus.sync_read("Present_Position") - follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict], dtype=np.float32) - - goal_pos = {f"{motor}": follower_pos[i] for i, motor in enumerate(self.robot_leader.bus.motors)} - self.robot_leader.bus.sync_write("Goal_Position", goal_pos) - - self.leader_tracking_error_queue.append(np.linalg.norm(follower_pos[:-1] - prev_leader_pos[:-1])) - - def step(self, action): - """ - Execute a step with possible human intervention. - - Args: - action: The action to take in the environment. - - Returns: - Tuple of (observation, reward, terminated, truncated, info). - """ - is_intervention = self._check_intervention() - - # NOTE: - if is_intervention: - action = self._handle_intervention(action) - else: - self._handle_leader_teleoperation() - - # NOTE: - obs, reward, terminated, truncated, info = self.env.step(action) - - if isinstance(action, np.ndarray): - action = torch.from_numpy(action) - - # Add intervention info - info["is_intervention"] = is_intervention - info["action_intervention"] = action - - self.prev_leader_gripper = np.clip( - self.robot_leader.bus.sync_read("Present_Position")["gripper"], - 0, - self.robot_follower.config.max_gripper_pos, - ) - - # Check for success or manual termination - success = self.keyboard_events["episode_success"] - terminated = terminated or self.keyboard_events["episode_end"] or success - - if success: - reward = 1.0 - logging.info("Episode ended successfully with reward 1.0") - - return obs, reward, terminated, truncated, info - - def reset(self, **kwargs): - """ - Reset the environment and intervention state. - - Args: - **kwargs: Keyword arguments passed to the wrapped environment's reset. - - Returns: - The initial observation and info. - """ - self.keyboard_events = dict.fromkeys(self.keyboard_events, False) - self.leader_tracking_error_queue.clear() - return super().reset(**kwargs) - - def close(self): - """ - Clean up resources, including stopping keyboard listener. - - Returns: - Result of closing the wrapped environment. - """ - if hasattr(self, "listener") and self.listener is not None: - self.listener.stop() - return self.env.close() - - -class GearedLeaderControlWrapper(BaseLeaderControlWrapper): - """ - Wrapper that enables manual intervention via keyboard. - - This wrapper extends the BaseLeaderControlWrapper to allow explicit toggling - of human intervention mode with keyboard controls. - """ - - def _init_keyboard_events(self): - """ - Initialize keyboard events including human intervention flag. - - Extends the base class dictionary with an additional flag for tracking - intervention state toggled by keyboard. - """ - super()._init_keyboard_events() - self.keyboard_events["human_intervention_step"] = False - - def _handle_key_press(self, key, keyboard_device): - """ - Handle key presses including space for intervention toggle. - - Args: - key: The key that was pressed. - keyboard: The keyboard module with key definitions. - - Extends the base handler to respond to space key for toggling intervention. - """ - super()._handle_key_press(key, keyboard_device) - if key == keyboard_device.Key.space: - if not self.keyboard_events["human_intervention_step"]: - logging.info( - "Space key pressed. Human intervention required.\n" - "Place the leader in similar pose to the follower and press space again." - ) - self.keyboard_events["human_intervention_step"] = True - log_say("Human intervention step.", play_sounds=True) - else: - self.keyboard_events["human_intervention_step"] = False - logging.info("Space key pressed for a second time.\nContinuing with policy actions.") - log_say("Continuing with policy actions.", play_sounds=True) - - def _check_intervention(self): - """ - Check if human intervention is active based on keyboard toggle. - - Returns: - Boolean indicating whether intervention mode is active. - """ - return self.keyboard_events["human_intervention_step"] - - -class GearedLeaderAutomaticControlWrapper(BaseLeaderControlWrapper): - """ - Wrapper with automatic intervention based on error thresholds. - - This wrapper monitors the error between leader and follower positions - and automatically triggers intervention when error exceeds thresholds. - """ - - def __init__( - self, - env, - teleop_device, - end_effector_step_sizes, - use_gripper=False, - intervention_threshold=10.0, - release_threshold=1e-2, - ): - """ - Initialize the automatic intervention wrapper. - - Args: - env: The environment to wrap. - teleop_device: The teleoperation device. - use_gripper: Whether to include gripper control. - intervention_threshold: Error threshold to trigger intervention. - release_threshold: Error threshold to release intervention. - queue_size: Number of error measurements to track for smoothing. - """ - super().__init__(env, teleop_device, end_effector_step_sizes, use_gripper=use_gripper) - - # Error tracking parameters - self.intervention_threshold = intervention_threshold # Threshold to trigger intervention - self.release_threshold = release_threshold # Threshold to release intervention - self.is_intervention_active = False - self.start_time = time.perf_counter() - - def _check_intervention(self): - """ - Determine if intervention should occur based on the rate of change of leader-follower error in end_effector space. - - This method monitors the rate of change of leader-follower error in end_effector space - and automatically triggers intervention when the rate of change exceeds - the intervention threshold, releasing when it falls below the release threshold. - - Returns: - Boolean indicating whether intervention should be active. - """ - - # Condition for starting the intervention - # If the error in teleoperation is too high, that means the a user has grasped the leader robot and he wants to take over - if ( - not self.is_intervention_active - and len(self.leader_tracking_error_queue) == self.leader_tracking_error_queue.maxlen - and np.var(list(self.leader_tracking_error_queue)[-2:]) > self.intervention_threshold - ): - self.is_intervention_active = True - self.leader_tracking_error_queue.clear() - log_say("Intervention started", play_sounds=True) - return True - - # Track the error over time in leader_tracking_error_queue - # If the variance of the tracking error is too low, that means the user has let go of the leader robot and the intervention is over - if ( - self.is_intervention_active - and len(self.leader_tracking_error_queue) == self.leader_tracking_error_queue.maxlen - and np.var(self.leader_tracking_error_queue) < self.release_threshold - ): - self.is_intervention_active = False - self.leader_tracking_error_queue.clear() - log_say("Intervention ended", play_sounds=True) - return False - - # If not change has happened that merits a change in the intervention state, return the current state - return self.is_intervention_active - - def reset(self, **kwargs): - """ - Reset error tracking on environment reset. - - Args: - **kwargs: Keyword arguments passed to the wrapped environment's reset. - - Returns: - The initial observation and info. - """ - self.is_intervention_active = False - return super().reset(**kwargs) - - -class GamepadControlWrapper(gym.Wrapper): - """ - Wrapper that allows controlling a gym environment with a gamepad. - - This wrapper intercepts the step method and allows human input via gamepad - to override the agent's actions when desired. - """ - - def __init__( - self, - env, - teleop_device, # Accepts an instantiated teleoperator - use_gripper=False, # This should align with teleop_device's config - auto_reset=False, - ): - """ - Initialize the gamepad controller wrapper. - - Args: - env: The environment to wrap. - teleop_device: The instantiated teleoperation device (e.g., GamepadTeleop). - use_gripper: Whether to include gripper control (should match teleop_device.config.use_gripper). - auto_reset: Whether to auto reset the environment when episode ends. - """ - super().__init__(env) - - self.teleop_device = teleop_device - # Ensure the teleop_device is connected if it has a connect method - if hasattr(self.teleop_device, "connect") and not self.teleop_device.is_connected: - self.teleop_device.connect() - - # self.controller attribute is removed - - self.auto_reset = auto_reset - # use_gripper from args should ideally match teleop_device.config.use_gripper - # For now, we use the one passed, but it can lead to inconsistency if not set correctly from config - self.use_gripper = use_gripper - - logging.info("Gamepad control wrapper initialized with provided teleop_device.") - print( - "Gamepad controls (managed by the provided teleop_device - specific button mappings might vary):" - ) - print(" Left analog stick: Move in X-Y plane") - print(" Right analog stick: Move in Z axis (up/down)") - print(" X/Square button: End episode (FAILURE)") - print(" Y/Triangle button: End episode (SUCCESS)") - print(" B/Circle button: Exit program") - - def get_teleop_commands( - self, - ) -> tuple[bool, np.ndarray, bool, bool, bool]: - """ - Get the current action from the gamepad if any input is active. - - Returns: - Tuple containing: - - is_active: Whether gamepad input is active (from teleop_device.gamepad.should_intervene()) - - action: The action derived from gamepad input (from teleop_device.get_action()) - - terminate_episode: Whether episode termination was requested - - success: Whether episode success was signaled - - rerecord_episode: Whether episode rerecording was requested - """ - if not hasattr(self.teleop_device, "gamepad") or self.teleop_device.gamepad is None: - raise AttributeError( - "teleop_device does not have a 'gamepad' attribute or it is None. Expected for GamepadControlWrapper." - ) - - # Get status flags from the underlying gamepad controller within the teleop_device - self.teleop_device.gamepad.update() # Ensure gamepad state is fresh - intervention_is_active = self.teleop_device.gamepad.should_intervene() - episode_end_status = self.teleop_device.gamepad.get_episode_end_status() - - terminate_episode = episode_end_status is not None - success = episode_end_status == "success" - rerecord_episode = episode_end_status == "rerecord_episode" - - # Get the action dictionary from the teleop_device - action_dict = self.teleop_device.get_action() - - # Convert action_dict to numpy array based on expected structure - # Order: delta_x, delta_y, delta_z, gripper (if use_gripper) - action_list = [action_dict["delta_x"], action_dict["delta_y"], action_dict["delta_z"]] - if self.use_gripper: - # GamepadTeleop returns gripper action as 0 (close), 1 (stay), 2 (open) - # This needs to be consistent with what EEActionWrapper expects if it's used downstream - # EEActionWrapper for gripper typically expects 0.0 (closed) to 2.0 (open) - # For now, we pass the direct value from GamepadTeleop, ensure downstream compatibility. - gripper_val = action_dict.get("gripper", 1.0) # Default to 1.0 (stay) if not present - action_list.append(float(gripper_val)) - - gamepad_action_np = np.array(action_list, dtype=np.float32) - - return ( - intervention_is_active, - gamepad_action_np, - terminate_episode, - success, - rerecord_episode, - ) - - def step(self, action): - """ - Step the environment, using gamepad input to override actions when active. - - Args: - action: Original action from agent. - - Returns: - Tuple of (observation, reward, terminated, truncated, info). - """ - # Get gamepad state and action - ( - is_intervention, - gamepad_action, - terminate_episode, - success, - rerecord_episode, - ) = self.get_teleop_commands() - - # Update episode ending state if requested - if terminate_episode: - logging.info(f"Episode manually ended: {'SUCCESS' if success else 'FAILURE'}") - - # Only override the action if gamepad is active - action = gamepad_action if is_intervention else action - - # Step the environment - obs, reward, terminated, truncated, info = self.env.step(action) - - # Add episode ending if requested via gamepad - terminated = terminated or truncated or terminate_episode - - if success: - reward = 1.0 - logging.info("Episode ended successfully with reward 1.0") - - if isinstance(action, np.ndarray): - action = torch.from_numpy(action) - - info["is_intervention"] = is_intervention - # The original `BaseLeaderControlWrapper` puts `action_intervention` in info. - # For Gamepad, if intervention, `gamepad_action` is the intervention. - # If not intervention, policy's action is `action`. - # For consistency, let's store the *human's* action if intervention occurred. - info["action_intervention"] = action - - info["rerecord_episode"] = rerecord_episode - - # If episode ended, reset the state - if terminated or truncated: - # Add success/failure information to info dict - info["next.success"] = success - - # Auto reset if configured - if self.auto_reset: - obs, reset_info = self.reset() - info.update(reset_info) - - return obs, reward, terminated, truncated, info - - def close(self): - """ - Clean up resources when environment closes. - - Returns: - Result of closing the wrapped environment. - """ - if hasattr(self.teleop_device, "disconnect"): - self.teleop_device.disconnect() - - # Call the parent close method - return self.env.close() - - -class KeyboardControlWrapper(GamepadControlWrapper): - """ - Wrapper that allows controlling a gym environment with a keyboard. - - This wrapper intercepts the step method and allows human input via keyboard - to override the agent's actions when desired. - - Inherits from GamepadControlWrapper to avoid code duplication. - """ - - def __init__( - self, - env, - teleop_device, # Accepts an instantiated teleoperator - use_gripper=False, # This should align with teleop_device's config - auto_reset=False, - ): - """ - Initialize the gamepad controller wrapper. - - Args: - env: The environment to wrap. - teleop_device: The instantiated teleoperation device (e.g., GamepadTeleop). - use_gripper: Whether to include gripper control (should match teleop_device.config.use_gripper). - auto_reset: Whether to auto reset the environment when episode ends. - """ - super().__init__(env, teleop_device, use_gripper, auto_reset) - - self.is_intervention_active = False - - logging.info("Keyboard control wrapper initialized with provided teleop_device.") - print("Keyboard controls:") - print(" Arrow keys: Move in X-Y plane") - print(" Shift and Shift_R: Move in Z axis") - print(" Right Ctrl and Left Ctrl: Open and close gripper") - print(" f: End episode with FAILURE") - print(" s: End episode with SUCCESS") - print(" r: End episode with RERECORD") - print(" i: Start/Stop Intervention") - - def get_teleop_commands( - self, - ) -> tuple[bool, np.ndarray, bool, bool, bool]: - action_dict = self.teleop_device.get_action() - episode_end_status = None - - # Unroll the misc_keys_queue to check for events related to intervention, episode success, etc. - while not self.teleop_device.misc_keys_queue.empty(): - key = self.teleop_device.misc_keys_queue.get() - if key == "i": - self.is_intervention_active = not self.is_intervention_active - elif key == "f": - episode_end_status = "failure" - elif key == "s": - episode_end_status = "success" - elif key == "r": - episode_end_status = "rerecord_episode" - - terminate_episode = episode_end_status is not None - success = episode_end_status == "success" - rerecord_episode = episode_end_status == "rerecord_episode" - - # Convert action_dict to numpy array based on expected structure - # Order: delta_x, delta_y, delta_z, gripper (if use_gripper) - action_list = [action_dict["delta_x"], action_dict["delta_y"], action_dict["delta_z"]] - if self.use_gripper: - # GamepadTeleop returns gripper action as 0 (close), 1 (stay), 2 (open) - # This needs to be consistent with what EEActionWrapper expects if it's used downstream - # EEActionWrapper for gripper typically expects 0.0 (closed) to 2.0 (open) - # For now, we pass the direct value from GamepadTeleop, ensure downstream compatibility. - gripper_val = action_dict.get("gripper", 1.0) # Default to 1.0 (stay) if not present - action_list.append(float(gripper_val)) - - gamepad_action_np = np.array(action_list, dtype=np.float32) - - return ( - self.is_intervention_active, - gamepad_action_np, - terminate_episode, - success, - rerecord_episode, - ) - - -class GymHilDeviceWrapper(gym.Wrapper): - def __init__(self, env, device="cpu"): - super().__init__(env) - self.device = device - - def step(self, action): - obs, reward, terminated, truncated, info = self.env.step(action) - for k in obs: - obs[k] = obs[k].to(self.device) - if "action_intervention" in info: - # NOTE: This is a hack to ensure the action intervention is a float32 tensor and supported on MPS device - info["action_intervention"] = info["action_intervention"].astype(np.float32) - info["action_intervention"] = torch.from_numpy(info["action_intervention"]).to(self.device) - return obs, reward, terminated, truncated, info - - def reset(self, *, seed: int | None = None, options: dict[str, Any] | None = None): - obs, info = self.env.reset(seed=seed, options=options) - for k in obs: - obs[k] = obs[k].to(self.device) - if "action_intervention" in info: - # NOTE: This is a hack to ensure the action intervention is a float32 tensor and supported on MPS device - info["action_intervention"] = info["action_intervention"].astype(np.float32) - info["action_intervention"] = torch.from_numpy(info["action_intervention"]).to(self.device) - return obs, info - - -class GymHilObservationProcessorWrapper(gym.ObservationWrapper): - def __init__(self, env: gym.Env): - super().__init__(env) - prev_space = self.observation_space - new_space = {} - - for key in prev_space: - if "pixels" in key: - for k in prev_space["pixels"]: - new_space[f"observation.images.{k}"] = gym.spaces.Box( - 0.0, 255.0, shape=(3, 128, 128), dtype=np.uint8 - ) - - if key == "agent_pos": - new_space["observation.state"] = prev_space["agent_pos"] - - self.observation_space = gym.spaces.Dict(new_space) - - def observation(self, observation: dict[str, Any]) -> dict[str, Any]: - return preprocess_observation(observation) - - -########################################################### -# Factory functions -########################################################### - - -def make_robot_env(cfg: EnvConfig) -> gym.Env: - """ - Factory function to create a robot environment. - - This function builds a robot environment with all necessary wrappers - based on the provided configuration. - - Args: - cfg: Configuration object containing environment parameters. - - Returns: - A gym environment with all necessary wrappers applied. - """ - if cfg.type == "hil": - import gym_hil # noqa: F401 - - # TODO (azouitine) - env = gym.make( - f"gym_hil/{cfg.task}", - image_obs=True, - render_mode="human", - use_gripper=cfg.wrapper.use_gripper, - gripper_penalty=cfg.wrapper.gripper_penalty, - ) - env = GymHilObservationProcessorWrapper(env=env) - env = GymHilDeviceWrapper(env=env, device=cfg.device) - env = BatchCompatibleWrapper(env=env) - env = TorchActionWrapper(env=env, device=cfg.device) - return env - - if not hasattr(cfg, "robot") or not hasattr(cfg, "teleop"): - raise ValueError( - "Configuration for 'gym_manipulator' must be HILSerlRobotEnvConfig with robot and teleop." - ) - - if cfg.robot is None: - raise ValueError("RobotConfig (cfg.robot) must be provided for gym_manipulator environment.") - robot = make_robot_from_config(cfg.robot) - teleop_device = make_teleoperator_from_config(cfg.teleop) - teleop_device.connect() - - # Create base environment - env = RobotEnv( - robot=robot, - use_gripper=cfg.wrapper.use_gripper, - display_cameras=cfg.wrapper.display_cameras if cfg.wrapper else False, - ) - - # Add observation and image processing - if cfg.wrapper: - if cfg.wrapper.add_joint_velocity_to_observation: - env = AddJointVelocityToObservation(env=env, fps=cfg.fps) - if cfg.wrapper.add_current_to_observation: - env = AddCurrentToObservation(env=env) - if cfg.wrapper.add_ee_pose_to_observation: - env = EEObservationWrapper(env=env, ee_pose_limits=robot.end_effector_bounds) - - env = ConvertToLeRobotObservation(env=env, device=cfg.device) - - if cfg.wrapper and cfg.wrapper.crop_params_dict is not None: - env = ImageCropResizeWrapper( - env=env, - crop_params_dict=cfg.wrapper.crop_params_dict, - resize_size=cfg.wrapper.resize_size, - ) - - # Add reward computation and control wrappers - reward_classifier = init_reward_classifier(cfg) - if reward_classifier is not None: - env = RewardWrapper(env=env, reward_classifier=reward_classifier, device=cfg.device) - - env = TimeLimitWrapper(env=env, control_time_s=cfg.wrapper.control_time_s, fps=cfg.fps) - if cfg.wrapper.use_gripper and cfg.wrapper.gripper_penalty is not None: - env = GripperPenaltyWrapper( - env=env, - penalty=cfg.wrapper.gripper_penalty, - ) - - # Control mode specific wrappers - control_mode = cfg.wrapper.control_mode - if control_mode == "gamepad": - assert isinstance(teleop_device, GamepadTeleop), ( - "teleop_device must be an instance of GamepadTeleop for gamepad control mode" - ) - env = GamepadControlWrapper( - env=env, - teleop_device=teleop_device, - use_gripper=cfg.wrapper.use_gripper, - ) - elif control_mode == "keyboard_ee": - assert isinstance(teleop_device, KeyboardEndEffectorTeleop), ( - "teleop_device must be an instance of KeyboardEndEffectorTeleop for keyboard control mode" - ) - env = KeyboardControlWrapper( - env=env, - teleop_device=teleop_device, - use_gripper=cfg.wrapper.use_gripper, - ) - elif control_mode == "leader": - env = GearedLeaderControlWrapper( - env=env, - teleop_device=teleop_device, - end_effector_step_sizes=cfg.robot.end_effector_step_sizes, - use_gripper=cfg.wrapper.use_gripper, - ) - elif control_mode == "leader_automatic": - env = GearedLeaderAutomaticControlWrapper( - env=env, - teleop_device=teleop_device, - end_effector_step_sizes=cfg.robot.end_effector_step_sizes, - use_gripper=cfg.wrapper.use_gripper, - ) - else: - raise ValueError(f"Invalid control mode: {control_mode}") - - env = ResetWrapper( - env=env, - reset_pose=cfg.wrapper.fixed_reset_joint_positions, - reset_time_s=cfg.wrapper.reset_time_s, - ) - - env = BatchCompatibleWrapper(env=env) - env = TorchActionWrapper(env=env, device=cfg.device) - - return env - - -def init_reward_classifier(cfg): - """ - Load a reward classifier policy from a pretrained path if configured. - - Args: - cfg: The environment configuration containing classifier paths. - - Returns: - The loaded classifier model or None if not configured. - """ - if cfg.reward_classifier_pretrained_path is None: - return None - - from lerobot.policies.sac.reward_model.modeling_classifier import Classifier - - # Get device from config or default to CUDA - device = getattr(cfg, "device", "cpu") - - # Load the classifier directly using from_pretrained - classifier = Classifier.from_pretrained( - pretrained_name_or_path=cfg.reward_classifier_pretrained_path, - ) - - # Ensure model is on the correct device - classifier.to(device) - classifier.eval() # Set to evaluation mode - - return classifier - - -########################################################### -# Record and replay functions -########################################################### - - -def record_dataset(env, policy, cfg): - """ - Record a dataset of robot interactions using either a policy or teleop. - - This function runs episodes in the environment and records the observations, - actions, and results for dataset creation. - - Args: - env: The environment to record from. - policy: Optional policy to generate actions (if None, uses teleop). - cfg: Configuration object containing recording parameters like: - - repo_id: Repository ID for dataset storage - - dataset_root: Local root directory for dataset - - num_episodes: Number of episodes to record - - fps: Frames per second for recording - - push_to_hub: Whether to push dataset to Hugging Face Hub - - task: Name/description of the task being recorded - - number_of_steps_after_success: Number of additional steps to continue recording after - a success (reward=1) is detected. This helps collect - more positive examples for reward classifier training. - """ - from lerobot.datasets.lerobot_dataset import LeRobotDataset - - # Setup initial action (zero action if using teleop) - action = env.action_space.sample() * 0.0 - - action_names = ["delta_x_ee", "delta_y_ee", "delta_z_ee"] - if cfg.wrapper.use_gripper: - action_names.append("gripper_delta") - - # Configure dataset features based on environment spaces - features = { - "observation.state": { - "dtype": "float32", - "shape": env.observation_space["observation.state"].shape, - "names": None, - }, - "action": { - "dtype": "float32", - "shape": (len(action_names),), - "names": action_names, - }, - "next.reward": {"dtype": "float32", "shape": (1,), "names": None}, - "next.done": {"dtype": "bool", "shape": (1,), "names": None}, - "complementary_info.discrete_penalty": { - "dtype": "float32", - "shape": (1,), - "names": ["discrete_penalty"], - }, - } - - # Add image features - for key in env.observation_space: - if "image" in key: - features[key] = { - "dtype": "video", - "shape": env.observation_space[key].shape, - "names": ["channels", "height", "width"], - } - - # Create dataset - dataset = LeRobotDataset.create( - cfg.repo_id, - cfg.fps, - root=cfg.dataset_root, - use_videos=True, - image_writer_threads=4, - image_writer_processes=0, - features=features, - ) - - # Record episodes - episode_index = 0 - recorded_action = None - while episode_index < cfg.num_episodes: - obs, _ = env.reset() - start_episode_t = time.perf_counter() - log_say(f"Recording episode {episode_index}", play_sounds=True) - - # Track success state collection - success_detected = False - success_steps_collected = 0 - - # Run episode steps - while time.perf_counter() - start_episode_t < cfg.wrapper.control_time_s: - start_loop_t = time.perf_counter() - - # Get action from policy if available - if cfg.pretrained_policy_name_or_path is not None: - action = policy.select_action(obs) - - # Step environment - obs, reward, terminated, truncated, info = env.step(action) - - # Check if episode needs to be rerecorded - if info.get("rerecord_episode", False): - break - - # For teleop, get action from intervention - recorded_action = { - "action": info["action_intervention"].cpu().squeeze(0).float() if policy is None else action - } - - # Process observation for dataset - obs_processed = {k: v.cpu().squeeze(0).float() for k, v in obs.items()} - - # Check if we've just detected success - if reward == 1.0 and not success_detected: - success_detected = True - logging.info("Success detected! Collecting additional success states.") - - # Add frame to dataset - continue marking as success even during extra collection steps - frame = {**obs_processed, **recorded_action} - - # If we're in the success collection phase, keep marking rewards as 1.0 - if success_detected: - frame["next.reward"] = np.array([1.0], dtype=np.float32) - else: - frame["next.reward"] = np.array([reward], dtype=np.float32) - - # Only mark as done if we're truly done (reached end or collected enough success states) - really_done = terminated or truncated - if success_detected: - success_steps_collected += 1 - really_done = success_steps_collected >= cfg.number_of_steps_after_success - - frame["next.done"] = np.array([really_done], dtype=bool) - frame["complementary_info.discrete_penalty"] = torch.tensor( - [info.get("discrete_penalty", 0.0)], dtype=torch.float32 - ) - frame["task"] = cfg.task - dataset.add_frame(frame) - - # Maintain consistent timing - if cfg.fps: - dt_s = time.perf_counter() - start_loop_t - busy_wait(1 / cfg.fps - dt_s) - - # Check if we should end the episode - if (terminated or truncated) and not success_detected: - # Regular termination without success - break - elif success_detected and success_steps_collected >= cfg.number_of_steps_after_success: - # We've collected enough success states - logging.info(f"Collected {success_steps_collected} additional success states") - break - - # Handle episode recording - if info.get("rerecord_episode", False): - dataset.clear_episode_buffer() - logging.info(f"Re-recording episode {episode_index}") - continue - - dataset.save_episode() - episode_index += 1 - - # Finalize dataset - # dataset.consolidate(run_compute_stats=True) - if cfg.push_to_hub: - dataset.push_to_hub() - - -def replay_episode(env, cfg): - """ - Replay a recorded episode in the environment. - - This function loads actions from a previously recorded episode - and executes them in the environment. - - Args: - env: The environment to replay in. - cfg: Configuration object containing replay parameters: - - repo_id: Repository ID for dataset - - dataset_root: Local root directory for dataset - - episode: Episode ID to replay - """ - from lerobot.datasets.lerobot_dataset import LeRobotDataset - - dataset = LeRobotDataset(cfg.repo_id, root=cfg.dataset_root, episodes=[cfg.episode]) - env.reset() - - actions = dataset.hf_dataset.select_columns("action") - - for idx in range(dataset.num_frames): - start_episode_t = time.perf_counter() - - action = actions[idx]["action"] - env.step(action) - - dt_s = time.perf_counter() - start_episode_t - busy_wait(1 / 10 - dt_s) - - -@parser.wrap() -def main(cfg: EnvConfig): - """Main entry point for the robot environment script. - - This function runs the robot environment in one of several modes - based on the provided configuration. - - Args: - cfg: Configuration object defining the run parameters, - including mode (record, replay, random) and other settings. - """ - env = make_robot_env(cfg) - - if cfg.mode == "record": - policy = None - if cfg.pretrained_policy_name_or_path is not None: - from lerobot.policies.sac.modeling_sac import SACPolicy - - policy = SACPolicy.from_pretrained(cfg.pretrained_policy_name_or_path) - policy.to(cfg.device) - policy.eval() - - record_dataset( - env, - policy=policy, - cfg=cfg, - ) - exit() - - if cfg.mode == "replay": - replay_episode( - env, - cfg=cfg, - ) - exit() - - env.reset() - - # Initialize the smoothed action as a random sample. - smoothed_action = env.action_space.sample() * 0.0 - - # Smoothing coefficient (alpha) defines how much of the new random sample to mix in. - # A value close to 0 makes the trajectory very smooth (slow to change), while a value close to 1 is less smooth. - alpha = 1.0 - - num_episode = 0 - successes = [] - while num_episode < 10: - start_loop_s = time.perf_counter() - # Sample a new random action from the robot's action space. - new_random_action = env.action_space.sample() - # Update the smoothed action using an exponential moving average. - smoothed_action = alpha * new_random_action + (1 - alpha) * smoothed_action - - # Execute the step: wrap the NumPy action in a torch tensor. - obs, reward, terminated, truncated, info = env.step(smoothed_action) - if terminated or truncated: - successes.append(reward) - env.reset() - num_episode += 1 - - dt_s = time.perf_counter() - start_loop_s - busy_wait(1 / cfg.fps - dt_s) - - logging.info(f"Success after 20 steps {successes}") - logging.info(f"success rate {sum(successes) / len(successes)}") - - -if __name__ == "__main__": - main() diff --git a/src/lerobot/teleoperators/__init__.py b/src/lerobot/teleoperators/__init__.py index 56f48af7e8d..ee508dddb3c 100644 --- a/src/lerobot/teleoperators/__init__.py +++ b/src/lerobot/teleoperators/__init__.py @@ -16,4 +16,4 @@ from .config import TeleoperatorConfig from .teleoperator import Teleoperator -from .utils import make_teleoperator_from_config +from .utils import TeleopEvents, make_teleoperator_from_config diff --git a/src/lerobot/teleoperators/gamepad/gamepad_utils.py b/src/lerobot/teleoperators/gamepad/gamepad_utils.py index 7ebed6b3145..9f94b6746a0 100644 --- a/src/lerobot/teleoperators/gamepad/gamepad_utils.py +++ b/src/lerobot/teleoperators/gamepad/gamepad_utils.py @@ -16,6 +16,8 @@ import logging +from ..utils import TeleopEvents + class InputController: """Base class for input controllers that generate motion deltas.""" @@ -50,10 +52,6 @@ def get_deltas(self): """Get the current movement deltas (dx, dy, dz) in meters.""" return 0.0, 0.0, 0.0 - def should_quit(self): - """Return True if the user has requested to quit.""" - return not self.running - def update(self): """Update controller state - call this once per frame.""" pass @@ -134,10 +132,10 @@ def on_press(key): return False elif key == keyboard.Key.enter: self.key_states["success"] = True - self.episode_end_status = "success" + self.episode_end_status = TeleopEvents.SUCCESS elif key == keyboard.Key.backspace: self.key_states["failure"] = True - self.episode_end_status = "failure" + self.episode_end_status = TeleopEvents.FAILURE except AttributeError: pass @@ -196,14 +194,6 @@ def get_deltas(self): return delta_x, delta_y, delta_z - def should_quit(self): - """Return True if ESC was pressed.""" - return self.key_states["quit"] - - def should_save(self): - """Return True if Enter was pressed (save episode).""" - return self.key_states["success"] or self.key_states["failure"] - class GamepadController(InputController): """Generate motion deltas from gamepad input.""" @@ -255,13 +245,13 @@ def update(self): for event in pygame.event.get(): if event.type == pygame.JOYBUTTONDOWN: if event.button == 3: - self.episode_end_status = "success" + self.episode_end_status = TeleopEvents.SUCCESS # A button (1) for failure elif event.button == 1: - self.episode_end_status = "failure" + self.episode_end_status = TeleopEvents.FAILURE # X button (0) for rerecord elif event.button == 0: - self.episode_end_status = "rerecord_episode" + self.episode_end_status = TeleopEvents.RERECORD_EPISODE # RB button (6) for closing gripper elif event.button == 6: @@ -295,8 +285,8 @@ def get_deltas(self): try: # Read joystick axes # Left stick X and Y (typically axes 0 and 1) - x_input = self.joystick.get_axis(0) # Left/Right - y_input = self.joystick.get_axis(1) # Up/Down (often inverted) + y_input = self.joystick.get_axis(0) # Up/Down (often inverted) + x_input = self.joystick.get_axis(1) # Left/Right # Right stick Y (typically axis 3 or 4) z_input = self.joystick.get_axis(3) # Up/Down for Z @@ -308,7 +298,7 @@ def get_deltas(self): # Calculate deltas (note: may need to invert axes depending on controller) delta_x = -x_input * self.x_step_size # Forward/backward - delta_y = y_input * self.y_step_size # Left/right + delta_y = -y_input * self.y_step_size # Left/right delta_z = -z_input * self.z_step_size # Up/down return delta_x, delta_y, delta_z @@ -349,8 +339,6 @@ def __init__( # Button states self.buttons = {} - self.quit_requested = False - self.save_requested = False def find_device(self): """Look for the gamepad device by vendor and product ID.""" @@ -451,11 +439,11 @@ def _update(self): # Check if X/Square button (bit 5) is pressed for failure # Check if A/Cross button (bit 4) is pressed for rerecording if buttons & 1 << 7: - self.episode_end_status = "success" + self.episode_end_status = TeleopEvents.SUCCESS elif buttons & 1 << 5: - self.episode_end_status = "failure" + self.episode_end_status = TeleopEvents.FAILURE elif buttons & 1 << 4: - self.episode_end_status = "rerecord_episode" + self.episode_end_status = TeleopEvents.RERECORD_EPISODE else: self.episode_end_status = None @@ -470,11 +458,3 @@ def get_deltas(self): delta_z = -self.right_y * self.z_step_size # Up/down return delta_x, delta_y, delta_z - - def should_quit(self): - """Return True if quit button was pressed.""" - return self.quit_requested - - def should_save(self): - """Return True if save button was pressed.""" - return self.save_requested diff --git a/src/lerobot/teleoperators/gamepad/teleop_gamepad.py b/src/lerobot/teleoperators/gamepad/teleop_gamepad.py index 98a0647e21c..c7072f4a7b0 100644 --- a/src/lerobot/teleoperators/gamepad/teleop_gamepad.py +++ b/src/lerobot/teleoperators/gamepad/teleop_gamepad.py @@ -21,6 +21,7 @@ import numpy as np from ..teleoperator import Teleoperator +from ..utils import TeleopEvents from .configuration_gamepad import GamepadTeleopConfig @@ -107,6 +108,48 @@ def get_action(self) -> dict[str, Any]: return action_dict + def get_teleop_events(self) -> dict[str, Any]: + """ + Get extra control events from the gamepad such as intervention status, + episode termination, success indicators, etc. + + Returns: + Dictionary containing: + - is_intervention: bool - Whether human is currently intervening + - terminate_episode: bool - Whether to terminate the current episode + - success: bool - Whether the episode was successful + - rerecord_episode: bool - Whether to rerecord the episode + """ + if self.gamepad is None: + return { + TeleopEvents.IS_INTERVENTION: False, + TeleopEvents.TERMINATE_EPISODE: False, + TeleopEvents.SUCCESS: False, + TeleopEvents.RERECORD_EPISODE: False, + } + + # Update gamepad state to get fresh inputs + self.gamepad.update() + + # Check if intervention is active + is_intervention = self.gamepad.should_intervene() + + # Get episode end status + episode_end_status = self.gamepad.get_episode_end_status() + terminate_episode = episode_end_status in [ + TeleopEvents.RERECORD_EPISODE, + TeleopEvents.FAILURE, + ] + success = episode_end_status == TeleopEvents.SUCCESS + rerecord_episode = episode_end_status == TeleopEvents.RERECORD_EPISODE + + return { + TeleopEvents.IS_INTERVENTION: is_intervention, + TeleopEvents.TERMINATE_EPISODE: terminate_episode, + TeleopEvents.SUCCESS: success, + TeleopEvents.RERECORD_EPISODE: rerecord_episode, + } + def disconnect(self) -> None: """Disconnect from the gamepad.""" if self.gamepad is not None: diff --git a/src/lerobot/teleoperators/homunculus/homunculus_arm.py b/src/lerobot/teleoperators/homunculus/homunculus_arm.py index 6f5137af93b..21d73de2ef6 100644 --- a/src/lerobot/teleoperators/homunculus/homunculus_arm.py +++ b/src/lerobot/teleoperators/homunculus/homunculus_arm.py @@ -18,12 +18,11 @@ import threading from collections import deque from pprint import pformat -from typing import Deque import serial -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors.motors_bus import MotorCalibration, MotorNormMode +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.utils.utils import enter_pressed, move_cursor_up from ..teleoperator import Teleoperator @@ -60,7 +59,7 @@ def __init__(self, config: HomunculusArmConfig): self.n: int = n self.alpha: float = 2 / (n + 1) # one deque *per joint* so we can inspect raw history if needed - self._buffers: dict[str, Deque[int]] = { + self._buffers: dict[str, deque[int]] = { joint: deque(maxlen=n) for joint in ( "shoulder_pitch", diff --git a/src/lerobot/teleoperators/homunculus/homunculus_glove.py b/src/lerobot/teleoperators/homunculus/homunculus_glove.py index 7b0ced9f601..251ecf56dcf 100644 --- a/src/lerobot/teleoperators/homunculus/homunculus_glove.py +++ b/src/lerobot/teleoperators/homunculus/homunculus_glove.py @@ -18,14 +18,13 @@ import threading from collections import deque from pprint import pformat -from typing import Deque import serial -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import MotorCalibration from lerobot.motors.motors_bus import MotorNormMode from lerobot.teleoperators.homunculus.joints_translation import homunculus_glove_to_hope_jr_hand +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.utils.utils import enter_pressed, move_cursor_up from ..teleoperator import Teleoperator @@ -97,7 +96,7 @@ def __init__(self, config: HomunculusGloveConfig): self.n: int = n self.alpha: float = 2 / (n + 1) # one deque *per joint* so we can inspect raw history if needed - self._buffers: dict[str, Deque[int]] = {joint: deque(maxlen=n) for joint in self.joints} + self._buffers: dict[str, deque[int]] = {joint: deque(maxlen=n) for joint in self.joints} # running EMA value per joint – lazily initialised on first read self._ema: dict[str, float | None] = dict.fromkeys(self._buffers) diff --git a/src/lerobot/teleoperators/keyboard/configuration_keyboard.py b/src/lerobot/teleoperators/keyboard/configuration_keyboard.py index 5d5ef364f77..6e070dedd4b 100644 --- a/src/lerobot/teleoperators/keyboard/configuration_keyboard.py +++ b/src/lerobot/teleoperators/keyboard/configuration_keyboard.py @@ -22,8 +22,9 @@ @TeleoperatorConfig.register_subclass("keyboard") @dataclass class KeyboardTeleopConfig(TeleoperatorConfig): + """KeyboardTeleopConfig""" + # TODO(Steven): Consider setting in here the keys that we want to capture/listen - mock: bool = False @TeleoperatorConfig.register_subclass("keyboard_ee") diff --git a/src/lerobot/teleoperators/keyboard/teleop_keyboard.py b/src/lerobot/teleoperators/keyboard/teleop_keyboard.py index d034982f126..6f53a17c7dc 100644 --- a/src/lerobot/teleoperators/keyboard/teleop_keyboard.py +++ b/src/lerobot/teleoperators/keyboard/teleop_keyboard.py @@ -21,9 +21,10 @@ from queue import Queue from typing import Any -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..teleoperator import Teleoperator +from ..utils import TeleopEvents from .configuration_keyboard import KeyboardEndEffectorTeleopConfig, KeyboardTeleopConfig PYNPUT_AVAILABLE = True @@ -176,16 +177,6 @@ def action_features(self) -> dict: "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2}, } - def _on_press(self, key): - if hasattr(key, "char"): - key = key.char - self.event_queue.put((key, True)) - - def _on_release(self, key): - if hasattr(key, "char"): - key = key.char - self.event_queue.put((key, False)) - def get_action(self) -> dict[str, Any]: if not self.is_connected: raise DeviceNotConnectedError( @@ -235,3 +226,66 @@ def get_action(self) -> dict[str, Any]: action_dict["gripper"] = gripper_action return action_dict + + def get_teleop_events(self) -> dict[str, Any]: + """ + Get extra control events from the keyboard such as intervention status, + episode termination, success indicators, etc. + + Keyboard mappings: + - Any movement keys pressed = intervention active + - 's' key = success (terminate episode successfully) + - 'r' key = rerecord episode (terminate and rerecord) + - 'q' key = quit episode (terminate without success) + + Returns: + Dictionary containing: + - is_intervention: bool - Whether human is currently intervening + - terminate_episode: bool - Whether to terminate the current episode + - success: bool - Whether the episode was successful + - rerecord_episode: bool - Whether to rerecord the episode + """ + if not self.is_connected: + return { + TeleopEvents.IS_INTERVENTION: False, + TeleopEvents.TERMINATE_EPISODE: False, + TeleopEvents.SUCCESS: False, + TeleopEvents.RERECORD_EPISODE: False, + } + + # Check if any movement keys are currently pressed (indicates intervention) + movement_keys = [ + keyboard.Key.up, + keyboard.Key.down, + keyboard.Key.left, + keyboard.Key.right, + keyboard.Key.shift, + keyboard.Key.shift_r, + keyboard.Key.ctrl_r, + keyboard.Key.ctrl_l, + ] + is_intervention = any(self.current_pressed.get(key, False) for key in movement_keys) + + # Check for episode control commands from misc_keys_queue + terminate_episode = False + success = False + rerecord_episode = False + + # Process any pending misc keys + while not self.misc_keys_queue.empty(): + key = self.misc_keys_queue.get_nowait() + if key == "s": + success = True + elif key == "r": + terminate_episode = True + rerecord_episode = True + elif key == "q": + terminate_episode = True + success = False + + return { + TeleopEvents.IS_INTERVENTION: is_intervention, + TeleopEvents.TERMINATE_EPISODE: terminate_episode, + TeleopEvents.SUCCESS: success, + TeleopEvents.RERECORD_EPISODE: rerecord_episode, + } diff --git a/src/lerobot/teleoperators/koch_leader/koch_leader.py b/src/lerobot/teleoperators/koch_leader/koch_leader.py index f703d5b6e05..0409f2e5796 100644 --- a/src/lerobot/teleoperators/koch_leader/koch_leader.py +++ b/src/lerobot/teleoperators/koch_leader/koch_leader.py @@ -17,13 +17,13 @@ import logging import time -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.dynamixel import ( DriveMode, DynamixelMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..teleoperator import Teleoperator from .config_koch_leader import KochLeaderConfig diff --git a/src/lerobot/teleoperators/phone/__init__.py b/src/lerobot/teleoperators/phone/__init__.py new file mode 100644 index 00000000000..2b28c1f9746 --- /dev/null +++ b/src/lerobot/teleoperators/phone/__init__.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .config_phone import PhoneConfig +from .teleop_phone import Phone diff --git a/src/lerobot/teleoperators/phone/config_phone.py b/src/lerobot/teleoperators/phone/config_phone.py new file mode 100644 index 00000000000..380d5f5ff0b --- /dev/null +++ b/src/lerobot/teleoperators/phone/config_phone.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from enum import Enum + +import numpy as np + +from ..config import TeleoperatorConfig + + +class PhoneOS(Enum): + ANDROID = "android" + IOS = "ios" + + +@TeleoperatorConfig.register_subclass("phone") +@dataclass +class PhoneConfig(TeleoperatorConfig): + phone_os: PhoneOS = PhoneOS.IOS + camera_offset = np.array( + [0.0, -0.02, 0.04] + ) # iPhone 14 Pro camera is 2cm off center and 4cm above center diff --git a/src/lerobot/teleoperators/phone/phone_processor.py b/src/lerobot/teleoperators/phone/phone_processor.py new file mode 100644 index 00000000000..67e64c7d50c --- /dev/null +++ b/src/lerobot/teleoperators/phone/phone_processor.py @@ -0,0 +1,110 @@ +# !/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature +from lerobot.processor import ProcessorStepRegistry, RobotAction, RobotActionProcessorStep +from lerobot.teleoperators.phone.config_phone import PhoneOS + + +@ProcessorStepRegistry.register("map_phone_action_to_robot_action") +@dataclass +class MapPhoneActionToRobotAction(RobotActionProcessorStep): + """ + Maps calibrated phone pose actions to standardized robot action inputs. + + This processor step acts as a bridge between the phone teleoperator's output + and the robot's expected action format. It remaps the phone's 6-DoF pose + (position and rotation) to the robot's target end-effector pose, applying + necessary axis inversions and swaps. It also interprets platform-specific + button presses to generate a gripper command. + + Attributes: + platform: The operating system of the phone (iOS or Android), used + to determine the correct button mappings for the gripper. + """ + + # TODO(Steven): Gripper vel could be output of phone_teleop directly + platform: PhoneOS + _enabled_prev: bool = field(default=False, init=False, repr=False) + + def action(self, action: RobotAction) -> RobotAction: + """ + Processes the phone action dictionary to create a robot action dictionary. + + Args: + act: The input action dictionary from the phone teleoperator. + + Returns: + A new action dictionary formatted for the robot controller. + + Raises: + ValueError: If 'pos' or 'rot' keys are missing from the input action. + """ + # Pop them from the action + enabled = bool(action.pop("phone.enabled")) + pos = action.pop("phone.pos") + rot = action.pop("phone.rot") + inputs = action.pop("phone.raw_inputs") + + if pos is None or rot is None: + raise ValueError("pos and rot must be present in action") + + rotvec = rot.as_rotvec() # Absolute orientation as rotvec + + # Map certain inputs to certain actions + if self.platform == PhoneOS.IOS: + gripper_vel = float(inputs.get("a3", 0.0)) + else: + a = float(inputs.get("reservedButtonA", 0.0)) + b = float(inputs.get("reservedButtonB", 0.0)) + gripper_vel = ( + a - b + ) # Positive if a is pressed, negative if b is pressed, 0 if both or neither are pressed + + # For some actions we need to invert the axis + action["enabled"] = enabled + action["target_x"] = -pos[1] if enabled else 0.0 + action["target_y"] = pos[0] if enabled else 0.0 + action["target_z"] = pos[2] if enabled else 0.0 + action["target_wx"] = rotvec[1] if enabled else 0.0 + action["target_wy"] = rotvec[0] if enabled else 0.0 + action["target_wz"] = -rotvec[2] if enabled else 0.0 + action["gripper_vel"] = gripper_vel # Still send gripper action when disabled + return action + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + for feat in ["enabled", "pos", "rot", "raw_inputs"]: + features[PipelineFeatureType.ACTION].pop(f"phone.{feat}", None) + + for feat in [ + "enabled", + "target_x", + "target_y", + "target_z", + "target_wx", + "target_wy", + "target_wz", + "gripper_vel", + ]: + features[PipelineFeatureType.ACTION][f"{feat}"] = PolicyFeature( + type=FeatureType.ACTION, shape=(1,) + ) + + return features diff --git a/src/lerobot/teleoperators/phone/teleop_phone.py b/src/lerobot/teleoperators/phone/teleop_phone.py new file mode 100644 index 00000000000..91e6131904a --- /dev/null +++ b/src/lerobot/teleoperators/phone/teleop_phone.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Docs: +# hebi: https://docs.hebi.us/tools.html#mobile-io +# teleop: https://github.com/SpesRobotics/teleop + +import logging +import threading +import time + +import hebi +import numpy as np +from teleop import Teleop + +from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS +from lerobot.teleoperators.teleoperator import Teleoperator +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.rotation import Rotation + +logger = logging.getLogger(__name__) + + +class BasePhone: + _enabled: bool = False + _calib_pos: np.ndarray | None = None + _calib_rot_inv: Rotation | None = None + + def _reapply_position_calibration(self, pos: np.ndarray) -> None: + self._calib_pos = pos.copy() + + @property + def is_calibrated(self) -> bool: + return (self._calib_pos is not None) and (self._calib_rot_inv is not None) + + @property + def action_features(self) -> dict[str, type]: + return { + "phone.pos": np.ndarray, # shape (3,) + "phone.rot": Rotation, # scipy.spatial.transform.Rotation + "phone.raw_inputs": dict, # analogs/buttons or webXR meta + "phone.enabled": bool, + } + + @property + def feedback_features(self) -> dict[str, type]: + # No haptic or other feedback implemented yet + pass + + def configure(self) -> None: + # No additional configuration required for phone teleop + pass + + def send_feedback(self, feedback: dict[str, float]) -> None: + # We could add haptic feedback (vibrations) here, but it's not implemented yet + raise NotImplementedError + + +class IOSPhone(BasePhone, Teleoperator): + name = "ios_phone" + + def __init__(self, config: PhoneConfig): + super().__init__(config) + self.config = config + self._group = None + + @property + def is_connected(self) -> bool: + return self._group is not None + + def connect(self) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + logger.info("Connecting to IPhone, make sure to open the HEBI Mobile I/O app.") + lookup = hebi.Lookup() + time.sleep(2.0) + group = lookup.get_group_from_names(["HEBI"], ["mobileIO"]) + if group is None: + raise RuntimeError("Mobile I/O not found — check name/family settings in the app.") + self._group = group + logger.info(f"{self} connected to HEBI group with {group.size} module(s).") + + self.calibrate() + + def calibrate(self) -> None: + print( + "Hold the phone so that: top edge points forward in same direction as the robot (robot +x) and screen points up (robot +z)" + ) + print("Press and hold B1 in the HEBI Mobile I/O app to capture this pose...\n") + position, rotation = self._wait_for_capture_trigger() + self._calib_pos = position.copy() + self._calib_rot_inv = rotation.inv() + self._enabled = False + print("Calibration done\n") + + def _wait_for_capture_trigger(self) -> tuple[np.ndarray, Rotation]: + """ + Blocks execution until the calibration trigger is detected from the iOS device. + + This method enters a loop, continuously reading the phone's state. It waits for the user to press + and hold the 'B1' button in the HEBI Mobile I/O app. Once B1 is pressed, the loop breaks and + returns the phone's pose at that exact moment. + + Returns: + A tuple containing the position (np.ndarray) and rotation (Rotation) of the phone at the + moment the trigger was activated. + """ + while True: + has_pose, position, rotation, fb_pose = self._read_current_pose() + if not has_pose: + time.sleep(0.01) + continue + + io = getattr(fb_pose, "io", None) + button_b = getattr(io, "b", None) if io is not None else None + button_b1_pressed = False + if button_b is not None: + button_b1_pressed = bool(button_b.get_int(1)) + if button_b1_pressed: + return position, rotation + + time.sleep(0.01) + + def _read_current_pose(self) -> tuple[bool, np.ndarray | None, Rotation | None, object | None]: + """ + Reads the instantaneous 6-DoF pose from the connected iOS device via the HEBI SDK. + + This method fetches the latest feedback packet from the HEBI group, extracts the ARKit + position and orientation, and converts them into a standard format. It also applies a + configured camera offset to adjust the pose from the camera's frame to the phone's + physical frame. + + Returns: + A tuple containing: + - A boolean indicating if a valid pose was successfully read. + - The 3D position as a NumPy array, or None if not available. + - The orientation as a `Rotation` object, or None if not available. + - The raw HEBI feedback object for accessing other data like button presses. + """ + fbk = self._group.get_next_feedback() + pose = fbk[0] + ar_pos = getattr(pose, "ar_position", None) + ar_quat = getattr(pose, "ar_orientation", None) + if ar_pos is None or ar_quat is None: + return False, None, None, None + # HEBI provides orientation in w, x, y, z format. + # Scipy's Rotation expects x, y, z, w. + quat_xyzw = np.concatenate((ar_quat[1:], [ar_quat[0]])) # wxyz to xyzw + rot = Rotation.from_quat(quat_xyzw) + pos = ar_pos - rot.apply(self.config.camera_offset) + return True, pos, rot, pose + + def get_action(self) -> dict: + has_pose, raw_position, raw_rotation, fb_pose = self._read_current_pose() + if not has_pose or not self.is_calibrated: + return {} + + # Collect raw inputs (B1 / analogs on iOS, move/scale on Android) + raw_inputs: dict[str, float | int | bool] = {} + io = getattr(fb_pose, "io", None) + if io is not None: + bank_a, bank_b = io.a, io.b + if bank_a: + for ch in range(1, 9): + if bank_a.has_float(ch): + raw_inputs[f"a{ch}"] = float(bank_a.get_float(ch)) + if bank_b: + for ch in range(1, 9): + if bank_b.has_int(ch): + raw_inputs[f"b{ch}"] = int(bank_b.get_int(ch)) + elif hasattr(bank_b, "has_bool") and bank_b.has_bool(ch): + raw_inputs[f"b{ch}"] = int(bank_b.get_bool(ch)) + + enable = bool(raw_inputs.get("b1", 0)) + + # Rising edge then re-capture calibration immediately from current raw pose + if enable and not self._enabled: + self._reapply_position_calibration(raw_position) + + # Apply calibration + pos_cal = self._calib_rot_inv.apply(raw_position - self._calib_pos) + rot_cal = self._calib_rot_inv * raw_rotation + + self._enabled = enable + + return { + "phone.pos": pos_cal, + "phone.rot": rot_cal, + "phone.raw_inputs": raw_inputs, + "phone.enabled": self._enabled, + } + + def disconnect(self) -> None: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self._group = None + + +class AndroidPhone(BasePhone, Teleoperator): + name = "android_phone" + + def __init__(self, config: PhoneConfig): + super().__init__(config) + self.config = config + self._teleop = None + self._teleop_thread = None + self._latest_pose = None + self._latest_message = None + self._android_lock = threading.Lock() + + @property + def is_connected(self) -> bool: + return self._teleop is not None + + def connect(self) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + logger.info("Starting teleop stream for Android...") + self._teleop = Teleop() + self._teleop.subscribe(self._android_callback) + self._teleop_thread = threading.Thread(target=self._teleop.run, daemon=True) + self._teleop_thread.start() + logger.info(f"{self} connected, teleop stream started.") + + self.calibrate() + + def calibrate(self) -> None: + print( + "Hold the phone so that: top edge points forward in same direction as the robot (robot +x) and screen points up (robot +z)" + ) + print("Touch and move on the WebXR page to capture this pose...\n") + + pos, rot = self._wait_for_capture_trigger() + self._calib_pos = pos.copy() + self._calib_rot_inv = rot.inv() + self._enabled = False + print("Calibration done\n") + + def _wait_for_capture_trigger(self) -> tuple[np.ndarray, Rotation]: + """ + Blocks execution until the calibration trigger is detected from the Android device. + + This method enters a loop, continuously checking the latest message received from the WebXR + session. It waits for the user to touch and move their finger on the screen, which generates + a `move` event. Once this event is detected, the loop breaks and returns the phone's current + pose. + + Returns: + A tuple containing the position (np.ndarray) and rotation (Rotation) of the phone at the + moment the trigger was activated. + """ + while True: + with self._android_lock: + msg = self._latest_message or {} + + if bool(msg.get("move", False)): + ok, pos, rot, _pose = self._read_current_pose() + if ok: + return pos, rot + + time.sleep(0.01) + + def _read_current_pose(self) -> tuple[bool, np.ndarray | None, Rotation | None, object | None]: + """ + Reads the latest 6-DoF pose received from the Android device's WebXR session. + + This method accesses the most recent pose data stored by the `_android_callback`. It uses a + thread lock to safely read the shared `_latest_pose` variable. The pose, a 4x4 matrix, is + then decomposed into position and rotation, and the configured camera offset is applied. + + Returns: + A tuple containing: + - A boolean indicating if a valid pose was available. + - The 3D position as a NumPy array, or None if no pose has been received yet. + - The orientation as a `Rotation` object, or None if no pose has been received. + - The raw 4x4 pose matrix as received from the teleop stream. + """ + with self._android_lock: + if self._latest_pose is None: + return False, None, None, None + p = self._latest_pose.copy() + pose = self._latest_pose + rot = Rotation.from_matrix(p[:3, :3]) + pos = p[:3, 3] - rot.apply(self.config.camera_offset) + return True, pos, rot, pose + + def _android_callback(self, pose: np.ndarray, message: dict) -> None: + """ + Callback function to handle incoming data from the Android teleop stream. + + This method is executed by the `teleop` package's subscriber thread whenever a new + pose and message are received from the WebXR session on the Android phone. It updates + the internal state (`_latest_pose` and `_latest_message`) with the new data. + A thread lock is used to ensure that these shared variables are updated atomically, + preventing race conditions with the main thread that reads them. + + Args: + pose: A 4x4 NumPy array representing the phone's transformation matrix. + message: A dictionary containing additional data, such as button presses or touch events. + """ + with self._android_lock: + self._latest_pose = pose + self._latest_message = message + + def get_action(self) -> dict: + ok, raw_pos, raw_rot, pose = self._read_current_pose() + if not ok or not self.is_calibrated: + return {} + + # Collect raw inputs (B1 / analogs on iOS, move/scale on Android) + raw_inputs: dict[str, float | int | bool] = {} + msg = self._latest_message or {} + raw_inputs["move"] = bool(msg.get("move", False)) + raw_inputs["scale"] = float(msg.get("scale", 1.0)) + raw_inputs["reservedButtonA"] = bool(msg.get("reservedButtonA", False)) + raw_inputs["reservedButtonB"] = bool(msg.get("reservedButtonB", False)) + + enable = bool(raw_inputs.get("move", False)) + + # Rising edge then re-capture calibration immediately from current raw pose + if enable and not self._enabled: + self._reapply_position_calibration(raw_pos) + + # Apply calibration + pos_cal = self._calib_rot_inv.apply(raw_pos - self._calib_pos) + rot_cal = self._calib_rot_inv * raw_rot + + self._enabled = enable + + return { + "phone.pos": pos_cal, + "phone.rot": rot_cal, + "phone.raw_inputs": raw_inputs, + "phone.enabled": self._enabled, + } + + def disconnect(self) -> None: + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + self._teleop = None + if self._teleop_thread and self._teleop_thread.is_alive(): + self._teleop_thread.join(timeout=1.0) + self._teleop_thread = None + self._latest_pose = None + + +class Phone(Teleoperator): + """ + Phone-based teleoperator using ARKit (iOS via HEBI Mobile I/O App) or the teleop Python package (Android via WebXR API). + For HEBI Mobile I/O we also expose 8 analog (a1-a8) and 8 digital (b1-b8) inputs. + + Press and hold **B1** to enable teleoperation. While enabled, the first B1 press + captures a reference pose and rotation, when disabled and pressed again the position is reapplied. + """ + + config_class = PhoneConfig + name = "phone" + + def __init__(self, config: PhoneConfig): + super().__init__(config) + self.config = config + + self._phone_impl: Teleoperator + + if self.config.phone_os == PhoneOS.IOS: + self._phone_impl = IOSPhone(config) + elif self.config.phone_os == PhoneOS.ANDROID: + self._phone_impl = AndroidPhone(config) + else: + raise ValueError(f"Invalid config phone_os: {self.config.phone_os}") + + @property + def is_connected(self) -> bool: + return self._phone_impl.is_connected + + def connect(self) -> None: + return self._phone_impl.connect() + + def calibrate(self) -> None: + return self._phone_impl.calibrate() + + @property + def is_calibrated(self) -> bool: + return self._phone_impl.is_calibrated + + @property + def action_features(self) -> dict[str, type]: + return self._phone_impl.action_features + + @property + def feedback_features(self) -> dict[str, type]: + return self._phone_impl.feedback_features + + def configure(self) -> None: + return self._phone_impl.configure() + + def get_action(self) -> dict: + return self._phone_impl.get_action() + + def send_feedback(self, feedback: dict[str, float]) -> None: + return self._phone_impl.send_feedback(feedback) + + def disconnect(self) -> None: + return self._phone_impl.disconnect() diff --git a/src/lerobot/teleoperators/so100_leader/so100_leader.py b/src/lerobot/teleoperators/so100_leader/so100_leader.py index a8f6d29b536..edcfe53e6b8 100644 --- a/src/lerobot/teleoperators/so100_leader/so100_leader.py +++ b/src/lerobot/teleoperators/so100_leader/so100_leader.py @@ -17,12 +17,12 @@ import logging import time -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.feetech import ( FeetechMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..teleoperator import Teleoperator from .config_so100_leader import SO100LeaderConfig diff --git a/src/lerobot/teleoperators/so101_leader/so101_leader.py b/src/lerobot/teleoperators/so101_leader/so101_leader.py index 15a363e3784..be804bf702f 100644 --- a/src/lerobot/teleoperators/so101_leader/so101_leader.py +++ b/src/lerobot/teleoperators/so101_leader/so101_leader.py @@ -17,12 +17,12 @@ import logging import time -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.feetech import ( FeetechMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..teleoperator import Teleoperator from .config_so101_leader import SO101LeaderConfig diff --git a/src/lerobot/teleoperators/stretch3_gamepad/configuration_stretch3.py b/src/lerobot/teleoperators/stretch3_gamepad/configuration_stretch3.py index 507a2158984..3af0b5be105 100644 --- a/src/lerobot/teleoperators/stretch3_gamepad/configuration_stretch3.py +++ b/src/lerobot/teleoperators/stretch3_gamepad/configuration_stretch3.py @@ -22,4 +22,4 @@ @TeleoperatorConfig.register_subclass("stretch3") @dataclass class Stretch3GamePadConfig(TeleoperatorConfig): - mock: bool = False + """Stretch3GamePadConfig""" diff --git a/src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py b/src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py index bdcb57d4079..09fdfadd7ad 100644 --- a/src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py +++ b/src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py @@ -20,7 +20,7 @@ from stretch_body.gamepad_teleop import GamePadTeleop from stretch_body.robot_params import RobotParams -from lerobot.errors import DeviceAlreadyConnectedError +from lerobot.utils.errors import DeviceAlreadyConnectedError from ..teleoperator import Teleoperator from .configuration_stretch3 import Stretch3GamePadConfig @@ -112,10 +112,6 @@ def get_action(self) -> np.ndarray: def send_feedback(self, feedback: np.ndarray) -> None: pass - def print_logs(self) -> None: - pass - # TODO(aliberts): move robot-specific logs logic here - def disconnect(self) -> None: self.api.stop() self.is_connected = False diff --git a/src/lerobot/teleoperators/teleoperator.py b/src/lerobot/teleoperators/teleoperator.py index c360ee7bba6..95020a962f0 100644 --- a/src/lerobot/teleoperators/teleoperator.py +++ b/src/lerobot/teleoperators/teleoperator.py @@ -19,8 +19,8 @@ import draccus -from lerobot.constants import HF_LEROBOT_CALIBRATION, TELEOPERATORS from lerobot.motors.motors_bus import MotorCalibration +from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, TELEOPERATORS from .config import TeleoperatorConfig diff --git a/src/lerobot/teleoperators/utils.py b/src/lerobot/teleoperators/utils.py index 02e6fd22c54..ada7ee8a1d8 100644 --- a/src/lerobot/teleoperators/utils.py +++ b/src/lerobot/teleoperators/utils.py @@ -12,11 +12,27 @@ # See the License for the specific language governing permissions and # limitations under the License. +from enum import Enum +from typing import cast + +from lerobot.utils.import_utils import make_device_from_device_class + from .config import TeleoperatorConfig from .teleoperator import Teleoperator +class TeleopEvents(Enum): + """Shared constants for teleoperator events across teleoperators.""" + + SUCCESS = "success" + FAILURE = "failure" + RERECORD_EPISODE = "rerecord_episode" + IS_INTERVENTION = "is_intervention" + TERMINATE_EPISODE = "terminate_episode" + + def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator: + # TODO(Steven): Consider just using the make_device_from_device_class for all types if config.type == "keyboard": from .keyboard import KeyboardTeleop @@ -70,4 +86,7 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator: return Reachy2Teleoperator(config) else: - raise ValueError(config.type) + try: + return cast(Teleoperator, make_device_from_device_class(config)) + except Exception as e: + raise ValueError(f"Error creating robot with config {config}: {e}") from e diff --git a/src/lerobot/teleoperators/widowx/widowx.py b/src/lerobot/teleoperators/widowx/widowx.py index 6becd767fca..1a00bd4d23c 100644 --- a/src/lerobot/teleoperators/widowx/widowx.py +++ b/src/lerobot/teleoperators/widowx/widowx.py @@ -17,13 +17,13 @@ import logging import time -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.dynamixel import ( DriveMode, DynamixelMotorsBus, OperatingMode, ) +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from ..teleoperator import Teleoperator from .config_widowx import WidowXConfig diff --git a/src/lerobot/templates/lerobot_modelcard_template.md b/src/lerobot/templates/lerobot_modelcard_template.md index 9293d6ba767..34af282b0dd 100644 --- a/src/lerobot/templates/lerobot_modelcard_template.md +++ b/src/lerobot/templates/lerobot_modelcard_template.md @@ -19,10 +19,28 @@ [Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. {% elif model_name == "vqbet" %} [VQ-BET](https://huggingface.co/papers/2403.03181) combines vector-quantised action tokens with Behaviour Transformers to discretise control and achieve data-efficient imitation across diverse skills. -{% elif model_name == "pi0" %} -[Pi0](https://huggingface.co/papers/2410.24164) is a generalist vision-language-action transformer that converts multimodal observations and text instructions into robot actions for zero-shot task transfer. {% elif model_name == "pi0fast" %} [Pi0-Fast](https://huggingface.co/papers/2501.09747) is a variant of Pi0 that uses a new tokenization method called FAST, which enables training of an autoregressive vision-language-action policy for high-frequency robotic tasks with improved performance and reduced training time. +{% elif model_name == "pi0" %} +**π₀ (Pi0)** + +π₀ is a Vision-Language-Action model for general robot control, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository. + +**Model Overview** + +π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by Physical Intelligence. Unlike traditional robots that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks. + +For more details, see the [Physical Intelligence π₀ blog post](https://www.physicalintelligence.company/blog/pi0). +{% elif model_name == "pi05" %} +**π₀.₅ (Pi05) Policy** + +π₀.₅ is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository. + +**Model Overview** + +π₀.₅ represents a significant evolution from π₀, developed by Physical Intelligence to address a big challenge in robotics: open-world generalization. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training. + +For more details, see the [Physical Intelligence π₀.₅ blog post](https://www.physicalintelligence.company/blog/pi05). {% elif model_name == "sac" %} [Soft Actor-Critic (SAC)](https://huggingface.co/papers/1801.01290) is an entropy-regularised actor-critic algorithm offering stable, sample-efficient learning in continuous-control environments. {% elif model_name == "reward_classifier" %} diff --git a/src/lerobot/constants.py b/src/lerobot/utils/constants.py similarity index 74% rename from src/lerobot/constants.py rename to src/lerobot/utils/constants.py index 382435a9fa3..6847666eb98 100644 --- a/src/lerobot/constants.py +++ b/src/lerobot/utils/constants.py @@ -17,15 +17,22 @@ from huggingface_hub.constants import HF_HOME -OBS_ENV_STATE = "observation.environment_state" -OBS_STATE = "observation.state" -OBS_IMAGE = "observation.image" -OBS_IMAGES = "observation.images" +OBS_STR = "observation" +OBS_PREFIX = OBS_STR + "." +OBS_ENV_STATE = OBS_STR + ".environment_state" +OBS_STATE = OBS_STR + ".state" +OBS_IMAGE = OBS_STR + ".image" +OBS_IMAGES = OBS_IMAGE + "s" +OBS_LANGUAGE = OBS_STR + ".language" +OBS_LANGUAGE_TOKENS = OBS_LANGUAGE + ".tokens" +OBS_LANGUAGE_ATTENTION_MASK = OBS_LANGUAGE + ".attention_mask" + ACTION = "action" REWARD = "next.reward" +TRUNCATED = "next.truncated" +DONE = "next.done" ROBOTS = "robots" -ROBOT_TYPE = "robot_type" TELEOPERATORS = "teleoperators" # files & directories @@ -39,6 +46,9 @@ OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json" SCHEDULER_STATE = "scheduler_state.json" +POLICY_PREPROCESSOR_DEFAULT_NAME = "policy_preprocessor" +POLICY_POSTPROCESSOR_DEFAULT_NAME = "policy_postprocessor" + if "LEROBOT_HOME" in os.environ: raise ValueError( f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n" @@ -57,3 +67,6 @@ # streaming datasets LOOKBACK_BACKTRACKTABLE = 100 LOOKAHEAD_BACKTRACKTABLE = 100 + +# openpi +OPENPI_ATTENTION_MASK_VALUE = -2.3819763e38 # TODO(pepijn): Modify this when extending support to fp8 models diff --git a/src/lerobot/utils/control_utils.py b/src/lerobot/utils/control_utils.py index 4bcc241da51..17371921cdc 100644 --- a/src/lerobot/utils/control_utils.py +++ b/src/lerobot/utils/control_utils.py @@ -22,65 +22,31 @@ from contextlib import nullcontext from copy import copy from functools import cache +from typing import Any import numpy as np import torch from deepdiff import DeepDiff -from termcolor import colored from lerobot.datasets.lerobot_dataset import LeRobotDataset from lerobot.datasets.utils import DEFAULT_FEATURES from lerobot.policies.pretrained import PreTrainedPolicy +from lerobot.processor import PolicyAction, PolicyProcessorPipeline from lerobot.robots import Robot -def log_control_info(robot: Robot, dt_s, episode_index=None, frame_index=None, fps=None): - log_items = [] - if episode_index is not None: - log_items.append(f"ep:{episode_index}") - if frame_index is not None: - log_items.append(f"frame:{frame_index}") - - def log_dt(shortname, dt_val_s): - nonlocal log_items, fps - info_str = f"{shortname}:{dt_val_s * 1000:5.2f} ({1 / dt_val_s:3.1f}hz)" - if fps is not None: - actual_fps = 1 / dt_val_s - if actual_fps < fps - 1: - info_str = colored(info_str, "yellow") - log_items.append(info_str) - - # total step time displayed in milliseconds and its frequency - log_dt("dt", dt_s) - - # TODO(aliberts): move robot-specific logs logic in robot.print_logs() - if not robot.robot_type.startswith("stretch"): - for name in robot.leader_arms: - key = f"read_leader_{name}_pos_dt_s" - if key in robot.logs: - log_dt("dtRlead", robot.logs[key]) - - for name in robot.follower_arms: - key = f"write_follower_{name}_goal_pos_dt_s" - if key in robot.logs: - log_dt("dtWfoll", robot.logs[key]) - - key = f"read_follower_{name}_pos_dt_s" - if key in robot.logs: - log_dt("dtRfoll", robot.logs[key]) - - for name in robot.cameras: - key = f"read_camera_{name}_dt_s" - if key in robot.logs: - log_dt(f"dtR{name}", robot.logs[key]) - - info_str = " ".join(log_items) - logging.info(info_str) - - @cache def is_headless(): - """Detects if python is running without a monitor.""" + """ + Detects if the Python script is running in a headless environment (e.g., without a display). + + This function attempts to import `pynput`, a library that requires a graphical environment. + If the import fails, it assumes the environment is headless. The result is cached to avoid + re-running the check. + + Returns: + True if the environment is determined to be headless, False otherwise. + """ try: import pynput # noqa @@ -101,10 +67,35 @@ def predict_action( observation: dict[str, np.ndarray], policy: PreTrainedPolicy, device: torch.device, + preprocessor: PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + postprocessor: PolicyProcessorPipeline[PolicyAction, PolicyAction], use_amp: bool, task: str | None = None, robot_type: str | None = None, ): + """ + Performs a single-step inference to predict a robot action from an observation. + + This function encapsulates the full inference pipeline: + 1. Prepares the observation by converting it to PyTorch tensors and adding a batch dimension. + 2. Runs the preprocessor pipeline on the observation. + 3. Feeds the processed observation to the policy to get a raw action. + 4. Runs the postprocessor pipeline on the raw action. + 5. Formats the final action by removing the batch dimension and moving it to the CPU. + + Args: + observation: A dictionary of NumPy arrays representing the robot's current observation. + policy: The `PreTrainedPolicy` model to use for action prediction. + device: The `torch.device` (e.g., 'cuda' or 'cpu') to run inference on. + preprocessor: The `PolicyProcessorPipeline` for preprocessing observations. + postprocessor: The `PolicyProcessorPipeline` for postprocessing actions. + use_amp: A boolean to enable/disable Automatic Mixed Precision for CUDA inference. + task: An optional string identifier for the task. + robot_type: An optional string identifier for the robot type. + + Returns: + A `torch.Tensor` containing the predicted action, ready for the robot. + """ observation = copy(observation) with ( torch.inference_mode(), @@ -122,10 +113,14 @@ def predict_action( observation["task"] = task if task else "" observation["robot_type"] = robot_type if robot_type else "" + observation = preprocessor(observation) + # Compute the next action with the policy # based on the current observation action = policy.select_action(observation) + action = postprocessor(action) + # Remove batch dimension action = action.squeeze(0) @@ -136,6 +131,18 @@ def predict_action( def init_keyboard_listener(): + """ + Initializes a non-blocking keyboard listener for real-time user interaction. + + This function sets up a listener for specific keys (right arrow, left arrow, escape) to control + the program flow during execution, such as stopping recording or exiting loops. It gracefully + handles headless environments where keyboard listening is not possible. + + Returns: + A tuple containing: + - The `pynput.keyboard.Listener` instance, or `None` if in a headless environment. + - A dictionary of event flags (e.g., `exit_early`) that are set by key presses. + """ # Allow to exit early while recording an episode or resetting the environment, # by tapping the right arrow key '->'. This might require a sudo permission # to allow your terminal to monitor keyboard events. @@ -177,6 +184,19 @@ def on_press(key): def sanity_check_dataset_name(repo_id, policy_cfg): + """ + Validates the dataset repository name against the presence of a policy configuration. + + This function enforces a naming convention: a dataset repository ID should start with "eval_" + if and only if a policy configuration is provided for evaluation purposes. + + Args: + repo_id: The Hugging Face Hub repository ID of the dataset. + policy_cfg: The configuration object for the policy, or `None`. + + Raises: + ValueError: If the naming convention is violated. + """ _, dataset_name = repo_id.split("/") # either repo_id doesnt start with "eval_" and there is no policy # or repo_id starts with "eval_" and there is a policy @@ -197,6 +217,21 @@ def sanity_check_dataset_name(repo_id, policy_cfg): def sanity_check_dataset_robot_compatibility( dataset: LeRobotDataset, robot: Robot, fps: int, features: dict ) -> None: + """ + Checks if a dataset's metadata is compatible with the current robot and recording setup. + + This function compares key metadata fields (`robot_type`, `fps`, and `features`) from the + dataset against the current configuration to ensure that appended data will be consistent. + + Args: + dataset: The `LeRobotDataset` instance to check. + robot: The `Robot` instance representing the current hardware setup. + fps: The current recording frequency (frames per second). + features: The dictionary of features for the current recording session. + + Raises: + ValueError: If any of the checked metadata fields do not match. + """ fields = [ ("robot_type", dataset.meta.robot_type, robot.robot_type), ("fps", dataset.fps, fps), diff --git a/src/lerobot/errors.py b/src/lerobot/utils/errors.py similarity index 78% rename from src/lerobot/errors.py rename to src/lerobot/utils/errors.py index c02d568d447..31b73eacabc 100644 --- a/src/lerobot/errors.py +++ b/src/lerobot/utils/errors.py @@ -30,14 +30,3 @@ def __init__( ): self.message = message super().__init__(self.message) - - -class InvalidActionError(ValueError): - """Exception raised when an action is already invalid.""" - - def __init__( - self, - message="The action is invalid. Check the value follows what it is expected from the action space.", - ): - self.message = message - super().__init__(self.message) diff --git a/src/lerobot/utils/import_utils.py b/src/lerobot/utils/import_utils.py index 5c29b5a8471..de43e58dbf5 100644 --- a/src/lerobot/utils/import_utils.py +++ b/src/lerobot/utils/import_utils.py @@ -15,6 +15,10 @@ # limitations under the License. import importlib import logging +import pkgutil +from typing import Any + +from draccus.choice_types import ChoiceRegistry def is_package_available(pkg_name: str, return_version: bool = False) -> tuple[bool, str] | bool: @@ -57,7 +61,94 @@ def is_package_available(pkg_name: str, return_version: bool = False) -> tuple[b return package_exists -_torch_available, _torch_version = is_package_available("torch", return_version=True) -_gym_xarm_available = is_package_available("gym_xarm") -_gym_aloha_available = is_package_available("gym_aloha") -_gym_pusht_available = is_package_available("gym_pusht") +_transformers_available = is_package_available("transformers") + + +def make_device_from_device_class(config: ChoiceRegistry) -> Any: + """ + Dynamically instantiates an object from its `ChoiceRegistry` configuration. + + This factory uses the module path and class name from the `config` object's + type to locate and instantiate the corresponding device class (not the config). + It derives the device class name by removing a trailing 'Config' from the config + class name and tries a few candidate modules where the device implementation is + commonly located. + """ + if not isinstance(config, ChoiceRegistry): + raise ValueError(f"Config should be an instance of `ChoiceRegistry`, got {type(config)}") + + config_cls = config.__class__ + module_path = config_cls.__module__ # typical: lerobot_teleop_mydevice.config_mydevice + config_name = config_cls.__name__ # typical: MyDeviceConfig + + # Derive device class name (strip "Config") + if not config_name.endswith("Config"): + raise ValueError(f"Config class name '{config_name}' does not end with 'Config'") + + device_class_name = config_name[:-6] # typical: MyDeviceConfig -> MyDevice + + # Build candidate modules to search for the device class + parts = module_path.split(".") + parent_module = ".".join(parts[:-1]) if len(parts) > 1 else module_path + candidates = [ + parent_module, # typical: lerobot_teleop_mydevice + parent_module + "." + device_class_name.lower(), # typical: lerobot_teleop_mydevice.mydevice + ] + + # handle modules named like "config_xxx" -> try replacing that piece with "xxx" + last = parts[-1] if parts else "" + if last.startswith("config_"): + candidates.append(".".join(parts[:-1] + [last.replace("config_", "")])) + + # de-duplicate while preserving order + seen: set[str] = set() + candidates = [c for c in candidates if not (c in seen or seen.add(c))] + + tried: list[str] = [] + for candidate in candidates: + tried.append(candidate) + try: + module = importlib.import_module(candidate) + except ImportError: + continue + + if hasattr(module, device_class_name): + cls = getattr(module, device_class_name) + if callable(cls): + try: + return cls(config) + except TypeError as e: + raise TypeError( + f"Failed to instantiate '{device_class_name}' from module '{candidate}': {e}" + ) from e + + raise ImportError( + f"Could not locate device class '{device_class_name}' for config '{config_name}'. " + f"Tried modules: {tried}. Ensure your device class name is the config class name without " + f"'Config' and that it's importable from one of those modules." + ) + + +def register_third_party_devices() -> None: + """ + Discover and import third-party lerobot_* plugins so they can register themselves. + + Scans top-level modules on sys.path for packages starting with + 'lerobot_robot_', 'lerobot_camera_' or 'lerobot_teleoperator_' and imports them. + """ + prefixes = ("lerobot_robot_", "lerobot_camera_", "lerobot_teleoperator_") + imported: list[str] = [] + failed: list[str] = [] + + for module_info in pkgutil.iter_modules(): + name = module_info.name + if name.startswith(prefixes): + try: + importlib.import_module(name) + imported.append(name) + logging.info("Imported third-party plugin: %s", name) + except Exception: + logging.exception("Could not import third-party plugin: %s", name) + failed.append(name) + + logging.debug("Third-party plugin import summary: imported=%s failed=%s", imported, failed) diff --git a/src/lerobot/utils/random_utils.py b/src/lerobot/utils/random_utils.py index da3ecf37fe9..1bb1f0631c8 100644 --- a/src/lerobot/utils/random_utils.py +++ b/src/lerobot/utils/random_utils.py @@ -23,8 +23,8 @@ import torch from safetensors.torch import load_file, save_file -from lerobot.constants import RNG_STATE from lerobot.datasets.utils import flatten_dict, unflatten_dict +from lerobot.utils.constants import RNG_STATE def serialize_python_rng_state() -> dict[str, torch.Tensor]: diff --git a/src/lerobot/utils/robot_utils.py b/src/lerobot/utils/robot_utils.py index 8069b366230..42abcdda498 100644 --- a/src/lerobot/utils/robot_utils.py +++ b/src/lerobot/utils/robot_utils.py @@ -27,17 +27,3 @@ def busy_wait(seconds): # On Linux time.sleep is accurate if seconds > 0: time.sleep(seconds) - - -def safe_disconnect(func): - # TODO(aliberts): Allow to pass custom exceptions - # (e.g. ThreadServiceExit, KeyboardInterrupt, SystemExit, UnpluggedError, DynamixelCommError) - def wrapper(robot, *args, **kwargs): - try: - return func(robot, *args, **kwargs) - except Exception as e: - if robot.is_connected: - robot.disconnect() - raise e - - return wrapper diff --git a/src/lerobot/utils/rotation.py b/src/lerobot/utils/rotation.py new file mode 100644 index 00000000000..41b6529478a --- /dev/null +++ b/src/lerobot/utils/rotation.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Custom rotation utilities to replace scipy.spatial.transform.Rotation.""" + +import numpy as np + + +class Rotation: + """ + Custom rotation class that provides a subset of scipy.spatial.transform.Rotation functionality. + + Supports conversions between rotation vectors, rotation matrices, and quaternions. + """ + + def __init__(self, quat: np.ndarray) -> None: + """Initialize rotation from quaternion [x, y, z, w].""" + self._quat = np.asarray(quat, dtype=float) + # Normalize quaternion + norm = np.linalg.norm(self._quat) + if norm > 0: + self._quat = self._quat / norm + + @classmethod + def from_rotvec(cls, rotvec: np.ndarray) -> "Rotation": + """ + Create rotation from rotation vector using Rodrigues' formula. + + Args: + rotvec: Rotation vector [x, y, z] where magnitude is angle in radians + + Returns: + Rotation instance + """ + rotvec = np.asarray(rotvec, dtype=float) + angle = np.linalg.norm(rotvec) + + if angle < 1e-8: + # For very small angles, use identity quaternion + quat = np.array([0.0, 0.0, 0.0, 1.0]) + else: + axis = rotvec / angle + half_angle = angle / 2.0 + sin_half = np.sin(half_angle) + cos_half = np.cos(half_angle) + + # Quaternion [x, y, z, w] + quat = np.array([axis[0] * sin_half, axis[1] * sin_half, axis[2] * sin_half, cos_half]) + + return cls(quat) + + @classmethod + def from_matrix(cls, matrix: np.ndarray) -> "Rotation": + """ + Create rotation from 3x3 rotation matrix. + + Args: + matrix: 3x3 rotation matrix + + Returns: + Rotation instance + """ + matrix = np.asarray(matrix, dtype=float) + + # Shepherd's method for converting rotation matrix to quaternion + trace = np.trace(matrix) + + if trace > 0: + s = np.sqrt(trace + 1.0) * 2 # s = 4 * qw + qw = 0.25 * s + qx = (matrix[2, 1] - matrix[1, 2]) / s + qy = (matrix[0, 2] - matrix[2, 0]) / s + qz = (matrix[1, 0] - matrix[0, 1]) / s + elif matrix[0, 0] > matrix[1, 1] and matrix[0, 0] > matrix[2, 2]: + s = np.sqrt(1.0 + matrix[0, 0] - matrix[1, 1] - matrix[2, 2]) * 2 # s = 4 * qx + qw = (matrix[2, 1] - matrix[1, 2]) / s + qx = 0.25 * s + qy = (matrix[0, 1] + matrix[1, 0]) / s + qz = (matrix[0, 2] + matrix[2, 0]) / s + elif matrix[1, 1] > matrix[2, 2]: + s = np.sqrt(1.0 + matrix[1, 1] - matrix[0, 0] - matrix[2, 2]) * 2 # s = 4 * qy + qw = (matrix[0, 2] - matrix[2, 0]) / s + qx = (matrix[0, 1] + matrix[1, 0]) / s + qy = 0.25 * s + qz = (matrix[1, 2] + matrix[2, 1]) / s + else: + s = np.sqrt(1.0 + matrix[2, 2] - matrix[0, 0] - matrix[1, 1]) * 2 # s = 4 * qz + qw = (matrix[1, 0] - matrix[0, 1]) / s + qx = (matrix[0, 2] + matrix[2, 0]) / s + qy = (matrix[1, 2] + matrix[2, 1]) / s + qz = 0.25 * s + + quat = np.array([qx, qy, qz, qw]) + return cls(quat) + + @classmethod + def from_quat(cls, quat: np.ndarray) -> "Rotation": + """ + Create rotation from quaternion. + + Args: + quat: Quaternion [x, y, z, w] or [w, x, y, z] (specify convention in docstring) + This implementation expects [x, y, z, w] format + + Returns: + Rotation instance + """ + return cls(quat) + + def as_matrix(self) -> np.ndarray: + """ + Convert rotation to 3x3 rotation matrix. + + Returns: + 3x3 rotation matrix + """ + qx, qy, qz, qw = self._quat + + # Compute rotation matrix from quaternion + return np.array( + [ + [1 - 2 * (qy * qy + qz * qz), 2 * (qx * qy - qz * qw), 2 * (qx * qz + qy * qw)], + [2 * (qx * qy + qz * qw), 1 - 2 * (qx * qx + qz * qz), 2 * (qy * qz - qx * qw)], + [2 * (qx * qz - qy * qw), 2 * (qy * qz + qx * qw), 1 - 2 * (qx * qx + qy * qy)], + ], + dtype=float, + ) + + def as_rotvec(self) -> np.ndarray: + """ + Convert rotation to rotation vector. + + Returns: + Rotation vector [x, y, z] where magnitude is angle in radians + """ + qx, qy, qz, qw = self._quat + + # Ensure qw is positive for unique representation + if qw < 0: + qx, qy, qz, qw = -qx, -qy, -qz, -qw + + # Compute angle and axis + angle = 2.0 * np.arccos(np.clip(abs(qw), 0.0, 1.0)) + sin_half_angle = np.sqrt(1.0 - qw * qw) + + if sin_half_angle < 1e-8: + # For very small angles, use linearization: rotvec ≈ 2 * [qx, qy, qz] + return 2.0 * np.array([qx, qy, qz]) + + # Extract axis and scale by angle + axis = np.array([qx, qy, qz]) / sin_half_angle + return angle * axis + + def as_quat(self) -> np.ndarray: + """ + Get quaternion representation. + + Returns: + Quaternion [x, y, z, w] + """ + return self._quat.copy() + + def apply(self, vectors: np.ndarray, inverse: bool = False) -> np.ndarray: + """ + Apply this rotation to a set of vectors. + + This is equivalent to applying the rotation matrix to the vectors: + self.as_matrix() @ vectors (or self.as_matrix().T @ vectors if inverse=True). + + Args: + vectors: Array of shape (3,) or (N, 3) representing vectors in 3D space + inverse: If True, apply the inverse of the rotation. Default is False. + + Returns: + Rotated vectors with shape: + - (3,) if input was single vector with shape (3,) + - (N, 3) in all other cases + """ + vectors = np.asarray(vectors, dtype=float) + original_shape = vectors.shape + + # Handle single vector case - ensure it's 2D for matrix multiplication + if vectors.ndim == 1: + if len(vectors) != 3: + raise ValueError("Single vector must have length 3") + vectors = vectors.reshape(1, 3) + single_vector = True + elif vectors.ndim == 2: + if vectors.shape[1] != 3: + raise ValueError("Vectors must have shape (N, 3)") + single_vector = False + else: + raise ValueError("Vectors must be 1D or 2D array") + + # Get rotation matrix + rotation_matrix = self.as_matrix() + + # Apply inverse if requested (transpose for orthogonal rotation matrices) + if inverse: + rotation_matrix = rotation_matrix.T + + # Apply rotation: (N, 3) @ (3, 3).T -> (N, 3) + rotated_vectors = vectors @ rotation_matrix.T + + # Return original shape for single vector case + if single_vector and original_shape == (3,): + return rotated_vectors.flatten() + + return rotated_vectors + + def inv(self) -> "Rotation": + """ + Invert this rotation. + + Composition of a rotation with its inverse results in an identity transformation. + + Returns: + Rotation instance containing the inverse of this rotation + """ + qx, qy, qz, qw = self._quat + + # For a unit quaternion, the inverse is the conjugate: [-x, -y, -z, w] + inverse_quat = np.array([-qx, -qy, -qz, qw]) + + return Rotation(inverse_quat) + + def __mul__(self, other: "Rotation") -> "Rotation": + """ + Compose this rotation with another rotation using the * operator. + + The composition `r2 * r1` means "apply r1 first, then r2". + This is equivalent to applying rotation matrices: r2.as_matrix() @ r1.as_matrix() + + Args: + other: Another Rotation instance to compose with + + Returns: + Rotation instance representing the composition of rotations + """ + if not isinstance(other, Rotation): + return NotImplemented + + # Get quaternions [x, y, z, w] + x1, y1, z1, w1 = other._quat # Apply first + x2, y2, z2, w2 = self._quat # Apply second + + # Quaternion multiplication: q2 * q1 (apply q1 first, then q2) + composed_quat = np.array( + [ + w2 * x1 + x2 * w1 + y2 * z1 - z2 * y1, # x component + w2 * y1 - x2 * z1 + y2 * w1 + z2 * x1, # y component + w2 * z1 + x2 * y1 - y2 * x1 + z2 * w1, # z component + w2 * w1 - x2 * x1 - y2 * y1 - z2 * z1, # w component + ] + ) + + return Rotation(composed_quat) diff --git a/src/lerobot/utils/train_utils.py b/src/lerobot/utils/train_utils.py index 2859fe0579a..3ebe3197176 100644 --- a/src/lerobot/utils/train_utils.py +++ b/src/lerobot/utils/train_utils.py @@ -13,32 +13,27 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import logging from pathlib import Path -from termcolor import colored from torch.optim import Optimizer from torch.optim.lr_scheduler import LRScheduler from lerobot.configs.train import TrainPipelineConfig -from lerobot.constants import ( +from lerobot.datasets.utils import load_json, write_json +from lerobot.optim.optimizers import load_optimizer_state, save_optimizer_state +from lerobot.optim.schedulers import load_scheduler_state, save_scheduler_state +from lerobot.policies.pretrained import PreTrainedPolicy +from lerobot.processor import PolicyProcessorPipeline +from lerobot.utils.constants import ( CHECKPOINTS_DIR, LAST_CHECKPOINT_LINK, PRETRAINED_MODEL_DIR, TRAINING_STATE_DIR, TRAINING_STEP, ) -from lerobot.datasets.utils import load_json, write_json -from lerobot.optim.optimizers import load_optimizer_state, save_optimizer_state -from lerobot.optim.schedulers import load_scheduler_state, save_scheduler_state -from lerobot.policies.pretrained import PreTrainedPolicy from lerobot.utils.random_utils import load_rng_state, save_rng_state -def log_output_dir(out_dir): - logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}") - - def get_step_identifier(step: int, total_steps: int) -> str: num_digits = max(6, len(str(total_steps))) return f"{step:0{num_digits}d}" @@ -74,6 +69,8 @@ def save_checkpoint( policy: PreTrainedPolicy, optimizer: Optimizer, scheduler: LRScheduler | None = None, + preprocessor: PolicyProcessorPipeline | None = None, + postprocessor: PolicyProcessorPipeline | None = None, ) -> None: """This function creates the following directory structure: @@ -81,7 +78,9 @@ def save_checkpoint( ├── pretrained_model/ │ ├── config.json # policy config │ ├── model.safetensors # policy weights - │ └── train_config.json # train config + │ ├── train_config.json # train config + │ ├── processor.json # processor config (if preprocessor provided) + │ └── step_*.safetensors # processor state files (if any) └── training_state/ ├── optimizer_param_groups.json # optimizer param groups ├── optimizer_state.safetensors # optimizer state @@ -95,10 +94,15 @@ def save_checkpoint( policy (PreTrainedPolicy): The policy to save. optimizer (Optimizer | None, optional): The optimizer to save the state from. Defaults to None. scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None. + preprocessor: The preprocessor/pipeline to save. Defaults to None. """ pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR policy.save_pretrained(pretrained_dir) cfg.save_pretrained(pretrained_dir) + if preprocessor is not None: + preprocessor.save_pretrained(pretrained_dir) + if postprocessor is not None: + postprocessor.save_pretrained(pretrained_dir) save_training_state(checkpoint_dir, step, optimizer, scheduler) diff --git a/src/lerobot/utils/transition.py b/src/lerobot/utils/transition.py index db413c388f3..fe36208615b 100644 --- a/src/lerobot/utils/transition.py +++ b/src/lerobot/utils/transition.py @@ -18,6 +18,8 @@ import torch +from lerobot.utils.constants import ACTION + class Transition(TypedDict): state: dict[str, torch.Tensor] @@ -39,7 +41,7 @@ def move_transition_to_device(transition: Transition, device: str = "cpu") -> Tr } # Move action to device - transition["action"] = transition["action"].to(device, non_blocking=non_blocking) + transition[ACTION] = transition[ACTION].to(device, non_blocking=non_blocking) # Move reward and done if they are tensors if isinstance(transition["reward"], torch.Tensor): @@ -61,7 +63,7 @@ def move_transition_to_device(transition: Transition, device: str = "cpu") -> Tr for key, val in transition["complementary_info"].items(): if isinstance(val, torch.Tensor): transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking) - elif isinstance(val, (int, float, bool)): + elif isinstance(val, (int | float | bool)): transition["complementary_info"][key] = torch.tensor(val, device=device) else: raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]") diff --git a/src/lerobot/utils/utils.py b/src/lerobot/utils/utils.py index 107606fdade..8777d5a9db8 100644 --- a/src/lerobot/utils/utils.py +++ b/src/lerobot/utils/utils.py @@ -15,14 +15,13 @@ # limitations under the License. import logging import os -import os.path as osp import platform import select import subprocess import sys import time from copy import copy, deepcopy -from datetime import datetime, timezone +from datetime import datetime from pathlib import Path from statistics import mean @@ -30,12 +29,6 @@ import torch -def none_or_int(value): - if value == "None": - return None - return int(value) - - def inside_slurm(): """Check whether the python process was launched through slurm""" # TODO(rcadene): return False for interactive mode `--pty bash` @@ -165,36 +158,6 @@ def format_big_number(num, precision=0): return num -def _relative_path_between(path1: Path, path2: Path) -> Path: - """Returns path1 relative to path2.""" - path1 = path1.absolute() - path2 = path2.absolute() - try: - return path1.relative_to(path2) - except ValueError: # most likely because path1 is not a subpath of path2 - common_parts = Path(osp.commonpath([path1, path2])).parts - return Path( - "/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :])) - ) - - -def print_cuda_memory_usage(): - """Use this function to locate and debug memory leak.""" - import gc - - gc.collect() - # Also clear the cache if you want to fully release the memory - torch.cuda.empty_cache() - print(f"Current GPU Memory Allocated: {torch.cuda.memory_allocated(0) / 1024**2:.2f} MB") - print(f"Maximum GPU Memory Allocated: {torch.cuda.max_memory_allocated(0) / 1024**2:.2f} MB") - print(f"Current GPU Memory Reserved: {torch.cuda.memory_reserved(0) / 1024**2:.2f} MB") - print(f"Maximum GPU Memory Reserved: {torch.cuda.max_memory_reserved(0) / 1024**2:.2f} MB") - - -def capture_timestamp_utc(): - return datetime.now(timezone.utc) - - def say(text: str, blocking: bool = False): system = platform.system() @@ -367,10 +330,6 @@ def count(self) -> int: def history(self) -> list[float]: return deepcopy(self._history) - @property - def fps_history(self) -> list[float]: - return [1.0 / t for t in self._history] - @property def fps_last(self) -> float: return 0.0 if self.last == 0 else 1.0 / self.last diff --git a/src/lerobot/utils/visualization_utils.py b/src/lerobot/utils/visualization_utils.py index f0f9aebb74c..95fdb178a41 100644 --- a/src/lerobot/utils/visualization_utils.py +++ b/src/lerobot/utils/visualization_utils.py @@ -12,14 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. +import numbers import os from typing import Any import numpy as np import rerun as rr +from .constants import OBS_PREFIX, OBS_STR -def _init_rerun(session_name: str = "lerobot_control_loop") -> None: + +def init_rerun(session_name: str = "lerobot_control_loop") -> None: """Initializes the Rerun SDK for visualizing the control loop.""" batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000") os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size @@ -28,19 +31,66 @@ def _init_rerun(session_name: str = "lerobot_control_loop") -> None: rr.spawn(memory_limit=memory_limit) -def log_rerun_data(observation: dict[str | Any], action: dict[str | Any]): - for obs, val in observation.items(): - if isinstance(val, float): - rr.log(f"observation.{obs}", rr.Scalar(val)) - elif isinstance(val, np.ndarray): - if val.ndim == 1: - for i, v in enumerate(val): - rr.log(f"observation.{obs}_{i}", rr.Scalar(float(v))) - else: - rr.log(f"observation.{obs}", rr.Image(val), static=True) - for act, val in action.items(): - if isinstance(val, float): - rr.log(f"action.{act}", rr.Scalar(val)) - elif isinstance(val, np.ndarray): - for i, v in enumerate(val): - rr.log(f"action.{act}_{i}", rr.Scalar(float(v))) +def _is_scalar(x): + return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or ( + isinstance(x, np.ndarray) and x.ndim == 0 + ) + + +def log_rerun_data( + observation: dict[str, Any] | None = None, + action: dict[str, Any] | None = None, +) -> None: + """ + Logs observation and action data to Rerun for real-time visualization. + + This function iterates through the provided observation and action dictionaries and sends their contents + to the Rerun viewer. It handles different data types appropriately: + - Scalar values (floats, ints) are logged as `rr.Scalar`. + - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed + from CHW to HWC format and logged as `rr.Image`. + - 1D NumPy arrays are logged as a series of individual scalars, with each element indexed. + - Other multi-dimensional arrays are flattened and logged as individual scalars. + + Keys are automatically namespaced with "observation." or "action." if not already present. + + Args: + observation: An optional dictionary containing observation data to log. + action: An optional dictionary containing action data to log. + """ + if observation: + for k, v in observation.items(): + if v is None: + continue + key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}" + + if _is_scalar(v): + rr.log(key, rr.Scalar(float(v))) + elif isinstance(v, np.ndarray): + arr = v + # Convert CHW -> HWC when needed + if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4): + arr = np.transpose(arr, (1, 2, 0)) + if arr.ndim == 1: + for i, vi in enumerate(arr): + rr.log(f"{key}_{i}", rr.Scalar(float(vi))) + else: + rr.log(key, rr.Image(arr), static=True) + + if action: + for k, v in action.items(): + if v is None: + continue + key = k if str(k).startswith("action.") else f"action.{k}" + + if _is_scalar(v): + rr.log(key, rr.Scalar(float(v))) + elif isinstance(v, np.ndarray): + if v.ndim == 1: + for i, vi in enumerate(v): + rr.log(f"{key}_{i}", rr.Scalar(float(vi))) + else: + # Fall back to flattening higher-dimensional arrays + flat = v.flatten() + for i, vi in enumerate(flat): + rr.log(f"{key}_{i}", rr.Scalar(float(vi))) diff --git a/tests/artifacts/policies/aloha_sim_insertion_human_act_/actions.safetensors b/tests/artifacts/policies/aloha_sim_insertion_human_act_/actions.safetensors index 8bd63e8948f..771af244500 100644 --- a/tests/artifacts/policies/aloha_sim_insertion_human_act_/actions.safetensors +++ b/tests/artifacts/policies/aloha_sim_insertion_human_act_/actions.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f3e4c8e85e146b043fd4e4984947c2a6f01627f174a19f18b5914cf690579d77 +oid sha256:ee0c29d3782aa1cadcf4dc6ed767d9460ff00fff9fc70b460502340b832eefcc size 5104 diff --git a/tests/artifacts/policies/aloha_sim_insertion_human_act_/param_stats.safetensors b/tests/artifacts/policies/aloha_sim_insertion_human_act_/param_stats.safetensors index 724d22b58e3..3e8df708e77 100644 --- a/tests/artifacts/policies/aloha_sim_insertion_human_act_/param_stats.safetensors +++ b/tests/artifacts/policies/aloha_sim_insertion_human_act_/param_stats.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:9b5f557e30aead3731c38cbd85af8c706395d8689a918ad88805b5a886245603 -size 33400 +oid sha256:ea76e6711959fd3f905ec2bdc306f488920f00ec99421e4870d05f6205eb323e +size 31672 diff --git a/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/actions.safetensors b/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/actions.safetensors index 6d912d81ac9..dd7d4d0e773 100644 --- a/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/actions.safetensors +++ b/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/actions.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2e6625cabfeb4800abc80252cf9112a9271c154edd01eb291658f143c951610b +oid sha256:c2b8f8532c7a0b776de5e536b8b54e30b1a0c2e3d5cc25a2d86fe43e40ae5e8c size 515400 diff --git a/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors b/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors index cc6b4a24b5d..5da67a1af09 100644 --- a/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors +++ b/tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:021562ee3e4814425e367ed0c144d6fbe2eb28838247085716cf0b58fd69a075 -size 33400 +oid sha256:eca0d87a699620e4fec7e68539b0be91e4cc933f6bf12032da52c182ab6f38cf +size 31672 diff --git a/tests/artifacts/policies/pusht_diffusion_/actions.safetensors b/tests/artifacts/policies/pusht_diffusion_/actions.safetensors index 84e14b9756c..ef581727d65 100644 --- a/tests/artifacts/policies/pusht_diffusion_/actions.safetensors +++ b/tests/artifacts/policies/pusht_diffusion_/actions.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a32376dde65a1562403afd1db3e56c7e6b987ebaf6c3c601336e77155b9e608c +oid sha256:19eaaa85f66ba4aa6388dbb83819ffad6ea4363247208f871a8dc385689f6fc8 size 992 diff --git a/tests/artifacts/policies/pusht_diffusion_/grad_stats.safetensors b/tests/artifacts/policies/pusht_diffusion_/grad_stats.safetensors index 54229791003..e00ed3238c7 100644 --- a/tests/artifacts/policies/pusht_diffusion_/grad_stats.safetensors +++ b/tests/artifacts/policies/pusht_diffusion_/grad_stats.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:12ee532c53173d0361ebb979f087b229cc045aa3d9e6b94cfd4290af54fd1201 +oid sha256:227296eaeeb54acdc3dae2eb8af3d4d08fb87e245337624447140b1e91cfd002 size 47424 diff --git a/tests/artifacts/policies/pusht_diffusion_/param_stats.safetensors b/tests/artifacts/policies/pusht_diffusion_/param_stats.safetensors index e91cd08b79c..614cc754ef4 100644 --- a/tests/artifacts/policies/pusht_diffusion_/param_stats.safetensors +++ b/tests/artifacts/policies/pusht_diffusion_/param_stats.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:010c01181b95625051276d69cb4209423c21f2e30a3fa9464ae67064a2ba4c22 -size 49120 +oid sha256:778fddbbaa64248cee35cb377c02cc2b6076f7ce5855146de677128900617ddf +size 47424 diff --git a/tests/artifacts/policies/save_policy_to_safetensors.py b/tests/artifacts/policies/save_policy_to_safetensors.py index 6ccb47c3e88..64b125cc92f 100644 --- a/tests/artifacts/policies/save_policy_to_safetensors.py +++ b/tests/artifacts/policies/save_policy_to_safetensors.py @@ -23,7 +23,8 @@ from lerobot.configs.train import TrainPipelineConfig from lerobot.datasets.factory import make_dataset from lerobot.optim.factory import make_optimizer_and_scheduler -from lerobot.policies.factory import make_policy, make_policy_config +from lerobot.policies.factory import make_policy, make_policy_config, make_pre_post_processors +from lerobot.utils.constants import OBS_STR from lerobot.utils.random_utils import set_seed @@ -37,7 +38,9 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict): train_cfg.validate() # Needed for auto-setting some parameters dataset = make_dataset(train_cfg) + dataset_stats = dataset.meta.stats policy = make_policy(train_cfg.policy, ds_meta=dataset.meta) + preprocessor, postprocessor = make_pre_post_processors(train_cfg.policy, dataset_stats=dataset_stats) policy.train() optimizer, _ = make_optimizer_and_scheduler(train_cfg, policy) @@ -49,7 +52,9 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict): ) batch = next(iter(dataloader)) + batch = preprocessor(batch) loss, output_dict = policy.forward(batch) + if output_dict is not None: output_dict = {k: v for k, v in output_dict.items() if isinstance(v, torch.Tensor)} output_dict["loss"] = loss @@ -61,15 +66,13 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict): for key, param in policy.named_parameters(): if param.requires_grad: grad_stats[f"{key}_mean"] = param.grad.mean() - grad_stats[f"{key}_std"] = ( - param.grad.std() if param.grad.numel() > 1 else torch.tensor(float(0.0)) - ) + grad_stats[f"{key}_std"] = param.grad.std() if param.grad.numel() > 1 else torch.tensor(0.0) optimizer.step() param_stats = {} for key, param in policy.named_parameters(): param_stats[f"{key}_mean"] = param.mean() - param_stats[f"{key}_std"] = param.std() if param.numel() > 1 else torch.tensor(float(0.0)) + param_stats[f"{key}_std"] = param.std() if param.numel() > 1 else torch.tensor(0.0) optimizer.zero_grad() policy.reset() @@ -88,7 +91,7 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict): # for backward compatibility if k == "task": continue - if k.startswith("observation"): + if k.startswith(OBS_STR): obs[k] = batch[k] if hasattr(train_cfg.policy, "n_action_steps"): @@ -96,7 +99,12 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict): else: actions_queue = train_cfg.policy.n_action_repeats - actions = {str(i): policy.select_action(obs).contiguous() for i in range(actions_queue)} + actions = {} + for i in range(actions_queue): + unnormalized_action = policy.select_action(obs).contiguous() + action_robot = postprocessor(unnormalized_action) + actions[str(i)] = action_robot + return output_dict, grad_stats, param_stats, actions diff --git a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/actions.safetensors b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/actions.safetensors index fa9bf06abd1..e23eacffdfb 100644 --- a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/actions.safetensors +++ b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/actions.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c5edc5600d7206f027cb696a597bc99fcdd9073a15fa130b8031c52c0a7c134b +oid sha256:d640988f2269cf6aa03c8ee17f9d096edace83d837f90025011fafec5bf53c61 size 200 diff --git a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/grad_stats.safetensors b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/grad_stats.safetensors index 8d90a671f93..e665f73c67d 100644 --- a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/grad_stats.safetensors +++ b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/grad_stats.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a70e29263afdbff3a49d7041ff2d5065df75472b7c030cc8a5d12ab20d24cc10 +oid sha256:32ddf36af25791935b395c7641531cda14d5c4a2cf654a2e76ac45271665d07a size 16904 diff --git a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/output_dict.safetensors b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/output_dict.safetensors index cde6c6dcaa3..97d783580a2 100644 --- a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/output_dict.safetensors +++ b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/output_dict.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c49a5b4d4df92c9564009780f5e286ddfca84ca2b1753557024057b3b36afb8b +oid sha256:22a1031a2acfc36a455bff73ffbe097cfeb7742b6485e7422507e78d7a682703 size 164 diff --git a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/param_stats.safetensors b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/param_stats.safetensors index 692377d1fd6..3090b70511a 100644 --- a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/param_stats.safetensors +++ b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/param_stats.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5f8d19a86065937cffdd3ca49caef87c59e67d419b28f40f2817bad892dc3170 -size 36312 +oid sha256:b5dca7940998421ae58e9e26b2b2641b058d23b0270b7a147ebf85fbbdce7184 +size 35496 diff --git a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/actions.safetensors b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/actions.safetensors index 7a0b165e2fc..5ce44048f59 100644 --- a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/actions.safetensors +++ 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a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/output_dict.safetensors b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/output_dict.safetensors index cde6c6dcaa3..97d783580a2 100644 --- a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/output_dict.safetensors +++ b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/output_dict.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c49a5b4d4df92c9564009780f5e286ddfca84ca2b1753557024057b3b36afb8b +oid sha256:22a1031a2acfc36a455bff73ffbe097cfeb7742b6485e7422507e78d7a682703 size 164 diff --git a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/param_stats.safetensors b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/param_stats.safetensors index 692377d1fd6..3090b70511a 100644 --- a/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/param_stats.safetensors +++ b/tests/artifacts/policies/xarm_lift_medium_tdmpc_use_policy/param_stats.safetensors @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5f8d19a86065937cffdd3ca49caef87c59e67d419b28f40f2817bad892dc3170 -size 36312 +oid sha256:b5dca7940998421ae58e9e26b2b2641b058d23b0270b7a147ebf85fbbdce7184 +size 35496 diff --git a/tests/async_inference/test_e2e.py b/tests/async_inference/test_e2e.py index 1c0400e6608..ebaef2ef1a2 100644 --- a/tests/async_inference/test_e2e.py +++ b/tests/async_inference/test_e2e.py @@ -48,11 +48,11 @@ def test_async_inference_e2e(monkeypatch): # Import grpc-dependent modules inside the test function import grpc + from lerobot.async_inference.configs import PolicyServerConfig, RobotClientConfig + from lerobot.async_inference.helpers import map_robot_keys_to_lerobot_features + from lerobot.async_inference.policy_server import PolicyServer + from lerobot.async_inference.robot_client import RobotClient from lerobot.robots.utils import make_robot_from_config - from lerobot.scripts.server.configs import PolicyServerConfig, RobotClientConfig - from lerobot.scripts.server.helpers import map_robot_keys_to_lerobot_features - from lerobot.scripts.server.policy_server import PolicyServer - from lerobot.scripts.server.robot_client import RobotClient from lerobot.transport import ( services_pb2, # type: ignore services_pb2_grpc, # type: ignore @@ -91,6 +91,9 @@ def model(self, batch): policy_server.policy = MockPolicy() policy_server.actions_per_chunk = 20 policy_server.device = "cpu" + # NOTE(Steven): Smelly tests as the Server is a state machine being partially mocked. Adding these processors as a quick fix. + policy_server.preprocessor = lambda obs: obs + policy_server.postprocessor = lambda tensor: tensor # Set up robot config and features robot_config = MockRobotConfig() diff --git a/tests/async_inference/test_helpers.py b/tests/async_inference/test_helpers.py index e0b7973716b..1e2d1e31176 100644 --- a/tests/async_inference/test_helpers.py +++ b/tests/async_inference/test_helpers.py @@ -19,8 +19,7 @@ import numpy as np import torch -from lerobot.configs.types import FeatureType, PolicyFeature -from lerobot.scripts.server.helpers import ( +from lerobot.async_inference.helpers import ( FPSTracker, TimedAction, TimedObservation, @@ -30,6 +29,8 @@ raw_observation_to_observation, resize_robot_observation_image, ) +from lerobot.configs.types import FeatureType, PolicyFeature +from lerobot.utils.constants import OBS_IMAGES, OBS_STATE # --------------------------------------------------------------------- # FPSTracker @@ -115,7 +116,7 @@ def test_timed_action_getters(): def test_timed_observation_getters(): """TimedObservation stores & returns timestamp, dict and timestep.""" ts = time.time() - obs_dict = {"observation.state": torch.ones(6)} + obs_dict = {OBS_STATE: torch.ones(6)} to = TimedObservation(timestamp=ts, observation=obs_dict, timestep=0) assert math.isclose(to.get_timestamp(), ts, rel_tol=0, abs_tol=1e-6) @@ -151,7 +152,7 @@ def test_timed_data_deserialization_data_getters(): # ------------------------------------------------------------------ # TimedObservation # ------------------------------------------------------------------ - obs_dict = {"observation.state": torch.arange(4).float()} + obs_dict = {OBS_STATE: torch.arange(4).float()} to_in = TimedObservation(timestamp=ts, observation=obs_dict, timestep=7, must_go=True) to_bytes = pickle.dumps(to_in) # nosec @@ -161,7 +162,7 @@ def test_timed_data_deserialization_data_getters(): assert to_out.get_timestep() == 7 assert to_out.must_go is True assert to_out.get_observation().keys() == obs_dict.keys() - torch.testing.assert_close(to_out.get_observation()["observation.state"], obs_dict["observation.state"]) + torch.testing.assert_close(to_out.get_observation()[OBS_STATE], obs_dict[OBS_STATE]) # --------------------------------------------------------------------- @@ -187,7 +188,7 @@ def test_observations_similar_true(): """Distance below atol → observations considered similar.""" # Create mock lerobot features for the similarity check lerobot_features = { - "observation.state": { + OBS_STATE: { "dtype": "float32", "shape": [4], "names": ["shoulder", "elbow", "wrist", "gripper"], @@ -222,17 +223,17 @@ def _create_mock_robot_observation(): def _create_mock_lerobot_features(): """Create mock lerobot features mapping similar to what hw_to_dataset_features returns.""" return { - "observation.state": { + OBS_STATE: { "dtype": "float32", "shape": [4], "names": ["shoulder", "elbow", "wrist", "gripper"], }, - "observation.images.laptop": { + f"{OBS_IMAGES}.laptop": { "dtype": "image", "shape": [480, 640, 3], "names": ["height", "width", "channels"], }, - "observation.images.phone": { + f"{OBS_IMAGES}.phone": { "dtype": "image", "shape": [480, 640, 3], "names": ["height", "width", "channels"], @@ -243,11 +244,11 @@ def _create_mock_lerobot_features(): def _create_mock_policy_image_features(): """Create mock policy image features with different resolutions.""" return { - "observation.images.laptop": PolicyFeature( + f"{OBS_IMAGES}.laptop": PolicyFeature( type=FeatureType.VISUAL, shape=(3, 224, 224), # Policy expects smaller resolution ), - "observation.images.phone": PolicyFeature( + f"{OBS_IMAGES}.phone": PolicyFeature( type=FeatureType.VISUAL, shape=(3, 160, 160), # Different resolution for second camera ), @@ -306,21 +307,21 @@ def test_prepare_raw_observation(): prepared = prepare_raw_observation(robot_obs, lerobot_features, policy_image_features) # Check that state is properly extracted and batched - assert "observation.state" in prepared - state = prepared["observation.state"] + assert OBS_STATE in prepared + state = prepared[OBS_STATE] assert isinstance(state, torch.Tensor) assert state.shape == (1, 4) # Batched state # Check that images are processed and resized - assert "observation.images.laptop" in prepared - assert "observation.images.phone" in prepared + assert f"{OBS_IMAGES}.laptop" in prepared + assert f"{OBS_IMAGES}.phone" in prepared - laptop_img = prepared["observation.images.laptop"] - phone_img = prepared["observation.images.phone"] + laptop_img = prepared[f"{OBS_IMAGES}.laptop"] + phone_img = prepared[f"{OBS_IMAGES}.phone"] # Check image shapes match policy requirements - assert laptop_img.shape == policy_image_features["observation.images.laptop"].shape - assert phone_img.shape == policy_image_features["observation.images.phone"].shape + assert laptop_img.shape == policy_image_features[f"{OBS_IMAGES}.laptop"].shape + assert phone_img.shape == policy_image_features[f"{OBS_IMAGES}.phone"].shape # Check that images are tensors assert isinstance(laptop_img, torch.Tensor) @@ -332,33 +333,27 @@ def test_raw_observation_to_observation_basic(): robot_obs = _create_mock_robot_observation() lerobot_features = _create_mock_lerobot_features() policy_image_features = _create_mock_policy_image_features() - device = "cpu" - observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features, device) + observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features) # Check that all expected keys are present - assert "observation.state" in observation - assert "observation.images.laptop" in observation - assert "observation.images.phone" in observation + assert OBS_STATE in observation + assert f"{OBS_IMAGES}.laptop" in observation + assert f"{OBS_IMAGES}.phone" in observation # Check state processing - state = observation["observation.state"] + state = observation[OBS_STATE] assert isinstance(state, torch.Tensor) - assert state.device.type == device assert state.shape == (1, 4) # Batched # Check image processing - laptop_img = observation["observation.images.laptop"] - phone_img = observation["observation.images.phone"] + laptop_img = observation[f"{OBS_IMAGES}.laptop"] + phone_img = observation[f"{OBS_IMAGES}.phone"] # Images should have batch dimension: (B, C, H, W) assert laptop_img.shape == (1, 3, 224, 224) assert phone_img.shape == (1, 3, 160, 160) - # Check device placement - assert laptop_img.device.type == device - assert phone_img.device.type == device - # Check image dtype and range (should be float32 in [0, 1]) assert laptop_img.dtype == torch.float32 assert phone_img.dtype == torch.float32 @@ -373,9 +368,8 @@ def test_raw_observation_to_observation_with_non_tensor_data(): lerobot_features = _create_mock_lerobot_features() policy_image_features = _create_mock_policy_image_features() - device = "cpu" - observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features, device) + observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features) # Check that task string is preserved assert "task" in observation @@ -385,19 +379,17 @@ def test_raw_observation_to_observation_with_non_tensor_data(): @torch.no_grad() def test_raw_observation_to_observation_device_handling(): - """Test that tensors are properly moved to the specified device.""" - device = "mps" if torch.backends.mps.is_available() else "cpu" - + """Test that tensors are created (device placement is handled by preprocessor).""" robot_obs = _create_mock_robot_observation() lerobot_features = _create_mock_lerobot_features() policy_image_features = _create_mock_policy_image_features() - observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features, device) + observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features) - # Check that all tensors are on the correct device + # Check that all expected keys produce tensors (device placement handled by preprocessor later) for key, value in observation.items(): if isinstance(value, torch.Tensor): - assert value.device.type == device, f"Tensor {key} not on {device}" + assert value.device.type in ["cpu", "cuda", "mps"], f"Tensor {key} on unexpected device" def test_raw_observation_to_observation_deterministic(): @@ -405,11 +397,10 @@ def test_raw_observation_to_observation_deterministic(): robot_obs = _create_mock_robot_observation() lerobot_features = _create_mock_lerobot_features() policy_image_features = _create_mock_policy_image_features() - device = "cpu" # Run twice with same input - obs1 = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features, device) - obs2 = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features, device) + obs1 = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features) + obs2 = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features) # Results should be identical assert set(obs1.keys()) == set(obs2.keys()) @@ -429,27 +420,27 @@ def test_image_processing_pipeline_preserves_content(): robot_obs = {"shoulder": 1.0, "elbow": 1.0, "wrist": 1.0, "gripper": 1.0, "laptop": original_img} lerobot_features = { - "observation.state": { + OBS_STATE: { "dtype": "float32", "shape": [4], "names": ["shoulder", "elbow", "wrist", "gripper"], }, - "observation.images.laptop": { + f"{OBS_IMAGES}.laptop": { "dtype": "image", "shape": [100, 100, 3], "names": ["height", "width", "channels"], }, } policy_image_features = { - "observation.images.laptop": PolicyFeature( + f"{OBS_IMAGES}.laptop": PolicyFeature( type=FeatureType.VISUAL, shape=(3, 50, 50), # Downsamples from 100x100 ) } - observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features, "cpu") + observation = raw_observation_to_observation(robot_obs, lerobot_features, policy_image_features) - processed_img = observation["observation.images.laptop"].squeeze(0) # Remove batch dim + processed_img = observation[f"{OBS_IMAGES}.laptop"].squeeze(0) # Remove batch dim # Check that the center region has higher values than corners # Due to bilinear interpolation, exact values will change but pattern should remain diff --git a/tests/async_inference/test_policy_server.py b/tests/async_inference/test_policy_server.py index 5c795e7ecd8..29583d4fac6 100644 --- a/tests/async_inference/test_policy_server.py +++ b/tests/async_inference/test_policy_server.py @@ -23,6 +23,7 @@ import torch from lerobot.configs.types import PolicyFeature +from lerobot.utils.constants import OBS_STATE from tests.utils import require_package # ----------------------------------------------------------------------------- @@ -44,7 +45,7 @@ def image_features(self) -> dict[str, PolicyFeature]: def predict_action_chunk(self, observation: dict[str, torch.Tensor]) -> torch.Tensor: """Return a chunk of 20 dummy actions.""" - batch_size = len(observation["observation.state"]) + batch_size = len(observation[OBS_STATE]) return torch.zeros(batch_size, 20, 6) def __init__(self): @@ -65,8 +66,8 @@ def model(self, batch: dict) -> torch.Tensor: def policy_server(): """Fresh `PolicyServer` instance with a stubbed-out policy model.""" # Import only when the test actually runs (after decorator check) - from lerobot.scripts.server.configs import PolicyServerConfig - from lerobot.scripts.server.policy_server import PolicyServer + from lerobot.async_inference.configs import PolicyServerConfig + from lerobot.async_inference.policy_server import PolicyServer test_config = PolicyServerConfig(host="localhost", port=9999) server = PolicyServer(test_config) @@ -77,7 +78,7 @@ def policy_server(): # Add mock lerobot_features that the observation similarity functions need server.lerobot_features = { - "observation.state": { + OBS_STATE: { "dtype": "float32", "shape": [6], "names": ["joint1", "joint2", "joint3", "joint4", "joint5", "joint6"], @@ -95,7 +96,7 @@ def policy_server(): def _make_obs(state: torch.Tensor, timestep: int = 0, must_go: bool = False): """Create a TimedObservation with a given state vector.""" # Import only when needed - from lerobot.scripts.server.helpers import TimedObservation + from lerobot.async_inference.helpers import TimedObservation return TimedObservation( observation={ @@ -191,10 +192,13 @@ def test_obs_sanity_checks(policy_server): def test_predict_action_chunk(monkeypatch, policy_server): """End-to-end test of `_predict_action_chunk` with a stubbed _get_action_chunk.""" # Import only when needed - from lerobot.scripts.server.policy_server import PolicyServer + from lerobot.async_inference.policy_server import PolicyServer # Force server to act-style policy; patch method to return deterministic tensor policy_server.policy_type = "act" + # NOTE(Steven): Smelly tests as the Server is a state machine being partially mocked. Adding these processors as a quick fix. + policy_server.preprocessor = lambda obs: obs + policy_server.postprocessor = lambda tensor: tensor action_dim = 6 batch_size = 1 actions_per_chunk = policy_server.actions_per_chunk diff --git a/tests/async_inference/test_robot_client.py b/tests/async_inference/test_robot_client.py index 51db2c3a771..dfdb8ce4200 100644 --- a/tests/async_inference/test_robot_client.py +++ b/tests/async_inference/test_robot_client.py @@ -38,8 +38,8 @@ def robot_client(): """Fresh `RobotClient` instance for each test case (no threads started). Uses DummyRobot.""" # Import only when the test actually runs (after decorator check) - from lerobot.scripts.server.configs import RobotClientConfig - from lerobot.scripts.server.robot_client import RobotClient + from lerobot.async_inference.configs import RobotClientConfig + from lerobot.async_inference.robot_client import RobotClient from tests.mocks.mock_robot import MockRobotConfig test_config = MockRobotConfig() @@ -73,7 +73,7 @@ def robot_client(): def _make_actions(start_ts: float, start_t: int, count: int): """Generate `count` consecutive TimedAction objects starting at timestep `start_t`.""" - from lerobot.scripts.server.helpers import TimedAction + from lerobot.async_inference.helpers import TimedAction fps = 30 # emulates most common frame-rate actions = [] @@ -124,7 +124,7 @@ def test_aggregate_action_queues_combines_actions_in_overlap( ): """`_aggregate_action_queues` must combine actions on overlapping timesteps according to the provided aggregate_fn, here tested with multiple coefficients.""" - from lerobot.scripts.server.helpers import TimedAction + from lerobot.async_inference.helpers import TimedAction robot_client.chunks_received = 0 diff --git a/tests/cameras/test_opencv.py b/tests/cameras/test_opencv.py index a9c060c4f81..a3d98a679b4 100644 --- a/tests/cameras/test_opencv.py +++ b/tests/cameras/test_opencv.py @@ -26,7 +26,7 @@ from lerobot.cameras.configs import Cv2Rotation from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError # NOTE(Steven): more tests + assertions? TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "cameras" diff --git a/tests/cameras/test_reachy2_camera.py b/tests/cameras/test_reachy2_camera.py index 66c7675a6b7..0b38e8b0b29 100644 --- a/tests/cameras/test_reachy2_camera.py +++ b/tests/cameras/test_reachy2_camera.py @@ -21,7 +21,7 @@ import pytest from lerobot.cameras.reachy2_camera import Reachy2Camera, Reachy2CameraConfig -from lerobot.errors import DeviceNotConnectedError +from lerobot.utils.errors import DeviceNotConnectedError PARAMS = [ ("teleop", "left"), diff --git a/tests/cameras/test_realsense.py b/tests/cameras/test_realsense.py index 4b3fbae82e6..fb991225716 100644 --- a/tests/cameras/test_realsense.py +++ b/tests/cameras/test_realsense.py @@ -26,7 +26,7 @@ import pytest from lerobot.cameras.configs import Cv2Rotation -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError pytest.importorskip("pyrealsense2") diff --git a/tests/conftest.py b/tests/conftest.py index e273da50f5d..b14e9aed52f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -19,7 +19,7 @@ import pytest from serial import SerialException -from lerobot.configs.types import FeatureType, PolicyFeature +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature from tests.utils import DEVICE # Import fixture modules as plugins @@ -83,7 +83,9 @@ def _pf(ft: FeatureType, shape: tuple[int, ...]) -> PolicyFeature: return _pf -def assert_contract_is_typed(features: dict[str, PolicyFeature]) -> None: +def assert_contract_is_typed(features: dict[PipelineFeatureType, dict[str, PolicyFeature]]) -> None: assert isinstance(features, dict) - assert all(isinstance(k, str) for k in features.keys()) - assert all(isinstance(v, PolicyFeature) for v in features.values()) + assert all(isinstance(k, PipelineFeatureType) for k in features) + assert all(isinstance(v, dict) for v in features.values()) + assert all(all(isinstance(nk, str) for nk in v) for v in features.values()) + assert all(all(isinstance(nv, PolicyFeature) for nv in v.values()) for v in features.values()) diff --git a/tests/datasets/test_compute_stats.py b/tests/datasets/test_compute_stats.py index 8f8179c2930..973c80bd891 100644 --- a/tests/datasets/test_compute_stats.py +++ b/tests/datasets/test_compute_stats.py @@ -19,6 +19,7 @@ import pytest from lerobot.datasets.compute_stats import ( + RunningQuantileStats, _assert_type_and_shape, aggregate_feature_stats, aggregate_stats, @@ -28,6 +29,7 @@ sample_images, sample_indices, ) +from lerobot.utils.constants import OBS_IMAGE, OBS_STATE def mock_load_image_as_numpy(path, dtype, channel_first): @@ -101,6 +103,9 @@ def test_get_feature_stats_axis_1(sample_array): "count": np.array([3]), } result = get_feature_stats(sample_array, axis=(1,), keepdims=False) + + # Check that basic stats are correct (quantiles are also included now) + assert set(expected.keys()).issubset(set(result.keys())) for key in expected: np.testing.assert_allclose(result[key], expected[key]) @@ -114,6 +119,9 @@ def test_get_feature_stats_no_axis(sample_array): "count": np.array([3]), } result = get_feature_stats(sample_array, axis=None, keepdims=False) + + # Check that basic stats are correct (quantiles are also included now) + assert set(expected.keys()).issubset(set(result.keys())) for key in expected: np.testing.assert_allclose(result[key], expected[key]) @@ -136,21 +144,21 @@ def test_get_feature_stats_single_value(): def test_compute_episode_stats(): episode_data = { - "observation.image": [f"image_{i}.jpg" for i in range(100)], - "observation.state": np.random.rand(100, 10), + OBS_IMAGE: [f"image_{i}.jpg" for i in range(100)], + OBS_STATE: np.random.rand(100, 10), } features = { - "observation.image": {"dtype": "image"}, - "observation.state": {"dtype": "numeric"}, + OBS_IMAGE: {"dtype": "image"}, + OBS_STATE: {"dtype": "numeric"}, } with patch("lerobot.datasets.compute_stats.load_image_as_numpy", side_effect=mock_load_image_as_numpy): stats = compute_episode_stats(episode_data, features) - assert "observation.image" in stats and "observation.state" in stats - assert stats["observation.image"]["count"].item() == 100 - assert stats["observation.state"]["count"].item() == 100 - assert stats["observation.image"]["mean"].shape == (3, 1, 1) + assert OBS_IMAGE in stats and OBS_STATE in stats + assert stats[OBS_IMAGE]["count"].item() == 100 + assert stats[OBS_STATE]["count"].item() == 100 + assert stats[OBS_IMAGE]["mean"].shape == (3, 1, 1) def test_assert_type_and_shape_valid(): @@ -224,38 +232,38 @@ def test_aggregate_feature_stats(): def test_aggregate_stats(): all_stats = [ { - "observation.image": { + OBS_IMAGE: { "min": [1, 2, 3], "max": [10, 20, 30], "mean": [5.5, 10.5, 15.5], "std": [2.87, 5.87, 8.87], "count": 10, }, - "observation.state": {"min": 1, "max": 10, "mean": 5.5, "std": 2.87, "count": 10}, + OBS_STATE: {"min": 1, "max": 10, "mean": 5.5, "std": 2.87, "count": 10}, "extra_key_0": {"min": 5, "max": 25, "mean": 15, "std": 6, "count": 6}, }, { - "observation.image": { + OBS_IMAGE: { "min": [2, 1, 0], "max": [15, 10, 5], "mean": [8.5, 5.5, 2.5], "std": [3.42, 2.42, 1.42], "count": 15, }, - "observation.state": {"min": 2, "max": 15, "mean": 8.5, "std": 3.42, "count": 15}, + OBS_STATE: {"min": 2, "max": 15, "mean": 8.5, "std": 3.42, "count": 15}, "extra_key_1": {"min": 0, "max": 20, "mean": 10, "std": 5, "count": 5}, }, ] expected_agg_stats = { - "observation.image": { + OBS_IMAGE: { "min": [1, 1, 0], "max": [15, 20, 30], "mean": [7.3, 7.5, 7.7], "std": [3.5317, 4.8267, 8.5581], "count": 25, }, - "observation.state": { + OBS_STATE: { "min": 1, "max": 15, "mean": 7.3, @@ -283,7 +291,7 @@ def test_aggregate_stats(): for fkey, stats in ep_stats.items(): for k in stats: stats[k] = np.array(stats[k], dtype=np.int64 if k == "count" else np.float32) - if fkey == "observation.image" and k != "count": + if fkey == OBS_IMAGE and k != "count": stats[k] = stats[k].reshape(3, 1, 1) # for normalization on image channels else: stats[k] = stats[k].reshape(1) @@ -292,7 +300,7 @@ def test_aggregate_stats(): for fkey, stats in expected_agg_stats.items(): for k in stats: stats[k] = np.array(stats[k], dtype=np.int64 if k == "count" else np.float32) - if fkey == "observation.image" and k != "count": + if fkey == OBS_IMAGE and k != "count": stats[k] = stats[k].reshape(3, 1, 1) # for normalization on image channels else: stats[k] = stats[k].reshape(1) @@ -307,3 +315,520 @@ def test_aggregate_stats(): results[fkey]["std"], expected_agg_stats[fkey]["std"], atol=1e-04, rtol=1e-04 ) np.testing.assert_allclose(results[fkey]["count"], expected_agg_stats[fkey]["count"]) + + +def test_running_quantile_stats_initialization(): + """Test proper initialization of RunningQuantileStats.""" + running_stats = RunningQuantileStats() + assert running_stats._count == 0 + assert running_stats._mean is None + assert running_stats._num_quantile_bins == 5000 + + # Test custom bin size + running_stats_custom = RunningQuantileStats(num_quantile_bins=1000) + assert running_stats_custom._num_quantile_bins == 1000 + + +def test_running_quantile_stats_single_batch_update(): + """Test updating with a single batch.""" + np.random.seed(42) + data = np.random.normal(0, 1, (100, 3)) + + running_stats = RunningQuantileStats() + running_stats.update(data) + + assert running_stats._count == 100 + assert running_stats._mean.shape == (3,) + assert len(running_stats._histograms) == 3 + assert len(running_stats._bin_edges) == 3 + + # Verify basic statistics are reasonable + np.testing.assert_allclose(running_stats._mean, np.mean(data, axis=0), atol=1e-10) + + +def test_running_quantile_stats_multiple_batch_updates(): + """Test updating with multiple batches.""" + np.random.seed(42) + data1 = np.random.normal(0, 1, (100, 2)) + data2 = np.random.normal(1, 1, (150, 2)) + + running_stats = RunningQuantileStats() + running_stats.update(data1) + running_stats.update(data2) + + assert running_stats._count == 250 + + # Verify running mean is correct + combined_data = np.vstack([data1, data2]) + expected_mean = np.mean(combined_data, axis=0) + np.testing.assert_allclose(running_stats._mean, expected_mean, atol=1e-10) + + +def test_running_quantile_stats_get_statistics_basic(): + """Test getting basic statistics without quantiles.""" + np.random.seed(42) + data = np.random.normal(0, 1, (100, 2)) + + running_stats = RunningQuantileStats() + running_stats.update(data) + + stats = running_stats.get_statistics() + + # Should have basic stats + expected_keys = {"min", "max", "mean", "std", "count"} + assert expected_keys.issubset(set(stats.keys())) + + # Verify values + np.testing.assert_allclose(stats["mean"], np.mean(data, axis=0), atol=1e-10) + np.testing.assert_allclose(stats["std"], np.std(data, axis=0), atol=1e-6) + np.testing.assert_equal(stats["count"], np.array([100])) + + +def test_running_quantile_stats_get_statistics_with_quantiles(): + """Test getting statistics with quantiles.""" + np.random.seed(42) + data = np.random.normal(0, 1, (1000, 2)) + + running_stats = RunningQuantileStats() + running_stats.update(data) + + stats = running_stats.get_statistics() + + # Should have basic stats plus quantiles + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert expected_keys.issubset(set(stats.keys())) + + # Verify quantile values are reasonable + from lerobot.datasets.compute_stats import DEFAULT_QUANTILES + + for i, q in enumerate(DEFAULT_QUANTILES): + q_key = f"q{int(q * 100):02d}" + assert q_key in stats + assert stats[q_key].shape == (2,) + + # Check that quantiles are in reasonable order + if i > 0: + prev_q_key = f"q{int(DEFAULT_QUANTILES[i - 1] * 100):02d}" + assert np.all(stats[prev_q_key] <= stats[q_key]) + + +def test_running_quantile_stats_histogram_adjustment(): + """Test that histograms adjust when min/max change.""" + running_stats = RunningQuantileStats() + + # Initial data with small range + data1 = np.array([[0.0, 1.0], [0.1, 1.1], [0.2, 1.2]]) + running_stats.update(data1) + + initial_edges_0 = running_stats._bin_edges[0].copy() + initial_edges_1 = running_stats._bin_edges[1].copy() + + # Add data with much larger range + data2 = np.array([[10.0, -10.0], [11.0, -11.0]]) + running_stats.update(data2) + + # Bin edges should have changed + assert not np.array_equal(initial_edges_0, running_stats._bin_edges[0]) + assert not np.array_equal(initial_edges_1, running_stats._bin_edges[1]) + + # New edges should cover the expanded range + # First dimension: min should still be ~0.0, max should be ~11.0 + assert running_stats._bin_edges[0][0] <= 0.0 + assert running_stats._bin_edges[0][-1] >= 11.0 + + # Second dimension: min should be ~-11.0, max should be ~1.2 + assert running_stats._bin_edges[1][0] <= -11.0 + assert running_stats._bin_edges[1][-1] >= 1.2 + + +def test_running_quantile_stats_insufficient_data_error(): + """Test error when trying to get stats with insufficient data.""" + running_stats = RunningQuantileStats() + + with pytest.raises(ValueError, match="Cannot compute statistics for less than 2 vectors"): + running_stats.get_statistics() + + # Single vector should also fail + running_stats.update(np.array([[1.0]])) + with pytest.raises(ValueError, match="Cannot compute statistics for less than 2 vectors"): + running_stats.get_statistics() + + +def test_running_quantile_stats_vector_length_consistency(): + """Test error when vector lengths don't match.""" + running_stats = RunningQuantileStats() + running_stats.update(np.array([[1.0, 2.0], [3.0, 4.0]])) + + with pytest.raises(ValueError, match="The length of new vectors does not match"): + running_stats.update(np.array([[1.0, 2.0, 3.0]])) # Different length + + +def test_running_quantile_stats_reshape_handling(): + """Test that various input shapes are handled correctly.""" + running_stats = RunningQuantileStats() + + # Test 3D input (e.g., images) + data_3d = np.random.normal(0, 1, (10, 32, 32)) + running_stats.update(data_3d) + + assert running_stats._count == 10 * 32 + assert running_stats._mean.shape == (32,) + + # Test 1D input + running_stats_1d = RunningQuantileStats() + data_1d = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) + running_stats_1d.update(data_1d) + + assert running_stats_1d._count == 5 + assert running_stats_1d._mean.shape == (1,) + + +def test_get_feature_stats_quantiles_enabled_by_default(): + """Test that quantiles are computed by default.""" + data = np.random.normal(0, 1, (100, 5)) + stats = get_feature_stats(data, axis=0, keepdims=False) + + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert set(stats.keys()) == expected_keys + + +def test_get_feature_stats_quantiles_with_vector_data(): + """Test quantile computation with vector data.""" + np.random.seed(42) + data = np.random.normal(0, 1, (100, 5)) + + stats = get_feature_stats(data, axis=0, keepdims=False) + + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert set(stats.keys()) == expected_keys + + # Verify shapes + assert stats["q01"].shape == (5,) + assert stats["q99"].shape == (5,) + + # Verify quantiles are reasonable + assert np.all(stats["q01"] < stats["q99"]) + + +def test_get_feature_stats_quantiles_with_image_data(): + """Test quantile computation with image data.""" + np.random.seed(42) + data = np.random.normal(0, 1, (50, 3, 32, 32)) # batch, channels, height, width + + stats = get_feature_stats(data, axis=(0, 2, 3), keepdims=True) + + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert set(stats.keys()) == expected_keys + + # Verify shapes for images (should be (1, channels, 1, 1)) + assert stats["q01"].shape == (1, 3, 1, 1) + assert stats["q50"].shape == (1, 3, 1, 1) + assert stats["q99"].shape == (1, 3, 1, 1) + + +def test_get_feature_stats_fixed_quantiles(): + """Test that fixed quantiles are always computed.""" + data = np.random.normal(0, 1, (200, 3)) + + stats = get_feature_stats(data, axis=0, keepdims=False) + + expected_quantile_keys = {"q01", "q10", "q50", "q90", "q99"} + assert expected_quantile_keys.issubset(set(stats.keys())) + + +def test_get_feature_stats_unsupported_axis_error(): + """Test error for unsupported axis configuration.""" + data = np.random.normal(0, 1, (10, 5)) + + with pytest.raises(ValueError, match="Unsupported axis configuration"): + get_feature_stats( + data, + axis=(1, 2), # Unsupported axis + keepdims=False, + ) + + +def test_compute_episode_stats_backward_compatibility(): + """Test that existing functionality is preserved.""" + episode_data = { + "action": np.random.normal(0, 1, (100, 7)), + "observation.state": np.random.normal(0, 1, (100, 10)), + } + features = { + "action": {"dtype": "float32", "shape": (7,)}, + "observation.state": {"dtype": "float32", "shape": (10,)}, + } + + stats = compute_episode_stats(episode_data, features) + + for key in ["action", "observation.state"]: + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert set(stats[key].keys()) == expected_keys + + +def test_compute_episode_stats_with_custom_quantiles(): + """Test quantile computation with custom quantile values.""" + np.random.seed(42) + episode_data = { + "action": np.random.normal(0, 1, (100, 7)), + "observation.state": np.random.normal(2, 1, (100, 10)), + } + features = { + "action": {"dtype": "float32", "shape": (7,)}, + "observation.state": {"dtype": "float32", "shape": (10,)}, + } + + stats = compute_episode_stats(episode_data, features) + + # Should have quantiles + for key in ["action", "observation.state"]: + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert set(stats[key].keys()) == expected_keys + + # Verify shapes + assert stats[key]["q01"].shape == (features[key]["shape"][0],) + assert stats[key]["q99"].shape == (features[key]["shape"][0],) + + +def test_compute_episode_stats_with_image_data(): + """Test quantile computation with image features.""" + image_paths = [f"image_{i}.jpg" for i in range(50)] + episode_data = { + "observation.image": image_paths, + "action": np.random.normal(0, 1, (50, 5)), + } + features = { + "observation.image": {"dtype": "image"}, + "action": {"dtype": "float32", "shape": (5,)}, + } + + with patch("lerobot.datasets.compute_stats.load_image_as_numpy", side_effect=mock_load_image_as_numpy): + stats = compute_episode_stats(episode_data, features) + + # Image quantiles should be normalized and have correct shape + assert "q01" in stats["observation.image"] + assert "q50" in stats["observation.image"] + assert "q99" in stats["observation.image"] + assert stats["observation.image"]["q01"].shape == (3, 1, 1) + assert stats["observation.image"]["q50"].shape == (3, 1, 1) + assert stats["observation.image"]["q99"].shape == (3, 1, 1) + + # Action quantiles should have correct shape + assert stats["action"]["q01"].shape == (5,) + assert stats["action"]["q50"].shape == (5,) + assert stats["action"]["q99"].shape == (5,) + + +def test_compute_episode_stats_string_features_skipped(): + """Test that string features are properly skipped.""" + episode_data = { + "task": ["pick_apple"] * 100, # String feature + "action": np.random.normal(0, 1, (100, 5)), + } + features = { + "task": {"dtype": "string"}, + "action": {"dtype": "float32", "shape": (5,)}, + } + + stats = compute_episode_stats( + episode_data, + features, + ) + + # String features should be skipped + assert "task" not in stats + assert "action" in stats + assert "q01" in stats["action"] + + +def test_aggregate_feature_stats_with_quantiles(): + """Test aggregating feature stats that include quantiles.""" + stats_ft_list = [ + { + "min": np.array([1.0]), + "max": np.array([10.0]), + "mean": np.array([5.0]), + "std": np.array([2.0]), + "count": np.array([100]), + "q01": np.array([1.5]), + "q99": np.array([9.5]), + }, + { + "min": np.array([2.0]), + "max": np.array([12.0]), + "mean": np.array([6.0]), + "std": np.array([2.5]), + "count": np.array([150]), + "q01": np.array([2.5]), + "q99": np.array([11.5]), + }, + ] + + result = aggregate_feature_stats(stats_ft_list) + + # Should preserve quantiles + assert "q01" in result + assert "q99" in result + + # Verify quantile aggregation (weighted average) + expected_q01 = (1.5 * 100 + 2.5 * 150) / 250 # ≈ 2.1 + expected_q99 = (9.5 * 100 + 11.5 * 150) / 250 # ≈ 10.7 + + np.testing.assert_allclose(result["q01"], np.array([expected_q01]), atol=1e-6) + np.testing.assert_allclose(result["q99"], np.array([expected_q99]), atol=1e-6) + + +def test_aggregate_stats_mixed_quantiles(): + """Test aggregating stats where some have quantiles and some don't.""" + stats_with_quantiles = { + "feature1": { + "min": np.array([1.0]), + "max": np.array([10.0]), + "mean": np.array([5.0]), + "std": np.array([2.0]), + "count": np.array([100]), + "q01": np.array([1.5]), + "q99": np.array([9.5]), + } + } + + stats_without_quantiles = { + "feature2": { + "min": np.array([0.0]), + "max": np.array([5.0]), + "mean": np.array([2.5]), + "std": np.array([1.5]), + "count": np.array([50]), + } + } + + all_stats = [stats_with_quantiles, stats_without_quantiles] + result = aggregate_stats(all_stats) + + # Feature1 should keep its quantiles + assert "q01" in result["feature1"] + assert "q99" in result["feature1"] + + # Feature2 should not have quantiles + assert "q01" not in result["feature2"] + assert "q99" not in result["feature2"] + + +def test_assert_type_and_shape_with_quantiles(): + """Test validation works correctly with quantile keys.""" + # Valid stats with quantiles + valid_stats = [ + { + "observation.image": { + "min": np.array([0.0, 0.0, 0.0]).reshape(3, 1, 1), + "max": np.array([1.0, 1.0, 1.0]).reshape(3, 1, 1), + "mean": np.array([0.5, 0.5, 0.5]).reshape(3, 1, 1), + "std": np.array([0.2, 0.2, 0.2]).reshape(3, 1, 1), + "count": np.array([100]), + "q01": np.array([0.1, 0.1, 0.1]).reshape(3, 1, 1), + "q99": np.array([0.9, 0.9, 0.9]).reshape(3, 1, 1), + } + } + ] + + # Should not raise error + _assert_type_and_shape(valid_stats) + + # Invalid shape for quantile + invalid_stats = [ + { + "observation.image": { + "count": np.array([100]), + "q01": np.array([0.1, 0.2]), # Wrong shape for image quantile + } + } + ] + + with pytest.raises(ValueError, match="Shape of quantile 'q01' must be \\(3,1,1\\)"): + _assert_type_and_shape(invalid_stats) + + +def test_quantile_integration_single_value_quantiles(): + """Test quantile computation with single repeated value.""" + data = np.ones((100, 3)) # All ones + + running_stats = RunningQuantileStats() + running_stats.update(data) + + stats = running_stats.get_statistics() + + # All quantiles should be approximately 1.0 + np.testing.assert_allclose(stats["q01"], np.array([1.0, 1.0, 1.0]), atol=1e-6) + np.testing.assert_allclose(stats["q50"], np.array([1.0, 1.0, 1.0]), atol=1e-6) + np.testing.assert_allclose(stats["q99"], np.array([1.0, 1.0, 1.0]), atol=1e-6) + + +def test_quantile_integration_fixed_quantiles(): + """Test that fixed quantiles are computed.""" + np.random.seed(42) + data = np.random.normal(0, 1, (1000, 2)) + + stats = get_feature_stats(data, axis=0, keepdims=False) + + # Check all fixed quantiles are present + assert "q01" in stats + assert "q10" in stats + assert "q50" in stats + assert "q90" in stats + assert "q99" in stats + + +def test_quantile_integration_large_dataset_quantiles(): + """Test quantile computation efficiency with large datasets.""" + np.random.seed(42) + large_data = np.random.normal(0, 1, (10000, 5)) + + running_stats = RunningQuantileStats(num_quantile_bins=1000) # Reduced bins for speed + running_stats.update(large_data) + + stats = running_stats.get_statistics() + + # Should complete without issues and produce reasonable results + assert stats["count"][0] == 10000 + assert len(stats["q01"]) == 5 + + +def test_fixed_quantiles_always_computed(): + """Test that the fixed quantiles [0.01, 0.10, 0.50, 0.90, 0.99] are always computed.""" + np.random.seed(42) + # Test with vector data + vector_data = np.random.normal(0, 1, (100, 5)) + vector_stats = get_feature_stats(vector_data, axis=0, keepdims=False) + + # Check all fixed quantiles are present + expected_quantiles = ["q01", "q10", "q50", "q90", "q99"] + for q_key in expected_quantiles: + assert q_key in vector_stats + assert vector_stats[q_key].shape == (5,) + + # Test with image data + image_data = np.random.randint(0, 256, (50, 3, 32, 32), dtype=np.uint8) + image_stats = get_feature_stats(image_data, axis=(0, 2, 3), keepdims=True) + + # Check all fixed quantiles are present for images + for q_key in expected_quantiles: + assert q_key in image_stats + assert image_stats[q_key].shape == (1, 3, 1, 1) + + # Test with episode data + episode_data = { + "action": np.random.normal(0, 1, (100, 7)), + "observation.state": np.random.normal(0, 1, (100, 10)), + } + features = { + "action": {"dtype": "float32", "shape": (7,)}, + "observation.state": {"dtype": "float32", "shape": (10,)}, + } + + episode_stats = compute_episode_stats(episode_data, features) + + # Check all fixed quantiles are present in episode stats + for key in ["action", "observation.state"]: + for q_key in expected_quantiles: + assert q_key in episode_stats[key] + assert episode_stats[key][q_key].shape == (features[key]["shape"][0],) diff --git a/tests/datasets/test_dataset_utils.py b/tests/datasets/test_dataset_utils.py new file mode 100644 index 00000000000..99b832e55d5 --- /dev/null +++ b/tests/datasets/test_dataset_utils.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python + +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +import torch +from datasets import Dataset +from huggingface_hub import DatasetCard + +from lerobot.datasets.push_dataset_to_hub.utils import calculate_episode_data_index +from lerobot.datasets.utils import combine_feature_dicts, create_lerobot_dataset_card, hf_transform_to_torch +from lerobot.utils.constants import ACTION, OBS_IMAGES + + +def test_default_parameters(): + card = create_lerobot_dataset_card() + assert isinstance(card, DatasetCard) + assert card.data.tags == ["LeRobot"] + assert card.data.task_categories == ["robotics"] + assert card.data.configs == [ + { + "config_name": "default", + "data_files": "data/*/*.parquet", + } + ] + + +def test_with_tags(): + tags = ["tag1", "tag2"] + card = create_lerobot_dataset_card(tags=tags) + assert card.data.tags == ["LeRobot", "tag1", "tag2"] + + +def test_calculate_episode_data_index(): + dataset = Dataset.from_dict( + { + "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], + "index": [0, 1, 2, 3, 4, 5], + "episode_index": [0, 0, 1, 2, 2, 2], + }, + ) + dataset.set_transform(hf_transform_to_torch) + episode_data_index = calculate_episode_data_index(dataset) + assert torch.equal(episode_data_index["from"], torch.tensor([0, 2, 3])) + assert torch.equal(episode_data_index["to"], torch.tensor([2, 3, 6])) + + +def test_merge_simple_vectors(): + g1 = { + ACTION: { + "dtype": "float32", + "shape": (2,), + "names": ["ee.x", "ee.y"], + } + } + g2 = { + ACTION: { + "dtype": "float32", + "shape": (2,), + "names": ["ee.y", "ee.z"], + } + } + + out = combine_feature_dicts(g1, g2) + + assert ACTION in out + assert out[ACTION]["dtype"] == "float32" + # Names merged with preserved order and de-dupuplication + assert out[ACTION]["names"] == ["ee.x", "ee.y", "ee.z"] + # Shape correctly recomputed from names length + assert out[ACTION]["shape"] == (3,) + + +def test_merge_multiple_groups_order_and_dedup(): + g1 = {ACTION: {"dtype": "float32", "shape": (2,), "names": ["a", "b"]}} + g2 = {ACTION: {"dtype": "float32", "shape": (2,), "names": ["b", "c"]}} + g3 = {ACTION: {"dtype": "float32", "shape": (3,), "names": ["a", "c", "d"]}} + + out = combine_feature_dicts(g1, g2, g3) + + assert out[ACTION]["names"] == ["a", "b", "c", "d"] + assert out[ACTION]["shape"] == (4,) + + +def test_non_vector_last_wins_for_images(): + # Non-vector (images) with same name should be overwritten by the last image specified + g1 = { + f"{OBS_IMAGES}.front": { + "dtype": "image", + "shape": (3, 480, 640), + "names": ["channels", "height", "width"], + } + } + g2 = { + f"{OBS_IMAGES}.front": { + "dtype": "image", + "shape": (3, 720, 1280), + "names": ["channels", "height", "width"], + } + } + + out = combine_feature_dicts(g1, g2) + assert out[f"{OBS_IMAGES}.front"]["shape"] == (3, 720, 1280) + assert out[f"{OBS_IMAGES}.front"]["dtype"] == "image" + + +def test_dtype_mismatch_raises(): + g1 = {ACTION: {"dtype": "float32", "shape": (1,), "names": ["a"]}} + g2 = {ACTION: {"dtype": "float64", "shape": (1,), "names": ["b"]}} + + with pytest.raises(ValueError, match="dtype mismatch for 'action'"): + _ = combine_feature_dicts(g1, g2) + + +def test_non_dict_passthrough_last_wins(): + g1 = {"misc": 123} + g2 = {"misc": 456} + + out = combine_feature_dicts(g1, g2) + # For non-dict entries the last one wins + assert out["misc"] == 456 diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 2eca82346f7..2bc3bea43be 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -46,6 +46,7 @@ from lerobot.envs.factory import make_env_config from lerobot.policies.factory import make_policy_config from lerobot.robots import make_robot_from_config +from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, OBS_STR, REWARD from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_REPO_ID from tests.mocks.mock_robot import MockRobotConfig from tests.utils import require_x86_64_kernel @@ -74,8 +75,8 @@ def test_same_attributes_defined(tmp_path, lerobot_dataset_factory): """ # Instantiate both ways robot = make_robot_from_config(MockRobotConfig()) - action_features = hw_to_dataset_features(robot.action_features, "action", True) - obs_features = hw_to_dataset_features(robot.observation_features, "observation", True) + action_features = hw_to_dataset_features(robot.action_features, ACTION, True) + obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR, True) dataset_features = {**action_features, **obs_features} root_create = tmp_path / "create" dataset_create = LeRobotDataset.create( @@ -112,7 +113,7 @@ def test_dataset_initialization(tmp_path, lerobot_dataset_factory): # and test the small resulting function that validates the features def test_dataset_feature_with_forward_slash_raises_error(): # make sure dir does not exist - from lerobot.constants import HF_LEROBOT_HOME + from lerobot.utils.constants import HF_LEROBOT_HOME dataset_dir = HF_LEROBOT_HOME / "lerobot/test/with/slash" # make sure does not exist @@ -392,14 +393,14 @@ def test_factory(env_name, repo_id, policy_name): item = dataset[0] keys_ndim_required = [ - ("action", 1, True), + (ACTION, 1, True), ("episode_index", 0, True), ("frame_index", 0, True), ("timestamp", 0, True), # TODO(rcadene): should we rename it agent_pos? - ("observation.state", 1, True), - ("next.reward", 0, False), - ("next.done", 0, False), + (OBS_STATE, 1, True), + (REWARD, 0, False), + (DONE, 0, False), ] # test number of dimensions @@ -662,12 +663,12 @@ def test_check_cached_episodes_sufficient(tmp_path, lerobot_dataset_factory): def test_update_chunk_settings(tmp_path, empty_lerobot_dataset_factory): """Test the update_chunk_settings functionality for both LeRobotDataset and LeRobotDatasetMetadata.""" features = { - "observation.state": { + OBS_STATE: { "dtype": "float32", "shape": (6,), "names": ["shoulder_pan", "shoulder_lift", "elbow", "wrist_1", "wrist_2", "wrist_3"], }, - "action": { + ACTION: { "dtype": "float32", "shape": (6,), "names": ["shoulder_pan", "shoulder_lift", "elbow", "wrist_1", "wrist_2", "wrist_3"], @@ -769,12 +770,12 @@ def test_update_chunk_settings(tmp_path, empty_lerobot_dataset_factory): def test_update_chunk_settings_video_dataset(tmp_path): """Test update_chunk_settings with a video dataset to ensure video-specific logic works.""" features = { - "observation.images.cam": { + f"{OBS_IMAGES}.cam": { "dtype": "video", "shape": (480, 640, 3), "names": ["height", "width", "channels"], }, - "action": {"dtype": "float32", "shape": (6,), "names": ["j1", "j2", "j3", "j4", "j5", "j6"]}, + ACTION: {"dtype": "float32", "shape": (6,), "names": ["j1", "j2", "j3", "j4", "j5", "j6"]}, } # Create video dataset @@ -841,7 +842,7 @@ def test_multi_episode_metadata_consistency(tmp_path, empty_lerobot_dataset_fact """Test episode metadata consistency across multiple episodes.""" features = { "state": {"dtype": "float32", "shape": (3,), "names": ["x", "y", "z"]}, - "action": {"dtype": "float32", "shape": (2,), "names": ["v", "w"]}, + ACTION: {"dtype": "float32", "shape": (2,), "names": ["v", "w"]}, } dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False) @@ -851,7 +852,7 @@ def test_multi_episode_metadata_consistency(tmp_path, empty_lerobot_dataset_fact for episode_idx in range(num_episodes): for _ in range(frames_per_episode[episode_idx]): - dataset.add_frame({"state": torch.randn(3), "action": torch.randn(2), "task": tasks[episode_idx]}) + dataset.add_frame({"state": torch.randn(3), ACTION: torch.randn(2), "task": tasks[episode_idx]}) dataset.save_episode() # Load and validate episode metadata @@ -926,7 +927,7 @@ def test_statistics_metadata_validation(tmp_path, empty_lerobot_dataset_factory) """Test that statistics are properly computed and stored for all features.""" features = { "state": {"dtype": "float32", "shape": (2,), "names": ["pos", "vel"]}, - "action": {"dtype": "float32", "shape": (1,), "names": ["force"]}, + ACTION: {"dtype": "float32", "shape": (1,), "names": ["force"]}, } dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False) @@ -940,7 +941,7 @@ def test_statistics_metadata_validation(tmp_path, empty_lerobot_dataset_factory) for frame_idx in range(frames_per_episode[episode_idx]): state_data = torch.tensor([frame_idx * 0.1, frame_idx * 0.2], dtype=torch.float32) action_data = torch.tensor([frame_idx * 0.05], dtype=torch.float32) - dataset.add_frame({"state": state_data, "action": action_data, "task": "stats_test"}) + dataset.add_frame({"state": state_data, ACTION: action_data, "task": "stats_test"}) dataset.save_episode() loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root) @@ -948,7 +949,7 @@ def test_statistics_metadata_validation(tmp_path, empty_lerobot_dataset_factory) # Check that statistics exist for all features assert loaded_dataset.meta.stats is not None, "No statistics found" - for feature_name in features.keys(): + for feature_name in features: assert feature_name in loaded_dataset.meta.stats, f"No statistics for feature '{feature_name}'" feature_stats = loaded_dataset.meta.stats[feature_name] diff --git a/tests/datasets/test_image_transforms.py b/tests/datasets/test_image_transforms.py index 3ab93cb2caf..98f95707633 100644 --- a/tests/datasets/test_image_transforms.py +++ b/tests/datasets/test_image_transforms.py @@ -29,7 +29,7 @@ SharpnessJitter, make_transform_from_config, ) -from lerobot.scripts.visualize_image_transforms import ( +from lerobot.scripts.lerobot_imgtransform_viz import ( save_all_transforms, save_each_transform, ) diff --git a/tests/datasets/test_quantiles_dataset_integration.py b/tests/datasets/test_quantiles_dataset_integration.py new file mode 100644 index 00000000000..4df7fab068b --- /dev/null +++ b/tests/datasets/test_quantiles_dataset_integration.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Integration tests for quantile functionality in LeRobotDataset.""" + +import numpy as np +import pytest + +from lerobot.datasets.lerobot_dataset import LeRobotDataset + + +def mock_load_image_as_numpy(path, dtype, channel_first): + """Mock image loading for consistent test results.""" + return np.ones((3, 32, 32), dtype=dtype) if channel_first else np.ones((32, 32, 3), dtype=dtype) + + +@pytest.fixture +def simple_features(): + """Simple feature configuration for testing.""" + return { + "action": { + "dtype": "float32", + "shape": (4,), + "names": ["arm_x", "arm_y", "arm_z", "gripper"], + }, + "observation.state": { + "dtype": "float32", + "shape": (10,), + "names": [f"joint_{i}" for i in range(10)], + }, + } + + +def test_create_dataset_with_fixed_quantiles(tmp_path, simple_features): + """Test creating dataset with fixed quantiles.""" + dataset = LeRobotDataset.create( + repo_id="test_dataset_fixed_quantiles", + fps=30, + features=simple_features, + root=tmp_path / "create_fixed_quantiles", + ) + + # Dataset should be created successfully + assert dataset is not None + + +def test_save_episode_computes_all_quantiles(tmp_path, simple_features): + """Test that all fixed quantiles are computed when saving an episode.""" + dataset = LeRobotDataset.create( + repo_id="test_dataset_save_episode", + fps=30, + features=simple_features, + root=tmp_path / "save_episode_quantiles", + ) + + # Add some frames + for _ in range(10): + dataset.add_frame( + { + "action": np.random.randn(4).astype(np.float32), # Correct shape for action + "observation.state": np.random.randn(10).astype(np.float32), + "task": "test_task", + } + ) + + dataset.save_episode() + + # Check that all fixed quantiles were computed + stats = dataset.meta.stats + for key in ["action", "observation.state"]: + assert "q01" in stats[key] + assert "q10" in stats[key] + assert "q50" in stats[key] + assert "q90" in stats[key] + assert "q99" in stats[key] + + +def test_quantile_values_ordering(tmp_path, simple_features): + """Test that quantile values are properly ordered.""" + dataset = LeRobotDataset.create( + repo_id="test_dataset_quantile_ordering", + fps=30, + features=simple_features, + root=tmp_path / "quantile_ordering", + ) + + # Add data with known distribution + np.random.seed(42) + for _ in range(100): + dataset.add_frame( + { + "action": np.random.randn(4).astype(np.float32), # Correct shape for action + "observation.state": np.random.randn(10).astype(np.float32), + "task": "test_task", + } + ) + + dataset.save_episode() + stats = dataset.meta.stats + + # Verify quantile ordering + for key in ["action", "observation.state"]: + assert np.all(stats[key]["q01"] <= stats[key]["q10"]) + assert np.all(stats[key]["q10"] <= stats[key]["q50"]) + assert np.all(stats[key]["q50"] <= stats[key]["q90"]) + assert np.all(stats[key]["q90"] <= stats[key]["q99"]) + + +def test_save_episode_with_fixed_quantiles(tmp_path, simple_features): + """Test saving episode always computes fixed quantiles.""" + dataset = LeRobotDataset.create( + repo_id="test_dataset_save_fixed", + fps=30, + features=simple_features, + root=tmp_path / "save_fixed_quantiles", + ) + + # Add frames to episode + np.random.seed(42) + for _ in range(50): + frame = { + "action": np.random.normal(0, 1, (4,)).astype(np.float32), + "observation.state": np.random.normal(0, 1, (10,)).astype(np.float32), + "task": "test_task", + } + dataset.add_frame(frame) + + dataset.save_episode() + + # Check that all fixed quantiles are included + stats = dataset.meta.stats + for key in ["action", "observation.state"]: + feature_stats = stats[key] + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert set(feature_stats.keys()) == expected_keys + + +def test_quantile_aggregation_across_episodes(tmp_path, simple_features): + """Test quantile aggregation across multiple episodes.""" + dataset = LeRobotDataset.create( + repo_id="test_dataset_aggregation", + fps=30, + features=simple_features, + root=tmp_path / "quantile_aggregation", + ) + + # Add frames to episode + np.random.seed(42) + for _ in range(100): + frame = { + "action": np.random.normal(0, 1, (4,)).astype(np.float32), + "observation.state": np.random.normal(2, 1, (10,)).astype(np.float32), + "task": "test_task", + } + dataset.add_frame(frame) + + dataset.save_episode() + + # Check stats include all fixed quantiles + stats = dataset.meta.stats + for key in ["action", "observation.state"]: + feature_stats = stats[key] + expected_keys = {"min", "max", "mean", "std", "count", "q01", "q10", "q50", "q90", "q99"} + assert set(feature_stats.keys()) == expected_keys + assert feature_stats["q01"].shape == (simple_features[key]["shape"][0],) + assert feature_stats["q50"].shape == (simple_features[key]["shape"][0],) + assert feature_stats["q99"].shape == (simple_features[key]["shape"][0],) + assert np.all(feature_stats["q01"] <= feature_stats["q50"]) + assert np.all(feature_stats["q50"] <= feature_stats["q99"]) + + +def test_save_multiple_episodes_with_quantiles(tmp_path, simple_features): + """Test quantile aggregation across multiple episodes.""" + dataset = LeRobotDataset.create( + repo_id="test_dataset_multiple_episodes", + fps=30, + features=simple_features, + root=tmp_path / "multiple_episodes", + ) + + # Save multiple episodes + np.random.seed(42) + for episode_idx in range(3): + for _ in range(50): + frame = { + "action": np.random.normal(episode_idx * 2.0, 1, (4,)).astype(np.float32), + "observation.state": np.random.normal(-episode_idx * 1.5, 1, (10,)).astype(np.float32), + "task": f"task_{episode_idx}", + } + dataset.add_frame(frame) + + dataset.save_episode() + + # Verify final stats include properly aggregated quantiles + stats = dataset.meta.stats + for key in ["action", "observation.state"]: + feature_stats = stats[key] + assert "q01" in feature_stats and "q99" in feature_stats + assert feature_stats["count"][0] == 150 # 3 episodes * 50 frames diff --git a/tests/datasets/test_streaming.py b/tests/datasets/test_streaming.py index 506be3ecf58..1bd4c1787c4 100644 --- a/tests/datasets/test_streaming.py +++ b/tests/datasets/test_streaming.py @@ -19,6 +19,7 @@ from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset from lerobot.datasets.utils import safe_shard +from lerobot.utils.constants import ACTION from tests.fixtures.constants import DUMMY_REPO_ID @@ -234,7 +235,7 @@ def test_frames_with_delta_consistency(tmp_path, lerobot_dataset_factory, state_ delta_timestamps = { camera_key: state_deltas, "state": state_deltas, - "action": action_deltas, + ACTION: action_deltas, } ds = lerobot_dataset_factory( @@ -319,7 +320,7 @@ def test_frames_with_delta_consistency_with_shards( delta_timestamps = { camera_key: state_deltas, "state": state_deltas, - "action": action_deltas, + ACTION: action_deltas, } ds = lerobot_dataset_factory( diff --git a/tests/datasets/test_utils.py b/tests/datasets/test_utils.py deleted file mode 100644 index 91d661b3c3f..00000000000 --- a/tests/datasets/test_utils.py +++ /dev/null @@ -1,86 +0,0 @@ -#!/usr/bin/env python - -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -from copy import deepcopy - -import torch -from datasets import Dataset -from huggingface_hub import DatasetCard - -from lerobot.datasets.push_dataset_to_hub.utils import calculate_episode_data_index -from lerobot.datasets.utils import ( - create_lerobot_dataset_card, - flatten_dict, - hf_transform_to_torch, - unflatten_dict, -) - - -def test_default_parameters(): - card = create_lerobot_dataset_card() - assert isinstance(card, DatasetCard) - assert card.data.tags == ["LeRobot"] - assert card.data.task_categories == ["robotics"] - assert card.data.configs == [ - { - "config_name": "default", - "data_files": "data/*/*.parquet", - } - ] - - -def test_with_tags(): - tags = ["tag1", "tag2"] - card = create_lerobot_dataset_card(tags=tags) - assert card.data.tags == ["LeRobot", "tag1", "tag2"] - - -def test_calculate_episode_data_index(): - dataset = Dataset.from_dict( - { - "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], - "index": [0, 1, 2, 3, 4, 5], - "episode_index": [0, 0, 1, 2, 2, 2], - }, - ) - dataset.set_transform(hf_transform_to_torch) - episode_data_index = calculate_episode_data_index(dataset) - assert torch.equal(episode_data_index["from"], torch.tensor([0, 2, 3])) - assert torch.equal(episode_data_index["to"], torch.tensor([2, 3, 6])) - - -def test_flatten_unflatten_dict(): - d = { - "obs": { - "min": 0, - "max": 1, - "mean": 2, - "std": 3, - }, - "action": { - "min": 4, - "max": 5, - "mean": 6, - "std": 7, - }, - } - - original_d = deepcopy(d) - d = unflatten_dict(flatten_dict(d)) - - # test equality between nested dicts - assert json.dumps(original_d, sort_keys=True) == json.dumps(d, sort_keys=True), f"{original_d} != {d}" diff --git a/tests/datasets/test_visualize_dataset.py b/tests/datasets/test_visualize_dataset.py index 303342e3c05..8e92ec82e85 100644 --- a/tests/datasets/test_visualize_dataset.py +++ b/tests/datasets/test_visualize_dataset.py @@ -15,7 +15,7 @@ # limitations under the License. import pytest -from lerobot.scripts.visualize_dataset import visualize_dataset +from lerobot.scripts.lerobot_dataset_viz import visualize_dataset @pytest.mark.skip("TODO: add dummy videos") diff --git a/tests/envs/test_envs.py b/tests/envs/test_envs.py index 140e9dfb998..51ea564e5a6 100644 --- a/tests/envs/test_envs.py +++ b/tests/envs/test_envs.py @@ -46,7 +46,10 @@ def test_env(env_name, env_task, obs_type): @require_env def test_factory(env_name): cfg = make_env_config(env_name) - env = make_env(cfg, n_envs=1) + envs = make_env(cfg, n_envs=1) + suite_name = next(iter(envs)) + task_id = next(iter(envs[suite_name])) + env = envs[suite_name][task_id] obs, _ = env.reset() obs = preprocess_observation(obs) diff --git a/tests/examples/test_examples.py b/tests/examples/test_examples.py deleted file mode 100644 index aabec69a67c..00000000000 --- a/tests/examples/test_examples.py +++ /dev/null @@ -1,147 +0,0 @@ -#!/usr/bin/env python - -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import io -import subprocess -import sys -from pathlib import Path - -import pytest - -from tests.fixtures.constants import DUMMY_REPO_ID -from tests.utils import require_package - - -def _find_and_replace(text: str, finds_and_replaces: list[tuple[str, str]]) -> str: - for f, r in finds_and_replaces: - assert f in text - text = text.replace(f, r) - return text - - -# TODO(aliberts): Remove usage of subprocess calls and patch code with fixtures -def _run_script(path): - subprocess.run([sys.executable, path], check=True) - - -def _read_file(path): - with open(path) as file: - return file.read() - - -@pytest.mark.skip("TODO Fix and remove subprocess / excec calls") -def test_example_1(tmp_path, lerobot_dataset_factory): - _ = lerobot_dataset_factory(root=tmp_path, repo_id=DUMMY_REPO_ID) - path = "examples/1_load_lerobot_dataset.py" - file_contents = _read_file(path) - file_contents = _find_and_replace( - file_contents, - [ - ('repo_id = "lerobot/pusht"', f'repo_id = "{DUMMY_REPO_ID}"'), - ( - "LeRobotDataset(repo_id", - f"LeRobotDataset(repo_id, root='{str(tmp_path)}'", - ), - ], - ) - exec(file_contents, {}) - assert Path("outputs/examples/1_load_lerobot_dataset/episode_0.mp4").exists() - - -@pytest.mark.skip("TODO Fix and remove subprocess / excec calls") -@require_package("gym_pusht") -def test_examples_basic2_basic3_advanced1(): - """ - Train a model with example 3, check the outputs. - Evaluate the trained model with example 2, check the outputs. - Calculate the validation loss with advanced example 1, check the outputs. - """ - - ### Test example 3 - file_contents = _read_file("examples/3_train_policy.py") - - # Do fewer steps, use smaller batch, use CPU, and don't complicate things with dataloader workers. - file_contents = _find_and_replace( - file_contents, - [ - ("training_steps = 5000", "training_steps = 1"), - ("num_workers=4", "num_workers=0"), - ('device = torch.device("cuda")', 'device = torch.device("cpu")'), - ("batch_size=64", "batch_size=1"), - ], - ) - - # Pass empty globals to allow dictionary comprehension https://stackoverflow.com/a/32897127/4391249. - exec(file_contents, {}) - - for file_name in ["model.safetensors", "config.json"]: - assert Path(f"outputs/train/example_pusht_diffusion/{file_name}").exists() - - ### Test example 2 - file_contents = _read_file("examples/2_evaluate_pretrained_policy.py") - - # Do fewer evals, use CPU, and use the local model. - file_contents = _find_and_replace( - file_contents, - [ - ( - 'pretrained_policy_path = Path(snapshot_download("lerobot/diffusion_pusht"))', - "", - ), - ( - '# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")', - 'pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")', - ), - ('device = torch.device("cuda")', 'device = torch.device("cpu")'), - ("step += 1", "break"), - ], - ) - - exec(file_contents, {}) - - assert Path("outputs/eval/example_pusht_diffusion/rollout.mp4").exists() - - ## Test example 4 - file_contents = _read_file("examples/advanced/2_calculate_validation_loss.py") - - # Run on a single example from the last episode, use CPU, and use the local model. - file_contents = _find_and_replace( - file_contents, - [ - ( - 'pretrained_policy_path = Path(snapshot_download("lerobot/diffusion_pusht"))', - "", - ), - ( - '# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")', - 'pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")', - ), - ("train_episodes = episodes[:num_train_episodes]", "train_episodes = [0]"), - ("val_episodes = episodes[num_train_episodes:]", "val_episodes = [1]"), - ("num_workers=4", "num_workers=0"), - ('device = torch.device("cuda")', 'device = torch.device("cpu")'), - ("batch_size=64", "batch_size=1"), - ], - ) - - # Capture the output of the script - output_buffer = io.StringIO() - sys.stdout = output_buffer - exec(file_contents, {}) - printed_output = output_buffer.getvalue() - # Restore stdout to its original state - sys.stdout = sys.__stdout__ - assert "Average loss on validation set" in printed_output diff --git a/tests/fixtures/constants.py b/tests/fixtures/constants.py index 0af499364eb..35d8776ce88 100644 --- a/tests/fixtures/constants.py +++ b/tests/fixtures/constants.py @@ -11,13 +11,13 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from lerobot.constants import HF_LEROBOT_HOME +from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME LEROBOT_TEST_DIR = HF_LEROBOT_HOME / "_testing" DUMMY_REPO_ID = "dummy/repo" DUMMY_ROBOT_TYPE = "dummy_robot" DUMMY_MOTOR_FEATURES = { - "action": { + ACTION: { "dtype": "float32", "shape": (6,), "names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"], diff --git a/tests/mocks/mock_robot.py b/tests/mocks/mock_robot.py index 8108c7c250a..027ee45ed4d 100644 --- a/tests/mocks/mock_robot.py +++ b/tests/mocks/mock_robot.py @@ -20,8 +20,8 @@ from typing import Any from lerobot.cameras import CameraConfig, make_cameras_from_configs -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.robots import Robot, RobotConfig +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError @RobotConfig.register_subclass("mock_robot") diff --git a/tests/mocks/mock_teleop.py b/tests/mocks/mock_teleop.py index e37d4a2c53d..71b49947ccc 100644 --- a/tests/mocks/mock_teleop.py +++ b/tests/mocks/mock_teleop.py @@ -19,8 +19,8 @@ from functools import cached_property from typing import Any -from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError from lerobot.teleoperators import Teleoperator, TeleoperatorConfig +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError @TeleoperatorConfig.register_subclass("mock_teleop") diff --git a/tests/motors/test_dynamixel.py b/tests/motors/test_dynamixel.py index e0dbe713a2b..8b02d433088 100644 --- a/tests/motors/test_dynamixel.py +++ b/tests/motors/test_dynamixel.py @@ -24,7 +24,7 @@ from lerobot.motors import Motor, MotorCalibration, MotorNormMode from lerobot.motors.dynamixel import MODEL_NUMBER_TABLE, DynamixelMotorsBus from lerobot.motors.dynamixel.tables import X_SERIES_CONTROL_TABLE -from lerobot.utils.encoding_utils import encode_twos_complement +from lerobot.motors.encoding_utils import encode_twos_complement try: import dynamixel_sdk as dxl diff --git a/tests/motors/test_feetech.py b/tests/motors/test_feetech.py index 31e4a9018bd..673276e0519 100644 --- a/tests/motors/test_feetech.py +++ b/tests/motors/test_feetech.py @@ -22,9 +22,9 @@ import pytest from lerobot.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.motors.encoding_utils import encode_sign_magnitude from lerobot.motors.feetech import MODEL_NUMBER, MODEL_NUMBER_TABLE, FeetechMotorsBus from lerobot.motors.feetech.tables import STS_SMS_SERIES_CONTROL_TABLE -from lerobot.utils.encoding_utils import encode_sign_magnitude try: import scservo_sdk as scs diff --git a/tests/optim/test_optimizers.py b/tests/optim/test_optimizers.py index 4152c7f8dcd..d1856556235 100644 --- a/tests/optim/test_optimizers.py +++ b/tests/optim/test_optimizers.py @@ -14,10 +14,6 @@ import pytest import torch -from lerobot.constants import ( - OPTIMIZER_PARAM_GROUPS, - OPTIMIZER_STATE, -) from lerobot.optim.optimizers import ( AdamConfig, AdamWConfig, @@ -26,6 +22,10 @@ load_optimizer_state, save_optimizer_state, ) +from lerobot.utils.constants import ( + OPTIMIZER_PARAM_GROUPS, + OPTIMIZER_STATE, +) @pytest.mark.parametrize( diff --git a/tests/optim/test_schedulers.py b/tests/optim/test_schedulers.py index 43851c45894..1e566a6ba37 100644 --- a/tests/optim/test_schedulers.py +++ b/tests/optim/test_schedulers.py @@ -13,7 +13,6 @@ # limitations under the License. from torch.optim.lr_scheduler import LambdaLR -from lerobot.constants import SCHEDULER_STATE from lerobot.optim.schedulers import ( CosineDecayWithWarmupSchedulerConfig, DiffuserSchedulerConfig, @@ -21,6 +20,7 @@ load_scheduler_state, save_scheduler_state, ) +from lerobot.utils.constants import SCHEDULER_STATE def test_diffuser_scheduler(optimizer): diff --git a/tests/plugins/reachy2_sdk.py b/tests/plugins/reachy2_sdk.py index f56b59efbe4..457fcf0f9ba 100644 --- a/tests/plugins/reachy2_sdk.py +++ b/tests/plugins/reachy2_sdk.py @@ -1,3 +1,19 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import sys import types from unittest.mock import MagicMock diff --git a/tests/policies/hilserl/test_modeling_classifier.py b/tests/policies/hilserl/test_modeling_classifier.py index 0be1b9c7c34..a572ea9e1f8 100644 --- a/tests/policies/hilserl/test_modeling_classifier.py +++ b/tests/policies/hilserl/test_modeling_classifier.py @@ -19,6 +19,7 @@ from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig from lerobot.policies.sac.reward_model.modeling_classifier import ClassifierOutput +from lerobot.utils.constants import OBS_IMAGE, REWARD from tests.utils import require_package @@ -41,10 +42,10 @@ def test_binary_classifier_with_default_params(): config = RewardClassifierConfig() config.input_features = { - "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), } config.output_features = { - "next.reward": PolicyFeature(type=FeatureType.REWARD, shape=(1,)), + REWARD: PolicyFeature(type=FeatureType.REWARD, shape=(1,)), } config.normalization_mapping = { "VISUAL": NormalizationMode.IDENTITY, @@ -56,8 +57,8 @@ def test_binary_classifier_with_default_params(): batch_size = 10 input = { - "observation.image": torch.rand((batch_size, 3, 128, 128)), - "next.reward": torch.randint(low=0, high=2, size=(batch_size,)).float(), + OBS_IMAGE: torch.rand((batch_size, 3, 128, 128)), + REWARD: torch.randint(low=0, high=2, size=(batch_size,)).float(), } images, labels = classifier.extract_images_and_labels(input) @@ -83,10 +84,10 @@ def test_multiclass_classifier(): num_classes = 5 config = RewardClassifierConfig() config.input_features = { - "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), } config.output_features = { - "next.reward": PolicyFeature(type=FeatureType.REWARD, shape=(num_classes,)), + REWARD: PolicyFeature(type=FeatureType.REWARD, shape=(num_classes,)), } config.num_cameras = 1 config.num_classes = num_classes @@ -95,8 +96,8 @@ def test_multiclass_classifier(): batch_size = 10 input = { - "observation.image": torch.rand((batch_size, 3, 128, 128)), - "next.reward": torch.rand((batch_size, num_classes)), + OBS_IMAGE: torch.rand((batch_size, 3, 128, 128)), + REWARD: torch.rand((batch_size, num_classes)), } images, labels = classifier.extract_images_and_labels(input) diff --git a/tests/policies/pi0_pi05/test_pi0.py b/tests/policies/pi0_pi05/test_pi0.py new file mode 100644 index 00000000000..b580310eb46 --- /dev/null +++ b/tests/policies/pi0_pi05/test_pi0.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Test script to verify PI0 policy integration with LeRobot, only meant to be run locally!""" + +import os + +import pytest +import torch + +# Skip this entire module in CI +pytestmark = pytest.mark.skipif( + os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true", + reason="This test requires local OpenPI installation and is not meant for CI", +) + +from lerobot.policies.factory import make_policy_config # noqa: E402 +from lerobot.policies.pi0 import ( # noqa: E402 + PI0Config, + PI0Policy, + make_pi0_pre_post_processors, # noqa: E402 +) +from lerobot.utils.random_utils import set_seed # noqa: E402 +from tests.utils import require_cuda # noqa: E402 + + +@require_cuda +def test_policy_instantiation(): + # Create config + set_seed(42) + config = PI0Config(max_action_dim=7, max_state_dim=14, dtype="float32") + + # Set up input_features and output_features in the config + from lerobot.configs.types import FeatureType, PolicyFeature + + config.input_features = { + "observation.state": PolicyFeature( + type=FeatureType.STATE, + shape=(14,), + ), + "observation.images.base_0_rgb": PolicyFeature( + type=FeatureType.VISUAL, + shape=(3, 224, 224), + ), + } + + config.output_features = { + "action": PolicyFeature( + type=FeatureType.ACTION, + shape=(7,), + ), + } + + # Create dummy dataset stats + dataset_stats = { + "observation.state": { + "mean": torch.zeros(14), + "std": torch.ones(14), + }, + "action": { + "mean": torch.zeros(7), + "std": torch.ones(7), + }, + "observation.images.base_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + }, + } + + # Instantiate policy + policy = PI0Policy(config) + preprocessor, postprocessor = make_pi0_pre_post_processors(config=config, dataset_stats=dataset_stats) + # Test forward pass with dummy data + batch_size = 1 + device = config.device + batch = { + "observation.state": torch.randn(batch_size, 14, dtype=torch.float32, device=device), + "action": torch.randn(batch_size, config.chunk_size, 7, dtype=torch.float32, device=device), + "observation.images.base_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), # Use rand for [0,1] range + "task": ["Pick up the object"] * batch_size, + } + batch = preprocessor(batch) + try: + loss, loss_dict = policy.forward(batch) + print(f"Forward pass successful. Loss: {loss_dict['loss']:.4f}") + except Exception as e: + print(f"Forward pass failed: {e}") + raise + + try: + with torch.no_grad(): + action = policy.select_action(batch) + action = postprocessor(action) + print(f"Action: {action}") + print(f"Action prediction successful. Action shape: {action.shape}") + except Exception as e: + print(f"Action prediction failed: {e}") + raise + + +@require_cuda +def test_config_creation(): + """Test policy config creation through factory.""" + try: + config = make_policy_config( + policy_type="pi0", + max_action_dim=7, + max_state_dim=14, + ) + print("Config created successfully through factory") + print(f" Config type: {type(config).__name__}") + print(f" PaliGemma variant: {config.paligemma_variant}") + print(f" Action expert variant: {config.action_expert_variant}") + except Exception as e: + print(f"Config creation failed: {e}") + raise diff --git a/tests/policies/pi0_pi05/test_pi05.py b/tests/policies/pi0_pi05/test_pi05.py new file mode 100644 index 00000000000..964539446a5 --- /dev/null +++ b/tests/policies/pi0_pi05/test_pi05.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Test script to verify PI0.5 (pi05) support in PI0 policy, only meant to be run locally!""" + +import os + +import pytest +import torch + +from lerobot.utils.random_utils import set_seed + +# Skip this entire module in CI +pytestmark = pytest.mark.skipif( + os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true", + reason="This test requires local OpenPI installation and is not meant for CI", +) + +from lerobot.policies.factory import make_policy_config # noqa: E402 +from lerobot.policies.pi05 import ( # noqa: E402 + PI05Config, + PI05Policy, + make_pi05_pre_post_processors, # noqa: E402 +) +from tests.utils import require_cuda # noqa: E402 + + +@require_cuda +def test_policy_instantiation(): + # Create config + set_seed(42) + config = PI05Config(max_action_dim=7, max_state_dim=14, dtype="float32") + + # Set up input_features and output_features in the config + from lerobot.configs.types import FeatureType, PolicyFeature + + config.input_features = { + "observation.state": PolicyFeature( + type=FeatureType.STATE, + shape=(14,), + ), + "observation.images.base_0_rgb": PolicyFeature( + type=FeatureType.VISUAL, + shape=(3, 224, 224), + ), + } + + config.output_features = { + "action": PolicyFeature( + type=FeatureType.ACTION, + shape=(7,), + ), + } + + assert config.tokenizer_max_length == 200, ( + f"Expected tokenizer_max_length=200 for pi05, got {config.tokenizer_max_length}" + ) + + # Create dummy dataset stats + dataset_stats = { + "observation.state": { + "mean": torch.zeros(14), + "std": torch.ones(14), + "min": torch.zeros(14), + "max": torch.ones(14), + "q01": torch.zeros(14), + "q99": torch.ones(14), + }, + "action": { + "mean": torch.zeros(7), + "std": torch.ones(7), + "min": torch.zeros(7), + "max": torch.ones(7), + "q01": torch.zeros(7), + "q99": torch.ones(7), + }, + "observation.images.base_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + "q01": torch.zeros(3, 224, 224), + "q99": torch.ones(3, 224, 224), + }, + } + + # Instantiate policy + policy = PI05Policy(config) + # Test forward pass with dummy data + batch_size = 1 + preprocessor, postprocessor = make_pi05_pre_post_processors(config=config, dataset_stats=dataset_stats) + device = config.device + batch = { + "observation.state": torch.randn(batch_size, 14, dtype=torch.float32, device=device), + "action": torch.randn(batch_size, config.chunk_size, 7, dtype=torch.float32, device=device), + "observation.images.base_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), # Use rand for [0,1] range + "task": ["Pick up the object"] * batch_size, + } + batch = preprocessor(batch) + try: + loss, loss_dict = policy.forward(batch) + print(f"Forward pass successful. Loss: {loss_dict['loss']:.4f}") + except Exception as e: + print(f"Forward pass failed: {e}") + raise + try: + with torch.no_grad(): + action = policy.select_action(batch) + action = postprocessor(action) + print(f"Action: {action}") + print(f"Action prediction successful. Action shape: {action.shape}") + except Exception as e: + print(f"Action prediction failed: {e}") + raise + + # Verify pi05 model components exist + # Check that time_mlp layers exist (for AdaRMS conditioning) + assert hasattr(policy.model, "time_mlp_in"), "Missing time_mlp_in layer for pi05" + assert hasattr(policy.model, "time_mlp_out"), "Missing time_mlp_out layer for pi05" + + # Check that action_time_mlp layers don't exist (pi0 only) + assert not hasattr(policy.model, "action_time_mlp_in"), "action_time_mlp_in should not exist in pi05 mode" + assert not hasattr(policy.model, "action_time_mlp_out"), ( + "action_time_mlp_out should not exist in pi05 mode" + ) + + # Check that state_proj doesn't exist in pi05 mode + assert not hasattr(policy.model, "state_proj"), "state_proj should not exist in pi05 mode" + + # Check AdaRMS configuration in the underlying model + adarms_config = policy.model.paligemma_with_expert.paligemma.config.text_config.use_adarms + assert adarms_config == False, f"PaliGemma should not use AdaRMS, got {adarms_config}" # noqa: E712 + + adarms_expert_config = policy.model.paligemma_with_expert.gemma_expert.config.use_adarms + assert adarms_expert_config == True, ( # noqa: E712 + f"Action expert should use AdaRMS in pi05, got {adarms_expert_config}" + ) + + +@require_cuda +def test_config_creation(): + """Test policy config creation through factory.""" + try: + config = make_policy_config( + policy_type="pi0", + max_action_dim=7, + max_state_dim=14, + ) + print("Config created successfully through factory") + print(f" Config type: {type(config).__name__}") + print(f" PaliGemma variant: {config.paligemma_variant}") + print(f" Action expert variant: {config.action_expert_variant}") + except Exception as e: + print(f"Config creation failed: {e}") + raise diff --git a/tests/policies/pi0_pi05/test_pi05_original_vs_lerobot.py b/tests/policies/pi0_pi05/test_pi05_original_vs_lerobot.py new file mode 100644 index 00000000000..0d5244e1c36 --- /dev/null +++ b/tests/policies/pi0_pi05/test_pi05_original_vs_lerobot.py @@ -0,0 +1,435 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Test script to verify PI0OpenPI policy integration with LeRobot vs the original implementation, only meant to be run locally!""" + +import os +from copy import deepcopy +from typing import Any + +import numpy as np +import pytest +import torch + +# Skip if openpi or transformers is not available +pytest.importorskip("openpi") +pytest.importorskip("transformers") + +# Skip this entire module in CI +pytestmark = pytest.mark.skipif( + os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true", + reason="This test requires local OpenPI installation and is not meant for CI", +) + +from openpi.models_pytorch import preprocessing_pytorch as openpi_preprocessing # noqa: E402 + +# NOTE: Assumes PYTHONPATH is set to include OpenPI src as per instructions. +from openpi.models_pytorch.pi0_pytorch import PI0Pytorch # noqa: E402 +from transformers import AutoTokenizer # noqa: E402 + +from lerobot.policies.pi05 import PI05Config, PI05Policy # noqa: E402 +from lerobot.policies.pi05.processor_pi05 import make_pi05_pre_post_processors # noqa: E402 +from lerobot.processor import PolicyAction, PolicyProcessorPipeline # noqa: E402 + +# TODO: ADDING DEFAULT IMAGES_FEATURES TO CONFIG +DUMMY_ACTION_DIM = 32 +DUMMY_STATE_DIM = 32 +DUMMY_ACTION_HORIZON = 50 +DUMMY_MAX_TOKEN_LEN = 200 +DEVICE = "cpu" # Use CPU to avoid memory issues for testing + +DUMMY_DATASET_STATS = { + "observation.state": { + "mean": torch.zeros(DUMMY_STATE_DIM), + "std": torch.ones(DUMMY_STATE_DIM), + "q01": torch.zeros(DUMMY_STATE_DIM), + "q99": torch.ones(DUMMY_STATE_DIM), + }, + "action": { + "mean": torch.zeros(DUMMY_ACTION_DIM), + "std": torch.ones(DUMMY_ACTION_DIM), + "q01": torch.zeros(DUMMY_ACTION_DIM), + "q99": torch.ones(DUMMY_ACTION_DIM), + }, + "images": { + "base_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + "q01": torch.zeros(3, 224, 224), + "q99": torch.ones(3, 224, 224), + }, + "left_wrist_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + "q01": torch.zeros(3, 224, 224), + "q99": torch.ones(3, 224, 224), + }, + "right_wrist_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + "q01": torch.zeros(3, 224, 224), + "q99": torch.ones(3, 224, 224), + }, + }, +} + + +class PI05BaseOriginalConfig: + action_dim: int = DUMMY_ACTION_DIM + action_horizon: int = DUMMY_ACTION_HORIZON + paligemma_variant: str = "gemma_2b" + action_expert_variant: str = "gemma_300m" + precision: str = "float32" + pi05: bool = True + dtype: str = "float32" + + +def instantiate_lerobot_pi05( + from_pretrained: bool = False, +) -> tuple[ + PI05Policy, + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + if from_pretrained: + # Load the policy first + policy = PI05Policy.from_pretrained(pretrained_name_or_path="lerobot/pi05_base", strict=True) + else: + config = PI05Config(max_action_dim=DUMMY_ACTION_DIM, max_state_dim=DUMMY_STATE_DIM, dtype="float32") + policy = PI05Policy(config) + + policy.to(DEVICE) + policy.config.device = DEVICE + preprocessor, postprocessor = make_pi05_pre_post_processors( + config=policy.config, dataset_stats=DUMMY_DATASET_STATS + ) + return (policy, preprocessor, postprocessor) + + +def instantiate_original_pi05(from_pretrained: bool = False, model_path: str | None = None): + config = PI05BaseOriginalConfig() + policy = PI0Pytorch(config) + + if from_pretrained: + try: + print("Loading converted PyTorch weights from HuggingFace Hub (lerobot/pi05_base)...") + + # Download the model from HuggingFace Hub + import safetensors.torch + from huggingface_hub import snapshot_download + + # Download the entire repository + if model_path and os.path.exists(model_path): + cache_dir = model_path + print(f"Using cached model from: {cache_dir}") + else: + cache_dir = snapshot_download(repo_id="lerobot/pi05_base", repo_type="model") + print(f"Downloaded model to: {cache_dir}") + + # Try to load safetensors format first + model_file = os.path.join(cache_dir, "model.safetensors") + if os.path.exists(model_file): + state_dict = safetensors.torch.load_file(model_file) + print(f"Loaded {len(state_dict)} parameters from safetensors") + else: + raise FileNotFoundError(f"No safetensors file found in {cache_dir}") + + # Load the state dict into the model + missing_keys, unexpected_keys = policy.load_state_dict(state_dict, strict=False) + + if missing_keys: + print(f"Missing keys: {len(missing_keys)}") + if len(missing_keys) <= 5: + for key in missing_keys: + print(f" - {key}") + else: + for key in missing_keys[:5]: + print(f" - {key}") + print(f" ... and {len(missing_keys) - 5} more") + + if unexpected_keys: + print(f"Unexpected keys: {len(unexpected_keys)}") + if len(unexpected_keys) <= 5: + for key in unexpected_keys: + print(f" - {key}") + else: + for key in unexpected_keys[:5]: + print(f" - {key}") + print(f" ... and {len(unexpected_keys) - 5} more") + + if not missing_keys and not unexpected_keys: + print("All pretrained weights loaded successfully!") + else: + print("Pretrained weights loaded with some missing/unexpected keys (this may be normal)") + + except Exception as e: + print(f"Failed to load pretrained weights: {e}") + print(" Using randomly initialized weights...") + import traceback + + traceback.print_exc() + + policy.to(DEVICE) + return policy + + +def create_dummy_data(): + batch_size = 2 # Reduce batch size for testing + device = DEVICE + + # Use the exact same prompt for both implementations + prompt = "Pick up the red block and place it in the bin" + + batch = { + "observation.state": torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device), + "action": torch.randn( + batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM, dtype=torch.float32, device=device + ), + # Create images in [0, 1] range as expected by LeRobot (will be converted to [-1, 1] internally) + "observation.images.base_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), + "observation.images.left_wrist_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), + "observation.images.right_wrist_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), + # Add the task prompt for LeRobot - provide as list with single element to trigger expansion + "task": [prompt for _ in range(batch_size)], + } + return batch + + +def extract_lerobot_processed_inputs(lerobot_pi0, batch): + """Extract the exact same processed inputs that LeRobot uses internally.""" + # Get the tokenized language from LeRobot's internal method + lang_tokens, lang_masks = lerobot_pi0._tokenize_language(batch) + + # Get the preprocessed images from LeRobot's internal method + images, img_masks = lerobot_pi0._preprocess_images(batch, train=False) + + # Create dummy token_ar_mask and token_loss_mask for original implementation + token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32) + token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool) + + return images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask + + +class PI05Observation: + """Observation class that matches the original OpenPI format.""" + + def __init__( + self, + state, + images, + image_masks, + tokenized_prompt, + tokenized_prompt_mask, + token_ar_mask, + token_loss_mask, + ): + self.state = state + self.images = images + self.image_masks = image_masks + self.tokenized_prompt = tokenized_prompt + self.tokenized_prompt_mask = tokenized_prompt_mask + self.token_ar_mask = token_ar_mask + self.token_loss_mask = token_loss_mask + + +def create_original_observation_with_openpi_preprocessing(batch): + """Create observation object for OpenPI using OpenPI's own preprocessing with pi05 state tokenizer.""" + batch_size = batch["observation.state"].shape[0] + device = batch["observation.state"].device + + # Create tokenizer for OpenPI (same as LeRobot uses) + tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224") + + # Get task description (pi05 processor handles all text formatting) + tasks = batch.get("task", ["Pick up the object"] * batch_size) + if isinstance(tasks, str): + tasks = [tasks] * batch_size + elif len(tasks) == 1: + tasks = tasks * batch_size + + # Use pi05 state and input tokenizer logic (same as Pi05PrepareStateTokenizerProcessorStep) + state = batch["observation.state"] + state = deepcopy(state) + + # Prepare state (pad to max_state_dim) + from lerobot.policies.pi05.modeling_pi05 import pad_vector + + state = pad_vector(state, DUMMY_STATE_DIM) + + # Normalize state to [-1, 1] range if needed (assuming it's already normalized from normalize_inputs) + # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`) + state_np = state.cpu().numpy() + discretized_states = np.digitize(state_np, bins=np.linspace(-1, 1, 256 + 1)[:-1]) - 1 + + # Create pi05-formatted prompts that include state information + full_prompts = [] + for i, task in enumerate(tasks): + cleaned_text = task.strip().replace("_", " ").replace("\n", " ") + state_str = " ".join(map(str, discretized_states[i])) + full_prompt = f"Task: {cleaned_text}, State: {state_str};\nAction: " + full_prompts.append(full_prompt) + + # Tokenize with max_length padding to match OpenPI's expected format + tokenized = tokenizer( + full_prompts, + padding="max_length", + padding_side="right", + truncation=True, + max_length=DUMMY_MAX_TOKEN_LEN, + return_tensors="pt", + ) + + lang_tokens = tokenized["input_ids"].to(device) + lang_masks = tokenized["attention_mask"].to(device, dtype=torch.bool) + + # Create dummy token_ar_mask and token_loss_mask for OpenPI + token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32) + token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool) + + # Convert LeRobot images format to OpenPI format (convert [0,1] to [-1,1] range) + image_dict = { + "base_0_rgb": batch["observation.images.base_0_rgb"] * 2.0 - 1.0, + "left_wrist_0_rgb": batch["observation.images.left_wrist_0_rgb"] * 2.0 - 1.0, + "right_wrist_0_rgb": batch["observation.images.right_wrist_0_rgb"] * 2.0 - 1.0, + } + + # Create image masks (all ones for real images) + image_masks_dict = {} + for key in image_dict: + image_masks_dict[key] = torch.ones(batch_size, dtype=torch.bool, device=device) + + # Create raw observation object (before preprocessing) + raw_observation = PI05Observation( + state=batch["observation.state"], + images=image_dict, + image_masks=image_masks_dict, + tokenized_prompt=lang_tokens, + tokenized_prompt_mask=lang_masks, + token_ar_mask=token_ar_mask, + token_loss_mask=token_loss_mask, + ) + + # Now use OpenPI's preprocessing + processed_obs = openpi_preprocessing.preprocess_observation_pytorch(raw_observation, train=False) + + return processed_obs + + +def create_original_observation_from_lerobot(lerobot_pi0, batch): + """Create observation object compatible with original OpenPI using the exact same inputs as LeRobot.""" + _batch_size = batch["observation.state"].shape[0] + _device = batch["observation.state"].device + + # Extract the exact same processed inputs that LeRobot uses + images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask = ( + extract_lerobot_processed_inputs(lerobot_pi0, batch) + ) + + # Convert images list to dict with original OpenPI keys + image_dict = { + "base_0_rgb": images[0], + "left_wrist_0_rgb": images[1], + "right_wrist_0_rgb": images[2], + } + + # Convert image masks list to dict with original OpenPI keys + image_masks_dict = { + "base_0_rgb": img_masks[0], + "left_wrist_0_rgb": img_masks[1], + "right_wrist_0_rgb": img_masks[2], + } + + return PI05Observation( + state=batch["observation.state"], + images=image_dict, + image_masks=image_masks_dict, + tokenized_prompt=lang_tokens, + tokenized_prompt_mask=lang_masks, + token_ar_mask=token_ar_mask, + token_loss_mask=token_loss_mask, + ) + + +def test_pi05_original_vs_lerobot(): + """Test PI05 original implementation vs LeRobot implementation.""" + print("Initializing models...") + lerobot_pi05, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_pi05( + from_pretrained=True + ) # Load pretrained LeRobot model + original_pi0 = instantiate_original_pi05( + from_pretrained=True + ) # Load pretrained OpenPI model from HuggingFace Hub + + print("Creating dummy data...") + batch = create_dummy_data() + batch_lerobot = deepcopy(batch) + + # Test each model with its own preprocessing (more realistic end-to-end test) + print("\nTest each model with its own preprocessing") + print("Creating observation for OpenPI using OpenPI's own preprocessing...") + pi0_obs_openpi = create_original_observation_with_openpi_preprocessing(batch) + + print(f"Task prompt: '{batch['task'][0]}'") + print(f"OpenPI tokenized prompt shape: {pi0_obs_openpi.tokenized_prompt.shape}") + print(f"OpenPI image shapes: {[img.shape for img in pi0_obs_openpi.images.values()]}") + print(f"OpenPI state shape: {pi0_obs_openpi.state.shape}") + + print("Testing OpenPI with own preprocessing...") + original_pi0.eval() + torch.manual_seed(42) # Set seed for reproducibility + batch_size = batch["observation.state"].shape[0] + noise_shape = (batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM) + fixed_noise = torch.randn(noise_shape, dtype=torch.float32, device=DEVICE) + + with torch.no_grad(): + openpi_actions = original_pi0.sample_actions( + device=DEVICE, observation=pi0_obs_openpi, noise=fixed_noise, num_steps=10 + ) + openpi_actions_unit = openpi_actions[:, 0, :] + print(f"OpenPI (own preprocessing) Actions shape: {openpi_actions.shape}") + print(f"OpenPI (own preprocessing) Actions unit shape: {openpi_actions_unit.shape}") + print(f"OpenPI (own preprocessing) Actions mean: {openpi_actions.mean().item():.6f}") + print(f"OpenPI (own preprocessing) Actions std: {openpi_actions.std().item():.6f}") + + print("Testing LeRobot with own preprocessing...") + lerobot_pi05.eval() + torch.manual_seed(42) # Set the same seed + + batch_lerobot_processed = lerobot_preprocessor(batch_lerobot) + with torch.no_grad(): + lerobot_actions_own = lerobot_pi05.predict_action_chunk( + batch_lerobot_processed + ) # batch_size, n_action_steps, action_dim + lerobot_actions_unit = lerobot_actions_own[:, 0, :] + print(f"LeRobot (own preprocessing) Actions shape: {lerobot_actions_own.shape}") + print(f"LeRobot (own preprocessing) Actions unit shape: {lerobot_actions_unit.shape}") + print(f"LeRobot (own preprocessing) Actions mean: {lerobot_actions_own.mean().item():.6f}") + print(f"LeRobot (own preprocessing) Actions std: {lerobot_actions_own.std().item():.6f}") + + print("\nComparing end-to-end implementations:") + print(f"Actions close (atol=1e-4): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4)}") + print(f"Actions close (atol=1e-2): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2)}") + print(f"Max absolute difference: {torch.abs(lerobot_actions_own - openpi_actions).max().item():.6f}") + + assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4) + assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2) + assert torch.abs(lerobot_actions_own - openpi_actions).max().item() < 1e-4 diff --git a/tests/policies/pi0_pi05/test_pi0_original_vs_lerobot.py b/tests/policies/pi0_pi05/test_pi0_original_vs_lerobot.py new file mode 100644 index 00000000000..41db2dceb2e --- /dev/null +++ b/tests/policies/pi0_pi05/test_pi0_original_vs_lerobot.py @@ -0,0 +1,426 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Test script to verify PI0 policy integration with LeRobot vs the original implementation, only meant to be run locally!""" + +import os +from copy import deepcopy +from typing import Any + +import pytest +import torch + +# Skip if openpi or transformers is not available +pytest.importorskip("openpi") +pytest.importorskip("transformers") + +# Skip this entire module in CI +pytestmark = pytest.mark.skipif( + os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true", + reason="This test requires local OpenPI installation and is not meant for CI", +) + +from openpi.models_pytorch import preprocessing_pytorch as openpi_preprocessing # noqa: E402 + +# NOTE: Assumes PYTHONPATH is set to include OpenPI src as per instructions. +from openpi.models_pytorch.pi0_pytorch import PI0Pytorch # noqa: E402 +from transformers import AutoTokenizer # noqa: E402 + +from lerobot.policies.pi0 import PI0Config, PI0Policy # noqa: E402 +from lerobot.policies.pi0.processor_pi0 import make_pi0_pre_post_processors # noqa: E402 +from lerobot.processor import PolicyAction, PolicyProcessorPipeline # noqa: E402 + +# TODO: ADDING DEFAULT IMAGES_FEATURES TO CONFIG +DUMMY_ACTION_DIM = 32 +DUMMY_STATE_DIM = 32 +DUMMY_ACTION_HORIZON = 50 +DUMMY_MAX_TOKEN_LEN = 48 # Default for PI0 (non-pi05) +DEVICE = "cpu" # Use CPU to avoid memory issues for testing + +DUMMY_DATASET_STATS = { + "observation.state": { + "mean": torch.zeros(DUMMY_STATE_DIM), + "std": torch.ones(DUMMY_STATE_DIM), + "q01": torch.zeros(DUMMY_STATE_DIM), + "q99": torch.ones(DUMMY_STATE_DIM), + }, + "action": { + "mean": torch.zeros(DUMMY_ACTION_DIM), + "std": torch.ones(DUMMY_ACTION_DIM), + "q01": torch.zeros(DUMMY_ACTION_DIM), + "q99": torch.ones(DUMMY_ACTION_DIM), + }, + "images": { + "base_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + "q01": torch.zeros(3, 224, 224), + "q99": torch.ones(3, 224, 224), + }, + "left_wrist_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + "q01": torch.zeros(3, 224, 224), + "q99": torch.ones(3, 224, 224), + }, + "right_wrist_0_rgb": { + "mean": torch.zeros(3, 224, 224), + "std": torch.ones(3, 224, 224), + "q01": torch.zeros(3, 224, 224), + "q99": torch.ones(3, 224, 224), + }, + }, +} + + +class PI0BaseOriginalConfig: + action_dim: int = DUMMY_ACTION_DIM + action_horizon: int = DUMMY_ACTION_HORIZON + paligemma_variant: str = "gemma_2b" + action_expert_variant: str = "gemma_300m" + precision: str = "float32" + pi05: bool = False + dtype: str = "float32" + + +def instantiate_lerobot_pi0( + from_pretrained: bool = False, +) -> tuple[ + PI0Policy, + PolicyProcessorPipeline[dict[str, Any], dict[str, Any]], + PolicyProcessorPipeline[PolicyAction, PolicyAction], +]: + if from_pretrained: + # Load the policy first + policy = PI0Policy.from_pretrained(pretrained_name_or_path="lerobot/pi0_base", strict=True) + else: + config = PI0Config(max_action_dim=DUMMY_ACTION_DIM, max_state_dim=DUMMY_STATE_DIM, dtype="float32") + policy = PI0Policy(config) + + policy.to(DEVICE) + policy.config.device = DEVICE + preprocessor, postprocessor = make_pi0_pre_post_processors( + config=policy.config, dataset_stats=DUMMY_DATASET_STATS + ) + return (policy, preprocessor, postprocessor) + + +def instantiate_original_pi0(from_pretrained: bool = False, model_path: str = None): + config = PI0BaseOriginalConfig() + policy = PI0Pytorch(config) + + if from_pretrained: + try: + print("Loading converted PyTorch weights from HuggingFace Hub (lerobot/pi0_base)...") + + # Download the model from HuggingFace Hub + import safetensors.torch + from huggingface_hub import snapshot_download + + # Download the entire repository + if model_path and os.path.exists(model_path): + cache_dir = model_path + print(f"Using cached model from: {cache_dir}") + else: + cache_dir = snapshot_download(repo_id="lerobot/pi0_base", repo_type="model") + print(f"Downloaded model to: {cache_dir}") + + # Try to load safetensors format first + model_file = os.path.join(cache_dir, "model.safetensors") + if os.path.exists(model_file): + state_dict = safetensors.torch.load_file(model_file) + print(f"Loaded {len(state_dict)} parameters from safetensors") + else: + raise FileNotFoundError(f"No safetensors file found in {cache_dir}") + + # Load the state dict into the model + missing_keys, unexpected_keys = policy.load_state_dict(state_dict, strict=False) + + if missing_keys: + print(f"Missing keys: {len(missing_keys)}") + if len(missing_keys) <= 5: + for key in missing_keys: + print(f" - {key}") + else: + for key in missing_keys[:5]: + print(f" - {key}") + print(f" ... and {len(missing_keys) - 5} more") + + if unexpected_keys: + print(f"Unexpected keys: {len(unexpected_keys)}") + if len(unexpected_keys) <= 5: + for key in unexpected_keys: + print(f" - {key}") + else: + for key in unexpected_keys[:5]: + print(f" - {key}") + print(f" ... and {len(unexpected_keys) - 5} more") + + if not missing_keys and not unexpected_keys: + print("All pretrained weights loaded successfully!") + else: + print("Pretrained weights loaded with some missing/unexpected keys (this may be normal)") + + except Exception as e: + print(f"Failed to load pretrained weights: {e}") + print(" Using randomly initialized weights...") + import traceback + + traceback.print_exc() + + policy.to(DEVICE) + return policy + + +def create_dummy_data(): + batch_size = 2 # Reduce batch size for testing + device = DEVICE + + # Use the exact same prompt for both implementations + prompt = "Pick up the red block and place it in the bin" + + batch = { + "observation.state": torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device), + "action": torch.randn( + batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM, dtype=torch.float32, device=device + ), + # Create images in [0, 1] range as expected by LeRobot (will be converted to [-1, 1] internally) + "observation.images.base_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), + "observation.images.left_wrist_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), + "observation.images.right_wrist_0_rgb": torch.rand( + batch_size, 3, 224, 224, dtype=torch.float32, device=device + ), + # Add the task prompt for LeRobot - provide as list with single element to trigger expansion + "task": [prompt for _ in range(batch_size)], + } + return batch + + +def extract_lerobot_processed_inputs(lerobot_pi0, batch): + """Extract the exact same processed inputs that LeRobot uses internally.""" + # Get the tokenized language from LeRobot's internal method + lang_tokens, lang_masks = lerobot_pi0._tokenize_language(batch) + + # Get the preprocessed images from LeRobot's internal method + images, img_masks = lerobot_pi0._preprocess_images(batch, train=False) + + # Create dummy token_ar_mask and token_loss_mask for original implementation + token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32) + token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool) + + return images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask + + +class PI0Observation: + """Observation class that matches the original OpenPI format.""" + + def __init__( + self, + state, + images, + image_masks, + tokenized_prompt, + tokenized_prompt_mask, + token_ar_mask, + token_loss_mask, + ): + self.state = state + self.images = images + self.image_masks = image_masks + self.tokenized_prompt = tokenized_prompt + self.tokenized_prompt_mask = tokenized_prompt_mask + self.token_ar_mask = token_ar_mask + self.token_loss_mask = token_loss_mask + + +def create_original_observation_with_openpi_preprocessing(batch): + """Create observation object for OpenPI using OpenPI's own preprocessing.""" + batch_size = batch["observation.state"].shape[0] + device = batch["observation.state"].device + + # Create tokenizer for OpenPI (same as LeRobot uses) + tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224") + + # Get task description + if "task" in batch: + tasks = batch["task"] + if isinstance(tasks, str): + # Single string: add newline if not present, then convert to list + if not tasks.endswith("\n"): + tasks = f"{tasks}\n" + tasks = [tasks] + elif isinstance(tasks, list) and all(isinstance(t, str) for t in tasks): + # List of strings: add newline to each if not present + tasks = [t if t.endswith("\n") else f"{t}\n" for t in tasks] + if len(tasks) == 1: + # Expand to batch size + tasks = tasks * batch_size + if len(tasks) != batch_size: + raise ValueError(f"Expected batch size {batch_size}, got {len(tasks)}") + # If task is neither string nor list of strings, leave unchanged + else: + # Default task if not provided + tasks = ["Pick up the object\n"] * batch_size + + # Tokenize with max_length padding to match OpenPI's expected format + tokenized = tokenizer( + tasks, + padding="max_length", + padding_side="right", + truncation=True, + max_length=DUMMY_MAX_TOKEN_LEN, + return_tensors="pt", + ) + + lang_tokens = tokenized["input_ids"].to(device) + lang_masks = tokenized["attention_mask"].to(device, dtype=torch.bool) + + # Create dummy token_ar_mask and token_loss_mask for OpenPI + token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32) + token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool) + + # Convert LeRobot images format to OpenPI format (convert [0,1] to [-1,1] range) + image_dict = { + "base_0_rgb": batch["observation.images.base_0_rgb"] * 2.0 - 1.0, + "left_wrist_0_rgb": batch["observation.images.left_wrist_0_rgb"] * 2.0 - 1.0, + "right_wrist_0_rgb": batch["observation.images.right_wrist_0_rgb"] * 2.0 - 1.0, + } + + # Create image masks (all ones for real images) + image_masks_dict = {} + for key in image_dict: + image_masks_dict[key] = torch.ones(batch_size, dtype=torch.bool, device=device) + + # Create raw observation object (before preprocessing) + raw_observation = PI0Observation( + state=batch["observation.state"], + images=image_dict, + image_masks=image_masks_dict, + tokenized_prompt=lang_tokens, + tokenized_prompt_mask=lang_masks, + token_ar_mask=token_ar_mask, + token_loss_mask=token_loss_mask, + ) + + # Now use OpenPI's preprocessing + processed_obs = openpi_preprocessing.preprocess_observation_pytorch(raw_observation, train=False) + + return processed_obs + + +def create_original_observation_from_lerobot(lerobot_pi0, batch): + """Create observation object compatible with original OpenPI using the exact same inputs as LeRobot.""" + _batch_size = batch["observation.state"].shape[0] + _device = batch["observation.state"].device + + # Extract the exact same processed inputs that LeRobot uses + images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask = ( + extract_lerobot_processed_inputs(lerobot_pi0, batch) + ) + + # Convert images list to dict with original OpenPI keys + image_dict = { + "base_0_rgb": images[0], + "left_wrist_0_rgb": images[1], + "right_wrist_0_rgb": images[2], + } + + # Convert image masks list to dict with original OpenPI keys + image_masks_dict = { + "base_0_rgb": img_masks[0], + "left_wrist_0_rgb": img_masks[1], + "right_wrist_0_rgb": img_masks[2], + } + + return PI0Observation( + state=batch["observation.state"], + images=image_dict, + image_masks=image_masks_dict, + tokenized_prompt=lang_tokens, + tokenized_prompt_mask=lang_masks, + token_ar_mask=token_ar_mask, + token_loss_mask=token_loss_mask, + ) + + +def test_pi0_original_vs_lerobot(): + """Test PI0 original implementation vs LeRobot implementation.""" + print("Initializing models...") + lerobot_pi0, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_pi0( + from_pretrained=True + ) # Load pretrained LeRobot model + original_pi0 = instantiate_original_pi0( + from_pretrained=True + ) # Load pretrained OpenPI model from HuggingFace Hub + + print("Creating dummy data...") + batch = create_dummy_data() + batch_lerobot = deepcopy(batch) + + # Test each model with its own preprocessing (more realistic end-to-end test) + print("\nTest each model with its own preprocessing") + print("Creating observation for OpenPI using OpenPI's own preprocessing...") + pi0_obs_openpi = create_original_observation_with_openpi_preprocessing(batch) + + print(f"Task prompt: '{batch['task'][0]}'") + print(f"OpenPI tokenized prompt shape: {pi0_obs_openpi.tokenized_prompt.shape}") + print(f"OpenPI image shapes: {[img.shape for img in pi0_obs_openpi.images.values()]}") + print(f"OpenPI state shape: {pi0_obs_openpi.state.shape}") + + print("Testing OpenPI with own preprocessing...") + original_pi0.eval() + torch.manual_seed(42) # Set seed for reproducibility + batch_size = batch["observation.state"].shape[0] + noise_shape = (batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM) + fixed_noise = torch.randn(noise_shape, dtype=torch.float32, device=DEVICE) + + with torch.no_grad(): + openpi_actions = original_pi0.sample_actions( + device=DEVICE, observation=pi0_obs_openpi, noise=fixed_noise, num_steps=10 + ) + openpi_actions_unit = openpi_actions[:, 0, :] + print(f"OpenPI (own preprocessing) Actions shape: {openpi_actions.shape}") + print(f"OpenPI (own preprocessing) Actions unit shape: {openpi_actions_unit.shape}") + print(f"OpenPI (own preprocessing) Actions mean: {openpi_actions.mean().item():.6f}") + print(f"OpenPI (own preprocessing) Actions std: {openpi_actions.std().item():.6f}") + + print("Testing LeRobot with own preprocessing...") + lerobot_pi0.eval() + torch.manual_seed(42) # Set the same seed + + batch_lerobot_processed = lerobot_preprocessor(batch_lerobot) + with torch.no_grad(): + lerobot_actions_own = lerobot_pi0.predict_action_chunk( + batch_lerobot_processed + ) # batch_size, n_action_steps, action_dim + lerobot_actions_unit = lerobot_actions_own[:, 0, :] + print(f"LeRobot (own preprocessing) Actions shape: {lerobot_actions_own.shape}") + print(f"LeRobot (own preprocessing) Actions unit shape: {lerobot_actions_unit.shape}") + print(f"LeRobot (own preprocessing) Actions mean: {lerobot_actions_own.mean().item():.6f}") + print(f"LeRobot (own preprocessing) Actions std: {lerobot_actions_own.std().item():.6f}") + + print("\nComparing end-to-end implementations:") + print(f"Actions close (atol=1e-4): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4)}") + print(f"Actions close (atol=1e-2): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2)}") + print(f"Max absolute difference: {torch.abs(lerobot_actions_own - openpi_actions).max().item():.6f}") + + assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4) + assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2) + assert torch.abs(lerobot_actions_own - openpi_actions).max().item() < 1e-4 diff --git a/tests/policies/test_policies.py b/tests/policies/test_policies.py index ef2d4ecd8ff..34fa8939040 100644 --- a/tests/policies/test_policies.py +++ b/tests/policies/test_policies.py @@ -26,8 +26,7 @@ from lerobot import available_policies from lerobot.configs.default import DatasetConfig from lerobot.configs.train import TrainPipelineConfig -from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature -from lerobot.constants import ACTION, OBS_STATE +from lerobot.configs.types import FeatureType, PolicyFeature from lerobot.datasets.factory import make_dataset from lerobot.datasets.utils import cycle, dataset_to_policy_features from lerobot.envs.factory import make_env, make_env_config @@ -39,9 +38,10 @@ get_policy_class, make_policy, make_policy_config, + make_pre_post_processors, ) -from lerobot.policies.normalize import Normalize, Unnormalize from lerobot.policies.pretrained import PreTrainedPolicy +from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE from lerobot.utils.random_utils import seeded_context from tests.artifacts.policies.save_policy_to_safetensors import get_policy_stats from tests.utils import DEVICE, require_cpu, require_env, require_x86_64_kernel @@ -52,19 +52,19 @@ def dummy_dataset_metadata(lerobot_dataset_metadata_factory, info_factory, tmp_p # Create only one camera input which is squared to fit all current policy constraints # e.g. vqbet and tdmpc works with one camera only, and tdmpc requires it to be squared camera_features = { - "observation.images.laptop": { + f"{OBS_IMAGES}.laptop": { "shape": (84, 84, 3), "names": ["height", "width", "channels"], "info": None, }, } motor_features = { - "action": { + ACTION: { "dtype": "float32", "shape": (6,), "names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"], }, - "observation.state": { + OBS_STATE: { "dtype": "float32", "shape": (6,), "names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"], @@ -154,11 +154,12 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs): # Check that we can make the policy object. dataset = make_dataset(train_cfg) + preprocessor, _ = make_pre_post_processors(train_cfg.policy, None) policy = make_policy(train_cfg.policy, ds_meta=dataset.meta) assert isinstance(policy, PreTrainedPolicy) # Check that we run select_actions and get the appropriate output. - env = make_env(train_cfg.env, n_envs=2) + envs = make_env(train_cfg.env, n_envs=2) dataloader = torch.utils.data.DataLoader( dataset, @@ -187,6 +188,12 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs): # reset the policy and environment policy.reset() + # For testing purposes, we only need a single environment instance. + # So here we unwrap the first suite_name and first task_id to grab + # the actual env object (SyncVectorEnv) that exposes `.reset()`. + suite_name = next(iter(envs)) + task_id = next(iter(envs[suite_name])) + env = envs[suite_name][task_id] observation, _ = env.reset(seed=train_cfg.seed) # apply transform to normalize the observations @@ -227,6 +234,7 @@ def test_act_backbone_lr(): assert cfg.policy.optimizer_lr_backbone == 0.001 dataset = make_dataset(cfg) + preprocessor, _ = make_pre_post_processors(cfg.policy, None) policy = make_policy(cfg.policy, ds_meta=dataset.meta) optimizer, _ = make_optimizer_and_scheduler(cfg, policy) assert len(optimizer.param_groups) == 2 @@ -266,108 +274,6 @@ def test_save_and_load_pretrained(dummy_dataset_metadata, tmp_path, policy_name: torch.testing.assert_close(list(policy.parameters()), list(loaded_policy.parameters()), rtol=0, atol=0) -@pytest.mark.parametrize("insert_temporal_dim", [False, True]) -def test_normalize(insert_temporal_dim): - """ - Test that normalize/unnormalize can run without exceptions when properly set up, and that they raise - an exception when the forward pass is called without the stats having been provided. - - TODO(rcadene, alexander-soare): This should also test that the normalization / unnormalization works as - expected. - """ - - input_features = { - "observation.image": PolicyFeature( - type=FeatureType.VISUAL, - shape=(3, 96, 96), - ), - "observation.state": PolicyFeature( - type=FeatureType.STATE, - shape=(10,), - ), - } - output_features = { - "action": PolicyFeature( - type=FeatureType.ACTION, - shape=(5,), - ), - } - - norm_map = { - "VISUAL": NormalizationMode.MEAN_STD, - "STATE": NormalizationMode.MIN_MAX, - "ACTION": NormalizationMode.MIN_MAX, - } - - dataset_stats = { - "observation.image": { - "mean": torch.randn(3, 1, 1), - "std": torch.randn(3, 1, 1), - "min": torch.randn(3, 1, 1), - "max": torch.randn(3, 1, 1), - }, - "observation.state": { - "mean": torch.randn(10), - "std": torch.randn(10), - "min": torch.randn(10), - "max": torch.randn(10), - }, - "action": { - "mean": torch.randn(5), - "std": torch.randn(5), - "min": torch.randn(5), - "max": torch.randn(5), - }, - } - - bsize = 2 - input_batch = { - "observation.image": torch.randn(bsize, 3, 96, 96), - "observation.state": torch.randn(bsize, 10), - } - output_batch = { - "action": torch.randn(bsize, 5), - } - - if insert_temporal_dim: - tdim = 4 - - for key in input_batch: - # [2,3,96,96] -> [2,tdim,3,96,96] - input_batch[key] = torch.stack([input_batch[key]] * tdim, dim=1) - - for key in output_batch: - output_batch[key] = torch.stack([output_batch[key]] * tdim, dim=1) - - # test without stats - normalize = Normalize(input_features, norm_map, stats=None) - with pytest.raises(AssertionError): - normalize(input_batch) - - # test with stats - normalize = Normalize(input_features, norm_map, stats=dataset_stats) - normalize(input_batch) - - # test loading pretrained models - new_normalize = Normalize(input_features, norm_map, stats=None) - new_normalize.load_state_dict(normalize.state_dict()) - new_normalize(input_batch) - - # test without stats - unnormalize = Unnormalize(output_features, norm_map, stats=None) - with pytest.raises(AssertionError): - unnormalize(output_batch) - - # test with stats - unnormalize = Unnormalize(output_features, norm_map, stats=dataset_stats) - unnormalize(output_batch) - - # test loading pretrained models - new_unnormalize = Unnormalize(output_features, norm_map, stats=None) - new_unnormalize.load_state_dict(unnormalize.state_dict()) - unnormalize(output_batch) - - @pytest.mark.parametrize("multikey", [True, False]) def test_multikey_construction(multikey: bool): """ @@ -375,13 +281,13 @@ def test_multikey_construction(multikey: bool): preventing erroneous creation of the policy object. """ input_features = { - "observation.state": PolicyFeature( + OBS_STATE: PolicyFeature( type=FeatureType.STATE, shape=(10,), ), } output_features = { - "action": PolicyFeature( + ACTION: PolicyFeature( type=FeatureType.ACTION, shape=(5,), ), @@ -391,14 +297,14 @@ def test_multikey_construction(multikey: bool): """Simulates the complete state/action is constructed from more granular multiple keys, of the same type as the overall state/action""" input_features = {} - input_features["observation.state.subset1"] = PolicyFeature(type=FeatureType.STATE, shape=(5,)) - input_features["observation.state.subset2"] = PolicyFeature(type=FeatureType.STATE, shape=(5,)) - input_features["observation.state"] = PolicyFeature(type=FeatureType.STATE, shape=(10,)) + input_features[f"{OBS_STATE}.subset1"] = PolicyFeature(type=FeatureType.STATE, shape=(5,)) + input_features[f"{OBS_STATE}.subset2"] = PolicyFeature(type=FeatureType.STATE, shape=(5,)) + input_features[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=(10,)) output_features = {} output_features["action.first_three_motors"] = PolicyFeature(type=FeatureType.ACTION, shape=(3,)) output_features["action.last_two_motors"] = PolicyFeature(type=FeatureType.ACTION, shape=(2,)) - output_features["action"] = PolicyFeature( + output_features[ACTION] = PolicyFeature( type=FeatureType.ACTION, shape=(5,), ) @@ -467,6 +373,8 @@ def test_backward_compatibility(ds_repo_id: str, policy_name: str, policy_kwargs NOTE: If the test does not pass, and you don't change the policy, it is likely that the test artifact is out of date. For example, some PyTorch versions have different randomness, see this PR: https://github.com/huggingface/lerobot/pull/1127. + NOTE: If the test don't pass and you don't change the policy, and note the dependencies version, + and you changed your processor, you might have to update the test artifact. """ diff --git a/tests/policies/test_sac_config.py b/tests/policies/test_sac_config.py index a67815eedaa..724c331fffc 100644 --- a/tests/policies/test_sac_config.py +++ b/tests/policies/test_sac_config.py @@ -25,6 +25,7 @@ PolicyConfig, SACConfig, ) +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE def test_sac_config_default_initialization(): @@ -37,15 +38,15 @@ def test_sac_config_default_initialization(): "ACTION": NormalizationMode.MIN_MAX, } assert config.dataset_stats == { - "observation.image": { + OBS_IMAGE: { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], }, - "observation.state": { + OBS_STATE: { "min": [0.0, 0.0], "max": [1.0, 1.0], }, - "action": { + ACTION: { "min": [0.0, 0.0, 0.0], "max": [1.0, 1.0, 1.0], }, @@ -68,7 +69,6 @@ def test_sac_config_default_initialization(): # Training parameters assert config.online_steps == 1000000 - assert config.online_env_seed == 10000 assert config.online_buffer_capacity == 100000 assert config.offline_buffer_capacity == 100000 assert config.async_prefetch is False @@ -90,15 +90,15 @@ def test_sac_config_default_initialization(): # Dataset stats defaults expected_dataset_stats = { - "observation.image": { + OBS_IMAGE: { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], }, - "observation.state": { + OBS_STATE: { "min": [0.0, 0.0], "max": [1.0, 1.0], }, - "action": { + ACTION: { "min": [0.0, 0.0, 0.0], "max": [1.0, 1.0, 1.0], }, @@ -191,8 +191,8 @@ def test_sac_config_custom_initialization(): def test_validate_features(): config = SACConfig( - input_features={"observation.state": PolicyFeature(type=FeatureType.STATE, shape=(10,))}, - output_features={"action": PolicyFeature(type=FeatureType.ACTION, shape=(3,))}, + input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))}, + output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))}, ) config.validate_features() @@ -200,7 +200,7 @@ def test_validate_features(): def test_validate_features_missing_observation(): config = SACConfig( input_features={"wrong_key": PolicyFeature(type=FeatureType.STATE, shape=(10,))}, - output_features={"action": PolicyFeature(type=FeatureType.ACTION, shape=(3,))}, + output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))}, ) with pytest.raises( ValueError, match="You must provide either 'observation.state' or an image observation" @@ -210,7 +210,7 @@ def test_validate_features_missing_observation(): def test_validate_features_missing_action(): config = SACConfig( - input_features={"observation.state": PolicyFeature(type=FeatureType.STATE, shape=(10,))}, + input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))}, output_features={"wrong_key": PolicyFeature(type=FeatureType.ACTION, shape=(3,))}, ) with pytest.raises(ValueError, match="You must provide 'action' in the output features"): diff --git a/tests/policies/test_sac_policy.py b/tests/policies/test_sac_policy.py index 7891c2e527a..8576883bd22 100644 --- a/tests/policies/test_sac_policy.py +++ b/tests/policies/test_sac_policy.py @@ -23,6 +23,7 @@ from lerobot.configs.types import FeatureType, PolicyFeature from lerobot.policies.sac.configuration_sac import SACConfig from lerobot.policies.sac.modeling_sac import MLP, SACPolicy +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE from lerobot.utils.random_utils import seeded_context, set_seed try: @@ -85,14 +86,14 @@ def test_sac_policy_with_default_args(): def create_dummy_state(batch_size: int, state_dim: int = 10) -> Tensor: return { - "observation.state": torch.randn(batch_size, state_dim), + OBS_STATE: torch.randn(batch_size, state_dim), } def create_dummy_with_visual_input(batch_size: int, state_dim: int = 10) -> Tensor: return { - "observation.image": torch.randn(batch_size, 3, 84, 84), - "observation.state": torch.randn(batch_size, state_dim), + OBS_IMAGE: torch.randn(batch_size, 3, 84, 84), + OBS_STATE: torch.randn(batch_size, state_dim), } @@ -104,7 +105,7 @@ def create_default_train_batch( batch_size: int = 8, state_dim: int = 10, action_dim: int = 10 ) -> dict[str, Tensor]: return { - "action": create_dummy_action(batch_size, action_dim), + ACTION: create_dummy_action(batch_size, action_dim), "reward": torch.randn(batch_size), "state": create_dummy_state(batch_size, state_dim), "next_state": create_dummy_state(batch_size, state_dim), @@ -116,7 +117,7 @@ def create_train_batch_with_visual_input( batch_size: int = 8, state_dim: int = 10, action_dim: int = 10 ) -> dict[str, Tensor]: return { - "action": create_dummy_action(batch_size, action_dim), + ACTION: create_dummy_action(batch_size, action_dim), "reward": torch.randn(batch_size), "state": create_dummy_with_visual_input(batch_size, state_dim), "next_state": create_dummy_with_visual_input(batch_size, state_dim), @@ -126,14 +127,14 @@ def create_train_batch_with_visual_input( def create_observation_batch(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]: return { - "observation.state": torch.randn(batch_size, state_dim), + OBS_STATE: torch.randn(batch_size, state_dim), } def create_observation_batch_with_visual_input(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]: return { - "observation.state": torch.randn(batch_size, state_dim), - "observation.image": torch.randn(batch_size, 3, 84, 84), + OBS_STATE: torch.randn(batch_size, state_dim), + OBS_IMAGE: torch.randn(batch_size, 3, 84, 84), } @@ -180,14 +181,14 @@ def create_default_config( action_dim += 1 config = SACConfig( - input_features={"observation.state": PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))}, - output_features={"action": PolicyFeature(type=FeatureType.ACTION, shape=(continuous_action_dim,))}, + input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))}, + output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(continuous_action_dim,))}, dataset_stats={ - "observation.state": { + OBS_STATE: { "min": [0.0] * state_dim, "max": [1.0] * state_dim, }, - "action": { + ACTION: { "min": [0.0] * continuous_action_dim, "max": [1.0] * continuous_action_dim, }, @@ -205,8 +206,8 @@ def create_config_with_visual_input( continuous_action_dim=continuous_action_dim, has_discrete_action=has_discrete_action, ) - config.input_features["observation.image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(3, 84, 84)) - config.dataset_stats["observation.image"] = { + config.input_features[OBS_IMAGE] = PolicyFeature(type=FeatureType.VISUAL, shape=(3, 84, 84)) + config.dataset_stats[OBS_IMAGE] = { "mean": torch.randn(3, 1, 1), "std": torch.randn(3, 1, 1), } diff --git a/tests/processor/test_act_processor.py b/tests/processor/test_act_processor.py new file mode 100644 index 00000000000..134cff6849f --- /dev/null +++ b/tests/processor/test_act_processor.py @@ -0,0 +1,412 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for ACT policy processor.""" + +import tempfile + +import pytest +import torch + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.policies.act.configuration_act import ACTConfig +from lerobot.policies.act.processor_act import make_act_pre_post_processors +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DataProcessorPipeline, + DeviceProcessorStep, + NormalizerProcessorStep, + RenameObservationsProcessorStep, + TransitionKey, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import create_transition, transition_to_batch +from lerobot.utils.constants import ACTION, OBS_STATE + + +def create_default_config(): + """Create a default ACT configuration for testing.""" + config = ACTConfig() + config.input_features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(7,)), + } + config.output_features = { + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(4,)), + } + config.normalization_mapping = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + config.device = "cpu" + return config + + +def create_default_stats(): + """Create default dataset statistics for testing.""" + return { + OBS_STATE: {"mean": torch.zeros(7), "std": torch.ones(7)}, + ACTION: {"mean": torch.zeros(4), "std": torch.ones(4)}, + } + + +def test_make_act_processor_basic(): + """Test basic creation of ACT processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_act_pre_post_processors(config, stats) + + # Check processor names + assert preprocessor.name == "policy_preprocessor" + assert postprocessor.name == "policy_postprocessor" + + # Check steps in preprocessor + assert len(preprocessor.steps) == 4 + assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep) + assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep) + assert isinstance(preprocessor.steps[2], DeviceProcessorStep) + assert isinstance(preprocessor.steps[3], NormalizerProcessorStep) + + # Check steps in postprocessor + assert len(postprocessor.steps) == 2 + assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep) + assert isinstance(postprocessor.steps[1], DeviceProcessorStep) + + +def test_act_processor_normalization(): + """Test that ACT processor correctly normalizes and unnormalizes data.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_act_pre_post_processors( + config, + stats, + ) + + # Create test data + observation = {OBS_STATE: torch.randn(7)} + action = torch.randn(4) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data is normalized and batched + assert processed[OBS_STATE].shape == (1, 7) + assert processed[TransitionKey.ACTION.value].shape == (1, 4) + + # Process action through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is unnormalized + assert postprocessed.shape == (1, 4) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_act_processor_cuda(): + """Test ACT processor with CUDA device.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, postprocessor = make_act_pre_post_processors( + config, + stats, + ) + + # Create CPU data + observation = {OBS_STATE: torch.randn(7)} + action = torch.randn(4) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data is on CUDA + assert processed[OBS_STATE].device.type == "cuda" + assert processed[TransitionKey.ACTION.value].device.type == "cuda" + + # Process through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is back on CPU + assert postprocessed.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_act_processor_accelerate_scenario(): + """Test ACT processor in simulated Accelerate scenario (data already on GPU).""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_act_pre_post_processors( + config, + stats, + ) + + # Simulate Accelerate: data already on GPU + device = torch.device("cuda:0") + observation = {OBS_STATE: torch.randn(1, 7).to(device)} # Already batched and on GPU + action = torch.randn(1, 4).to(device) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data stays on same GPU (not moved unnecessarily) + assert processed[OBS_STATE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_act_processor_multi_gpu(): + """Test ACT processor with multi-GPU setup.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_act_pre_post_processors( + config, + stats, + ) + + # Simulate data on different GPU (like in multi-GPU training) + device = torch.device("cuda:1") + observation = {OBS_STATE: torch.randn(1, 7).to(device)} + action = torch.randn(1, 4).to(device) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data stays on cuda:1 (not moved to cuda:0) + assert processed[OBS_STATE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +def test_act_processor_without_stats(): + """Test ACT processor creation without dataset statistics.""" + config = create_default_config() + + preprocessor, postprocessor = make_act_pre_post_processors( + config, + dataset_stats=None, + ) + + # Should still create processors, but normalization won't have stats + assert preprocessor is not None + assert postprocessor is not None + + # Process should still work (but won't normalize without stats) + observation = {OBS_STATE: torch.randn(7)} + action = torch.randn(4) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed is not None + + +def test_act_processor_save_and_load(): + """Test saving and loading ACT processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_act_pre_post_processors( + config, + stats, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + # Save preprocessor + preprocessor.save_pretrained(tmpdir) + + # Load preprocessor + loaded_preprocessor = DataProcessorPipeline.from_pretrained( + tmpdir, config_filename="policy_preprocessor.json" + ) + + # Test that loaded processor works + observation = {OBS_STATE: torch.randn(7)} + action = torch.randn(4) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = loaded_preprocessor(batch) + assert processed[OBS_STATE].shape == (1, 7) + assert processed[TransitionKey.ACTION.value].shape == (1, 4) + + +def test_act_processor_device_placement_preservation(): + """Test that ACT processor preserves device placement correctly.""" + config = create_default_config() + stats = create_default_stats() + + # Test with CPU config + config.device = "cpu" + preprocessor, _ = make_act_pre_post_processors( + config, + stats, + ) + + # Process CPU data + observation = {OBS_STATE: torch.randn(7)} + action = torch.randn(4) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed[OBS_STATE].device.type == "cpu" + assert processed[TransitionKey.ACTION.value].device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_act_processor_mixed_precision(): + """Test ACT processor with mixed precision (float16).""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + # Modify the device processor to use float16 + preprocessor, postprocessor = make_act_pre_post_processors( + config, + stats, + ) + + # Replace DeviceProcessorStep with one that uses float16 + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="float16")) + elif isinstance(step, NormalizerProcessorStep): + # Update normalizer to use the same device as the device processor + norm_step = step # Now type checker knows this is NormalizerProcessorStep + modified_steps.append( + NormalizerProcessorStep( + features=norm_step.features, + norm_map=norm_step.norm_map, + stats=norm_step.stats, + device=config.device, + dtype=torch.float16, # Match the float16 dtype + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Create test data + observation = {OBS_STATE: torch.randn(7, dtype=torch.float32)} + action = torch.randn(4, dtype=torch.float32) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data is converted to float16 + assert processed[OBS_STATE].dtype == torch.float16 + assert processed[TransitionKey.ACTION.value].dtype == torch.float16 + + +def test_act_processor_batch_consistency(): + """Test that ACT processor handles different batch sizes correctly.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_act_pre_post_processors( + config, + stats, + ) + + # Test single sample (unbatched) + observation = {OBS_STATE: torch.randn(7)} + action = torch.randn(4) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed[OBS_STATE].shape[0] == 1 # Batched + + # Test already batched data + observation_batched = {OBS_STATE: torch.randn(8, 7)} # Batch of 8 + action_batched = torch.randn(8, 4) + transition_batched = create_transition(observation_batched, action_batched) + batch_batched = transition_to_batch(transition_batched) + + processed_batched = preprocessor(batch_batched) + assert processed_batched[OBS_STATE].shape[0] == 8 + assert processed_batched[TransitionKey.ACTION.value].shape[0] == 8 + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_act_processor_bfloat16_device_float32_normalizer(): + """Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, _ = make_act_pre_post_processors( + config, + stats, + ) + + # Modify the pipeline to use bfloat16 device processor with float32 normalizer + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + # Device processor converts to bfloat16 + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16")) + elif isinstance(step, NormalizerProcessorStep): + # Normalizer stays configured as float32 (will auto-adapt to bfloat16) + norm_step = step # Now type checker knows this is NormalizerProcessorStep + modified_steps.append( + NormalizerProcessorStep( + features=norm_step.features, + norm_map=norm_step.norm_map, + stats=norm_step.stats, + device=config.device, + dtype=torch.float32, # Deliberately configured as float32 + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Verify initial normalizer configuration + normalizer_step = preprocessor.steps[3] # NormalizerProcessorStep + assert normalizer_step.dtype == torch.float32 + + # Create test data + observation = {OBS_STATE: torch.randn(7, dtype=torch.float32)} # Start with float32 + action = torch.randn(4, dtype=torch.float32) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through full pipeline + processed = preprocessor(batch) + + # Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16 + assert processed[OBS_STATE].dtype == torch.bfloat16 + assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16 + + # Verify normalizer automatically adapted its internal state + assert normalizer_step.dtype == torch.bfloat16 + for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 diff --git a/tests/processor/test_batch_conversion.py b/tests/processor/test_batch_conversion.py index 63894025d3e..477381618e6 100644 --- a/tests/processor/test_batch_conversion.py +++ b/tests/processor/test_batch_conversion.py @@ -1,84 +1,97 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import torch -from lerobot.processor.pipeline import ( - RobotProcessor, - TransitionKey, - _default_batch_to_transition, - _default_transition_to_batch, -) +from lerobot.processor import DataProcessorPipeline, TransitionKey +from lerobot.processor.converters import batch_to_transition, transition_to_batch +from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_PREFIX, OBS_STATE, REWARD, TRUNCATED def _dummy_batch(): """Create a dummy batch using the new format with observation.* and next.* keys.""" return { - "observation.image.left": torch.randn(1, 3, 128, 128), - "observation.image.right": torch.randn(1, 3, 128, 128), - "observation.state": torch.tensor([[0.1, 0.2, 0.3, 0.4]]), - "action": torch.tensor([[0.5]]), - "next.reward": 1.0, - "next.done": False, - "next.truncated": False, + f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128), + f"{OBS_IMAGE}.right": torch.randn(1, 3, 128, 128), + OBS_STATE: torch.tensor([[0.1, 0.2, 0.3, 0.4]]), + ACTION: torch.tensor([[0.5]]), + REWARD: 1.0, + DONE: False, + TRUNCATED: False, "info": {"key": "value"}, } def test_observation_grouping_roundtrip(): """Test that observation.* keys are properly grouped and ungrouped.""" - proc = RobotProcessor([]) + proc = DataProcessorPipeline([]) batch_in = _dummy_batch() batch_out = proc(batch_in) # Check that all observation.* keys are preserved - original_obs_keys = {k: v for k, v in batch_in.items() if k.startswith("observation.")} - reconstructed_obs_keys = {k: v for k, v in batch_out.items() if k.startswith("observation.")} + original_obs_keys = {k: v for k, v in batch_in.items() if k.startswith(OBS_PREFIX)} + reconstructed_obs_keys = {k: v for k, v in batch_out.items() if k.startswith(OBS_PREFIX)} assert set(original_obs_keys.keys()) == set(reconstructed_obs_keys.keys()) # Check tensor values - assert torch.allclose(batch_out["observation.image.left"], batch_in["observation.image.left"]) - assert torch.allclose(batch_out["observation.image.right"], batch_in["observation.image.right"]) - assert torch.allclose(batch_out["observation.state"], batch_in["observation.state"]) + assert torch.allclose(batch_out[f"{OBS_IMAGE}.left"], batch_in[f"{OBS_IMAGE}.left"]) + assert torch.allclose(batch_out[f"{OBS_IMAGE}.right"], batch_in[f"{OBS_IMAGE}.right"]) + assert torch.allclose(batch_out[OBS_STATE], batch_in[OBS_STATE]) # Check other fields - assert torch.allclose(batch_out["action"], batch_in["action"]) - assert batch_out["next.reward"] == batch_in["next.reward"] - assert batch_out["next.done"] == batch_in["next.done"] - assert batch_out["next.truncated"] == batch_in["next.truncated"] + assert torch.allclose(batch_out[ACTION], batch_in[ACTION]) + assert batch_out[REWARD] == batch_in[REWARD] + assert batch_out[DONE] == batch_in[DONE] + assert batch_out[TRUNCATED] == batch_in[TRUNCATED] assert batch_out["info"] == batch_in["info"] def test_batch_to_transition_observation_grouping(): - """Test that _default_batch_to_transition correctly groups observation.* keys.""" + """Test that batch_to_transition correctly groups observation.* keys.""" batch = { - "observation.image.top": torch.randn(1, 3, 128, 128), - "observation.image.left": torch.randn(1, 3, 128, 128), - "observation.state": [1, 2, 3, 4], - "action": "action_data", - "next.reward": 1.5, - "next.done": True, - "next.truncated": False, + f"{OBS_IMAGE}.top": torch.randn(1, 3, 128, 128), + f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128), + OBS_STATE: [1, 2, 3, 4], + ACTION: torch.tensor([0.1, 0.2, 0.3, 0.4]), + REWARD: 1.5, + DONE: True, + TRUNCATED: False, "info": {"episode": 42}, } - transition = _default_batch_to_transition(batch) + transition = batch_to_transition(batch) # Check observation is a dict with all observation.* keys assert isinstance(transition[TransitionKey.OBSERVATION], dict) - assert "observation.image.top" in transition[TransitionKey.OBSERVATION] - assert "observation.image.left" in transition[TransitionKey.OBSERVATION] - assert "observation.state" in transition[TransitionKey.OBSERVATION] + assert f"{OBS_IMAGE}.top" in transition[TransitionKey.OBSERVATION] + assert f"{OBS_IMAGE}.left" in transition[TransitionKey.OBSERVATION] + assert OBS_STATE in transition[TransitionKey.OBSERVATION] # Check values are preserved assert torch.allclose( - transition[TransitionKey.OBSERVATION]["observation.image.top"], batch["observation.image.top"] + transition[TransitionKey.OBSERVATION][f"{OBS_IMAGE}.top"], batch[f"{OBS_IMAGE}.top"] ) assert torch.allclose( - transition[TransitionKey.OBSERVATION]["observation.image.left"], batch["observation.image.left"] + transition[TransitionKey.OBSERVATION][f"{OBS_IMAGE}.left"], batch[f"{OBS_IMAGE}.left"] ) - assert transition[TransitionKey.OBSERVATION]["observation.state"] == [1, 2, 3, 4] + assert transition[TransitionKey.OBSERVATION][OBS_STATE] == [1, 2, 3, 4] # Check other fields - assert transition[TransitionKey.ACTION] == "action_data" + assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([0.1, 0.2, 0.3, 0.4])) assert transition[TransitionKey.REWARD] == 1.5 assert transition[TransitionKey.DONE] assert not transition[TransitionKey.TRUNCATED] @@ -87,11 +100,11 @@ def test_batch_to_transition_observation_grouping(): def test_transition_to_batch_observation_flattening(): - """Test that _default_transition_to_batch correctly flattens observation dict.""" + """Test that transition_to_batch correctly flattens observation dict.""" observation_dict = { - "observation.image.top": torch.randn(1, 3, 128, 128), - "observation.image.left": torch.randn(1, 3, 128, 128), - "observation.state": [1, 2, 3, 4], + f"{OBS_IMAGE}.top": torch.randn(1, 3, 128, 128), + f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128), + OBS_STATE: [1, 2, 3, 4], } transition = { @@ -104,66 +117,66 @@ def test_transition_to_batch_observation_flattening(): TransitionKey.COMPLEMENTARY_DATA: {}, } - batch = _default_transition_to_batch(transition) + batch = transition_to_batch(transition) # Check that observation.* keys are flattened back to batch - assert "observation.image.top" in batch - assert "observation.image.left" in batch - assert "observation.state" in batch + assert f"{OBS_IMAGE}.top" in batch + assert f"{OBS_IMAGE}.left" in batch + assert OBS_STATE in batch # Check values are preserved - assert torch.allclose(batch["observation.image.top"], observation_dict["observation.image.top"]) - assert torch.allclose(batch["observation.image.left"], observation_dict["observation.image.left"]) - assert batch["observation.state"] == [1, 2, 3, 4] + assert torch.allclose(batch[f"{OBS_IMAGE}.top"], observation_dict[f"{OBS_IMAGE}.top"]) + assert torch.allclose(batch[f"{OBS_IMAGE}.left"], observation_dict[f"{OBS_IMAGE}.left"]) + assert batch[OBS_STATE] == [1, 2, 3, 4] # Check other fields are mapped to next.* format - assert batch["action"] == "action_data" - assert batch["next.reward"] == 1.5 - assert batch["next.done"] - assert not batch["next.truncated"] + assert batch[ACTION] == "action_data" + assert batch[REWARD] == 1.5 + assert batch[DONE] + assert not batch[TRUNCATED] assert batch["info"] == {"episode": 42} def test_no_observation_keys(): """Test behavior when there are no observation.* keys.""" batch = { - "action": "action_data", - "next.reward": 2.0, - "next.done": False, - "next.truncated": True, + ACTION: torch.tensor([1.0, 2.0]), + REWARD: 2.0, + DONE: False, + TRUNCATED: True, "info": {"test": "no_obs"}, } - transition = _default_batch_to_transition(batch) + transition = batch_to_transition(batch) # Observation should be None when no observation.* keys assert transition[TransitionKey.OBSERVATION] is None # Check other fields - assert transition[TransitionKey.ACTION] == "action_data" + assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([1.0, 2.0])) assert transition[TransitionKey.REWARD] == 2.0 assert not transition[TransitionKey.DONE] assert transition[TransitionKey.TRUNCATED] assert transition[TransitionKey.INFO] == {"test": "no_obs"} # Round trip should work - reconstructed_batch = _default_transition_to_batch(transition) - assert reconstructed_batch["action"] == "action_data" - assert reconstructed_batch["next.reward"] == 2.0 - assert not reconstructed_batch["next.done"] - assert reconstructed_batch["next.truncated"] + reconstructed_batch = transition_to_batch(transition) + assert torch.allclose(reconstructed_batch[ACTION], torch.tensor([1.0, 2.0])) + assert reconstructed_batch[REWARD] == 2.0 + assert not reconstructed_batch[DONE] + assert reconstructed_batch[TRUNCATED] assert reconstructed_batch["info"] == {"test": "no_obs"} def test_minimal_batch(): """Test with minimal batch containing only observation.* and action.""" - batch = {"observation.state": "minimal_state", "action": "minimal_action"} + batch = {OBS_STATE: "minimal_state", ACTION: torch.tensor([0.5])} - transition = _default_batch_to_transition(batch) + transition = batch_to_transition(batch) # Check observation - assert transition[TransitionKey.OBSERVATION] == {"observation.state": "minimal_state"} - assert transition[TransitionKey.ACTION] == "minimal_action" + assert transition[TransitionKey.OBSERVATION] == {OBS_STATE: "minimal_state"} + assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([0.5])) # Check defaults assert transition[TransitionKey.REWARD] == 0.0 @@ -173,12 +186,12 @@ def test_minimal_batch(): assert transition[TransitionKey.COMPLEMENTARY_DATA] == {} # Round trip - reconstructed_batch = _default_transition_to_batch(transition) - assert reconstructed_batch["observation.state"] == "minimal_state" - assert reconstructed_batch["action"] == "minimal_action" - assert reconstructed_batch["next.reward"] == 0.0 - assert not reconstructed_batch["next.done"] - assert not reconstructed_batch["next.truncated"] + reconstructed_batch = transition_to_batch(transition) + assert reconstructed_batch[OBS_STATE] == "minimal_state" + assert torch.allclose(reconstructed_batch[ACTION], torch.tensor([0.5])) + assert reconstructed_batch[REWARD] == 0.0 + assert not reconstructed_batch[DONE] + assert not reconstructed_batch[TRUNCATED] assert reconstructed_batch["info"] == {} @@ -186,7 +199,7 @@ def test_empty_batch(): """Test behavior with empty batch.""" batch = {} - transition = _default_batch_to_transition(batch) + transition = batch_to_transition(batch) # All fields should have defaults assert transition[TransitionKey.OBSERVATION] is None @@ -198,54 +211,54 @@ def test_empty_batch(): assert transition[TransitionKey.COMPLEMENTARY_DATA] == {} # Round trip - reconstructed_batch = _default_transition_to_batch(transition) - assert reconstructed_batch["action"] is None - assert reconstructed_batch["next.reward"] == 0.0 - assert not reconstructed_batch["next.done"] - assert not reconstructed_batch["next.truncated"] + reconstructed_batch = transition_to_batch(transition) + assert reconstructed_batch[ACTION] is None + assert reconstructed_batch[REWARD] == 0.0 + assert not reconstructed_batch[DONE] + assert not reconstructed_batch[TRUNCATED] assert reconstructed_batch["info"] == {} def test_complex_nested_observation(): """Test with complex nested observation data.""" batch = { - "observation.image.top": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567890}, - "observation.image.left": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567891}, - "observation.state": torch.randn(7), - "action": torch.randn(8), - "next.reward": 3.14, - "next.done": False, - "next.truncated": True, + f"{OBS_IMAGE}.top": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567890}, + f"{OBS_IMAGE}.left": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567891}, + OBS_STATE: torch.randn(7), + ACTION: torch.randn(8), + REWARD: 3.14, + DONE: False, + TRUNCATED: True, "info": {"episode_length": 200, "success": True}, } - transition = _default_batch_to_transition(batch) - reconstructed_batch = _default_transition_to_batch(transition) + transition = batch_to_transition(batch) + reconstructed_batch = transition_to_batch(transition) # Check that all observation keys are preserved - original_obs_keys = {k for k in batch if k.startswith("observation.")} - reconstructed_obs_keys = {k for k in reconstructed_batch if k.startswith("observation.")} + original_obs_keys = {k for k in batch if k.startswith(OBS_PREFIX)} + reconstructed_obs_keys = {k for k in reconstructed_batch if k.startswith(OBS_PREFIX)} assert original_obs_keys == reconstructed_obs_keys # Check tensor values - assert torch.allclose(batch["observation.state"], reconstructed_batch["observation.state"]) + assert torch.allclose(batch[OBS_STATE], reconstructed_batch[OBS_STATE]) # Check nested dict with tensors assert torch.allclose( - batch["observation.image.top"]["image"], reconstructed_batch["observation.image.top"]["image"] + batch[f"{OBS_IMAGE}.top"]["image"], reconstructed_batch[f"{OBS_IMAGE}.top"]["image"] ) assert torch.allclose( - batch["observation.image.left"]["image"], reconstructed_batch["observation.image.left"]["image"] + batch[f"{OBS_IMAGE}.left"]["image"], reconstructed_batch[f"{OBS_IMAGE}.left"]["image"] ) # Check action tensor - assert torch.allclose(batch["action"], reconstructed_batch["action"]) + assert torch.allclose(batch[ACTION], reconstructed_batch[ACTION]) # Check other fields - assert batch["next.reward"] == reconstructed_batch["next.reward"] - assert batch["next.done"] == reconstructed_batch["next.done"] - assert batch["next.truncated"] == reconstructed_batch["next.truncated"] + assert batch[REWARD] == reconstructed_batch[REWARD] + assert batch[DONE] == reconstructed_batch[DONE] + assert batch[TRUNCATED] == reconstructed_batch[TRUNCATED] assert batch["info"] == reconstructed_batch["info"] @@ -254,7 +267,7 @@ def test_custom_converter(): def to_tr(batch): # Custom converter that modifies the reward - tr = _default_batch_to_transition(batch) + tr = batch_to_transition(batch) # Double the reward reward = tr.get(TransitionKey.REWARD, 0.0) new_tr = tr.copy() @@ -262,21 +275,21 @@ def to_tr(batch): return new_tr def to_batch(tr): - batch = _default_transition_to_batch(tr) + batch = transition_to_batch(tr) return batch - processor = RobotProcessor(steps=[], to_transition=to_tr, to_output=to_batch) + processor = DataProcessorPipeline(steps=[], to_transition=to_tr, to_output=to_batch) batch = { - "observation.state": torch.randn(1, 4), - "action": torch.randn(1, 2), - "next.reward": 1.0, - "next.done": False, + OBS_STATE: torch.randn(1, 4), + ACTION: torch.randn(1, 2), + REWARD: 1.0, + DONE: False, } result = processor(batch) # Check the reward was doubled by our custom converter - assert result["next.reward"] == 2.0 - assert torch.allclose(result["observation.state"], batch["observation.state"]) - assert torch.allclose(result["action"], batch["action"]) + assert result[REWARD] == 2.0 + assert torch.allclose(result[OBS_STATE], batch[OBS_STATE]) + assert torch.allclose(result[ACTION], batch[ACTION]) diff --git a/tests/processor/test_batch_processor.py b/tests/processor/test_batch_processor.py new file mode 100644 index 00000000000..5c94b06570c --- /dev/null +++ b/tests/processor/test_batch_processor.py @@ -0,0 +1,1184 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tempfile +from pathlib import Path + +import numpy as np +import pytest +import torch + +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DataProcessorPipeline, + ProcessorStepRegistry, + TransitionKey, +) +from lerobot.processor.converters import create_transition, identity_transition +from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE + + +def test_state_1d_to_2d(): + """Test that 1D state tensors get unsqueezed to 2D.""" + processor = AddBatchDimensionProcessorStep() + + # Test observation.state + state_1d = torch.randn(7) + observation = {OBS_STATE: state_1d} + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + processed_state = result[TransitionKey.OBSERVATION][OBS_STATE] + assert processed_state.shape == (1, 7) + assert torch.allclose(processed_state.squeeze(0), state_1d) + + +def test_env_state_1d_to_2d(): + """Test that 1D environment state tensors get unsqueezed to 2D.""" + processor = AddBatchDimensionProcessorStep() + + # Test observation.environment_state + env_state_1d = torch.randn(10) + observation = {OBS_ENV_STATE: env_state_1d} + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + processed_env_state = result[TransitionKey.OBSERVATION][OBS_ENV_STATE] + assert processed_env_state.shape == (1, 10) + assert torch.allclose(processed_env_state.squeeze(0), env_state_1d) + + +def test_image_3d_to_4d(): + """Test that 3D image tensors get unsqueezed to 4D.""" + processor = AddBatchDimensionProcessorStep() + + # Test observation.image + image_3d = torch.randn(224, 224, 3) + observation = {OBS_IMAGE: image_3d} + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + processed_image = result[TransitionKey.OBSERVATION][OBS_IMAGE] + assert processed_image.shape == (1, 224, 224, 3) + assert torch.allclose(processed_image.squeeze(0), image_3d) + + +def test_multiple_images_3d_to_4d(): + """Test that 3D image tensors in observation.images.* get unsqueezed to 4D.""" + processor = AddBatchDimensionProcessorStep() + + # Test observation.images.camera1 and observation.images.camera2 + image1_3d = torch.randn(64, 64, 3) + image2_3d = torch.randn(128, 128, 3) + observation = { + f"{OBS_IMAGES}.camera1": image1_3d, + f"{OBS_IMAGES}.camera2": image2_3d, + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + processed_obs = result[TransitionKey.OBSERVATION] + processed_image1 = processed_obs[f"{OBS_IMAGES}.camera1"] + processed_image2 = processed_obs[f"{OBS_IMAGES}.camera2"] + + assert processed_image1.shape == (1, 64, 64, 3) + assert processed_image2.shape == (1, 128, 128, 3) + assert torch.allclose(processed_image1.squeeze(0), image1_3d) + assert torch.allclose(processed_image2.squeeze(0), image2_3d) + + +def test_already_batched_tensors_unchanged(): + """Test that already batched tensors remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + # Create already batched tensors + state_2d = torch.randn(1, 7) + env_state_2d = torch.randn(1, 10) + image_4d = torch.randn(1, 224, 224, 3) + + observation = { + OBS_STATE: state_2d, + OBS_ENV_STATE: env_state_2d, + OBS_IMAGE: image_4d, + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + processed_obs = result[TransitionKey.OBSERVATION] + + # Should remain unchanged + assert torch.allclose(processed_obs[OBS_STATE], state_2d) + assert torch.allclose(processed_obs[OBS_ENV_STATE], env_state_2d) + assert torch.allclose(processed_obs[OBS_IMAGE], image_4d) + + +def test_higher_dimensional_tensors_unchanged(): + """Test that tensors with more dimensions than expected remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + # Create tensors with more dimensions + state_3d = torch.randn(2, 7, 5) # More than 1D + image_5d = torch.randn(2, 3, 224, 224, 1) # More than 3D + + observation = { + OBS_STATE: state_3d, + OBS_IMAGE: image_5d, + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + processed_obs = result[TransitionKey.OBSERVATION] + + # Should remain unchanged + assert torch.allclose(processed_obs[OBS_STATE], state_3d) + assert torch.allclose(processed_obs[OBS_IMAGE], image_5d) + + +def test_non_tensor_values_unchanged(): + """Test that non-tensor values in observations remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + observation = { + OBS_STATE: [1, 2, 3], # List, not tensor + OBS_IMAGE: "not_a_tensor", # String + "custom_key": 42, # Integer + "another_key": {"nested": "dict"}, # Dict + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + processed_obs = result[TransitionKey.OBSERVATION] + + # Should remain unchanged + assert processed_obs[OBS_STATE] == [1, 2, 3] + assert processed_obs[OBS_IMAGE] == "not_a_tensor" + assert processed_obs["custom_key"] == 42 + assert processed_obs["another_key"] == {"nested": "dict"} + + +def test_none_observation(): + """Test processor handles None observation gracefully.""" + processor = AddBatchDimensionProcessorStep() + + transition = create_transition(observation={}, action=torch.empty(0)) + result = processor(transition) + + assert result[TransitionKey.OBSERVATION] == {} + + +def test_empty_observation(): + """Test processor handles empty observation dict.""" + processor = AddBatchDimensionProcessorStep() + + observation = {} + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + + assert result[TransitionKey.OBSERVATION] == {} + + +def test_mixed_observation(): + """Test processor with mixed observation containing various types and dimensions.""" + processor = AddBatchDimensionProcessorStep() + + state_1d = torch.randn(5) + env_state_2d = torch.randn(1, 8) # Already batched + image_3d = torch.randn(32, 32, 3) + other_tensor = torch.randn(3, 3, 3, 3) # 4D, should be unchanged + + observation = { + OBS_STATE: state_1d, + OBS_ENV_STATE: env_state_2d, + OBS_IMAGE: image_3d, + f"{OBS_IMAGES}.front": torch.randn(64, 64, 3), # 3D, should be batched + f"{OBS_IMAGES}.back": torch.randn(1, 64, 64, 3), # 4D, should be unchanged + "other_tensor": other_tensor, + "non_tensor": "string_value", + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + processed_obs = result[TransitionKey.OBSERVATION] + + # Check transformations + assert processed_obs[OBS_STATE].shape == (1, 5) + assert processed_obs[OBS_ENV_STATE].shape == (1, 8) # Unchanged + assert processed_obs[OBS_IMAGE].shape == (1, 32, 32, 3) + assert processed_obs[f"{OBS_IMAGES}.front"].shape == (1, 64, 64, 3) + assert processed_obs[f"{OBS_IMAGES}.back"].shape == (1, 64, 64, 3) # Unchanged + assert processed_obs["other_tensor"].shape == (3, 3, 3, 3) # Unchanged + assert processed_obs["non_tensor"] == "string_value" # Unchanged + + +def test_integration_with_robot_processor(): + """Test AddBatchDimensionProcessorStep integration with RobotProcessor.""" + to_batch_processor = AddBatchDimensionProcessorStep() + pipeline = DataProcessorPipeline( + [to_batch_processor], to_transition=identity_transition, to_output=identity_transition + ) + + # Create unbatched observation + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: torch.randn(224, 224, 3), + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = pipeline(transition) + processed_obs = result[TransitionKey.OBSERVATION] + + assert processed_obs[OBS_STATE].shape == (1, 7) + assert processed_obs[OBS_IMAGE].shape == (1, 224, 224, 3) + + +def test_serialization_methods(): + """Test get_config, state_dict, load_state_dict, and reset methods.""" + processor = AddBatchDimensionProcessorStep() + + # Test get_config + config = processor.get_config() + assert isinstance(config, dict) + assert config == {} + + # Test state_dict + state = processor.state_dict() + assert isinstance(state, dict) + assert state == {} + + # Test load_state_dict (should not raise an error) + processor.load_state_dict({}) + + # Test reset (should not raise an error) + processor.reset() + + +def test_save_and_load_pretrained(): + """Test saving and loading AddBatchDimensionProcessorStep with RobotProcessor.""" + processor = AddBatchDimensionProcessorStep() + pipeline = DataProcessorPipeline( + [processor], name="BatchPipeline", to_transition=identity_transition, to_output=identity_transition + ) + + with tempfile.TemporaryDirectory() as tmp_dir: + # Save pipeline + pipeline.save_pretrained(tmp_dir) + + # Check config file exists + config_path = Path(tmp_dir) / "batchpipeline.json" + assert config_path.exists() + + # Load pipeline + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="batchpipeline.json", + to_transition=identity_transition, + to_output=identity_transition, + ) + + assert loaded_pipeline.name == "BatchPipeline" + assert len(loaded_pipeline) == 1 + assert isinstance(loaded_pipeline.steps[0], AddBatchDimensionProcessorStep) + + # Test functionality of loaded processor + observation = {OBS_STATE: torch.randn(5)} + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = loaded_pipeline(transition) + assert result[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 5) + + +def test_registry_functionality(): + """Test that AddBatchDimensionProcessorStep is properly registered.""" + # Check that the processor is registered + registered_class = ProcessorStepRegistry.get("to_batch_processor") + assert registered_class is AddBatchDimensionProcessorStep + + # Check that it's in the list of registered processors + assert "to_batch_processor" in ProcessorStepRegistry.list() + + +def test_registry_based_save_load(): + """Test saving and loading using registry name.""" + processor = AddBatchDimensionProcessorStep() + pipeline = DataProcessorPipeline( + [processor], to_transition=identity_transition, to_output=identity_transition + ) + + with tempfile.TemporaryDirectory() as tmp_dir: + pipeline.save_pretrained(tmp_dir) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + to_transition=identity_transition, + to_output=identity_transition, + ) + + # Verify the loaded processor works + observation = { + OBS_STATE: torch.randn(3), + OBS_IMAGE: torch.randn(100, 100, 3), + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = loaded_pipeline(transition) + processed_obs = result[TransitionKey.OBSERVATION] + + assert processed_obs[OBS_STATE].shape == (1, 3) + assert processed_obs[OBS_IMAGE].shape == (1, 100, 100, 3) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_device_compatibility(): + """Test processor works with tensors on different devices.""" + processor = AddBatchDimensionProcessorStep() + + # Create tensors on GPU + state_1d = torch.randn(7, device="cuda") + image_3d = torch.randn(64, 64, 3, device="cuda") + + observation = { + OBS_STATE: state_1d, + OBS_IMAGE: image_3d, + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + processed_obs = result[TransitionKey.OBSERVATION] + + # Check shapes and that tensors stayed on GPU + assert processed_obs[OBS_STATE].shape == (1, 7) + assert processed_obs[OBS_IMAGE].shape == (1, 64, 64, 3) + assert processed_obs[OBS_STATE].device.type == "cuda" + assert processed_obs[OBS_IMAGE].device.type == "cuda" + + +def test_processor_preserves_other_transition_keys(): + """Test that processor only modifies observation and preserves other transition keys.""" + processor = AddBatchDimensionProcessorStep() + + action = torch.randn(5) + reward = 1.5 + done = True + truncated = False + info = {"step": 10} + comp_data = {"extra": "data"} + + observation = {OBS_STATE: torch.randn(7)} + + transition = create_transition( + observation=observation, + action=action, + reward=reward, + done=done, + truncated=truncated, + info=info, + complementary_data=comp_data, + ) + + result = processor(transition) + + # Check that non-observation keys are preserved + assert torch.allclose(result[TransitionKey.ACTION], action) + assert result[TransitionKey.REWARD] == reward + assert result[TransitionKey.DONE] == done + assert result[TransitionKey.TRUNCATED] == truncated + assert result[TransitionKey.INFO] == info + assert result[TransitionKey.COMPLEMENTARY_DATA] == comp_data + + # Check that observation was processed + assert result[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 7) + + +def test_edge_case_zero_dimensional_tensors(): + """Test processor handles 0D tensors (scalars) correctly.""" + processor = AddBatchDimensionProcessorStep() + + # 0D tensors should not be modified + scalar_tensor = torch.tensor(42.0) + + observation = { + OBS_STATE: scalar_tensor, + "scalar_value": scalar_tensor, + } + transition = create_transition(observation=observation, action=torch.empty(0)) + + result = processor(transition) + processed_obs = result[TransitionKey.OBSERVATION] + + # 0D tensors should remain unchanged + assert torch.allclose(processed_obs[OBS_STATE], scalar_tensor) + assert torch.allclose(processed_obs["scalar_value"], scalar_tensor) + + +# Action-specific tests +def test_action_1d_to_2d(): + """Test that 1D action tensors get batch dimension added.""" + processor = AddBatchDimensionProcessorStep() + + # Create 1D action tensor + action_1d = torch.randn(4) + transition = create_transition(observation={}, action=action_1d) + + result = processor(transition) + + # Should add batch dimension + assert result[TransitionKey.ACTION].shape == (1, 4) + assert torch.equal(result[TransitionKey.ACTION][0], action_1d) + + +def test_action_already_batched(): + """Test that already batched action tensors remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + # Test various batch sizes + action_batched_1 = torch.randn(1, 4) + action_batched_5 = torch.randn(5, 4) + + # Single batch + transition = create_transition(action=action_batched_1, observation={}) + result = processor(transition) + assert torch.equal(result[TransitionKey.ACTION], action_batched_1) + + # Multiple batch + transition = create_transition(action=action_batched_5, observation={}) + result = processor(transition) + assert torch.equal(result[TransitionKey.ACTION], action_batched_5) + + +def test_action_higher_dimensional(): + """Test that higher dimensional action tensors remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + # 3D action tensor (e.g., sequence of actions) + action_3d = torch.randn(2, 4, 3) + transition = create_transition(action=action_3d, observation={}) + result = processor(transition) + assert torch.equal(result[TransitionKey.ACTION], action_3d) + + # 4D action tensor + action_4d = torch.randn(2, 10, 4, 3) + transition = create_transition(action=action_4d, observation={}) + result = processor(transition) + assert torch.equal(result[TransitionKey.ACTION], action_4d) + + +def test_action_scalar_tensor(): + """Test that scalar (0D) action tensors remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + action_scalar = torch.tensor(1.5) + transition = create_transition(action=action_scalar, observation={}) + result = processor(transition) + + # Should remain scalar + assert result[TransitionKey.ACTION].dim() == 0 + assert torch.equal(result[TransitionKey.ACTION], action_scalar) + + +def test_action_non_tensor_raises_error(): + """Test that non-tensor actions raise ValueError for PolicyAction processors.""" + processor = AddBatchDimensionProcessorStep() + + # List action should raise error + action_list = [0.1, 0.2, 0.3, 0.4] + transition = create_transition(action=action_list) + with pytest.raises(ValueError, match="Action should be a PolicyAction type"): + processor(transition) + + # Numpy array action should raise error + action_numpy = np.array([1, 2, 3, 4]) + transition = create_transition(action=action_numpy) + with pytest.raises(ValueError, match="Action should be a PolicyAction type"): + processor(transition) + + # String action should raise error + action_string = "forward" + transition = create_transition(action=action_string) + with pytest.raises(ValueError, match="Action should be a PolicyAction type"): + processor(transition) + + # Dict action should raise error + action_dict = {"linear": [0.5, 0.0], "angular": 0.2} + transition = create_transition(action=action_dict) + with pytest.raises(ValueError, match="Action should be a PolicyAction type"): + processor(transition) + + +def test_action_none(): + """Test that empty action tensor is handled correctly.""" + processor = AddBatchDimensionProcessorStep() + + transition = create_transition(action=torch.empty(0), observation={}) + result = processor(transition) + # Empty 1D tensor becomes empty 2D tensor with batch dimension + assert result[TransitionKey.ACTION].shape == (1, 0) + + +def test_action_with_observation(): + """Test action processing together with observation processing.""" + processor = AddBatchDimensionProcessorStep() + + # Both need batching + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: torch.randn(64, 64, 3), + } + action = torch.randn(4) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Both should be batched + assert result[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 7) + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (1, 64, 64, 3) + assert result[TransitionKey.ACTION].shape == (1, 4) + + +def test_action_different_sizes(): + """Test action processing with various action dimensions.""" + processor = AddBatchDimensionProcessorStep() + + # Different action sizes (robot with different DOF) + action_sizes = [1, 2, 4, 7, 10, 20] + + for size in action_sizes: + action = torch.randn(size) + transition = create_transition(action=action, observation={}) + result = processor(transition) + + assert result[TransitionKey.ACTION].shape == (1, size) + assert torch.equal(result[TransitionKey.ACTION][0], action) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_action_device_compatibility(): + """Test action processing on different devices.""" + processor = AddBatchDimensionProcessorStep() + + # CUDA action + action_cuda = torch.randn(4, device="cuda") + transition = create_transition(action=action_cuda, observation={}) + result = processor(transition) + + assert result[TransitionKey.ACTION].shape == (1, 4) + assert result[TransitionKey.ACTION].device.type == "cuda" + + # CPU action + action_cpu = torch.randn(4, device="cpu") + transition = create_transition(action=action_cpu, observation={}) + result = processor(transition) + + assert result[TransitionKey.ACTION].shape == (1, 4) + assert result[TransitionKey.ACTION].device.type == "cpu" + + +def test_action_dtype_preservation(): + """Test that action dtype is preserved during processing.""" + processor = AddBatchDimensionProcessorStep() + + # Different dtypes + dtypes = [torch.float32, torch.float64, torch.int32, torch.int64] + + for dtype in dtypes: + action = torch.randn(4).to(dtype) + transition = create_transition(action=action, observation={}) + result = processor(transition) + + assert result[TransitionKey.ACTION].dtype == dtype + assert result[TransitionKey.ACTION].shape == (1, 4) + + +def test_empty_action_tensor(): + """Test handling of empty action tensors.""" + processor = AddBatchDimensionProcessorStep() + + # Empty 1D tensor + action_empty = torch.tensor([]) + transition = create_transition(action=action_empty, observation={}) + result = processor(transition) + + # Should add batch dimension even to empty tensor + assert result[TransitionKey.ACTION].shape == (1, 0) + + # Empty 2D tensor (already batched) + action_empty_2d = torch.randn(1, 0) + transition = create_transition(action=action_empty_2d, observation={}) + result = processor(transition) + + # Should remain unchanged + assert result[TransitionKey.ACTION].shape == (1, 0) + + +# Task-specific tests +def test_task_string_to_list(): + """Test that string tasks get wrapped in lists to add batch dimension.""" + processor = AddBatchDimensionProcessorStep() + + # Create complementary data with string task + complementary_data = {"task": "pick_cube"} + transition = create_transition( + action=torch.empty(0), observation={}, complementary_data=complementary_data + ) + + result = processor(transition) + + # String task should be wrapped in list + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == ["pick_cube"] + assert isinstance(processed_comp_data["task"], list) + assert len(processed_comp_data["task"]) == 1 + + +def test_task_string_validation(): + """Test that only string and list of strings are valid task values.""" + processor = AddBatchDimensionProcessorStep() + + # Valid string task - should be converted to list + complementary_data = {"task": "valid_task"} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + result = processor(transition) + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == ["valid_task"] + + # Valid list of strings - should remain unchanged + complementary_data = {"task": ["task1", "task2"]} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + result = processor(transition) + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == ["task1", "task2"] + + +def test_task_list_of_strings(): + """Test that lists of strings remain unchanged (already batched).""" + processor = AddBatchDimensionProcessorStep() + + # Test various list of strings + test_lists = [ + ["pick_cube"], # Single string in list + ["pick_cube", "place_cube"], # Multiple strings + ["task1", "task2", "task3"], # Three strings + [], # Empty list + [""], # List with empty string + ["task with spaces", "task_with_underscores"], # Mixed formats + ] + + for task_list in test_lists: + complementary_data = {"task": task_list} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + # Should remain unchanged since it's already a list + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == task_list + assert isinstance(processed_comp_data["task"], list) + + +def test_complementary_data_none(): + """Test processor handles None complementary_data gracefully.""" + processor = AddBatchDimensionProcessorStep() + + transition = create_transition(complementary_data=None, action=torch.empty(0), observation={}) + result = processor(transition) + + assert result[TransitionKey.COMPLEMENTARY_DATA] == {} + + +def test_complementary_data_empty(): + """Test processor handles empty complementary_data dict.""" + processor = AddBatchDimensionProcessorStep() + + complementary_data = {} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + assert result[TransitionKey.COMPLEMENTARY_DATA] == {} + + +def test_complementary_data_no_task(): + """Test processor handles complementary_data without task field.""" + processor = AddBatchDimensionProcessorStep() + + complementary_data = { + "episode_id": 123, + "timestamp": 1234567890.0, + "extra_info": "some data", + } + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + # Should remain unchanged + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data == complementary_data + + +def test_complementary_data_mixed(): + """Test processor with mixed complementary_data containing task and other fields.""" + processor = AddBatchDimensionProcessorStep() + + complementary_data = { + "task": "stack_blocks", + "episode_id": 456, + "difficulty": "hard", + "metadata": {"scene": "kitchen"}, + } + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Task should be batched + assert processed_comp_data["task"] == ["stack_blocks"] + + # Other fields should remain unchanged + assert processed_comp_data["episode_id"] == 456 + assert processed_comp_data["difficulty"] == "hard" + assert processed_comp_data["metadata"] == {"scene": "kitchen"} + + +def test_task_with_observation_and_action(): + """Test task processing together with observation and action processing.""" + processor = AddBatchDimensionProcessorStep() + + # All components need batching + observation = { + OBS_STATE: torch.randn(5), + OBS_IMAGE: torch.randn(32, 32, 3), + } + action = torch.randn(4) + complementary_data = {"task": "navigate_to_goal"} + + transition = create_transition( + observation=observation, action=action, complementary_data=complementary_data + ) + + result = processor(transition) + + # All should be batched + assert result[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 5) + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (1, 32, 32, 3) + assert result[TransitionKey.ACTION].shape == (1, 4) + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == ["navigate_to_goal"] + + +def test_task_comprehensive_string_cases(): + """Test task processing with comprehensive string cases and edge cases.""" + processor = AddBatchDimensionProcessorStep() + + # Test various string formats + string_tasks = [ + "pick_and_place", + "navigate", + "open_drawer", + "", # Empty string (valid but edge case) + "task with spaces", + "task_with_underscores", + "task-with-dashes", + "UPPERCASE_TASK", + "MixedCaseTask", + "task123", + "数字任务", # Unicode task + "🤖 robot task", # Emoji in task + "task\nwith\nnewlines", # Special characters + "task\twith\ttabs", + "task with 'quotes'", + 'task with "double quotes"', + ] + + # Test that all string tasks get properly batched + for task in string_tasks: + complementary_data = {"task": task} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == [task] + assert isinstance(processed_comp_data["task"], list) + assert len(processed_comp_data["task"]) == 1 + + # Test various list of strings (should remain unchanged) + list_tasks = [ + ["single_task"], + ["task1", "task2"], + ["pick", "place", "navigate"], + [], # Empty list + [""], # List with empty string + ["task with spaces", "task_with_underscores", "UPPERCASE"], + ["🤖 task", "数字任务", "normal_task"], # Mixed formats + ] + + for task_list in list_tasks: + complementary_data = {"task": task_list} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == task_list + assert isinstance(processed_comp_data["task"], list) + + +def test_task_preserves_other_keys(): + """Test that task processing preserves other keys in complementary_data.""" + processor = AddBatchDimensionProcessorStep() + + complementary_data = { + "task": "clean_table", + "robot_id": "robot_123", + "motor_id": "motor_456", + "config": {"speed": "slow", "precision": "high"}, + "metrics": [1.0, 2.0, 3.0], + } + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Task should be processed + assert processed_comp_data["task"] == ["clean_table"] + + # All other keys should be preserved exactly + assert processed_comp_data["robot_id"] == "robot_123" + assert processed_comp_data["motor_id"] == "motor_456" + assert processed_comp_data["config"] == {"speed": "slow", "precision": "high"} + assert processed_comp_data["metrics"] == [1.0, 2.0, 3.0] + + +# Index and task_index specific tests +def test_index_scalar_to_1d(): + """Test that 0D index tensor gets unsqueezed to 1D.""" + processor = AddBatchDimensionProcessorStep() + + # Create 0D index tensor (scalar) + index_0d = torch.tensor(42, dtype=torch.int64) + complementary_data = {"index": index_0d} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["index"].shape == (1,) + assert processed_comp_data["index"].dtype == torch.int64 + assert processed_comp_data["index"][0] == 42 + + +def test_task_index_scalar_to_1d(): + """Test that 0D task_index tensor gets unsqueezed to 1D.""" + processor = AddBatchDimensionProcessorStep() + + # Create 0D task_index tensor (scalar) + task_index_0d = torch.tensor(7, dtype=torch.int64) + complementary_data = {"task_index": task_index_0d} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task_index"].shape == (1,) + assert processed_comp_data["task_index"].dtype == torch.int64 + assert processed_comp_data["task_index"][0] == 7 + + +def test_index_and_task_index_together(): + """Test processing both index and task_index together.""" + processor = AddBatchDimensionProcessorStep() + + # Create 0D tensors for both + index_0d = torch.tensor(100, dtype=torch.int64) + task_index_0d = torch.tensor(3, dtype=torch.int64) + complementary_data = { + "index": index_0d, + "task_index": task_index_0d, + "task": "pick_object", + } + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Check index + assert processed_comp_data["index"].shape == (1,) + assert processed_comp_data["index"][0] == 100 + + # Check task_index + assert processed_comp_data["task_index"].shape == (1,) + assert processed_comp_data["task_index"][0] == 3 + + # Check task is also processed + assert processed_comp_data["task"] == ["pick_object"] + + +def test_index_already_batched(): + """Test that already batched index tensors remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + # Create already batched tensors + index_1d = torch.tensor([42], dtype=torch.int64) + index_2d = torch.tensor([[42, 43]], dtype=torch.int64) + + # Test 1D (already batched) + complementary_data = {"index": index_1d} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + result = processor(transition) + assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["index"], index_1d) + + # Test 2D + complementary_data = {"index": index_2d} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + result = processor(transition) + assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["index"], index_2d) + + +def test_task_index_already_batched(): + """Test that already batched task_index tensors remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + # Create already batched tensors + task_index_1d = torch.tensor([7], dtype=torch.int64) + task_index_2d = torch.tensor([[7, 8]], dtype=torch.int64) + + # Test 1D (already batched) + complementary_data = {"task_index": task_index_1d} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + result = processor(transition) + assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["task_index"], task_index_1d) + + # Test 2D + complementary_data = {"task_index": task_index_2d} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + result = processor(transition) + assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["task_index"], task_index_2d) + + +def test_index_non_tensor_unchanged(): + """Test that non-tensor index values remain unchanged.""" + processor = AddBatchDimensionProcessorStep() + + complementary_data = { + "index": 42, # Plain int, not tensor + "task_index": [1, 2, 3], # List, not tensor + } + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["index"] == 42 + assert processed_comp_data["task_index"] == [1, 2, 3] + + +def test_index_dtype_preservation(): + """Test that index and task_index dtype is preserved during processing.""" + processor = AddBatchDimensionProcessorStep() + + # Test different dtypes + dtypes = [torch.int32, torch.int64, torch.long] + + for dtype in dtypes: + index_0d = torch.tensor(42, dtype=dtype) + task_index_0d = torch.tensor(7, dtype=dtype) + complementary_data = { + "index": index_0d, + "task_index": task_index_0d, + } + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["index"].dtype == dtype + assert processed_comp_data["task_index"].dtype == dtype + + +def test_index_with_full_transition(): + """Test index/task_index processing with full transition data.""" + processor = AddBatchDimensionProcessorStep() + + # Create full transition with all components + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: torch.randn(64, 64, 3), + } + action = torch.randn(4) + complementary_data = { + "task": "navigate_to_goal", + "index": torch.tensor(1000, dtype=torch.int64), + "task_index": torch.tensor(5, dtype=torch.int64), + "episode_id": 123, + } + + transition = create_transition( + observation=observation, + action=action, + reward=0.5, + done=False, + complementary_data=complementary_data, + ) + + result = processor(transition) + + # Check all components are processed correctly + assert result[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 7) + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].shape == (1, 64, 64, 3) + assert result[TransitionKey.ACTION].shape == (1, 4) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == ["navigate_to_goal"] + assert processed_comp_data["index"].shape == (1,) + assert processed_comp_data["index"][0] == 1000 + assert processed_comp_data["task_index"].shape == (1,) + assert processed_comp_data["task_index"][0] == 5 + assert processed_comp_data["episode_id"] == 123 # Non-tensor field unchanged + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_index_device_compatibility(): + """Test processor works with index/task_index tensors on different devices.""" + processor = AddBatchDimensionProcessorStep() + + # Create tensors on GPU + index_0d = torch.tensor(42, dtype=torch.int64, device="cuda") + task_index_0d = torch.tensor(7, dtype=torch.int64, device="cuda") + + complementary_data = { + "index": index_0d, + "task_index": task_index_0d, + } + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Check shapes and that tensors stayed on GPU + assert processed_comp_data["index"].shape == (1,) + assert processed_comp_data["task_index"].shape == (1,) + assert processed_comp_data["index"].device.type == "cuda" + assert processed_comp_data["task_index"].device.type == "cuda" + + +def test_empty_index_tensor(): + """Test handling of empty index tensors.""" + processor = AddBatchDimensionProcessorStep() + + # Empty 0D tensor doesn't make sense, but test empty 1D + index_empty = torch.tensor([], dtype=torch.int64) + complementary_data = {"index": index_empty} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + result = processor(transition) + + # Should remain unchanged (already 1D) + assert result[TransitionKey.COMPLEMENTARY_DATA]["index"].shape == (0,) + + +def test_action_processing_creates_new_transition(): + """Test that the processor creates a new transition object with correctly processed action.""" + processor = AddBatchDimensionProcessorStep() + + action = torch.randn(4) + transition = create_transition(action=action, observation={}) + + # Store reference to original transition + original_transition = transition + + # Process + result = processor(transition) + + # Should be a different object (functional design, not in-place mutation) + assert result is not original_transition + # Original transition should remain unchanged + assert original_transition[TransitionKey.ACTION].shape == (4,) + # Result should have correctly processed action with batch dimension + assert result[TransitionKey.ACTION].shape == (1, 4) + assert torch.equal(result[TransitionKey.ACTION][0], action) + + +def test_task_processing_creates_new_transition(): + """Test that the processor creates a new transition object with correctly processed task.""" + processor = AddBatchDimensionProcessorStep() + + complementary_data = {"task": "sort_objects"} + transition = create_transition( + complementary_data=complementary_data, observation={}, action=torch.empty(0) + ) + + # Store reference to original transition and complementary_data + original_transition = transition + original_comp_data = complementary_data + + # Process + result = processor(transition) + + # Should be different transition object (functional design) + assert result is not original_transition + # The task should be processed correctly (wrapped in list) + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == ["sort_objects"] + # Original complementary data is also modified (current behavior) + assert original_comp_data["task"] == "sort_objects" diff --git a/tests/processor/test_classifier_processor.py b/tests/processor/test_classifier_processor.py new file mode 100644 index 00000000000..e1567bf29d0 --- /dev/null +++ b/tests/processor/test_classifier_processor.py @@ -0,0 +1,362 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for Reward Classifier processor.""" + +import tempfile + +import pytest +import torch + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig +from lerobot.policies.sac.reward_model.processor_classifier import make_classifier_processor +from lerobot.processor import ( + DataProcessorPipeline, + DeviceProcessorStep, + IdentityProcessorStep, + NormalizerProcessorStep, + TransitionKey, +) +from lerobot.processor.converters import create_transition, transition_to_batch +from lerobot.utils.constants import OBS_IMAGE, OBS_STATE + + +def create_default_config(): + """Create a default Reward Classifier configuration for testing.""" + config = RewardClassifierConfig() + config.input_features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)), + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), + } + config.output_features = { + "reward": PolicyFeature(type=FeatureType.ACTION, shape=(1,)), # Classifier output + } + config.normalization_mapping = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.IDENTITY, # No normalization for classifier output + } + config.device = "cpu" + return config + + +def create_default_stats(): + """Create default dataset statistics for testing.""" + return { + OBS_STATE: {"mean": torch.zeros(10), "std": torch.ones(10)}, + OBS_IMAGE: {}, # No normalization for images + "reward": {}, # No normalization for classifier output + } + + +def test_make_classifier_processor_basic(): + """Test basic creation of Classifier processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor(config, stats) + + # Check processor names + assert preprocessor.name == "classifier_preprocessor" + assert postprocessor.name == "classifier_postprocessor" + + # Check steps in preprocessor + assert len(preprocessor.steps) == 3 + assert isinstance(preprocessor.steps[0], NormalizerProcessorStep) # For input features + assert isinstance(preprocessor.steps[1], NormalizerProcessorStep) # For output features + assert isinstance(preprocessor.steps[2], DeviceProcessorStep) + + # Check steps in postprocessor + assert len(postprocessor.steps) == 2 + assert isinstance(postprocessor.steps[0], DeviceProcessorStep) + assert isinstance(postprocessor.steps[1], IdentityProcessorStep) + + +def test_classifier_processor_normalization(): + """Test that Classifier processor correctly normalizes data.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor( + config, + stats, + ) + + # Create test data + observation = { + OBS_STATE: torch.randn(10), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(1) # Dummy action/reward + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data is processed + assert processed[OBS_STATE].shape == (10,) + assert processed[OBS_IMAGE].shape == (3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1,) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_classifier_processor_cuda(): + """Test Classifier processor with CUDA device.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor( + config, + stats, + ) + + # Create CPU data + observation = { + OBS_STATE: torch.randn(10), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(1) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is on CUDA + assert processed[OBS_STATE].device.type == "cuda" + assert processed[OBS_IMAGE].device.type == "cuda" + assert processed[TransitionKey.ACTION.value].device.type == "cuda" + + # Process through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that output is back on CPU + assert postprocessed.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_classifier_processor_accelerate_scenario(): + """Test Classifier processor in simulated Accelerate scenario.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor( + config, + stats, + ) + + # Simulate Accelerate: data already on GPU + device = torch.device("cuda:0") + observation = { + OBS_STATE: torch.randn(10).to(device), + OBS_IMAGE: torch.randn(3, 224, 224).to(device), + } + action = torch.randn(1).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on same GPU + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_classifier_processor_multi_gpu(): + """Test Classifier processor with multi-GPU setup.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor(config, stats) + + # Simulate data on different GPU + device = torch.device("cuda:1") + observation = { + OBS_STATE: torch.randn(10).to(device), + OBS_IMAGE: torch.randn(3, 224, 224).to(device), + } + action = torch.randn(1).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on cuda:1 + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +def test_classifier_processor_without_stats(): + """Test Classifier processor creation without dataset statistics.""" + config = create_default_config() + + preprocessor, postprocessor = make_classifier_processor(config, dataset_stats=None) + + # Should still create processors + assert preprocessor is not None + assert postprocessor is not None + + # Process should still work + observation = { + OBS_STATE: torch.randn(10), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(1) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed is not None + + +def test_classifier_processor_save_and_load(): + """Test saving and loading Classifier processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor(config, stats) + + with tempfile.TemporaryDirectory() as tmpdir: + # Save preprocessor + preprocessor.save_pretrained(tmpdir) + + # Load preprocessor + loaded_preprocessor = DataProcessorPipeline.from_pretrained( + tmpdir, config_filename="classifier_preprocessor.json" + ) + + # Test that loaded processor works + observation = { + OBS_STATE: torch.randn(10), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(1) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = loaded_preprocessor(batch) + assert processed[OBS_STATE].shape == (10,) + assert processed[OBS_IMAGE].shape == (3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1,) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_classifier_processor_mixed_precision(): + """Test Classifier processor with mixed precision.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor(config, stats) + + # Replace DeviceProcessorStep with one that uses float16 + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="float16")) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Create test data + observation = { + OBS_STATE: torch.randn(10, dtype=torch.float32), + OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32), + } + action = torch.randn(1, dtype=torch.float32) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is converted to float16 + assert processed[OBS_STATE].dtype == torch.float16 + assert processed[OBS_IMAGE].dtype == torch.float16 + assert processed[TransitionKey.ACTION.value].dtype == torch.float16 + + +def test_classifier_processor_batch_data(): + """Test Classifier processor with batched data.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor( + config, + stats, + ) + + # Test with batched data + batch_size = 16 + observation = { + OBS_STATE: torch.randn(batch_size, 10), + OBS_IMAGE: torch.randn(batch_size, 3, 224, 224), + } + action = torch.randn(batch_size, 1) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that batch dimension is preserved + assert processed[OBS_STATE].shape == (batch_size, 10) + assert processed[OBS_IMAGE].shape == (batch_size, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (batch_size, 1) + + +def test_classifier_processor_postprocessor_identity(): + """Test that Classifier postprocessor uses IdentityProcessor correctly.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_classifier_processor( + config, + stats, + ) + + # Create test data for postprocessor + reward = torch.tensor([[0.8], [0.3], [0.9]]) # Batch of rewards/predictions + transition = create_transition(action=reward) + + _ = transition_to_batch(transition) + + # Process through postprocessor + processed = postprocessor(reward) + + # IdentityProcessor should leave values unchanged (except device) + assert torch.allclose(processed.cpu(), reward.cpu()) + assert processed.device.type == "cpu" diff --git a/tests/processor/test_converters.py b/tests/processor/test_converters.py new file mode 100644 index 00000000000..47a6eea1823 --- /dev/null +++ b/tests/processor/test_converters.py @@ -0,0 +1,309 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pytest +import torch + +from lerobot.processor import TransitionKey +from lerobot.processor.converters import ( + batch_to_transition, + create_transition, + to_tensor, + transition_to_batch, +) +from lerobot.utils.constants import ACTION, DONE, OBS_STATE, OBS_STR, REWARD + + +# Tests for the unified to_tensor function +def test_to_tensor_numpy_arrays(): + """Test to_tensor with various numpy arrays.""" + # Regular numpy array + arr = np.array([1.0, 2.0, 3.0]) + result = to_tensor(arr) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0])) + + # Different numpy dtypes should convert to float32 by default + int_arr = np.array([1, 2, 3], dtype=np.int64) + result = to_tensor(int_arr) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0])) + + # uint8 arrays (previously "preserved") should now convert + uint8_arr = np.array([100, 150, 200], dtype=np.uint8) + result = to_tensor(uint8_arr) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert torch.allclose(result, torch.tensor([100.0, 150.0, 200.0])) + + +def test_to_tensor_numpy_scalars(): + """Test to_tensor with numpy scalars (0-dimensional arrays).""" + # numpy float32 scalar + scalar = np.float32(3.14) + result = to_tensor(scalar) + assert isinstance(result, torch.Tensor) + assert result.ndim == 0 # Should be 0-dimensional tensor + assert result.dtype == torch.float32 + assert result.item() == pytest.approx(3.14) + + # numpy int32 scalar + int_scalar = np.int32(42) + result = to_tensor(int_scalar) + assert isinstance(result, torch.Tensor) + assert result.ndim == 0 + assert result.dtype == torch.float32 + assert result.item() == pytest.approx(42.0) + + +def test_to_tensor_python_scalars(): + """Test to_tensor with Python scalars.""" + # Python int + result = to_tensor(42) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert result.item() == pytest.approx(42.0) + + # Python float + result = to_tensor(3.14) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert result.item() == pytest.approx(3.14) + + +def test_to_tensor_sequences(): + """Test to_tensor with lists and tuples.""" + # List + result = to_tensor([1, 2, 3]) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0])) + + # Tuple + result = to_tensor((4.5, 5.5, 6.5)) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert torch.allclose(result, torch.tensor([4.5, 5.5, 6.5])) + + +def test_to_tensor_existing_tensors(): + """Test to_tensor with existing PyTorch tensors.""" + # Tensor with same dtype should pass through with potential device change + tensor = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32) + result = to_tensor(tensor) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert torch.allclose(result, tensor) + + # Tensor with different dtype should convert + int_tensor = torch.tensor([1, 2, 3], dtype=torch.int64) + result = to_tensor(int_tensor) + assert isinstance(result, torch.Tensor) + assert result.dtype == torch.float32 + assert torch.allclose(result, torch.tensor([1.0, 2.0, 3.0])) + + +def test_to_tensor_dictionaries(): + """Test to_tensor with nested dictionaries.""" + # Simple dictionary + data = {"mean": [0.1, 0.2], "std": np.array([1.0, 2.0]), "count": 42} + result = to_tensor(data) + assert isinstance(result, dict) + assert isinstance(result["mean"], torch.Tensor) + assert isinstance(result["std"], torch.Tensor) + assert isinstance(result["count"], torch.Tensor) + assert torch.allclose(result["mean"], torch.tensor([0.1, 0.2])) + assert torch.allclose(result["std"], torch.tensor([1.0, 2.0])) + assert result["count"].item() == pytest.approx(42.0) + + # Nested dictionary + nested = { + ACTION: {"mean": [0.1, 0.2], "std": [1.0, 2.0]}, + OBS_STR: {"mean": np.array([0.5, 0.6]), "count": 10}, + } + result = to_tensor(nested) + assert isinstance(result, dict) + assert isinstance(result[ACTION], dict) + assert isinstance(result[OBS_STR], dict) + assert isinstance(result[ACTION]["mean"], torch.Tensor) + assert isinstance(result[OBS_STR]["mean"], torch.Tensor) + assert torch.allclose(result[ACTION]["mean"], torch.tensor([0.1, 0.2])) + assert torch.allclose(result[OBS_STR]["mean"], torch.tensor([0.5, 0.6])) + + +def test_to_tensor_none_filtering(): + """Test that None values are filtered out from dictionaries.""" + data = {"valid": [1, 2, 3], "none_value": None, "nested": {"valid": [4, 5], "also_none": None}} + result = to_tensor(data) + assert "none_value" not in result + assert "also_none" not in result["nested"] + assert "valid" in result + assert "valid" in result["nested"] + assert torch.allclose(result["valid"], torch.tensor([1.0, 2.0, 3.0])) + + +def test_to_tensor_dtype_parameter(): + """Test to_tensor with different dtype parameters.""" + arr = np.array([1, 2, 3]) + + # Default dtype (float32) + result = to_tensor(arr) + assert result.dtype == torch.float32 + + # Explicit float32 + result = to_tensor(arr, dtype=torch.float32) + assert result.dtype == torch.float32 + + # Float64 + result = to_tensor(arr, dtype=torch.float64) + assert result.dtype == torch.float64 + + # Preserve original dtype + float64_arr = np.array([1.0, 2.0, 3.0], dtype=np.float64) + result = to_tensor(float64_arr, dtype=None) + assert result.dtype == torch.float64 + + +def test_to_tensor_device_parameter(): + """Test to_tensor with device parameter.""" + arr = np.array([1.0, 2.0, 3.0]) + + # CPU device (default) + result = to_tensor(arr, device="cpu") + assert result.device.type == "cpu" + + # CUDA device (if available) + if torch.cuda.is_available(): + result = to_tensor(arr, device="cuda") + assert result.device.type == "cuda" + + +def test_to_tensor_empty_dict(): + """Test to_tensor with empty dictionary.""" + result = to_tensor({}) + assert isinstance(result, dict) + assert len(result) == 0 + + +def test_to_tensor_unsupported_type(): + """Test to_tensor with unsupported types raises TypeError.""" + with pytest.raises(TypeError, match="Unsupported type for tensor conversion"): + to_tensor("unsupported_string") + + with pytest.raises(TypeError, match="Unsupported type for tensor conversion"): + to_tensor(object()) + + +def test_batch_to_transition_with_index_fields(): + """Test that batch_to_transition handles index and task_index fields correctly.""" + + # Create batch with index and task_index fields + batch = { + OBS_STATE: torch.randn(1, 7), + ACTION: torch.randn(1, 4), + REWARD: 1.5, + DONE: False, + "task": ["pick_cube"], + "index": torch.tensor([42], dtype=torch.int64), + "task_index": torch.tensor([3], dtype=torch.int64), + } + + transition = batch_to_transition(batch) + + # Check basic transition structure + assert TransitionKey.OBSERVATION in transition + assert TransitionKey.ACTION in transition + assert TransitionKey.COMPLEMENTARY_DATA in transition + + # Check that index and task_index are in complementary_data + comp_data = transition[TransitionKey.COMPLEMENTARY_DATA] + assert "index" in comp_data + assert "task_index" in comp_data + assert "task" in comp_data + + # Verify values + assert torch.equal(comp_data["index"], batch["index"]) + assert torch.equal(comp_data["task_index"], batch["task_index"]) + assert comp_data["task"] == batch["task"] + + +def testtransition_to_batch_with_index_fields(): + """Test that transition_to_batch handles index and task_index fields correctly.""" + + # Create transition with index and task_index in complementary_data + transition = create_transition( + observation={OBS_STATE: torch.randn(1, 7)}, + action=torch.randn(1, 4), + reward=1.5, + done=False, + complementary_data={ + "task": ["navigate"], + "index": torch.tensor([100], dtype=torch.int64), + "task_index": torch.tensor([5], dtype=torch.int64), + }, + ) + + batch = transition_to_batch(transition) + + # Check that index and task_index are in the batch + assert "index" in batch + assert "task_index" in batch + assert "task" in batch + + # Verify values + assert torch.equal(batch["index"], transition[TransitionKey.COMPLEMENTARY_DATA]["index"]) + assert torch.equal(batch["task_index"], transition[TransitionKey.COMPLEMENTARY_DATA]["task_index"]) + assert batch["task"] == transition[TransitionKey.COMPLEMENTARY_DATA]["task"] + + +def test_batch_to_transition_without_index_fields(): + """Test that conversion works without index and task_index fields.""" + + # Batch without index/task_index + batch = { + OBS_STATE: torch.randn(1, 7), + ACTION: torch.randn(1, 4), + "task": ["pick_cube"], + } + + transition = batch_to_transition(batch) + comp_data = transition[TransitionKey.COMPLEMENTARY_DATA] + + # Should have task but not index/task_index + assert "task" in comp_data + assert "index" not in comp_data + assert "task_index" not in comp_data + + +def test_transition_to_batch_without_index_fields(): + """Test that conversion works without index and task_index fields.""" + + # Transition without index/task_index + transition = create_transition( + observation={OBS_STATE: torch.randn(1, 7)}, + action=torch.randn(1, 4), + complementary_data={"task": ["navigate"]}, + ) + + batch = transition_to_batch(transition) + + # Should have task but not index/task_index + assert "task" in batch + assert "index" not in batch + assert "task_index" not in batch diff --git a/tests/processor/test_device_processor.py b/tests/processor/test_device_processor.py new file mode 100644 index 00000000000..bb7d467bf3a --- /dev/null +++ b/tests/processor/test_device_processor.py @@ -0,0 +1,1158 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tempfile + +import pytest +import torch + +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature +from lerobot.processor import DataProcessorPipeline, DeviceProcessorStep, TransitionKey +from lerobot.processor.converters import create_transition, identity_transition +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE + + +def test_basic_functionality(): + """Test basic device processor functionality on CPU.""" + processor = DeviceProcessorStep(device="cpu") + + # Create a transition with CPU tensors + observation = {OBS_STATE: torch.randn(10), OBS_IMAGE: torch.randn(3, 224, 224)} + action = torch.randn(5) + reward = torch.tensor(1.0) + done = torch.tensor(False) + truncated = torch.tensor(False) + + transition = create_transition( + observation=observation, action=action, reward=reward, done=done, truncated=truncated + ) + + result = processor(transition) + + # Check that all tensors are on CPU + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cpu" + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].device.type == "cpu" + assert result[TransitionKey.ACTION].device.type == "cpu" + assert result[TransitionKey.REWARD].device.type == "cpu" + assert result[TransitionKey.DONE].device.type == "cpu" + assert result[TransitionKey.TRUNCATED].device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_cuda_functionality(): + """Test device processor functionality on CUDA.""" + processor = DeviceProcessorStep(device="cuda") + + # Create a transition with CPU tensors + observation = {OBS_STATE: torch.randn(10), OBS_IMAGE: torch.randn(3, 224, 224)} + action = torch.randn(5) + reward = torch.tensor(1.0) + done = torch.tensor(False) + truncated = torch.tensor(False) + + transition = create_transition( + observation=observation, action=action, reward=reward, done=done, truncated=truncated + ) + + result = processor(transition) + + # Check that all tensors are on CUDA + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda" + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].device.type == "cuda" + assert result[TransitionKey.ACTION].device.type == "cuda" + assert result[TransitionKey.REWARD].device.type == "cuda" + assert result[TransitionKey.DONE].device.type == "cuda" + assert result[TransitionKey.TRUNCATED].device.type == "cuda" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_specific_cuda_device(): + """Test device processor with specific CUDA device.""" + processor = DeviceProcessorStep(device="cuda:0") + + observation = {OBS_STATE: torch.randn(10)} + action = torch.randn(5) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda" + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.index == 0 + assert result[TransitionKey.ACTION].device.type == "cuda" + assert result[TransitionKey.ACTION].device.index == 0 + + +def test_non_tensor_values(): + """Test that non-tensor values are preserved.""" + processor = DeviceProcessorStep(device="cpu") + + observation = { + OBS_STATE: torch.randn(10), + "observation.metadata": {"key": "value"}, # Non-tensor data + "observation.list": [1, 2, 3], # Non-tensor data + } + action = torch.randn(5) + info = {"episode": 1, "step": 42} + + transition = create_transition(observation=observation, action=action, info=info) + + result = processor(transition) + + # Check tensors are processed + assert isinstance(result[TransitionKey.OBSERVATION][OBS_STATE], torch.Tensor) + assert isinstance(result[TransitionKey.ACTION], torch.Tensor) + + # Check non-tensor values are preserved + assert result[TransitionKey.OBSERVATION]["observation.metadata"] == {"key": "value"} + assert result[TransitionKey.OBSERVATION]["observation.list"] == [1, 2, 3] + assert result[TransitionKey.INFO] == {"episode": 1, "step": 42} + + +def test_none_values(): + """Test handling of None values.""" + processor = DeviceProcessorStep(device="cpu") + + # Test with None observation + transition = create_transition(observation=None, action=torch.randn(5)) + result = processor(transition) + assert result[TransitionKey.OBSERVATION] is None + assert result[TransitionKey.ACTION].device.type == "cpu" + + # Test with None action + transition = create_transition(observation={OBS_STATE: torch.randn(10)}, action=None) + result = processor(transition) + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cpu" + assert result[TransitionKey.ACTION] is None + + +def test_empty_observation(): + """Test handling of empty observation dictionary.""" + processor = DeviceProcessorStep(device="cpu") + + transition = create_transition(observation={}, action=torch.randn(5)) + result = processor(transition) + + assert result[TransitionKey.OBSERVATION] == {} + assert result[TransitionKey.ACTION].device.type == "cpu" + + +def test_scalar_tensors(): + """Test handling of scalar tensors.""" + processor = DeviceProcessorStep(device="cpu") + + observation = {"observation.scalar": torch.tensor(1.5)} + action = torch.tensor(2.0) + reward = torch.tensor(0.5) + + transition = create_transition(observation=observation, action=action, reward=reward) + + result = processor(transition) + + assert result[TransitionKey.OBSERVATION]["observation.scalar"].item() == 1.5 + assert result[TransitionKey.ACTION].item() == 2.0 + assert result[TransitionKey.REWARD].item() == 0.5 + + +def test_dtype_preservation(): + """Test that tensor dtypes are preserved.""" + processor = DeviceProcessorStep(device="cpu") + + observation = { + "observation.float32": torch.randn(5, dtype=torch.float32), + "observation.float64": torch.randn(5, dtype=torch.float64), + "observation.int32": torch.randint(0, 10, (5,), dtype=torch.int32), + "observation.bool": torch.tensor([True, False, True], dtype=torch.bool), + } + action = torch.randn(3, dtype=torch.float16) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float32 + assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float64 + assert result[TransitionKey.OBSERVATION]["observation.int32"].dtype == torch.int32 + assert result[TransitionKey.OBSERVATION]["observation.bool"].dtype == torch.bool + assert result[TransitionKey.ACTION].dtype == torch.float16 + + +def test_shape_preservation(): + """Test that tensor shapes are preserved.""" + processor = DeviceProcessorStep(device="cpu") + + observation = { + "observation.1d": torch.randn(10), + "observation.2d": torch.randn(5, 10), + "observation.3d": torch.randn(3, 224, 224), + "observation.4d": torch.randn(2, 3, 224, 224), + } + action = torch.randn(2, 5, 3) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + assert result[TransitionKey.OBSERVATION]["observation.1d"].shape == (10,) + assert result[TransitionKey.OBSERVATION]["observation.2d"].shape == (5, 10) + assert result[TransitionKey.OBSERVATION]["observation.3d"].shape == (3, 224, 224) + assert result[TransitionKey.OBSERVATION]["observation.4d"].shape == (2, 3, 224, 224) + assert result[TransitionKey.ACTION].shape == (2, 5, 3) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_mixed_devices(): + """Test handling of tensors already on different devices.""" + processor = DeviceProcessorStep(device="cuda") + + # Create tensors on different devices + observation = { + "observation.cpu": torch.randn(5), # CPU + "observation.cuda": torch.randn(5).cuda(), # Already on CUDA + } + action = torch.randn(3).cuda() # Already on CUDA + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # All should be on CUDA + assert result[TransitionKey.OBSERVATION]["observation.cpu"].device.type == "cuda" + assert result[TransitionKey.OBSERVATION]["observation.cuda"].device.type == "cuda" + assert result[TransitionKey.ACTION].device.type == "cuda" + + +def test_non_blocking_flag(): + """Test that non_blocking flag is set correctly.""" + # CPU processor should have non_blocking=False + cpu_processor = DeviceProcessorStep(device="cpu") + assert cpu_processor.non_blocking is False + + if torch.cuda.is_available(): + # CUDA processor should have non_blocking=True + cuda_processor = DeviceProcessorStep(device="cuda") + assert cuda_processor.non_blocking is True + + cuda_0_processor = DeviceProcessorStep(device="cuda:0") + assert cuda_0_processor.non_blocking is True + + +def test_serialization_methods(): + """Test get_config, state_dict, and load_state_dict methods.""" + device = "cuda" if torch.cuda.is_available() else "cpu" + processor = DeviceProcessorStep(device=device) + + # Test get_config + config = processor.get_config() + assert config == {"device": device, "float_dtype": None} + + # Test state_dict (should be empty) + state = processor.state_dict() + assert state == {} + + # Test load_state_dict (should be no-op) + processor.load_state_dict({}) + assert processor.device == device + + # Test reset (should be no-op) + processor.reset() + assert processor.device == device + + +def test_features(): + """Test that features returns features unchanged.""" + processor = DeviceProcessorStep(device="cpu") + + features = { + PipelineFeatureType.OBSERVATION: {OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))}, + PipelineFeatureType.ACTION: {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(5,))}, + } + + result = processor.transform_features(features) + assert result == features + assert result is features # Should return the same object + + +def test_integration_with_robot_processor(): + """Test integration with RobotProcessor.""" + from lerobot.processor import AddBatchDimensionProcessorStep + from lerobot.utils.constants import OBS_STATE + + # Create a pipeline with DeviceProcessorStep + device_processor = DeviceProcessorStep(device="cpu") + batch_processor = AddBatchDimensionProcessorStep() + + processor = DataProcessorPipeline( + steps=[batch_processor, device_processor], + name="test_pipeline", + to_transition=identity_transition, + to_output=identity_transition, + ) + + # Create test data + observation = {OBS_STATE: torch.randn(10)} + action = torch.randn(5) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Check that tensors are batched and on correct device + # The result has TransitionKey.OBSERVATION as the key, with observation.state inside + assert result[TransitionKey.OBSERVATION][OBS_STATE].shape[0] == 1 # Batched + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cpu" + assert result[TransitionKey.ACTION].shape[0] == 1 # Batched + assert result[TransitionKey.ACTION].device.type == "cpu" + + +def test_save_and_load_pretrained(): + """Test saving and loading processor with DeviceProcessorStep.""" + device = "cuda:0" if torch.cuda.is_available() else "cpu" + processor = DeviceProcessorStep(device=device, float_dtype="float16") + robot_processor = DataProcessorPipeline(steps=[processor], name="device_test_processor") + + with tempfile.TemporaryDirectory() as tmpdir: + # Save + robot_processor.save_pretrained(tmpdir) + + # Load + loaded_processor = DataProcessorPipeline.from_pretrained( + tmpdir, config_filename="device_test_processor.json" + ) + + assert len(loaded_processor.steps) == 1 + loaded_device_processor = loaded_processor.steps[0] + assert isinstance(loaded_device_processor, DeviceProcessorStep) + # Use getattr to access attributes safely + assert ( + getattr(loaded_device_processor, "device", None) == device.split(":")[0] + ) # Device normalizes cuda:0 to cuda + assert getattr(loaded_device_processor, "float_dtype", None) == "float16" + + +def test_registry_functionality(): + """Test that DeviceProcessorStep is properly registered.""" + from lerobot.processor import ProcessorStepRegistry + + # Check that DeviceProcessorStep is registered + registered_class = ProcessorStepRegistry.get("device_processor") + assert registered_class is DeviceProcessorStep + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_performance_with_large_tensors(): + """Test performance with large tensors and non_blocking flag.""" + processor = DeviceProcessorStep(device="cuda") + + # Create large tensors + observation = { + "observation.large_image": torch.randn(10, 3, 512, 512), # Large image batch + "observation.features": torch.randn(10, 2048), # Large feature vector + } + action = torch.randn(10, 100) # Large action space + + transition = create_transition(observation=observation, action=action) + + # Process should not raise any errors + result = processor(transition) + + # Verify all tensors are on CUDA + assert result[TransitionKey.OBSERVATION]["observation.large_image"].device.type == "cuda" + assert result[TransitionKey.OBSERVATION]["observation.features"].device.type == "cuda" + assert result[TransitionKey.ACTION].device.type == "cuda" + + +def test_reward_done_truncated_types(): + """Test handling of different types for reward, done, and truncated.""" + processor = DeviceProcessorStep(device="cpu") + + # Test with scalar values (not tensors) + transition = create_transition( + observation={OBS_STATE: torch.randn(5)}, + action=torch.randn(3), + reward=1.0, # float + done=False, # bool + truncated=True, # bool + ) + + result = processor(transition) + + # Non-tensor values should be preserved as-is + assert result[TransitionKey.REWARD] == 1.0 + assert result[TransitionKey.DONE] is False + assert result[TransitionKey.TRUNCATED] is True + + # Test with tensor values + transition = create_transition( + observation={OBS_STATE: torch.randn(5)}, + action=torch.randn(3), + reward=torch.tensor(1.0), + done=torch.tensor(False), + truncated=torch.tensor(True), + ) + + result = processor(transition) + + # Tensor values should be moved to device + assert isinstance(result[TransitionKey.REWARD], torch.Tensor) + assert isinstance(result[TransitionKey.DONE], torch.Tensor) + assert isinstance(result[TransitionKey.TRUNCATED], torch.Tensor) + assert result[TransitionKey.REWARD].device.type == "cpu" + assert result[TransitionKey.DONE].device.type == "cpu" + assert result[TransitionKey.TRUNCATED].device.type == "cpu" + + +def test_complementary_data_preserved(): + """Test that complementary_data is preserved unchanged.""" + processor = DeviceProcessorStep(device="cpu") + + complementary_data = { + "task": "pick_object", + "episode_id": 42, + "metadata": {"sensor": "camera_1"}, + "observation_is_pad": torch.tensor([False, False, True]), # This should be moved to device + } + + transition = create_transition( + observation={OBS_STATE: torch.randn(5)}, complementary_data=complementary_data + ) + + result = processor(transition) + + # Check that complementary_data is preserved + assert TransitionKey.COMPLEMENTARY_DATA in result + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "pick_object" + assert result[TransitionKey.COMPLEMENTARY_DATA]["episode_id"] == 42 + assert result[TransitionKey.COMPLEMENTARY_DATA]["metadata"] == {"sensor": "camera_1"} + # Note: Currently DeviceProcessorStep doesn't process tensors in complementary_data + # This is intentional as complementary_data is typically metadata + + +def test_float_dtype_conversion(): + """Test float dtype conversion functionality.""" + processor = DeviceProcessorStep(device="cpu", float_dtype="float16") + + # Create tensors of different types + observation = { + "observation.float32": torch.randn(5, dtype=torch.float32), + "observation.float64": torch.randn(5, dtype=torch.float64), + "observation.int32": torch.randint(0, 10, (5,), dtype=torch.int32), + "observation.int64": torch.randint(0, 10, (5,), dtype=torch.int64), + "observation.bool": torch.tensor([True, False, True], dtype=torch.bool), + } + action = torch.randn(3, dtype=torch.float32) + reward = torch.tensor(1.0, dtype=torch.float32) + + transition = create_transition(observation=observation, action=action, reward=reward) + result = processor(transition) + + # Check that float tensors are converted to float16 + assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float16 + assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float16 + assert result[TransitionKey.ACTION].dtype == torch.float16 + assert result[TransitionKey.REWARD].dtype == torch.float16 + + # Check that non-float tensors are preserved + assert result[TransitionKey.OBSERVATION]["observation.int32"].dtype == torch.int32 + assert result[TransitionKey.OBSERVATION]["observation.int64"].dtype == torch.int64 + assert result[TransitionKey.OBSERVATION]["observation.bool"].dtype == torch.bool + + +def test_float_dtype_none(): + """Test that when float_dtype is None, no dtype conversion occurs.""" + processor = DeviceProcessorStep(device="cpu", float_dtype=None) + + observation = { + "observation.float32": torch.randn(5, dtype=torch.float32), + "observation.float64": torch.randn(5, dtype=torch.float64), + "observation.int32": torch.randint(0, 10, (5,), dtype=torch.int32), + } + action = torch.randn(3, dtype=torch.float64) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Check that dtypes are preserved when float_dtype is None + assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float32 + assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float64 + assert result[TransitionKey.OBSERVATION]["observation.int32"].dtype == torch.int32 + assert result[TransitionKey.ACTION].dtype == torch.float64 + + +def test_float_dtype_bfloat16(): + """Test conversion to bfloat16.""" + processor = DeviceProcessorStep(device="cpu", float_dtype="bfloat16") + + observation = {OBS_STATE: torch.randn(5, dtype=torch.float32)} + action = torch.randn(3, dtype=torch.float64) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + assert result[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.bfloat16 + assert result[TransitionKey.ACTION].dtype == torch.bfloat16 + + +def test_float_dtype_float64(): + """Test conversion to float64.""" + processor = DeviceProcessorStep(device="cpu", float_dtype="float64") + + observation = {OBS_STATE: torch.randn(5, dtype=torch.float16)} + action = torch.randn(3, dtype=torch.float32) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + assert result[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.float64 + assert result[TransitionKey.ACTION].dtype == torch.float64 + + +def test_float_dtype_invalid(): + """Test that invalid float_dtype raises ValueError.""" + with pytest.raises(ValueError, match="Invalid float_dtype 'invalid_dtype'"): + DeviceProcessorStep(device="cpu", float_dtype="invalid_dtype") + + +def test_float_dtype_aliases(): + """Test that dtype aliases work correctly.""" + # Test 'half' alias for float16 + processor_half = DeviceProcessorStep(device="cpu", float_dtype="half") + assert processor_half._target_float_dtype == torch.float16 + + # Test 'float' alias for float32 + processor_float = DeviceProcessorStep(device="cpu", float_dtype="float") + assert processor_float._target_float_dtype == torch.float32 + + # Test 'double' alias for float64 + processor_double = DeviceProcessorStep(device="cpu", float_dtype="double") + assert processor_double._target_float_dtype == torch.float64 + + +def test_float_dtype_with_mixed_tensors(): + """Test float dtype conversion with mixed tensor types.""" + processor = DeviceProcessorStep(device="cpu", float_dtype="float32") + + observation = { + OBS_IMAGE: torch.randint(0, 255, (3, 64, 64), dtype=torch.uint8), # Should not convert + OBS_STATE: torch.randn(10, dtype=torch.float64), # Should convert + "observation.mask": torch.tensor([True, False, True], dtype=torch.bool), # Should not convert + "observation.indices": torch.tensor([1, 2, 3], dtype=torch.long), # Should not convert + } + action = torch.randn(5, dtype=torch.float16) # Should convert + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Check conversions + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].dtype == torch.uint8 # Unchanged + assert result[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.float32 # Converted + assert result[TransitionKey.OBSERVATION]["observation.mask"].dtype == torch.bool # Unchanged + assert result[TransitionKey.OBSERVATION]["observation.indices"].dtype == torch.long # Unchanged + assert result[TransitionKey.ACTION].dtype == torch.float32 # Converted + + +def test_float_dtype_serialization(): + """Test that float_dtype is properly serialized in get_config.""" + device = "cuda" if torch.cuda.is_available() else "cpu" + processor = DeviceProcessorStep(device=device, float_dtype="float16") + config = processor.get_config() + + assert config == {"device": device, "float_dtype": "float16"} + + # Test with None float_dtype + processor_none = DeviceProcessorStep(device="cpu", float_dtype=None) + config_none = processor_none.get_config() + + assert config_none == {"device": "cpu", "float_dtype": None} + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_float_dtype_with_cuda(): + """Test float dtype conversion combined with CUDA device.""" + processor = DeviceProcessorStep(device="cuda", float_dtype="float16") + + # Create tensors on CPU with different dtypes + observation = { + "observation.float32": torch.randn(5, dtype=torch.float32), + "observation.int64": torch.tensor([1, 2, 3], dtype=torch.int64), + } + action = torch.randn(3, dtype=torch.float64) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Check that tensors are on CUDA and float types are converted + assert result[TransitionKey.OBSERVATION]["observation.float32"].device.type == "cuda" + assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float16 + + assert result[TransitionKey.OBSERVATION]["observation.int64"].device.type == "cuda" + assert result[TransitionKey.OBSERVATION]["observation.int64"].dtype == torch.int64 # Unchanged + + assert result[TransitionKey.ACTION].device.type == "cuda" + assert result[TransitionKey.ACTION].dtype == torch.float16 + + +def test_complementary_data_index_fields(): + """Test processing of index and task_index fields in complementary_data.""" + processor = DeviceProcessorStep(device="cpu") + + # Create transition with index and task_index in complementary_data + complementary_data = { + "task": ["pick_cube"], + "index": torch.tensor([42], dtype=torch.int64), + "task_index": torch.tensor([3], dtype=torch.int64), + "episode_id": 123, # Non-tensor field + } + transition = create_transition( + observation={OBS_STATE: torch.randn(1, 7)}, + action=torch.randn(1, 4), + complementary_data=complementary_data, + ) + + result = processor(transition) + + # Check that tensors in complementary_data are processed + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Check index tensor + assert isinstance(processed_comp_data["index"], torch.Tensor) + assert processed_comp_data["index"].device.type == "cpu" + assert torch.equal(processed_comp_data["index"], complementary_data["index"]) + + # Check task_index tensor + assert isinstance(processed_comp_data["task_index"], torch.Tensor) + assert processed_comp_data["task_index"].device.type == "cpu" + assert torch.equal(processed_comp_data["task_index"], complementary_data["task_index"]) + + # Check non-tensor fields remain unchanged + assert processed_comp_data["task"] == ["pick_cube"] + assert processed_comp_data["episode_id"] == 123 + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_complementary_data_index_fields_cuda(): + """Test moving index and task_index fields to CUDA.""" + processor = DeviceProcessorStep(device="cuda:0") + + # Create CPU tensors + complementary_data = { + "index": torch.tensor([100, 101], dtype=torch.int64), + "task_index": torch.tensor([5], dtype=torch.int64), + } + transition = create_transition(complementary_data=complementary_data) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Check tensors moved to CUDA + assert processed_comp_data["index"].device.type == "cuda" + assert processed_comp_data["index"].device.index == 0 + assert processed_comp_data["task_index"].device.type == "cuda" + assert processed_comp_data["task_index"].device.index == 0 + + +def test_complementary_data_without_index_fields(): + """Test that complementary_data without index/task_index fields works correctly.""" + processor = DeviceProcessorStep(device="cpu") + + complementary_data = { + "task": ["navigate"], + "episode_id": 456, + } + transition = create_transition(complementary_data=complementary_data) + + result = processor(transition) + + # Should process without errors and preserve non-tensor fields + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["task"] == ["navigate"] + assert processed_comp_data["episode_id"] == 456 + + +def test_complementary_data_mixed_tensors(): + """Test complementary_data with mix of tensors and non-tensors.""" + processor = DeviceProcessorStep(device="cpu") + + complementary_data = { + "task": ["pick_and_place"], + "index": torch.tensor([42], dtype=torch.int64), + "task_index": torch.tensor([3], dtype=torch.int64), + "metrics": [1.0, 2.0, 3.0], # List, not tensor + "config": {"speed": "fast"}, # Dict + "episode_id": 789, # Int + } + transition = create_transition(complementary_data=complementary_data) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Check tensors are processed + assert isinstance(processed_comp_data["index"], torch.Tensor) + assert isinstance(processed_comp_data["task_index"], torch.Tensor) + + # Check non-tensors remain unchanged + assert processed_comp_data["task"] == ["pick_and_place"] + assert processed_comp_data["metrics"] == [1.0, 2.0, 3.0] + assert processed_comp_data["config"] == {"speed": "fast"} + assert processed_comp_data["episode_id"] == 789 + + +def test_complementary_data_float_dtype_conversion(): + """Test that float dtype conversion doesn't affect int tensors in complementary_data.""" + processor = DeviceProcessorStep(device="cpu", float_dtype="float16") + + complementary_data = { + "index": torch.tensor([42], dtype=torch.int64), + "task_index": torch.tensor([3], dtype=torch.int64), + "float_tensor": torch.tensor([1.5, 2.5], dtype=torch.float32), # Should be converted + } + transition = create_transition(complementary_data=complementary_data) + + result = processor(transition) + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Int tensors should keep their dtype + assert processed_comp_data["index"].dtype == torch.int64 + assert processed_comp_data["task_index"].dtype == torch.int64 + + # Float tensor should be converted + assert processed_comp_data["float_tensor"].dtype == torch.float16 + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_complementary_data_full_pipeline_cuda(): + """Test full transition with complementary_data on CUDA.""" + processor = DeviceProcessorStep(device="cuda:0", float_dtype="float16") + + # Create full transition with mixed CPU tensors + observation = {OBS_STATE: torch.randn(1, 7, dtype=torch.float32)} + action = torch.randn(1, 4, dtype=torch.float32) + reward = torch.tensor(1.5, dtype=torch.float32) + done = torch.tensor(False) + complementary_data = { + "task": ["reach_target"], + "index": torch.tensor([1000], dtype=torch.int64), + "task_index": torch.tensor([10], dtype=torch.int64), + } + + transition = create_transition( + observation=observation, + action=action, + reward=reward, + done=done, + complementary_data=complementary_data, + ) + + result = processor(transition) + + # Check all components moved to CUDA + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda" + assert result[TransitionKey.ACTION].device.type == "cuda" + assert result[TransitionKey.REWARD].device.type == "cuda" + assert result[TransitionKey.DONE].device.type == "cuda" + + # Check complementary_data tensors + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["index"].device.type == "cuda" + assert processed_comp_data["task_index"].device.type == "cuda" + + # Check float conversion happened for float tensors + assert result[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.float16 + assert result[TransitionKey.ACTION].dtype == torch.float16 + assert result[TransitionKey.REWARD].dtype == torch.float16 + + # Check int tensors kept their dtype + assert processed_comp_data["index"].dtype == torch.int64 + assert processed_comp_data["task_index"].dtype == torch.int64 + + +def test_complementary_data_empty(): + """Test empty complementary_data handling.""" + processor = DeviceProcessorStep(device="cpu") + + transition = create_transition( + observation={OBS_STATE: torch.randn(1, 7)}, + complementary_data={}, + ) + + result = processor(transition) + + # Should have empty dict + assert result[TransitionKey.COMPLEMENTARY_DATA] == {} + + +def test_complementary_data_none(): + """Test None complementary_data handling.""" + processor = DeviceProcessorStep(device="cpu") + + transition = create_transition( + observation={OBS_STATE: torch.randn(1, 7)}, + complementary_data=None, + ) + + result = processor(transition) + + # Complementary data should not be in the result (same as input) + assert result[TransitionKey.COMPLEMENTARY_DATA] == {} + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_preserves_gpu_placement(): + """Test that DeviceProcessorStep preserves GPU placement when tensor is already on GPU.""" + processor = DeviceProcessorStep(device="cuda:0") + + # Create tensors already on GPU + observation = { + OBS_STATE: torch.randn(10).cuda(), # Already on GPU + OBS_IMAGE: torch.randn(3, 224, 224).cuda(), # Already on GPU + } + action = torch.randn(5).cuda() # Already on GPU + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Check that tensors remain on their original GPU + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda" + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].device.type == "cuda" + assert result[TransitionKey.ACTION].device.type == "cuda" + + # Verify no unnecessary copies were made (same data pointer) + assert torch.equal(result[TransitionKey.OBSERVATION][OBS_STATE], observation[OBS_STATE]) + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_multi_gpu_preservation(): + """Test that DeviceProcessorStep preserves placement on different GPUs in multi-GPU setup.""" + # Test 1: GPU-to-GPU preservation (cuda:0 config, cuda:1 input) + processor_gpu = DeviceProcessorStep(device="cuda:0") + + # Create tensors on cuda:1 (simulating Accelerate placement) + cuda1_device = torch.device("cuda:1") + observation = { + OBS_STATE: torch.randn(10).to(cuda1_device), + OBS_IMAGE: torch.randn(3, 224, 224).to(cuda1_device), + } + action = torch.randn(5).to(cuda1_device) + + transition = create_transition(observation=observation, action=action) + result = processor_gpu(transition) + + # Check that tensors remain on cuda:1 (not moved to cuda:0) + assert result[TransitionKey.OBSERVATION][OBS_STATE].device == cuda1_device + assert result[TransitionKey.OBSERVATION][OBS_IMAGE].device == cuda1_device + assert result[TransitionKey.ACTION].device == cuda1_device + + # Test 2: GPU-to-CPU should move to CPU (not preserve GPU) + processor_cpu = DeviceProcessorStep(device="cpu") + + transition_gpu = create_transition( + observation={OBS_STATE: torch.randn(10).cuda()}, action=torch.randn(5).cuda() + ) + result_cpu = processor_cpu(transition_gpu) + + # Check that tensors are moved to CPU + assert result_cpu[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cpu" + assert result_cpu[TransitionKey.ACTION].device.type == "cpu" + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_multi_gpu_with_cpu_tensors(): + """Test that CPU tensors are moved to configured device even in multi-GPU context.""" + # Processor configured for cuda:1 + processor = DeviceProcessorStep(device="cuda:1") + + # Mix of CPU and GPU tensors + observation = { + "observation.cpu": torch.randn(10), # CPU tensor + "observation.gpu0": torch.randn(10).cuda(0), # Already on cuda:0 + "observation.gpu1": torch.randn(10).cuda(1), # Already on cuda:1 + } + action = torch.randn(5) # CPU tensor + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # CPU tensor should move to configured device (cuda:1) + assert result[TransitionKey.OBSERVATION]["observation.cpu"].device.type == "cuda" + assert result[TransitionKey.OBSERVATION]["observation.cpu"].device.index == 1 + assert result[TransitionKey.ACTION].device.type == "cuda" + assert result[TransitionKey.ACTION].device.index == 1 + + # GPU tensors should stay on their original devices + assert result[TransitionKey.OBSERVATION]["observation.gpu0"].device.index == 0 + assert result[TransitionKey.OBSERVATION]["observation.gpu1"].device.index == 1 + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_multi_gpu_with_float_dtype(): + """Test float dtype conversion works correctly with multi-GPU preservation.""" + processor = DeviceProcessorStep(device="cuda:0", float_dtype="float16") + + # Create float tensors on different GPUs + observation = { + "observation.gpu0": torch.randn(5, dtype=torch.float32).cuda(0), + "observation.gpu1": torch.randn(5, dtype=torch.float32).cuda(1), + "observation.cpu": torch.randn(5, dtype=torch.float32), # CPU + } + + transition = create_transition(observation=observation) + result = processor(transition) + + # Check device placement + assert result[TransitionKey.OBSERVATION]["observation.gpu0"].device.index == 0 + assert result[TransitionKey.OBSERVATION]["observation.gpu1"].device.index == 1 + assert result[TransitionKey.OBSERVATION]["observation.cpu"].device.index == 0 # Moved to cuda:0 + + # Check dtype conversion happened for all + assert result[TransitionKey.OBSERVATION]["observation.gpu0"].dtype == torch.float16 + assert result[TransitionKey.OBSERVATION]["observation.gpu1"].dtype == torch.float16 + assert result[TransitionKey.OBSERVATION]["observation.cpu"].dtype == torch.float16 + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_simulated_accelerate_scenario(): + """Test a scenario simulating how Accelerate would use the processor.""" + # Simulate different processes getting different GPU assignments + for gpu_id in range(min(torch.cuda.device_count(), 2)): + # Each "process" has a processor configured for cuda:0 + # but data comes in already placed on the process's GPU + processor = DeviceProcessorStep(device="cuda:0") + + # Simulate data already placed by Accelerate + device = torch.device(f"cuda:{gpu_id}") + observation = {OBS_STATE: torch.randn(1, 10).to(device)} + action = torch.randn(1, 5).to(device) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Verify data stays on the GPU where Accelerate placed it + assert result[TransitionKey.OBSERVATION][OBS_STATE].device == device + assert result[TransitionKey.ACTION].device == device + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_policy_processor_integration(): + """Test integration with policy processors - input on GPU, output on CPU.""" + from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature + from lerobot.processor import ( + AddBatchDimensionProcessorStep, + NormalizerProcessorStep, + UnnormalizerProcessorStep, + ) + from lerobot.utils.constants import ACTION, OBS_STATE + + # Create features and stats + features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(5,)), + } + + stats = { + OBS_STATE: {"mean": torch.zeros(10), "std": torch.ones(10)}, + ACTION: {"mean": torch.zeros(5), "std": torch.ones(5)}, + } + + norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD, FeatureType.ACTION: NormalizationMode.MEAN_STD} + + # Create input processor (preprocessor) that moves to GPU + input_processor = DataProcessorPipeline( + steps=[ + NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats), + AddBatchDimensionProcessorStep(), + DeviceProcessorStep(device="cuda"), + ], + name="test_preprocessor", + to_transition=identity_transition, + to_output=identity_transition, + ) + + # Create output processor (postprocessor) that moves to CPU + output_processor = DataProcessorPipeline( + steps=[ + DeviceProcessorStep(device="cpu"), + UnnormalizerProcessorStep(features={ACTION: features[ACTION]}, norm_map=norm_map, stats=stats), + ], + name="test_postprocessor", + to_transition=identity_transition, + to_output=identity_transition, + ) + + # Test data on CPU + observation = {OBS_STATE: torch.randn(10)} + action = torch.randn(5) + transition = create_transition(observation=observation, action=action) + + # Process through input processor + input_result = input_processor(transition) + + # Verify tensors are on GPU and batched + # The result has TransitionKey.OBSERVATION as the key, with observation.state inside + assert input_result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda" + assert input_result[TransitionKey.OBSERVATION][OBS_STATE].shape[0] == 1 + assert input_result[TransitionKey.ACTION].device.type == "cuda" + assert input_result[TransitionKey.ACTION].shape[0] == 1 + + # Simulate model output on GPU + model_output = create_transition(action=torch.randn(1, 5).cuda()) + + # Process through output processor + output_result = output_processor(model_output) + + # Verify action is back on CPU and unnormalized + assert output_result[TransitionKey.ACTION].device.type == "cpu" + assert output_result[TransitionKey.ACTION].shape == (1, 5) + + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_mps_float64_compatibility(): + """Test MPS device compatibility with float64 tensors (automatic conversion to float32).""" + processor = DeviceProcessorStep(device="mps") + + # Create tensors with different dtypes, including float64 which MPS doesn't support + observation = { + "observation.float64": torch.randn(5, dtype=torch.float64), # Should be converted to float32 + "observation.float32": torch.randn(5, dtype=torch.float32), # Should remain float32 + "observation.float16": torch.randn(5, dtype=torch.float16), # Should remain float16 + "observation.int64": torch.randint(0, 10, (5,), dtype=torch.int64), # Should remain int64 + "observation.bool": torch.tensor([True, False, True], dtype=torch.bool), # Should remain bool + } + action = torch.randn(3, dtype=torch.float64) # Should be converted to float32 + reward = torch.tensor(1.0, dtype=torch.float64) # Should be converted to float32 + done = torch.tensor(False, dtype=torch.bool) # Should remain bool + truncated = torch.tensor(True, dtype=torch.bool) # Should remain bool + + transition = create_transition( + observation=observation, action=action, reward=reward, done=done, truncated=truncated + ) + + result = processor(transition) + + # Check that all tensors are on MPS device + assert result[TransitionKey.OBSERVATION]["observation.float64"].device.type == "mps" + assert result[TransitionKey.OBSERVATION]["observation.float32"].device.type == "mps" + assert result[TransitionKey.OBSERVATION]["observation.float16"].device.type == "mps" + assert result[TransitionKey.OBSERVATION]["observation.int64"].device.type == "mps" + assert result[TransitionKey.OBSERVATION]["observation.bool"].device.type == "mps" + assert result[TransitionKey.ACTION].device.type == "mps" + assert result[TransitionKey.REWARD].device.type == "mps" + assert result[TransitionKey.DONE].device.type == "mps" + assert result[TransitionKey.TRUNCATED].device.type == "mps" + + # Check that float64 tensors were automatically converted to float32 + assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float32 + assert result[TransitionKey.ACTION].dtype == torch.float32 + assert result[TransitionKey.REWARD].dtype == torch.float32 + + # Check that other dtypes were preserved + assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float32 + assert result[TransitionKey.OBSERVATION]["observation.float16"].dtype == torch.float16 + assert result[TransitionKey.OBSERVATION]["observation.int64"].dtype == torch.int64 + assert result[TransitionKey.OBSERVATION]["observation.bool"].dtype == torch.bool + assert result[TransitionKey.DONE].dtype == torch.bool + assert result[TransitionKey.TRUNCATED].dtype == torch.bool + + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_mps_float64_with_complementary_data(): + """Test MPS float64 conversion with complementary_data tensors.""" + processor = DeviceProcessorStep(device="mps") + + # Create complementary_data with float64 tensors + complementary_data = { + "task": ["pick_object"], + "index": torch.tensor([42], dtype=torch.int64), # Should remain int64 + "task_index": torch.tensor([3], dtype=torch.int64), # Should remain int64 + "float64_tensor": torch.tensor([1.5, 2.5], dtype=torch.float64), # Should convert to float32 + "float32_tensor": torch.tensor([3.5], dtype=torch.float32), # Should remain float32 + } + + transition = create_transition( + observation={OBS_STATE: torch.randn(5, dtype=torch.float64)}, + action=torch.randn(3, dtype=torch.float64), + complementary_data=complementary_data, + ) + + result = processor(transition) + + # Check that all tensors are on MPS device + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "mps" + assert result[TransitionKey.ACTION].device.type == "mps" + + processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + assert processed_comp_data["index"].device.type == "mps" + assert processed_comp_data["task_index"].device.type == "mps" + assert processed_comp_data["float64_tensor"].device.type == "mps" + assert processed_comp_data["float32_tensor"].device.type == "mps" + + # Check dtype conversions + assert result[TransitionKey.OBSERVATION][OBS_STATE].dtype == torch.float32 # Converted + assert result[TransitionKey.ACTION].dtype == torch.float32 # Converted + assert processed_comp_data["float64_tensor"].dtype == torch.float32 # Converted + assert processed_comp_data["float32_tensor"].dtype == torch.float32 # Unchanged + assert processed_comp_data["index"].dtype == torch.int64 # Unchanged + assert processed_comp_data["task_index"].dtype == torch.int64 # Unchanged + + # Check non-tensor data preserved + assert processed_comp_data["task"] == ["pick_object"] + + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_mps_with_explicit_float_dtype(): + """Test MPS device with explicit float_dtype setting.""" + # Test that explicit float_dtype still works on MPS + processor = DeviceProcessorStep(device="mps", float_dtype="float16") + + observation = { + "observation.float64": torch.randn( + 5, dtype=torch.float64 + ), # First converted to float32, then to float16 + "observation.float32": torch.randn(5, dtype=torch.float32), # Converted to float16 + "observation.int32": torch.randint(0, 10, (5,), dtype=torch.int32), # Should remain int32 + } + action = torch.randn(3, dtype=torch.float64) + + transition = create_transition(observation=observation, action=action) + result = processor(transition) + + # Check device placement + assert result[TransitionKey.OBSERVATION]["observation.float64"].device.type == "mps" + assert result[TransitionKey.OBSERVATION]["observation.float32"].device.type == "mps" + assert result[TransitionKey.OBSERVATION]["observation.int32"].device.type == "mps" + assert result[TransitionKey.ACTION].device.type == "mps" + + # Check that all float tensors end up as float16 (the target dtype) + assert result[TransitionKey.OBSERVATION]["observation.float64"].dtype == torch.float16 + assert result[TransitionKey.OBSERVATION]["observation.float32"].dtype == torch.float16 + assert result[TransitionKey.ACTION].dtype == torch.float16 + + # Check that non-float tensors are preserved + assert result[TransitionKey.OBSERVATION]["observation.int32"].dtype == torch.int32 + + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_mps_serialization(): + """Test that MPS device processor can be serialized and loaded correctly.""" + processor = DeviceProcessorStep(device="mps", float_dtype="float32") + + # Test get_config + config = processor.get_config() + assert config == {"device": "mps", "float_dtype": "float32"} + + # Test state_dict (should be empty) + state = processor.state_dict() + assert state == {} + + # Test load_state_dict (should be no-op) + processor.load_state_dict({}) + assert processor.device == "mps" diff --git a/tests/processor/test_diffusion_processor.py b/tests/processor/test_diffusion_processor.py new file mode 100644 index 00000000000..67981c70d5c --- /dev/null +++ b/tests/processor/test_diffusion_processor.py @@ -0,0 +1,398 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for Diffusion policy processor.""" + +import tempfile + +import pytest +import torch + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig +from lerobot.policies.diffusion.processor_diffusion import make_diffusion_pre_post_processors +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DataProcessorPipeline, + DeviceProcessorStep, + NormalizerProcessorStep, + RenameObservationsProcessorStep, + TransitionKey, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import create_transition, transition_to_batch +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE + + +def create_default_config(): + """Create a default Diffusion configuration for testing.""" + config = DiffusionConfig() + config.input_features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(7,)), + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), + } + config.output_features = { + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(6,)), + } + config.normalization_mapping = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.MIN_MAX, + } + config.device = "cpu" + return config + + +def create_default_stats(): + """Create default dataset statistics for testing.""" + return { + OBS_STATE: {"mean": torch.zeros(7), "std": torch.ones(7)}, + OBS_IMAGE: {}, # No normalization for images + ACTION: {"min": torch.full((6,), -1.0), "max": torch.ones(6)}, + } + + +def test_make_diffusion_processor_basic(): + """Test basic creation of Diffusion processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors(config, stats) + + # Check processor names + assert preprocessor.name == "policy_preprocessor" + assert postprocessor.name == "policy_postprocessor" + + # Check steps in preprocessor + assert len(preprocessor.steps) == 4 + assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep) + assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep) + assert isinstance(preprocessor.steps[2], DeviceProcessorStep) + assert isinstance(preprocessor.steps[3], NormalizerProcessorStep) + + # Check steps in postprocessor + assert len(postprocessor.steps) == 2 + assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep) + assert isinstance(postprocessor.steps[1], DeviceProcessorStep) + + +def test_diffusion_processor_with_images(): + """Test Diffusion processor with image observations.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors( + config, + stats, + ) + + # Create test data with images + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is batched + assert processed[OBS_STATE].shape == (1, 7) + assert processed[OBS_IMAGE].shape == (1, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1, 6) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_diffusion_processor_cuda(): + """Test Diffusion processor with CUDA device.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors( + config, + stats, + ) + + # Create CPU data + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is on CUDA + assert processed[OBS_STATE].device.type == "cuda" + assert processed[OBS_IMAGE].device.type == "cuda" + assert processed[TransitionKey.ACTION.value].device.type == "cuda" + + # Process through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is back on CPU + assert postprocessed.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_diffusion_processor_accelerate_scenario(): + """Test Diffusion processor in simulated Accelerate scenario.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors( + config, + stats, + ) + + # Simulate Accelerate: data already on GPU + device = torch.device("cuda:0") + observation = { + OBS_STATE: torch.randn(1, 7).to(device), + OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device), + } + action = torch.randn(1, 6).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on same GPU + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_diffusion_processor_multi_gpu(): + """Test Diffusion processor with multi-GPU setup.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors(config, stats) + + # Simulate data on different GPU + device = torch.device("cuda:1") + observation = { + OBS_STATE: torch.randn(1, 7).to(device), + OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device), + } + action = torch.randn(1, 6).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on cuda:1 + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +def test_diffusion_processor_without_stats(): + """Test Diffusion processor creation without dataset statistics.""" + config = create_default_config() + + preprocessor, postprocessor = make_diffusion_pre_post_processors( + config, + dataset_stats=None, + ) + + # Should still create processors + assert preprocessor is not None + assert postprocessor is not None + + # Process should still work + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed is not None + + +def test_diffusion_processor_save_and_load(): + """Test saving and loading Diffusion processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors(config, stats) + + with tempfile.TemporaryDirectory() as tmpdir: + # Save preprocessor + preprocessor.save_pretrained(tmpdir) + + # Load preprocessor + loaded_preprocessor = DataProcessorPipeline.from_pretrained( + tmpdir, config_filename="policy_preprocessor.json" + ) + + # Test that loaded processor works + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = loaded_preprocessor(batch) + assert processed[OBS_STATE].shape == (1, 7) + assert processed[OBS_IMAGE].shape == (1, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1, 6) + + +def test_diffusion_processor_identity_normalization(): + """Test that images with IDENTITY normalization are not normalized.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors( + config, + stats, + ) + + # Create test data + image_value = torch.rand(3, 224, 224) * 255 # Large values + observation = { + OBS_STATE: torch.randn(7), + OBS_IMAGE: image_value.clone(), + } + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Image should not be normalized (IDENTITY mode) + # Just batched + assert torch.allclose(processed[OBS_IMAGE][0], image_value, rtol=1e-5) + + +def test_diffusion_processor_batch_consistency(): + """Test Diffusion processor with different batch sizes.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_diffusion_pre_post_processors( + config, + stats, + ) + + # Test with different batch sizes + for batch_size in [1, 8, 32]: + observation = { + OBS_STATE: torch.randn(batch_size, 7) if batch_size > 1 else torch.randn(7), + OBS_IMAGE: torch.randn(batch_size, 3, 224, 224) if batch_size > 1 else torch.randn(3, 224, 224), + } + action = torch.randn(batch_size, 6) if batch_size > 1 else torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + + # Check correct batch size + expected_batch = batch_size if batch_size > 1 else 1 + assert processed[OBS_STATE].shape[0] == expected_batch + assert processed[OBS_IMAGE].shape[0] == expected_batch + assert processed[TransitionKey.ACTION.value].shape[0] == expected_batch + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_diffusion_processor_bfloat16_device_float32_normalizer(): + """Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, _ = make_diffusion_pre_post_processors(config, stats) + + # Modify the pipeline to use bfloat16 device processor with float32 normalizer + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + # Device processor converts to bfloat16 + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16")) + elif isinstance(step, NormalizerProcessorStep): + # Normalizer stays configured as float32 (will auto-adapt to bfloat16) + norm_step = step # Now type checker knows this is NormalizerProcessorStep + modified_steps.append( + NormalizerProcessorStep( + features=norm_step.features, + norm_map=norm_step.norm_map, + stats=norm_step.stats, + device=config.device, + dtype=torch.float32, # Deliberately configured as float32 + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Verify initial normalizer configuration + normalizer_step = preprocessor.steps[3] # NormalizerProcessorStep + assert normalizer_step.dtype == torch.float32 + + # Create test data with both state and visual observations + observation = { + OBS_STATE: torch.randn(7, dtype=torch.float32), + OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32), + } + action = torch.randn(6, dtype=torch.float32) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through full pipeline + processed = preprocessor(batch) + + # Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16 + assert processed[OBS_STATE].dtype == torch.bfloat16 + assert processed[OBS_IMAGE].dtype == torch.bfloat16 # IDENTITY normalization still gets dtype conversion + assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16 + + # Verify normalizer automatically adapted its internal state + assert normalizer_step.dtype == torch.bfloat16 + # Check state stats (has normalization) + for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 + # OBS_IMAGE uses IDENTITY normalization, so no stats to check diff --git a/tests/processor/test_migration_detection.py b/tests/processor/test_migration_detection.py new file mode 100644 index 00000000000..1ddc87d1ece --- /dev/null +++ b/tests/processor/test_migration_detection.py @@ -0,0 +1,342 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Tests for processor migration detection functionality. +""" + +import json +import tempfile +from pathlib import Path + +import pytest + +from lerobot.processor.pipeline import DataProcessorPipeline, ProcessorMigrationError +from lerobot.utils.constants import ACTION, OBS_STATE + + +def test_is_processor_config_valid_configs(): + """Test processor config detection with valid configurations.""" + valid_configs = [ + {"steps": []}, # Empty steps + {"steps": [{"class": "MyClass"}]}, # Class-based step + {"steps": [{"registry_name": "my_step"}]}, # Registry-based step + {"steps": [{"class": "A"}, {"registry_name": "B"}]}, # Mixed + {"name": "Test", "steps": [{"class": "MyClass"}]}, # With name + ] + + for i, config in enumerate(valid_configs): + assert DataProcessorPipeline._is_processor_config(config), ( + f"Valid config {i} should be detected as processor config: {config}" + ) + + +def test_is_processor_config_invalid_configs(): + """Test processor config detection with invalid configurations.""" + invalid_configs = [ + {}, # No steps field + {"steps": "not a list"}, # Steps is not a list + {"steps": [{}]}, # Step without class or registry_name + {"steps": ["not a dict"]}, # Step is not a dict + {"steps": [{"other_field": "value"}]}, # Step with wrong fields + {"other_field": "value"}, # Completely different structure + ] + + for i, config in enumerate(invalid_configs): + assert not DataProcessorPipeline._is_processor_config(config), ( + f"Invalid config {i} should not be detected as processor config: {config}" + ) + + +def test_should_suggest_migration_with_processor_config(): + """Test that migration is NOT suggested when processor config exists.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create a valid processor config + processor_config = { + "name": "TestProcessor", + "steps": [ + { + "class": "lerobot.processor.normalize.NormalizeStep", + "config": {"mean": 0.0, "std": 1.0}, + } + ], + } + + with open(tmp_path / "processor.json", "w") as f: + json.dump(processor_config, f) + + # Should NOT suggest migration (processor config exists) + result = DataProcessorPipeline._should_suggest_migration(tmp_path) + assert not result + + +def test_should_suggest_migration_with_empty_processor_config(): + """Test that migration is NOT suggested when empty processor config exists.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create an empty processor config + empty_processor_config = { + "name": "EmptyProcessor", + "steps": [], # Empty steps is valid + } + + with open(tmp_path / "empty_processor.json", "w") as f: + json.dump(empty_processor_config, f) + + # Should NOT suggest migration (processor config exists, even if empty) + result = DataProcessorPipeline._should_suggest_migration(tmp_path) + assert not result + + +def test_should_suggest_migration_with_model_config_only(): + """Test that migration IS suggested when only model config exists.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create a model config (like old LeRobot format) + model_config = { + "type": "act", + "input_features": {OBS_STATE: {"shape": [7]}}, + "output_features": {ACTION: {"shape": [7]}}, + "hidden_dim": 256, + "n_obs_steps": 1, + "n_action_steps": 1, + } + + with open(tmp_path / "config.json", "w") as f: + json.dump(model_config, f) + + # SHOULD suggest migration (model config exists but no processor) + result = DataProcessorPipeline._should_suggest_migration(tmp_path) + assert result + + +def test_should_suggest_migration_no_json_files(): + """Test that migration is NOT suggested when no JSON files exist.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create some non-JSON files + with open(tmp_path / "model.safetensors", "w") as f: + f.write("fake model data") + + with open(tmp_path / "README.md", "w") as f: + f.write("# Model README") + + # Should NOT suggest migration (no JSON files) + result = DataProcessorPipeline._should_suggest_migration(tmp_path) + assert not result + + +def test_should_suggest_migration_random_json_files(): + """Test that migration IS suggested when JSON files exist but none are processor configs.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create some random JSON file (not a processor config) + random_config = {"some_field": "some_value", "another_field": 123} + + with open(tmp_path / "random.json", "w") as f: + json.dump(random_config, f) + + # SHOULD suggest migration (JSON files exist but none are processor configs) + result = DataProcessorPipeline._should_suggest_migration(tmp_path) + assert result + + +def test_should_suggest_migration_mixed_configs(): + """Test that migration is NOT suggested when processor config exists alongside other configs.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create both a processor config and a model config + processor_config = {"name": "TestProcessor", "steps": [{"registry_name": "normalize_step"}]} + + model_config = {"type": "diffusion", "hidden_dim": 512} + + with open(tmp_path / "processor.json", "w") as f: + json.dump(processor_config, f) + + with open(tmp_path / "config.json", "w") as f: + json.dump(model_config, f) + + # Should NOT suggest migration (processor config exists) + result = DataProcessorPipeline._should_suggest_migration(tmp_path) + assert not result + + +def test_should_suggest_migration_invalid_json(): + """Test that invalid JSON is handled gracefully.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create an invalid JSON file + with open(tmp_path / "invalid.json", "w") as f: + f.write("{ invalid json") + + # Create a valid non-processor config + model_config = {"type": "act"} + with open(tmp_path / "model.json", "w") as f: + json.dump(model_config, f) + + # SHOULD suggest migration (invalid JSON is ignored, but we have non-processor JSON) + result = DataProcessorPipeline._should_suggest_migration(tmp_path) + assert result + + +def test_from_pretrained_multiple_json_files_migration_error(): + """Test that multiple JSON files trigger ProcessorMigrationError.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create multiple non-processor configs + model_config = {"type": "act", "hidden_dim": 128} + train_config = {"batch_size": 32, "lr": 0.001} + + with open(tmp_path / "config.json", "w") as f: + json.dump(model_config, f) + + with open(tmp_path / "train_config.json", "w") as f: + json.dump(train_config, f) + + # Should raise ProcessorMigrationError + with pytest.raises(ProcessorMigrationError) as exc_info: + DataProcessorPipeline.from_pretrained(tmp_path, config_filename="config.json") + + # Check the error details + error = exc_info.value + assert str(tmp_path) in str(error.model_path) + assert "migrate_policy_normalization.py" in error.migration_command + assert "not a valid processor configuration" in error.original_error + + +def test_from_pretrained_no_processor_config_migration_error(): + """Test that missing processor config triggers ProcessorMigrationError.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create a model config but no processor + model_config = {"type": "diffusion", "hidden_dim": 256} + + with open(tmp_path / "config.json", "w") as f: + json.dump(model_config, f) + + # Should raise ProcessorMigrationError + with pytest.raises(ProcessorMigrationError) as exc_info: + DataProcessorPipeline.from_pretrained(tmp_path, config_filename="config.json") + + # Check the error details + error = exc_info.value + assert str(tmp_path) in str(error.model_path) + assert "migrate_policy_normalization.py" in error.migration_command + assert "not a valid processor configuration" in error.original_error + + +def test_from_pretrained_valid_processor_no_migration_error(): + """Test that valid processor config does NOT trigger migration error.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create a valid processor config + processor_config = { + "name": "TestProcessor", + "steps": [], # Empty is valid + } + + with open(tmp_path / "processor.json", "w") as f: + json.dump(processor_config, f) + + # Should succeed and create pipeline + pipeline = DataProcessorPipeline.from_pretrained(tmp_path, config_filename="processor.json") + assert pipeline is not None + assert pipeline.name == "TestProcessor" + assert len(pipeline) == 0 + + +def test_from_pretrained_no_json_files_no_migration_error(): + """Test that directories with no JSON files don't trigger migration errors.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create some non-JSON files + with open(tmp_path / "model.safetensors", "w") as f: + f.write("fake model data") + + # Should raise FileNotFoundError (config file not found) + with pytest.raises(FileNotFoundError, match="not found in directory"): + DataProcessorPipeline.from_pretrained(tmp_path, config_filename="processor.json") + + +def test_processor_migration_error_creation(): + """Test that ProcessorMigrationError is created correctly.""" + model_path = "/path/to/model" + migration_command = "python migrate.py --path /path/to/model" + original_error = "Config not found" + + error = ProcessorMigrationError(model_path, migration_command, original_error) + + assert error.model_path == model_path + assert error.migration_command == migration_command + assert error.original_error == original_error + assert model_path in str(error) + assert migration_command in str(error) + assert original_error in str(error) + + +def test_processor_migration_error_attributes(): + """Test that ProcessorMigrationError has correct attributes.""" + model_path = Path("/test/path") + migration_command = "python test.py" + original_error = "Test error" + + error = ProcessorMigrationError(model_path, migration_command, original_error) + + # Test that attributes are accessible + assert hasattr(error, "model_path") + assert hasattr(error, "migration_command") + assert hasattr(error, "original_error") + + # Test that it's still an Exception + assert isinstance(error, Exception) + + +def test_migration_suggestion_raises_error(): + """Test that migration suggestion always raises ProcessorMigrationError.""" + with pytest.raises(ProcessorMigrationError) as exc_info: + DataProcessorPipeline._suggest_processor_migration("/test/path", "Test error") + + error = exc_info.value + assert "/test/path" in str(error.model_path) + assert "Test error" in error.original_error + assert "migrate_policy_normalization.py" in error.migration_command + + +def test_migration_error_always_raised_for_invalid_configs(): + """Test that ProcessorMigrationError is always raised for invalid configs.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create a model config + model_config = {"type": "test", "param": "value"} + with open(tmp_path / "config.json", "w") as f: + json.dump(model_config, f) + + # Should always raise ProcessorMigrationError + with pytest.raises(ProcessorMigrationError): + DataProcessorPipeline.from_pretrained(tmp_path, config_filename="config.json") diff --git a/tests/processor/test_normalize_processor.py b/tests/processor/test_normalize_processor.py index 26aea56c767..208a6b5c537 100644 --- a/tests/processor/test_normalize_processor.py +++ b/tests/processor/test_normalize_processor.py @@ -20,55 +20,45 @@ import torch from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature -from lerobot.processor.normalize_processor import ( - NormalizerProcessor, - UnnormalizerProcessor, - _convert_stats_to_tensors, +from lerobot.processor import ( + DataProcessorPipeline, + IdentityProcessorStep, + NormalizerProcessorStep, + TransitionKey, + UnnormalizerProcessorStep, + hotswap_stats, ) -from lerobot.processor.pipeline import RobotProcessor, TransitionKey - - -def create_transition( - observation=None, action=None, reward=None, done=None, truncated=None, info=None, complementary_data=None -): - """Helper to create an EnvTransition dictionary.""" - return { - TransitionKey.OBSERVATION: observation, - TransitionKey.ACTION: action, - TransitionKey.REWARD: reward, - TransitionKey.DONE: done, - TransitionKey.TRUNCATED: truncated, - TransitionKey.INFO: info, - TransitionKey.COMPLEMENTARY_DATA: complementary_data, - } +from lerobot.processor.converters import create_transition, identity_transition, to_tensor +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE, OBS_STR +from lerobot.utils.utils import auto_select_torch_device def test_numpy_conversion(): stats = { - "observation.image": { + OBS_IMAGE: { "mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2]), } } - tensor_stats = _convert_stats_to_tensors(stats) + tensor_stats = to_tensor(stats) - assert isinstance(tensor_stats["observation.image"]["mean"], torch.Tensor) - assert isinstance(tensor_stats["observation.image"]["std"], torch.Tensor) - assert torch.allclose(tensor_stats["observation.image"]["mean"], torch.tensor([0.5, 0.5, 0.5])) - assert torch.allclose(tensor_stats["observation.image"]["std"], torch.tensor([0.2, 0.2, 0.2])) + assert isinstance(tensor_stats[OBS_IMAGE]["mean"], torch.Tensor) + assert isinstance(tensor_stats[OBS_IMAGE]["std"], torch.Tensor) + assert torch.allclose(tensor_stats[OBS_IMAGE]["mean"], torch.tensor([0.5, 0.5, 0.5])) + assert torch.allclose(tensor_stats[OBS_IMAGE]["std"], torch.tensor([0.2, 0.2, 0.2])) def test_tensor_conversion(): stats = { - "action": { + ACTION: { "mean": torch.tensor([0.0, 0.0]), "std": torch.tensor([1.0, 1.0]), } } - tensor_stats = _convert_stats_to_tensors(stats) + tensor_stats = to_tensor(stats) - assert tensor_stats["action"]["mean"].dtype == torch.float32 - assert tensor_stats["action"]["std"].dtype == torch.float32 + assert tensor_stats[ACTION]["mean"].dtype == torch.float32 + assert tensor_stats[ACTION]["std"].dtype == torch.float32 def test_scalar_conversion(): @@ -78,7 +68,7 @@ def test_scalar_conversion(): "std": 0.1, } } - tensor_stats = _convert_stats_to_tensors(stats) + tensor_stats = to_tensor(stats) assert torch.allclose(tensor_stats["reward"]["mean"], torch.tensor(0.5)) assert torch.allclose(tensor_stats["reward"]["std"], torch.tensor(0.1)) @@ -86,15 +76,15 @@ def test_scalar_conversion(): def test_list_conversion(): stats = { - "observation.state": { + OBS_STATE: { "min": [0.0, -1.0, -2.0], "max": [1.0, 1.0, 2.0], } } - tensor_stats = _convert_stats_to_tensors(stats) + tensor_stats = to_tensor(stats) - assert torch.allclose(tensor_stats["observation.state"]["min"], torch.tensor([0.0, -1.0, -2.0])) - assert torch.allclose(tensor_stats["observation.state"]["max"], torch.tensor([1.0, 1.0, 2.0])) + assert torch.allclose(tensor_stats[OBS_STATE]["min"], torch.tensor([0.0, -1.0, -2.0])) + assert torch.allclose(tensor_stats[OBS_STATE]["max"], torch.tensor([1.0, 1.0, 2.0])) def test_unsupported_type(): @@ -104,14 +94,14 @@ def test_unsupported_type(): } } with pytest.raises(TypeError, match="Unsupported type"): - _convert_stats_to_tensors(stats) + to_tensor(stats) # Helper functions to create feature maps and norm maps def _create_observation_features(): return { - "observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), - "observation.state": PolicyFeature(FeatureType.STATE, (2,)), + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), } @@ -122,15 +112,15 @@ def _create_observation_norm_map(): } -# Fixtures for observation normalisation tests using NormalizerProcessor +# Fixtures for observation normalisation tests using NormalizerProcessorStep @pytest.fixture def observation_stats(): return { - "observation.image": { + OBS_IMAGE: { "mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2]), }, - "observation.state": { + OBS_STATE: { "min": np.array([0.0, -1.0]), "max": np.array([1.0, 1.0]), }, @@ -139,16 +129,16 @@ def observation_stats(): @pytest.fixture def observation_normalizer(observation_stats): - """Return a NormalizerProcessor that only has observation stats (no action).""" + """Return a NormalizerProcessorStep that only has observation stats (no action).""" features = _create_observation_features() norm_map = _create_observation_norm_map() - return NormalizerProcessor(features=features, norm_map=norm_map, stats=observation_stats) + return NormalizerProcessorStep(features=features, norm_map=norm_map, stats=observation_stats) def test_mean_std_normalization(observation_normalizer): observation = { - "observation.image": torch.tensor([0.7, 0.5, 0.3]), - "observation.state": torch.tensor([0.5, 0.0]), + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([0.5, 0.0]), } transition = create_transition(observation=observation) @@ -157,12 +147,12 @@ def test_mean_std_normalization(observation_normalizer): # Check mean/std normalization expected_image = (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2 - assert torch.allclose(normalized_obs["observation.image"], expected_image) + assert torch.allclose(normalized_obs[OBS_IMAGE], expected_image) def test_min_max_normalization(observation_normalizer): observation = { - "observation.state": torch.tensor([0.5, 0.0]), + OBS_STATE: torch.tensor([0.5, 0.0]), } transition = create_transition(observation=observation) @@ -173,19 +163,242 @@ def test_min_max_normalization(observation_normalizer): # For state[0]: 2 * (0.5 - 0.0) / (1.0 - 0.0) - 1 = 0.0 # For state[1]: 2 * (0.0 - (-1.0)) / (1.0 - (-1.0)) - 1 = 0.0 expected_state = torch.tensor([0.0, 0.0]) + assert torch.allclose(normalized_obs[OBS_STATE], expected_state, atol=1e-6) + + +def test_quantile_normalization(): + """Test QUANTILES mode using 1st-99th percentiles.""" + features = { + "observation.state": PolicyFeature(FeatureType.STATE, (2,)), + } + norm_map = { + FeatureType.STATE: NormalizationMode.QUANTILES, + } + stats = { + "observation.state": { + "q01": np.array([0.1, -0.8]), # 1st percentile + "q99": np.array([0.9, 0.8]), # 99th percentile + }, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + "observation.state": torch.tensor([0.5, 0.0]), + } + transition = create_transition(observation=observation) + + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + + # Check quantile normalization to [-1, 1] + # For state[0]: 2 * (0.5 - 0.1) / (0.9 - 0.1) - 1 = 2 * 0.4 / 0.8 - 1 = 0.0 + # For state[1]: 2 * (0.0 - (-0.8)) / (0.8 - (-0.8)) - 1 = 2 * 0.8 / 1.6 - 1 = 0.0 + expected_state = torch.tensor([0.0, 0.0]) + assert torch.allclose(normalized_obs["observation.state"], expected_state, atol=1e-6) + + +def test_quantile10_normalization(): + """Test QUANTILE10 mode using 10th-90th percentiles.""" + features = { + "observation.state": PolicyFeature(FeatureType.STATE, (2,)), + } + norm_map = { + FeatureType.STATE: NormalizationMode.QUANTILE10, + } + stats = { + "observation.state": { + "q10": np.array([0.2, -0.6]), # 10th percentile + "q90": np.array([0.8, 0.6]), # 90th percentile + }, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + "observation.state": torch.tensor([0.5, 0.0]), + } + transition = create_transition(observation=observation) + + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + + # Check quantile normalization to [-1, 1] + # For state[0]: 2 * (0.5 - 0.2) / (0.8 - 0.2) - 1 = 2 * 0.3 / 0.6 - 1 = 0.0 + # For state[1]: 2 * (0.0 - (-0.6)) / (0.6 - (-0.6)) - 1 = 2 * 0.6 / 1.2 - 1 = 0.0 + expected_state = torch.tensor([0.0, 0.0]) + assert torch.allclose(normalized_obs["observation.state"], expected_state, atol=1e-6) + + +def test_quantile_unnormalization(): + """Test that quantile normalization can be reversed properly.""" + features = { + "action": PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = { + FeatureType.ACTION: NormalizationMode.QUANTILES, + } + stats = { + "action": { + "q01": np.array([0.1, -0.8]), + "q99": np.array([0.9, 0.8]), + }, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + # Test round-trip normalization + original_action = torch.tensor([0.5, 0.0]) + transition = create_transition(action=original_action) + + # Normalize then unnormalize + normalized = normalizer(transition) + unnormalized = unnormalizer(normalized) + + # Should recover original values + recovered_action = unnormalized[TransitionKey.ACTION] + assert torch.allclose(recovered_action, original_action, atol=1e-6) + + +def test_quantile_division_by_zero(): + """Test quantile normalization handles edge case where q01 == q99.""" + features = { + "observation.state": PolicyFeature(FeatureType.STATE, (1,)), + } + norm_map = { + FeatureType.STATE: NormalizationMode.QUANTILES, + } + stats = { + "observation.state": { + "q01": np.array([0.5]), # Same value + "q99": np.array([0.5]), # Same value -> division by zero case + }, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + "observation.state": torch.tensor([0.5]), + } + transition = create_transition(observation=observation) + + # Should not crash and should handle gracefully + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + + # When quantiles are identical, should normalize to 0 (due to epsilon handling) + assert torch.isfinite(normalized_obs["observation.state"]).all() + + +def test_quantile_partial_stats(): + """Test that quantile normalization handles missing quantile stats by raising.""" + features = { + "observation.state": PolicyFeature(FeatureType.STATE, (2,)), + } + norm_map = { + FeatureType.STATE: NormalizationMode.QUANTILES, + } + + # Missing q99 - should pass through unchanged + stats_partial = { + "observation.state": { + "q01": np.array([0.1, -0.8]), # Only q01, missing q99 + }, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats_partial) + + observation = { + "observation.state": torch.tensor([0.5, 0.0]), + } + transition = create_transition(observation=observation) + + with pytest.raises(ValueError, match="QUANTILES normalization mode requires q01 and q99 stats"): + _ = normalizer(transition) + + +def test_quantile_mixed_with_other_modes(): + """Test quantile normalization mixed with other normalization modes.""" + features = { + "observation.image": PolicyFeature(FeatureType.VISUAL, (3,)), + "observation.state": PolicyFeature(FeatureType.STATE, (2,)), + "action": PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, # Standard normalization + FeatureType.STATE: NormalizationMode.QUANTILES, # Quantile normalization + FeatureType.ACTION: NormalizationMode.QUANTILE10, # Different quantile mode + } + stats = { + "observation.image": {"mean": [0.5, 0.5, 0.5], "std": [0.2, 0.2, 0.2]}, + "observation.state": {"q01": [0.1, -0.8], "q99": [0.9, 0.8]}, + "action": {"q10": [0.2, -0.6], "q90": [0.8, 0.6]}, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + "observation.image": torch.tensor([0.7, 0.5, 0.3]), + "observation.state": torch.tensor([0.5, 0.0]), # Should use QUANTILES + } + action = torch.tensor([0.5, 0.0]) # Should use QUANTILE10 + transition = create_transition(observation=observation, action=action) + + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + normalized_action = normalized_transition[TransitionKey.ACTION] + + # Image should be mean/std normalized: (0.7 - 0.5) / 0.2 = 1.0, etc. + expected_image = (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2 + assert torch.allclose(normalized_obs["observation.image"], expected_image) + + # State should be quantile normalized: 2 * (0.5 - 0.1) / (0.9 - 0.1) - 1 = 0.0, etc. + expected_state = torch.tensor([0.0, 0.0]) assert torch.allclose(normalized_obs["observation.state"], expected_state, atol=1e-6) + # Action should be quantile10 normalized: 2 * (0.5 - 0.2) / (0.8 - 0.2) - 1 = 0.0, etc. + expected_action = torch.tensor([0.0, 0.0]) + assert torch.allclose(normalized_action, expected_action, atol=1e-6) + + +def test_quantile_with_missing_stats(): + """Test that quantile normalization handles completely missing stats gracefully.""" + features = { + "observation.state": PolicyFeature(FeatureType.STATE, (2,)), + } + norm_map = { + FeatureType.STATE: NormalizationMode.QUANTILES, + } + stats = {} # No stats provided + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + "observation.state": torch.tensor([0.5, 0.0]), + } + transition = create_transition(observation=observation) + + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + + # Should pass through unchanged when no stats available + assert torch.allclose(normalized_obs["observation.state"], observation["observation.state"]) + def test_selective_normalization(observation_stats): features = _create_observation_features() norm_map = _create_observation_norm_map() - normalizer = NormalizerProcessor( - features=features, norm_map=norm_map, stats=observation_stats, normalize_keys={"observation.image"} + normalizer = NormalizerProcessorStep( + features=features, + norm_map=norm_map, + stats=observation_stats, + normalize_observation_keys={OBS_IMAGE}, ) observation = { - "observation.image": torch.tensor([0.7, 0.5, 0.3]), - "observation.state": torch.tensor([0.5, 0.0]), + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([0.5, 0.0]), } transition = create_transition(observation=observation) @@ -193,69 +406,70 @@ def test_selective_normalization(observation_stats): normalized_obs = normalized_transition[TransitionKey.OBSERVATION] # Only image should be normalized - assert torch.allclose(normalized_obs["observation.image"], (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2) + assert torch.allclose(normalized_obs[OBS_IMAGE], (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2) # State should remain unchanged - assert torch.allclose(normalized_obs["observation.state"], observation["observation.state"]) + assert torch.allclose(normalized_obs[OBS_STATE], observation[OBS_STATE]) @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") def test_device_compatibility(observation_stats): features = _create_observation_features() norm_map = _create_observation_norm_map() - normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=observation_stats) + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=observation_stats) observation = { - "observation.image": torch.tensor([0.7, 0.5, 0.3]).cuda(), + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]).cuda(), } transition = create_transition(observation=observation) normalized_transition = normalizer(transition) normalized_obs = normalized_transition[TransitionKey.OBSERVATION] - assert normalized_obs["observation.image"].device.type == "cuda" + assert normalized_obs[OBS_IMAGE].device.type == "cuda" def test_from_lerobot_dataset(): # Mock dataset mock_dataset = Mock() mock_dataset.meta.stats = { - "observation.image": {"mean": [0.5], "std": [0.2]}, - "action": {"mean": [0.0], "std": [1.0]}, + OBS_IMAGE: {"mean": [0.5], "std": [0.2]}, + ACTION: {"mean": [0.0], "std": [1.0]}, } features = { - "observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), - "action": PolicyFeature(FeatureType.ACTION, (1,)), + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), + ACTION: PolicyFeature(FeatureType.ACTION, (1,)), } norm_map = { FeatureType.VISUAL: NormalizationMode.MEAN_STD, FeatureType.ACTION: NormalizationMode.MEAN_STD, } - normalizer = NormalizerProcessor.from_lerobot_dataset(mock_dataset, features, norm_map) + normalizer = NormalizerProcessorStep.from_lerobot_dataset(mock_dataset, features, norm_map) # Both observation and action statistics should be present in tensor stats - assert "observation.image" in normalizer._tensor_stats - assert "action" in normalizer._tensor_stats + assert OBS_IMAGE in normalizer._tensor_stats + assert ACTION in normalizer._tensor_stats def test_state_dict_save_load(observation_normalizer): # Save state state_dict = observation_normalizer.state_dict() + print("State dict:", state_dict) # Create new normalizer and load state features = _create_observation_features() norm_map = _create_observation_norm_map() - new_normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats={}) + new_normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats={}) new_normalizer.load_state_dict(state_dict) # Test that it works the same - observation = {"observation.image": torch.tensor([0.7, 0.5, 0.3])} + observation = {OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3])} transition = create_transition(observation=observation) result1 = observation_normalizer(transition)[TransitionKey.OBSERVATION] result2 = new_normalizer(transition)[TransitionKey.OBSERVATION] - assert torch.allclose(result1["observation.image"], result2["observation.image"]) + assert torch.allclose(result1[OBS_IMAGE], result2[OBS_IMAGE]) # Fixtures for ActionUnnormalizer tests @@ -277,7 +491,7 @@ def action_stats_min_max(): def _create_action_features(): return { - "action": PolicyFeature(FeatureType.ACTION, (3,)), + ACTION: PolicyFeature(FeatureType.ACTION, (3,)), } @@ -296,8 +510,8 @@ def _create_action_norm_map_min_max(): def test_mean_std_unnormalization(action_stats_mean_std): features = _create_action_features() norm_map = _create_action_norm_map_mean_std() - unnormalizer = UnnormalizerProcessor( - features=features, norm_map=norm_map, stats={"action": action_stats_mean_std} + unnormalizer = UnnormalizerProcessorStep( + features=features, norm_map=norm_map, stats={ACTION: action_stats_mean_std} ) normalized_action = torch.tensor([1.0, -0.5, 2.0]) @@ -314,8 +528,8 @@ def test_mean_std_unnormalization(action_stats_mean_std): def test_min_max_unnormalization(action_stats_min_max): features = _create_action_features() norm_map = _create_action_norm_map_min_max() - unnormalizer = UnnormalizerProcessor( - features=features, norm_map=norm_map, stats={"action": action_stats_min_max} + unnormalizer = UnnormalizerProcessorStep( + features=features, norm_map=norm_map, stats={ACTION: action_stats_min_max} ) # Actions in [-1, 1] @@ -337,14 +551,14 @@ def test_min_max_unnormalization(action_stats_min_max): assert torch.allclose(unnormalized_action, expected) -def test_numpy_action_input(action_stats_mean_std): +def test_tensor_action_input(action_stats_mean_std): features = _create_action_features() norm_map = _create_action_norm_map_mean_std() - unnormalizer = UnnormalizerProcessor( - features=features, norm_map=norm_map, stats={"action": action_stats_mean_std} + unnormalizer = UnnormalizerProcessorStep( + features=features, norm_map=norm_map, stats={ACTION: action_stats_mean_std} ) - normalized_action = np.array([1.0, -0.5, 2.0], dtype=np.float32) + normalized_action = torch.tensor([1.0, -0.5, 2.0], dtype=torch.float32) transition = create_transition(action=normalized_action) unnormalized_transition = unnormalizer(transition) @@ -358,8 +572,8 @@ def test_numpy_action_input(action_stats_mean_std): def test_none_action(action_stats_mean_std): features = _create_action_features() norm_map = _create_action_norm_map_mean_std() - unnormalizer = UnnormalizerProcessor( - features=features, norm_map=norm_map, stats={"action": action_stats_mean_std} + unnormalizer = UnnormalizerProcessorStep( + features=features, norm_map=norm_map, stats={ACTION: action_stats_mean_std} ) transition = create_transition() @@ -371,26 +585,26 @@ def test_none_action(action_stats_mean_std): def test_action_from_lerobot_dataset(): mock_dataset = Mock() - mock_dataset.meta.stats = {"action": {"mean": [0.0], "std": [1.0]}} - features = {"action": PolicyFeature(FeatureType.ACTION, (1,))} + mock_dataset.meta.stats = {ACTION: {"mean": [0.0], "std": [1.0]}} + features = {ACTION: PolicyFeature(FeatureType.ACTION, (1,))} norm_map = {FeatureType.ACTION: NormalizationMode.MEAN_STD} - unnormalizer = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset, features, norm_map) - assert "mean" in unnormalizer._tensor_stats["action"] + unnormalizer = UnnormalizerProcessorStep.from_lerobot_dataset(mock_dataset, features, norm_map) + assert "mean" in unnormalizer._tensor_stats[ACTION] -# Fixtures for NormalizerProcessor tests +# Fixtures for NormalizerProcessorStep tests @pytest.fixture def full_stats(): return { - "observation.image": { + OBS_IMAGE: { "mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2]), }, - "observation.state": { + OBS_STATE: { "min": np.array([0.0, -1.0]), "max": np.array([1.0, 1.0]), }, - "action": { + ACTION: { "mean": np.array([0.0, 0.0]), "std": np.array([1.0, 2.0]), }, @@ -399,9 +613,9 @@ def full_stats(): def _create_full_features(): return { - "observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), - "observation.state": PolicyFeature(FeatureType.STATE, (2,)), - "action": PolicyFeature(FeatureType.ACTION, (2,)), + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), } @@ -417,13 +631,13 @@ def _create_full_norm_map(): def normalizer_processor(full_stats): features = _create_full_features() norm_map = _create_full_norm_map() - return NormalizerProcessor(features=features, norm_map=norm_map, stats=full_stats) + return NormalizerProcessorStep(features=features, norm_map=norm_map, stats=full_stats) def test_combined_normalization(normalizer_processor): observation = { - "observation.image": torch.tensor([0.7, 0.5, 0.3]), - "observation.state": torch.tensor([0.5, 0.0]), + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([0.5, 0.0]), } action = torch.tensor([1.0, -0.5]) transition = create_transition( @@ -441,7 +655,7 @@ def test_combined_normalization(normalizer_processor): # Check normalized observations processed_obs = processed_transition[TransitionKey.OBSERVATION] expected_image = (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2 - assert torch.allclose(processed_obs["observation.image"], expected_image) + assert torch.allclose(processed_obs[OBS_IMAGE], expected_image) # Check normalized action processed_action = processed_transition[TransitionKey.ACTION] @@ -461,30 +675,34 @@ def test_processor_from_lerobot_dataset(full_stats): features = _create_full_features() norm_map = _create_full_norm_map() - processor = NormalizerProcessor.from_lerobot_dataset( - mock_dataset, features, norm_map, normalize_keys={"observation.image"} + processor = NormalizerProcessorStep.from_lerobot_dataset( + mock_dataset, features, norm_map, normalize_observation_keys={OBS_IMAGE} ) - assert processor.normalize_keys == {"observation.image"} - assert "observation.image" in processor._tensor_stats - assert "action" in processor._tensor_stats + assert processor.normalize_observation_keys == {OBS_IMAGE} + assert OBS_IMAGE in processor._tensor_stats + assert ACTION in processor._tensor_stats def test_get_config(full_stats): features = _create_full_features() norm_map = _create_full_norm_map() - processor = NormalizerProcessor( - features=features, norm_map=norm_map, stats=full_stats, normalize_keys={"observation.image"}, eps=1e-6 + processor = NormalizerProcessorStep( + features=features, + norm_map=norm_map, + stats=full_stats, + normalize_observation_keys={OBS_IMAGE}, + eps=1e-6, ) config = processor.get_config() expected_config = { - "normalize_keys": ["observation.image"], + "normalize_observation_keys": [OBS_IMAGE], "eps": 1e-6, "features": { - "observation.image": {"type": "VISUAL", "shape": (3, 96, 96)}, - "observation.state": {"type": "STATE", "shape": (2,)}, - "action": {"type": "ACTION", "shape": (2,)}, + OBS_IMAGE: {"type": "VISUAL", "shape": (3, 96, 96)}, + OBS_STATE: {"type": "STATE", "shape": (2,)}, + ACTION: {"type": "ACTION", "shape": (2,)}, }, "norm_map": { "VISUAL": "MEAN_STD", @@ -497,11 +715,13 @@ def test_get_config(full_stats): def test_integration_with_robot_processor(normalizer_processor): """Test integration with RobotProcessor pipeline""" - robot_processor = RobotProcessor([normalizer_processor]) + robot_processor = DataProcessorPipeline( + [normalizer_processor], to_transition=identity_transition, to_output=identity_transition + ) observation = { - "observation.image": torch.tensor([0.7, 0.5, 0.3]), - "observation.state": torch.tensor([0.5, 0.0]), + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([0.5, 0.0]), } action = torch.tensor([1.0, -0.5]) transition = create_transition( @@ -523,10 +743,10 @@ def test_integration_with_robot_processor(normalizer_processor): # Edge case tests def test_empty_observation(): - stats = {"observation.image": {"mean": [0.5], "std": [0.2]}} - features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} + stats = {OBS_IMAGE: {"mean": [0.5], "std": [0.2]}} + features = {OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} - normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=stats) + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) transition = create_transition() result = normalizer(transition) @@ -535,68 +755,70 @@ def test_empty_observation(): def test_empty_stats(): - features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} + features = {OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} - normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats={}) - observation = {"observation.image": torch.tensor([0.5])} + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats={}) + observation = {OBS_IMAGE: torch.tensor([0.5])} transition = create_transition(observation=observation) result = normalizer(transition) # Should return observation unchanged since no stats are available - assert torch.allclose( - result[TransitionKey.OBSERVATION]["observation.image"], observation["observation.image"] - ) + assert torch.allclose(result[TransitionKey.OBSERVATION][OBS_IMAGE], observation[OBS_IMAGE]) def test_partial_stats(): - """If statistics are incomplete, the value should pass through unchanged.""" - stats = {"observation.image": {"mean": [0.5]}} # Missing std / (min,max) - features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} + """If statistics are incomplete, we should raise.""" + stats = {OBS_IMAGE: {"mean": [0.5]}} # Missing std / (min,max) + features = {OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} - normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=stats) - observation = {"observation.image": torch.tensor([0.7])} + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + observation = {OBS_IMAGE: torch.tensor([0.7])} transition = create_transition(observation=observation) - processed = normalizer(transition)[TransitionKey.OBSERVATION] - assert torch.allclose(processed["observation.image"], observation["observation.image"]) + with pytest.raises(ValueError, match="MEAN_STD normalization mode requires mean and std stats"): + _ = normalizer(transition)[TransitionKey.OBSERVATION] def test_missing_action_stats_no_error(): mock_dataset = Mock() - mock_dataset.meta.stats = {"observation.image": {"mean": [0.5], "std": [0.2]}} + mock_dataset.meta.stats = {OBS_IMAGE: {"mean": [0.5], "std": [0.2]}} - features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} + features = {OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96))} norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} - processor = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset, features, norm_map) + processor = UnnormalizerProcessorStep.from_lerobot_dataset(mock_dataset, features, norm_map) # The tensor stats should not contain the 'action' key - assert "action" not in processor._tensor_stats + assert ACTION not in processor._tensor_stats def test_serialization_roundtrip(full_stats): """Test that features and norm_map can be serialized and deserialized correctly.""" features = _create_full_features() norm_map = _create_full_norm_map() - original_processor = NormalizerProcessor( - features=features, norm_map=norm_map, stats=full_stats, normalize_keys={"observation.image"}, eps=1e-6 + original_processor = NormalizerProcessorStep( + features=features, + norm_map=norm_map, + stats=full_stats, + normalize_observation_keys={OBS_IMAGE}, + eps=1e-6, ) # Get config (serialization) config = original_processor.get_config() # Create a new processor from the config (deserialization) - new_processor = NormalizerProcessor( + new_processor = NormalizerProcessorStep( features=config["features"], norm_map=config["norm_map"], stats=full_stats, - normalize_keys=set(config["normalize_keys"]), + normalize_observation_keys=set(config["normalize_observation_keys"]), eps=config["eps"], ) # Test that both processors work the same way observation = { - "observation.image": torch.tensor([0.7, 0.5, 0.3]), - "observation.state": torch.tensor([0.5, 0.0]), + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([0.5, 0.0]), } action = torch.tensor([1.0, -0.5]) transition = create_transition( @@ -614,15 +836,1295 @@ def test_serialization_roundtrip(full_stats): # Compare results assert torch.allclose( - result1[TransitionKey.OBSERVATION]["observation.image"], - result2[TransitionKey.OBSERVATION]["observation.image"], + result1[TransitionKey.OBSERVATION][OBS_IMAGE], + result2[TransitionKey.OBSERVATION][OBS_IMAGE], ) assert torch.allclose(result1[TransitionKey.ACTION], result2[TransitionKey.ACTION]) # Verify features and norm_map are correctly reconstructed - assert new_processor.features.keys() == original_processor.features.keys() - for key in new_processor.features: - assert new_processor.features[key].type == original_processor.features[key].type - assert new_processor.features[key].shape == original_processor.features[key].shape + assert ( + new_processor.transform_features(features).keys() + == original_processor.transform_features(features).keys() + ) + for key in new_processor.transform_features(features): + assert ( + new_processor.transform_features(features)[key].type + == original_processor.transform_features(features)[key].type + ) + assert ( + new_processor.transform_features(features)[key].shape + == original_processor.transform_features(features)[key].shape + ) assert new_processor.norm_map == original_processor.norm_map + + +# Identity normalization tests +def test_identity_normalization_observations(): + """Test that IDENTITY mode skips normalization for observations.""" + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.IDENTITY, # IDENTITY mode + FeatureType.STATE: NormalizationMode.MEAN_STD, # Normal mode for comparison + } + stats = { + OBS_IMAGE: {"mean": [0.5, 0.5, 0.5], "std": [0.2, 0.2, 0.2]}, + OBS_STATE: {"mean": [0.0, 0.0], "std": [1.0, 1.0]}, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([1.0, -0.5]), + } + transition = create_transition(observation=observation) + + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + + # Image should remain unchanged (IDENTITY) + assert torch.allclose(normalized_obs[OBS_IMAGE], observation[OBS_IMAGE]) + + # State should be normalized (MEAN_STD) + expected_state = (torch.tensor([1.0, -0.5]) - torch.tensor([0.0, 0.0])) / torch.tensor([1.0, 1.0]) + assert torch.allclose(normalized_obs[OBS_STATE], expected_state) + + +def test_identity_normalization_actions(): + """Test that IDENTITY mode skips normalization for actions.""" + features = {ACTION: PolicyFeature(FeatureType.ACTION, (2,))} + norm_map = {FeatureType.ACTION: NormalizationMode.IDENTITY} + stats = {ACTION: {"mean": [0.0, 0.0], "std": [1.0, 2.0]}} + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + action = torch.tensor([1.0, -0.5]) + transition = create_transition(action=action) + + normalized_transition = normalizer(transition) + + # Action should remain unchanged + assert torch.allclose(normalized_transition[TransitionKey.ACTION], action) + + +def test_identity_unnormalization_observations(): + """Test that IDENTITY mode skips unnormalization for observations.""" + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.IDENTITY, # IDENTITY mode + FeatureType.STATE: NormalizationMode.MIN_MAX, # Normal mode for comparison + } + stats = { + OBS_IMAGE: {"mean": [0.5, 0.5, 0.5], "std": [0.2, 0.2, 0.2]}, + OBS_STATE: {"min": [-1.0, -1.0], "max": [1.0, 1.0]}, + } + + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([0.0, -1.0]), # Normalized values in [-1, 1] + } + transition = create_transition(observation=observation) + + unnormalized_transition = unnormalizer(transition) + unnormalized_obs = unnormalized_transition[TransitionKey.OBSERVATION] + + # Image should remain unchanged (IDENTITY) + assert torch.allclose(unnormalized_obs[OBS_IMAGE], observation[OBS_IMAGE]) + + # State should be unnormalized (MIN_MAX) + # (0.0 + 1) / 2 * (1.0 - (-1.0)) + (-1.0) = 0.0 + # (-1.0 + 1) / 2 * (1.0 - (-1.0)) + (-1.0) = -1.0 + expected_state = torch.tensor([0.0, -1.0]) + assert torch.allclose(unnormalized_obs[OBS_STATE], expected_state) + + +def test_identity_unnormalization_actions(): + """Test that IDENTITY mode skips unnormalization for actions.""" + features = {ACTION: PolicyFeature(FeatureType.ACTION, (2,))} + norm_map = {FeatureType.ACTION: NormalizationMode.IDENTITY} + stats = {ACTION: {"min": [-1.0, -2.0], "max": [1.0, 2.0]}} + + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + action = torch.tensor([0.5, -0.8]) # Normalized values + transition = create_transition(action=action) + + unnormalized_transition = unnormalizer(transition) + + # Action should remain unchanged + assert torch.allclose(unnormalized_transition[TransitionKey.ACTION], action) + + +def test_identity_with_missing_stats(): + """Test that IDENTITY mode works even when stats are missing.""" + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.IDENTITY, + } + stats = {} # No stats provided + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = {OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3])} + action = torch.tensor([1.0, -0.5]) + transition = create_transition(observation=observation, action=action) + + # Both should work without errors and return unchanged data + normalized_transition = normalizer(transition) + unnormalized_transition = unnormalizer(transition) + + assert torch.allclose( + normalized_transition[TransitionKey.OBSERVATION][OBS_IMAGE], + observation[OBS_IMAGE], + ) + assert torch.allclose(normalized_transition[TransitionKey.ACTION], action) + assert torch.allclose( + unnormalized_transition[TransitionKey.OBSERVATION][OBS_IMAGE], + observation[OBS_IMAGE], + ) + assert torch.allclose(unnormalized_transition[TransitionKey.ACTION], action) + + +def test_identity_mixed_with_other_modes(): + """Test IDENTITY mode mixed with other normalization modes.""" + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3,)), + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MIN_MAX, + } + stats = { + OBS_IMAGE: {"mean": [0.5, 0.5, 0.5], "std": [0.2, 0.2, 0.2]}, # Will be ignored + OBS_STATE: {"mean": [0.0, 0.0], "std": [1.0, 1.0]}, + ACTION: {"min": [-1.0, -1.0], "max": [1.0, 1.0]}, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([1.0, -0.5]), + } + action = torch.tensor([0.5, 0.0]) + transition = create_transition(observation=observation, action=action) + + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + normalized_action = normalized_transition[TransitionKey.ACTION] + + # Image should remain unchanged (IDENTITY) + assert torch.allclose(normalized_obs[OBS_IMAGE], observation[OBS_IMAGE]) + + # State should be normalized (MEAN_STD) + expected_state = torch.tensor([1.0, -0.5]) # (x - 0) / 1 = x + assert torch.allclose(normalized_obs[OBS_STATE], expected_state) + + # Action should be normalized (MIN_MAX) to [-1, 1] + # 2 * (0.5 - (-1)) / (1 - (-1)) - 1 = 2 * 1.5 / 2 - 1 = 0.5 + # 2 * (0.0 - (-1)) / (1 - (-1)) - 1 = 2 * 1.0 / 2 - 1 = 0.0 + expected_action = torch.tensor([0.5, 0.0]) + assert torch.allclose(normalized_action, expected_action) + + +def test_identity_defaults_when_not_in_norm_map(): + """Test that IDENTITY is used as default when feature type not in norm_map.""" + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3,)), + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), + } + norm_map = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + # VISUAL not specified, should default to IDENTITY + } + stats = { + OBS_IMAGE: {"mean": [0.5, 0.5, 0.5], "std": [0.2, 0.2, 0.2]}, + OBS_STATE: {"mean": [0.0, 0.0], "std": [1.0, 1.0]}, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + observation = { + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([1.0, -0.5]), + } + transition = create_transition(observation=observation) + + normalized_transition = normalizer(transition) + normalized_obs = normalized_transition[TransitionKey.OBSERVATION] + + # Image should remain unchanged (defaults to IDENTITY) + assert torch.allclose(normalized_obs[OBS_IMAGE], observation[OBS_IMAGE]) + + # State should be normalized (explicitly MEAN_STD) + expected_state = torch.tensor([1.0, -0.5]) + assert torch.allclose(normalized_obs[OBS_STATE], expected_state) + + +def test_identity_roundtrip(): + """Test that IDENTITY normalization and unnormalization are true inverses.""" + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3,)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.IDENTITY, + } + stats = { + OBS_IMAGE: {"mean": [0.5, 0.5, 0.5], "std": [0.2, 0.2, 0.2]}, + ACTION: {"min": [-1.0, -1.0], "max": [1.0, 1.0]}, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + original_observation = {OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3])} + original_action = torch.tensor([0.5, -0.2]) + original_transition = create_transition(observation=original_observation, action=original_action) + + # Normalize then unnormalize + normalized = normalizer(original_transition) + roundtrip = unnormalizer(normalized) + + # Should be identical to original + assert torch.allclose(roundtrip[TransitionKey.OBSERVATION][OBS_IMAGE], original_observation[OBS_IMAGE]) + assert torch.allclose(roundtrip[TransitionKey.ACTION], original_action) + + +def test_identity_config_serialization(): + """Test that IDENTITY mode is properly saved and loaded in config.""" + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3,)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + stats = { + OBS_IMAGE: {"mean": [0.5], "std": [0.2]}, + ACTION: {"mean": [0.0, 0.0], "std": [1.0, 1.0]}, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + # Get config + config = normalizer.get_config() + + # Check that IDENTITY is properly serialized + assert config["norm_map"]["VISUAL"] == "IDENTITY" + assert config["norm_map"]["ACTION"] == "MEAN_STD" + + # Create new processor from config (simulating load) + new_normalizer = NormalizerProcessorStep( + features=config["features"], + norm_map=config["norm_map"], + stats=stats, + eps=config["eps"], + ) + + # Test that both work the same way + observation = {OBS_IMAGE: torch.tensor([0.7])} + action = torch.tensor([1.0, -0.5]) + transition = create_transition(observation=observation, action=action) + + result1 = normalizer(transition) + result2 = new_normalizer(transition) + + # Results should be identical + assert torch.allclose( + result1[TransitionKey.OBSERVATION][OBS_IMAGE], + result2[TransitionKey.OBSERVATION][OBS_IMAGE], + ) + assert torch.allclose(result1[TransitionKey.ACTION], result2[TransitionKey.ACTION]) + + +# def test_unsupported_normalization_mode_error(): +# """Test that unsupported normalization modes raise appropriate errors.""" +# features = {OBS_STATE: PolicyFeature(FeatureType.STATE, (2,))} + +# # Create an invalid norm_map (this would never happen in practice, but tests error handling) +# from enum import Enum + +# class InvalidMode(str, Enum): +# INVALID = "INVALID" + +# # We can't actually pass an invalid enum to the processor due to type checking, +# # but we can test the error by manipulating the norm_map after creation +# norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD} +# stats = {OBS_STATE: {"mean": [0.0, 0.0], "std": [1.0, 1.0]}} + +# normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + +# # Manually inject an invalid mode to test error handling +# normalizer.norm_map[FeatureType.STATE] = "INVALID_MODE" + +# observation = {OBS_STATE: torch.tensor([1.0, -0.5])} +# transition = create_transition(observation=observation) + +# with pytest.raises(ValueError, match="Unsupported normalization mode"): +# normalizer(transition) + + +def test_hotswap_stats_basic_functionality(): + """Test that hotswap_stats correctly updates stats in normalizer/unnormalizer steps.""" + # Create initial stats + initial_stats = { + OBS_IMAGE: {"mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2])}, + ACTION: {"mean": np.array([0.0, 0.0]), "std": np.array([1.0, 1.0])}, + } + + # Create new stats for hotswapping + new_stats = { + OBS_IMAGE: {"mean": np.array([0.3, 0.3, 0.3]), "std": np.array([0.1, 0.1, 0.1])}, + ACTION: {"mean": np.array([0.1, 0.1]), "std": np.array([0.5, 0.5])}, + } + + # Create features and norm_map + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + + # Create processors + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + identity = IdentityProcessorStep() + + # Create robot processor + robot_processor = DataProcessorPipeline(steps=[normalizer, unnormalizer, identity]) + + # Hotswap stats + new_processor = hotswap_stats(robot_processor, new_stats) + + # Check that normalizer and unnormalizer have new stats + assert new_processor.steps[0].stats == new_stats + assert new_processor.steps[1].stats == new_stats + + # Check that tensor stats are updated correctly + expected_tensor_stats = to_tensor(new_stats) + for key in expected_tensor_stats: + for stat_name in expected_tensor_stats[key]: + torch.testing.assert_close( + new_processor.steps[0]._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name] + ) + torch.testing.assert_close( + new_processor.steps[1]._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name] + ) + + +def test_hotswap_stats_deep_copy(): + """Test that hotswap_stats creates a deep copy and doesn't modify the original processor.""" + initial_stats = { + OBS_IMAGE: {"mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2])}, + } + + new_stats = { + OBS_IMAGE: {"mean": np.array([0.3, 0.3, 0.3]), "std": np.array([0.1, 0.1, 0.1])}, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + } + norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + original_processor = DataProcessorPipeline(steps=[normalizer]) + + # Store reference to original stats + original_stats_reference = original_processor.steps[0].stats + original_tensor_stats_reference = original_processor.steps[0]._tensor_stats + + # Hotswap stats + new_processor = hotswap_stats(original_processor, new_stats) + + # Original processor should be unchanged + assert original_processor.steps[0].stats is original_stats_reference + assert original_processor.steps[0]._tensor_stats is original_tensor_stats_reference + assert original_processor.steps[0].stats == initial_stats + + # New processor should have new stats + assert new_processor.steps[0].stats == new_stats + assert new_processor.steps[0].stats is not original_stats_reference + + # Processors should be different objects + assert new_processor is not original_processor + assert new_processor.steps[0] is not original_processor.steps[0] + + +def test_hotswap_stats_only_affects_normalizer_steps(): + """Test that hotswap_stats only modifies NormalizerProcessorStep and UnnormalizerProcessorStep steps.""" + stats = { + OBS_IMAGE: {"mean": np.array([0.5]), "std": np.array([0.2])}, + } + + new_stats = { + OBS_IMAGE: {"mean": np.array([0.3]), "std": np.array([0.1])}, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + } + norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} + + # Create mixed steps + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + identity = IdentityProcessorStep() + + robot_processor = DataProcessorPipeline(steps=[normalizer, identity, unnormalizer]) + + # Hotswap stats + new_processor = hotswap_stats(robot_processor, new_stats) + + # Check that only normalizer and unnormalizer steps are affected + assert new_processor.steps[0].stats == new_stats # normalizer + assert new_processor.steps[2].stats == new_stats # unnormalizer + + # Identity processor should remain unchanged (and it doesn't have stats attribute) + assert not hasattr(new_processor.steps[1], "stats") + + +def test_hotswap_stats_empty_stats(): + """Test hotswap_stats with empty stats dictionary.""" + initial_stats = { + OBS_IMAGE: {"mean": np.array([0.5]), "std": np.array([0.2])}, + } + + empty_stats = {} + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + } + norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + robot_processor = DataProcessorPipeline(steps=[normalizer]) + + # Hotswap with empty stats + new_processor = hotswap_stats(robot_processor, empty_stats) + + # Should update to empty stats + assert new_processor.steps[0].stats == empty_stats + assert new_processor.steps[0]._tensor_stats == {} + + +def test_hotswap_stats_no_normalizer_steps(): + """Test hotswap_stats with a processor that has no normalizer/unnormalizer steps.""" + stats = { + OBS_IMAGE: {"mean": np.array([0.5]), "std": np.array([0.2])}, + } + + # Create processor with only identity steps + robot_processor = DataProcessorPipeline(steps=[IdentityProcessorStep(), IdentityProcessorStep()]) + + # Hotswap stats - should work without error + new_processor = hotswap_stats(robot_processor, stats) + + # Should return a different object (deep copy) + assert new_processor is not robot_processor + + # Steps should be deep copied but unchanged + assert len(new_processor.steps) == len(robot_processor.steps) + for i, step in enumerate(new_processor.steps): + assert step is not robot_processor.steps[i] # Different objects + assert isinstance(step, type(robot_processor.steps[i])) # Same type + + +def test_hotswap_stats_preserves_other_attributes(): + """Test that hotswap_stats preserves other processor attributes like features and norm_map.""" + initial_stats = { + OBS_IMAGE: {"mean": np.array([0.5]), "std": np.array([0.2])}, + } + + new_stats = { + OBS_IMAGE: {"mean": np.array([0.3]), "std": np.array([0.1])}, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + } + norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} + normalize_observation_keys = {OBS_IMAGE} + eps = 1e-6 + + normalizer = NormalizerProcessorStep( + features=features, + norm_map=norm_map, + stats=initial_stats, + normalize_observation_keys=normalize_observation_keys, + eps=eps, + ) + robot_processor = DataProcessorPipeline(steps=[normalizer]) + + # Hotswap stats + new_processor = hotswap_stats(robot_processor, new_stats) + + # Check that other attributes are preserved + new_normalizer = new_processor.steps[0] + assert new_normalizer.features == features + assert new_normalizer.norm_map == norm_map + assert new_normalizer.normalize_observation_keys == normalize_observation_keys + assert new_normalizer.eps == eps + + # But stats should be updated + assert new_normalizer.stats == new_stats + + +def test_hotswap_stats_multiple_normalizer_types(): + """Test hotswap_stats with multiple normalizer and unnormalizer steps.""" + initial_stats = { + OBS_IMAGE: {"mean": np.array([0.5]), "std": np.array([0.2])}, + ACTION: {"min": np.array([-1.0]), "max": np.array([1.0])}, + } + + new_stats = { + OBS_IMAGE: {"mean": np.array([0.3]), "std": np.array([0.1])}, + ACTION: {"min": np.array([-2.0]), "max": np.array([2.0])}, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(1,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MIN_MAX, + } + + # Create multiple normalizers and unnormalizers + normalizer1 = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + normalizer2 = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + unnormalizer1 = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + unnormalizer2 = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + + robot_processor = DataProcessorPipeline(steps=[normalizer1, unnormalizer1, normalizer2, unnormalizer2]) + + # Hotswap stats + new_processor = hotswap_stats(robot_processor, new_stats) + + # All normalizer/unnormalizer steps should be updated + for step in new_processor.steps: + assert step.stats == new_stats + + # Check tensor stats conversion + expected_tensor_stats = to_tensor(new_stats) + for key in expected_tensor_stats: + for stat_name in expected_tensor_stats[key]: + torch.testing.assert_close( + step._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name] + ) + + +def test_hotswap_stats_with_different_data_types(): + """Test hotswap_stats with various data types in stats.""" + initial_stats = { + OBS_IMAGE: {"mean": np.array([0.5]), "std": np.array([0.2])}, + } + + # New stats with different data types (int, float, list, tuple) + new_stats = { + OBS_IMAGE: { + "mean": [0.3, 0.4, 0.5], # list + "std": (0.1, 0.2, 0.3), # tuple + "min": 0, # int + "max": 1.0, # float + }, + ACTION: { + "mean": np.array([0.1, 0.2]), # numpy array + "std": torch.tensor([0.5, 0.6]), # torch tensor + }, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + robot_processor = DataProcessorPipeline(steps=[normalizer]) + + # Hotswap stats + new_processor = hotswap_stats(robot_processor, new_stats) + + # Check that stats are updated + assert new_processor.steps[0].stats == new_stats + + # Check that tensor conversion worked correctly + tensor_stats = new_processor.steps[0]._tensor_stats + assert isinstance(tensor_stats[OBS_IMAGE]["mean"], torch.Tensor) + assert isinstance(tensor_stats[OBS_IMAGE]["std"], torch.Tensor) + assert isinstance(tensor_stats[OBS_IMAGE]["min"], torch.Tensor) + assert isinstance(tensor_stats[OBS_IMAGE]["max"], torch.Tensor) + assert isinstance(tensor_stats[ACTION]["mean"], torch.Tensor) + assert isinstance(tensor_stats[ACTION]["std"], torch.Tensor) + + # Check values + torch.testing.assert_close(tensor_stats[OBS_IMAGE]["mean"], torch.tensor([0.3, 0.4, 0.5])) + torch.testing.assert_close(tensor_stats[OBS_IMAGE]["std"], torch.tensor([0.1, 0.2, 0.3])) + torch.testing.assert_close(tensor_stats[OBS_IMAGE]["min"], torch.tensor(0.0)) + torch.testing.assert_close(tensor_stats[OBS_IMAGE]["max"], torch.tensor(1.0)) + + +def test_hotswap_stats_functional_test(): + """Test that hotswapped processor actually works functionally.""" + # Create test data + observation = { + OBS_IMAGE: torch.tensor([[[0.6, 0.7], [0.8, 0.9]], [[0.5, 0.6], [0.7, 0.8]]]), + } + action = torch.tensor([0.5, -0.5]) + transition = create_transition(observation=observation, action=action) + + # Initial stats + initial_stats = { + OBS_IMAGE: {"mean": np.array([0.5, 0.4]), "std": np.array([0.2, 0.3])}, + ACTION: {"mean": np.array([0.0, 0.0]), "std": np.array([1.0, 1.0])}, + } + + # New stats + new_stats = { + OBS_IMAGE: {"mean": np.array([0.3, 0.2]), "std": np.array([0.1, 0.2])}, + ACTION: {"mean": np.array([0.1, -0.1]), "std": np.array([0.5, 0.5])}, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(2, 2, 2)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + + # Create original processor + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=initial_stats) + original_processor = DataProcessorPipeline( + steps=[normalizer], to_transition=identity_transition, to_output=identity_transition + ) + + # Process with original stats + original_result = original_processor(transition) + + # Hotswap stats + new_processor = hotswap_stats(original_processor, new_stats) + + # Process with new stats + new_result = new_processor(transition) + + # Results should be different since normalization changed + assert not torch.allclose( + original_result[OBS_STR][OBS_IMAGE], + new_result[OBS_STR][OBS_IMAGE], + rtol=1e-3, + atol=1e-3, + ) + assert not torch.allclose(original_result[ACTION], new_result[ACTION], rtol=1e-3, atol=1e-3) + + # Verify that the new processor is actually using the new stats by checking internal state + assert new_processor.steps[0].stats == new_stats + assert torch.allclose(new_processor.steps[0]._tensor_stats[OBS_IMAGE]["mean"], torch.tensor([0.3, 0.2])) + assert torch.allclose(new_processor.steps[0]._tensor_stats[OBS_IMAGE]["std"], torch.tensor([0.1, 0.2])) + assert torch.allclose(new_processor.steps[0]._tensor_stats[ACTION]["mean"], torch.tensor([0.1, -0.1])) + assert torch.allclose(new_processor.steps[0]._tensor_stats[ACTION]["std"], torch.tensor([0.5, 0.5])) + + # Test that normalization actually happens (output should not equal input) + assert not torch.allclose(new_result[OBS_STR][OBS_IMAGE], observation[OBS_IMAGE]) + assert not torch.allclose(new_result[ACTION], action) + + +def test_zero_std_uses_eps(): + """When std == 0, (x-mean)/(std+eps) is well-defined; x==mean should map to 0.""" + features = {OBS_STATE: PolicyFeature(FeatureType.STATE, (1,))} + norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD} + stats = {OBS_STATE: {"mean": np.array([0.5]), "std": np.array([0.0])}} + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats, eps=1e-6) + + observation = {OBS_STATE: torch.tensor([0.5])} # equals mean + out = normalizer(create_transition(observation=observation)) + assert torch.allclose(out[TransitionKey.OBSERVATION][OBS_STATE], torch.tensor([0.0])) + + +def test_min_equals_max_maps_to_minus_one(): + """When min == max, MIN_MAX path maps to -1 after [-1,1] scaling for x==min.""" + features = {OBS_STATE: PolicyFeature(FeatureType.STATE, (1,))} + norm_map = {FeatureType.STATE: NormalizationMode.MIN_MAX} + stats = {OBS_STATE: {"min": np.array([2.0]), "max": np.array([2.0])}} + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats, eps=1e-6) + + observation = {OBS_STATE: torch.tensor([2.0])} + out = normalizer(create_transition(observation=observation)) + assert torch.allclose(out[TransitionKey.OBSERVATION][OBS_STATE], torch.tensor([-1.0])) + + +def test_action_normalized_despite_normalize_observation_keys(): + """Action normalization is independent of normalize_observation_keys filter for observations.""" + features = { + OBS_STATE: PolicyFeature(FeatureType.STATE, (1,)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = {FeatureType.STATE: NormalizationMode.IDENTITY, FeatureType.ACTION: NormalizationMode.MEAN_STD} + stats = {ACTION: {"mean": np.array([1.0, -1.0]), "std": np.array([2.0, 4.0])}} + normalizer = NormalizerProcessorStep( + features=features, norm_map=norm_map, stats=stats, normalize_observation_keys={OBS_STATE} + ) + + transition = create_transition( + observation={OBS_STATE: torch.tensor([3.0])}, action=torch.tensor([3.0, 3.0]) + ) + out = normalizer(transition) + # (3-1)/2 = 1.0 ; (3-(-1))/4 = 1.0 + assert torch.allclose(out[TransitionKey.ACTION], torch.tensor([1.0, 1.0])) + + +def test_unnormalize_observations_mean_std_and_min_max(): + features = { + "observation.ms": PolicyFeature(FeatureType.STATE, (2,)), + "observation.mm": PolicyFeature(FeatureType.STATE, (2,)), + } + # Build two processors: one mean/std and one min/max + unnorm_ms = UnnormalizerProcessorStep( + features={"observation.ms": features["observation.ms"]}, + norm_map={FeatureType.STATE: NormalizationMode.MEAN_STD}, + stats={"observation.ms": {"mean": np.array([1.0, -1.0]), "std": np.array([2.0, 4.0])}}, + ) + unnorm_mm = UnnormalizerProcessorStep( + features={"observation.mm": features["observation.mm"]}, + norm_map={FeatureType.STATE: NormalizationMode.MIN_MAX}, + stats={"observation.mm": {"min": np.array([0.0, -2.0]), "max": np.array([2.0, 2.0])}}, + ) + + tr = create_transition( + observation={ + "observation.ms": torch.tensor([0.0, 0.0]), # → mean + "observation.mm": torch.tensor([0.0, 0.0]), # → mid-point + } + ) + out_ms = unnorm_ms(tr)[TransitionKey.OBSERVATION]["observation.ms"] + out_mm = unnorm_mm(tr)[TransitionKey.OBSERVATION]["observation.mm"] + assert torch.allclose(out_ms, torch.tensor([1.0, -1.0])) + assert torch.allclose(out_mm, torch.tensor([1.0, 0.0])) # mid of [0,2] and [-2,2] + + +def test_unknown_observation_keys_ignored(): + features = {OBS_STATE: PolicyFeature(FeatureType.STATE, (1,))} + norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD} + stats = {OBS_STATE: {"mean": np.array([0.0]), "std": np.array([1.0])}} + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + obs = {OBS_STATE: torch.tensor([1.0]), "observation.unknown": torch.tensor([5.0])} + tr = create_transition(observation=obs) + out = normalizer(tr) + + # Unknown key should pass through unchanged and not be tracked + assert torch.allclose(out[TransitionKey.OBSERVATION]["observation.unknown"], obs["observation.unknown"]) + + +def test_batched_action_normalization(): + features = {ACTION: PolicyFeature(FeatureType.ACTION, (2,))} + norm_map = {FeatureType.ACTION: NormalizationMode.MEAN_STD} + stats = {ACTION: {"mean": np.array([1.0, -1.0]), "std": np.array([2.0, 4.0])}} + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + actions = torch.tensor([[1.0, -1.0], [3.0, 3.0]]) # first equals mean → zeros; second → [1, 1] + out = normalizer(create_transition(action=actions))[TransitionKey.ACTION] + expected = torch.tensor([[0.0, 0.0], [1.0, 1.0]]) + assert torch.allclose(out, expected) + + +def test_complementary_data_preservation(): + features = {OBS_STATE: PolicyFeature(FeatureType.STATE, (1,))} + norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD} + stats = {OBS_STATE: {"mean": np.array([0.0]), "std": np.array([1.0])}} + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + comp = {"existing": 123} + tr = create_transition(observation={OBS_STATE: torch.tensor([1.0])}, complementary_data=comp) + out = normalizer(tr) + new_comp = out[TransitionKey.COMPLEMENTARY_DATA] + assert new_comp["existing"] == 123 + + +def test_roundtrip_normalize_unnormalize_non_identity(): + features = { + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD, FeatureType.ACTION: NormalizationMode.MIN_MAX} + stats = { + OBS_STATE: {"mean": np.array([1.0, -1.0]), "std": np.array([2.0, 4.0])}, + ACTION: {"min": np.array([-2.0, 0.0]), "max": np.array([2.0, 4.0])}, + } + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + # Add a time dimension in action for broadcasting check (B,T,D) + obs = {OBS_STATE: torch.tensor([[3.0, 3.0], [1.0, -1.0]])} + act = torch.tensor([[[0.0, -1.0], [1.0, 1.0]]]) # shape (1,2,2) already in [-1,1] + + tr = create_transition(observation=obs, action=act) + out = unnormalizer(normalizer(tr)) + + assert torch.allclose(out[TransitionKey.OBSERVATION][OBS_STATE], obs[OBS_STATE], atol=1e-5) + assert torch.allclose(out[TransitionKey.ACTION], act, atol=1e-5) + + +def test_dtype_adaptation_bfloat16_input_float32_normalizer(): + """Test automatic dtype adaptation: NormalizerProcessor(float32) adapts to bfloat16 input → bfloat16 output""" + features = {OBS_STATE: PolicyFeature(FeatureType.STATE, (5,))} + norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD} + stats = { + OBS_STATE: { + "mean": np.array([0.0, 0.0, 0.0, 0.0, 0.0]), + "std": np.array([1.0, 1.0, 1.0, 1.0, 1.0]), + } + } + + # Create normalizer configured with float32 dtype + normalizer = NormalizerProcessorStep( + features=features, norm_map=norm_map, stats=stats, dtype=torch.float32 + ) + + # Verify initial configuration + assert normalizer.dtype == torch.float32 + for stat_tensor in normalizer._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.float32 + + # Create bfloat16 input tensor + observation = {OBS_STATE: torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], dtype=torch.bfloat16)} + transition = create_transition(observation=observation) + + # Process the transition + result = normalizer(transition) + + # Verify that: + # 1. Stats were automatically adapted to bfloat16 + assert normalizer.dtype == torch.bfloat16 + for stat_tensor in normalizer._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 + + # 2. Output is in bfloat16 + output_tensor = result[TransitionKey.OBSERVATION][OBS_STATE] + assert output_tensor.dtype == torch.bfloat16 + + # 3. Normalization was applied correctly (mean should be close to original - mean) / std + expected = ( + torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], dtype=torch.bfloat16) + - torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0], dtype=torch.bfloat16) + ) / torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0], dtype=torch.bfloat16) + assert torch.allclose(output_tensor, expected, atol=1e-2) # bfloat16 has lower precision + + +def test_stats_override_preservation_in_load_state_dict(): + """ + Test that explicitly provided stats are preserved during load_state_dict. + + This tests the fix for the bug where stats provided via overrides were + being overwritten when load_state_dict was called. + """ + # Create original stats + original_stats = { + OBS_IMAGE: {"mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2])}, + ACTION: {"mean": np.array([0.0, 0.0]), "std": np.array([1.0, 1.0])}, + } + + # Create override stats (what user wants to use) + override_stats = { + OBS_IMAGE: {"mean": np.array([0.3, 0.3, 0.3]), "std": np.array([0.1, 0.1, 0.1])}, + ACTION: {"mean": np.array([0.1, 0.1]), "std": np.array([0.5, 0.5])}, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + + # Create a normalizer with original stats and save its state + original_normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=original_stats) + saved_state_dict = original_normalizer.state_dict() + + # Create a new normalizer with override stats (simulating from_pretrained with overrides) + override_normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=override_stats) + + # Verify that the override stats are initially set correctly + assert set(override_normalizer.stats.keys()) == set(override_stats.keys()) + for key in override_stats: + assert set(override_normalizer.stats[key].keys()) == set(override_stats[key].keys()) + for stat_name in override_stats[key]: + np.testing.assert_array_equal( + override_normalizer.stats[key][stat_name], override_stats[key][stat_name] + ) + assert override_normalizer._stats_explicitly_provided is True + + # This is the critical test: load_state_dict should NOT overwrite the override stats + override_normalizer.load_state_dict(saved_state_dict) + + # After loading state_dict, stats should still be the override stats, not the original stats + # Check that loaded stats match override stats + assert set(override_normalizer.stats.keys()) == set(override_stats.keys()) + for key in override_stats: + assert set(override_normalizer.stats[key].keys()) == set(override_stats[key].keys()) + for stat_name in override_stats[key]: + np.testing.assert_array_equal( + override_normalizer.stats[key][stat_name], override_stats[key][stat_name] + ) + # Compare individual arrays to avoid numpy array comparison ambiguity + for key in override_stats: + for stat_name in override_stats[key]: + assert not np.array_equal( + override_normalizer.stats[key][stat_name], original_stats[key][stat_name] + ), f"Stats for {key}.{stat_name} should not match original stats" + + # Verify that _tensor_stats are also correctly set to match the override stats + expected_tensor_stats = to_tensor(override_stats) + for key in expected_tensor_stats: + for stat_name in expected_tensor_stats[key]: + if isinstance(expected_tensor_stats[key][stat_name], torch.Tensor): + torch.testing.assert_close( + override_normalizer._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name] + ) + + +def test_stats_without_override_loads_normally(): + """ + Test that when stats are not explicitly provided (normal case), + load_state_dict works as before. + """ + original_stats = { + OBS_IMAGE: {"mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2])}, + ACTION: {"mean": np.array([0.0, 0.0]), "std": np.array([1.0, 1.0])}, + } + + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + + # Create a normalizer with original stats and save its state + original_normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=original_stats) + saved_state_dict = original_normalizer.state_dict() + + # Create a new normalizer without stats (simulating normal from_pretrained) + new_normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats={}) + + # Verify that stats are not explicitly provided + assert new_normalizer._stats_explicitly_provided is False + + # Load state dict - this should work normally and load the saved stats + new_normalizer.load_state_dict(saved_state_dict) + + # Stats should now match the original stats (normal behavior) + # Check that all keys and values match + assert set(new_normalizer.stats.keys()) == set(original_stats.keys()) + for key in original_stats: + assert set(new_normalizer.stats[key].keys()) == set(original_stats[key].keys()) + for stat_name in original_stats[key]: + np.testing.assert_allclose( + new_normalizer.stats[key][stat_name], original_stats[key][stat_name], rtol=1e-6, atol=1e-6 + ) + + +def test_stats_explicit_provided_flag_detection(): + """Test that the _stats_explicitly_provided flag is set correctly in different scenarios.""" + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)), + } + norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD} + + # Test 1: Explicitly provided stats (non-empty dict) + stats = {OBS_IMAGE: {"mean": [0.5], "std": [0.2]}} + normalizer1 = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + assert normalizer1._stats_explicitly_provided is True + + # Test 2: Empty stats dict + normalizer2 = NormalizerProcessorStep(features=features, norm_map=norm_map, stats={}) + assert normalizer2._stats_explicitly_provided is False + + # Test 3: None stats + normalizer3 = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=None) + assert normalizer3._stats_explicitly_provided is False + + # Test 4: Stats not provided (defaults to None) + normalizer4 = NormalizerProcessorStep(features=features, norm_map=norm_map) + assert normalizer4._stats_explicitly_provided is False + + +def test_pipeline_from_pretrained_with_stats_overrides(): + """ + Test the actual use case: DataProcessorPipeline.from_pretrained with stat overrides. + + This is an integration test that verifies the fix works in the real scenario + where users provide stat overrides when loading a pipeline. + """ + import tempfile + + # Create test data + features = { + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 32, 32)), + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + + original_stats = { + OBS_IMAGE: {"mean": np.array([0.5, 0.5, 0.5]), "std": np.array([0.2, 0.2, 0.2])}, + ACTION: {"mean": np.array([0.0, 0.0]), "std": np.array([1.0, 1.0])}, + } + + override_stats = { + OBS_IMAGE: {"mean": np.array([0.3, 0.3, 0.3]), "std": np.array([0.1, 0.1, 0.1])}, + ACTION: {"mean": np.array([0.1, 0.1]), "std": np.array([0.5, 0.5])}, + } + + # Create and save a pipeline with the original stats + normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=original_stats) + identity = IdentityProcessorStep() + original_pipeline = DataProcessorPipeline(steps=[normalizer, identity], name="test_pipeline") + + with tempfile.TemporaryDirectory() as temp_dir: + # Save the pipeline + original_pipeline.save_pretrained(temp_dir) + + # Load the pipeline with stat overrides + overrides = {"normalizer_processor": {"stats": override_stats}} + + loaded_pipeline = DataProcessorPipeline.from_pretrained( + temp_dir, config_filename="test_pipeline.json", overrides=overrides + ) + + # The critical test: the loaded pipeline should use override stats, not original stats + loaded_normalizer = loaded_pipeline.steps[0] + assert isinstance(loaded_normalizer, NormalizerProcessorStep) + + # Check that loaded stats match override stats + assert set(loaded_normalizer.stats.keys()) == set(override_stats.keys()) + for key in override_stats: + assert set(loaded_normalizer.stats[key].keys()) == set(override_stats[key].keys()) + for stat_name in override_stats[key]: + np.testing.assert_array_equal( + loaded_normalizer.stats[key][stat_name], override_stats[key][stat_name] + ) + + # Verify stats don't match original stats + for key in override_stats: + for stat_name in override_stats[key]: + assert not np.array_equal( + loaded_normalizer.stats[key][stat_name], original_stats[key][stat_name] + ), f"Stats for {key}.{stat_name} should not match original stats" + + # Test that the override stats are actually used in processing + observation = { + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + } + action = torch.tensor([1.0, -0.5]) + transition = create_transition(observation=observation, action=action) + + # Process with override pipeline + override_result = loaded_pipeline(transition) + + # Create a reference pipeline with override stats for comparison + reference_normalizer = NormalizerProcessorStep( + features=features, norm_map=norm_map, stats=override_stats + ) + reference_pipeline = DataProcessorPipeline( + steps=[reference_normalizer, identity], + to_transition=identity_transition, + to_output=identity_transition, + ) + _ = reference_pipeline(transition) + + # The critical part was verified above: loaded_normalizer.stats == override_stats + # This confirms that override stats are preserved during load_state_dict. + # Let's just verify the pipeline processes data successfully. + assert ACTION in override_result + assert isinstance(override_result[ACTION], torch.Tensor) + + +def test_dtype_adaptation_device_processor_bfloat16_normalizer_float32(): + """Test policy pipeline scenario: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → bfloat16 output""" + from lerobot.processor import DeviceProcessorStep + + features = {OBS_STATE: PolicyFeature(FeatureType.STATE, (3,))} + norm_map = {FeatureType.STATE: NormalizationMode.MEAN_STD} + stats = {OBS_STATE: {"mean": np.array([0.0, 0.0, 0.0]), "std": np.array([1.0, 1.0, 1.0])}} + + # Create pipeline: DeviceProcessor(bfloat16) → NormalizerProcessor(float32) + device_processor = DeviceProcessorStep(device=str(auto_select_torch_device()), float_dtype="bfloat16") + normalizer = NormalizerProcessorStep( + features=features, norm_map=norm_map, stats=stats, dtype=torch.float32 + ) + + # Verify initial normalizer configuration + assert normalizer.dtype == torch.float32 + + # Create CPU input + observation = {OBS_STATE: torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)} + transition = create_transition(observation=observation) + + # Step 1: DeviceProcessor converts to bfloat16 + moves to CUDA + processed_1 = device_processor(transition) + intermediate_tensor = processed_1[TransitionKey.OBSERVATION][OBS_STATE] + assert intermediate_tensor.dtype == torch.bfloat16 + assert intermediate_tensor.device.type == str(auto_select_torch_device()) + + # Step 2: NormalizerProcessor receives bfloat16 input and adapts + final_result = normalizer(processed_1) + final_tensor = final_result[TransitionKey.OBSERVATION][OBS_STATE] + + # Verify final output is bfloat16 (automatic adaptation worked) + assert final_tensor.dtype == torch.bfloat16 + assert final_tensor.device.type == str(auto_select_torch_device()) + + # Verify normalizer adapted its internal state + assert normalizer.dtype == torch.bfloat16 + for stat_tensor in normalizer._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 + assert stat_tensor.device.type == str(auto_select_torch_device()) + + +def test_stats_reconstruction_after_load_state_dict(): + """ + Test that stats dict is properly reconstructed from _tensor_stats after loading. + + This test ensures the bug where stats became empty after loading is fixed. + The bug occurred when: + 1. Only _tensor_stats were saved via state_dict() + 2. stats field became empty {} after loading + 3. Calling to() method or hotswap_stats would fail because they depend on self.stats + """ + + # Create normalizer with stats + features = { + OBS_IMAGE: PolicyFeature(FeatureType.VISUAL, (3, 96, 96)), + OBS_STATE: PolicyFeature(FeatureType.STATE, (2,)), + ACTION: PolicyFeature(FeatureType.ACTION, (2,)), + } + norm_map = { + FeatureType.VISUAL: NormalizationMode.MEAN_STD, + FeatureType.STATE: NormalizationMode.MIN_MAX, + FeatureType.ACTION: NormalizationMode.MEAN_STD, + } + stats = { + OBS_IMAGE: { + "mean": np.array([0.5, 0.5, 0.5]), + "std": np.array([0.2, 0.2, 0.2]), + }, + OBS_STATE: { + "min": np.array([0.0, -1.0]), + "max": np.array([1.0, 1.0]), + }, + ACTION: { + "mean": np.array([0.0, 0.0]), + "std": np.array([1.0, 2.0]), + }, + } + + original_normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats) + + # Save state dict (simulating save/load) + state_dict = original_normalizer.state_dict() + + # Create new normalizer with empty stats (simulating load) + new_normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats={}) + + # Before fix: this would cause stats to remain empty + new_normalizer.load_state_dict(state_dict) + + # Verify that stats dict is properly reconstructed from _tensor_stats + assert new_normalizer.stats is not None + assert new_normalizer.stats != {} + + # Check that all expected keys are present + assert OBS_IMAGE in new_normalizer.stats + assert OBS_STATE in new_normalizer.stats + assert ACTION in new_normalizer.stats + + # Check that values are correct (converted back from tensors) + np.testing.assert_allclose(new_normalizer.stats[OBS_IMAGE]["mean"], [0.5, 0.5, 0.5]) + np.testing.assert_allclose(new_normalizer.stats[OBS_IMAGE]["std"], [0.2, 0.2, 0.2]) + np.testing.assert_allclose(new_normalizer.stats[OBS_STATE]["min"], [0.0, -1.0]) + np.testing.assert_allclose(new_normalizer.stats[OBS_STATE]["max"], [1.0, 1.0]) + np.testing.assert_allclose(new_normalizer.stats[ACTION]["mean"], [0.0, 0.0]) + np.testing.assert_allclose(new_normalizer.stats[ACTION]["std"], [1.0, 2.0]) + + # Test that methods that depend on self.stats work correctly after loading + # This would fail before the bug fix because self.stats was empty + + # Test 1: to() method should work without crashing + try: + new_normalizer.to(device="cpu", dtype=torch.float32) + # If we reach here, the bug is fixed + except (KeyError, AttributeError) as e: + pytest.fail(f"to() method failed after loading state_dict: {e}") + + # Test 2: hotswap_stats should work + new_stats = { + OBS_IMAGE: {"mean": [0.3, 0.3, 0.3], "std": [0.1, 0.1, 0.1]}, + OBS_STATE: {"min": [-1.0, -2.0], "max": [2.0, 2.0]}, + ACTION: {"mean": [0.1, 0.1], "std": [0.5, 0.5]}, + } + + pipeline = DataProcessorPipeline([new_normalizer]) + try: + new_pipeline = hotswap_stats(pipeline, new_stats) + # If we reach here, hotswap_stats worked correctly + assert new_pipeline.steps[0].stats == new_stats + except (KeyError, AttributeError) as e: + pytest.fail(f"hotswap_stats failed after loading state_dict: {e}") + + # Test 3: The normalizer should work functionally the same as the original + observation = { + OBS_IMAGE: torch.tensor([0.7, 0.5, 0.3]), + OBS_STATE: torch.tensor([0.5, 0.0]), + } + action = torch.tensor([1.0, -0.5]) + transition = create_transition(observation=observation, action=action) + + original_result = original_normalizer(transition) + new_result = new_normalizer(transition) + + # Results should be identical (within floating point precision) + torch.testing.assert_close( + original_result[TransitionKey.OBSERVATION][OBS_IMAGE], + new_result[TransitionKey.OBSERVATION][OBS_IMAGE], + ) + torch.testing.assert_close( + original_result[TransitionKey.OBSERVATION][OBS_STATE], + new_result[TransitionKey.OBSERVATION][OBS_STATE], + ) + torch.testing.assert_close(original_result[TransitionKey.ACTION], new_result[TransitionKey.ACTION]) diff --git a/tests/processor/test_observation_processor.py b/tests/processor/test_observation_processor.py index e48b6bc08e1..11b58a66c80 100644 --- a/tests/processor/test_observation_processor.py +++ b/tests/processor/test_observation_processor.py @@ -18,31 +18,16 @@ import pytest import torch -from lerobot.configs.types import FeatureType -from lerobot.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE -from lerobot.processor import VanillaObservationProcessor -from lerobot.processor.pipeline import TransitionKey +from lerobot.configs.types import FeatureType, PipelineFeatureType +from lerobot.processor import TransitionKey, VanillaObservationProcessorStep +from lerobot.processor.converters import create_transition +from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE from tests.conftest import assert_contract_is_typed -def create_transition( - observation=None, action=None, reward=None, done=None, truncated=None, info=None, complementary_data=None -): - """Helper to create an EnvTransition dictionary.""" - return { - TransitionKey.OBSERVATION: observation, - TransitionKey.ACTION: action, - TransitionKey.REWARD: reward, - TransitionKey.DONE: done, - TransitionKey.TRUNCATED: truncated, - TransitionKey.INFO: info, - TransitionKey.COMPLEMENTARY_DATA: complementary_data, - } - - def test_process_single_image(): """Test processing a single image.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Create a mock image (H, W, C) format, uint8 image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8) @@ -54,8 +39,8 @@ def test_process_single_image(): processed_obs = result[TransitionKey.OBSERVATION] # Check that the image was processed correctly - assert "observation.image" in processed_obs - processed_img = processed_obs["observation.image"] + assert OBS_IMAGE in processed_obs + processed_img = processed_obs[OBS_IMAGE] # Check shape: should be (1, 3, 64, 64) - batch, channels, height, width assert processed_img.shape == (1, 3, 64, 64) @@ -68,7 +53,7 @@ def test_process_single_image(): def test_process_image_dict(): """Test processing multiple images in a dictionary.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Create mock images image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8) @@ -81,17 +66,17 @@ def test_process_image_dict(): processed_obs = result[TransitionKey.OBSERVATION] # Check that both images were processed - assert "observation.images.camera1" in processed_obs - assert "observation.images.camera2" in processed_obs + assert f"{OBS_IMAGES}.camera1" in processed_obs + assert f"{OBS_IMAGES}.camera2" in processed_obs # Check shapes - assert processed_obs["observation.images.camera1"].shape == (1, 3, 32, 32) - assert processed_obs["observation.images.camera2"].shape == (1, 3, 48, 48) + assert processed_obs[f"{OBS_IMAGES}.camera1"].shape == (1, 3, 32, 32) + assert processed_obs[f"{OBS_IMAGES}.camera2"].shape == (1, 3, 48, 48) def test_process_batched_image(): """Test processing already batched images.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Create a batched image (B, H, W, C) image = np.random.randint(0, 256, size=(2, 64, 64, 3), dtype=np.uint8) @@ -103,12 +88,12 @@ def test_process_batched_image(): processed_obs = result[TransitionKey.OBSERVATION] # Check that batch dimension is preserved - assert processed_obs["observation.image"].shape == (2, 3, 64, 64) + assert processed_obs[OBS_IMAGE].shape == (2, 3, 64, 64) def test_invalid_image_format(): """Test error handling for invalid image formats.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Test wrong channel order (channels first) image = np.random.randint(0, 256, size=(3, 64, 64), dtype=np.uint8) @@ -121,7 +106,7 @@ def test_invalid_image_format(): def test_invalid_image_dtype(): """Test error handling for invalid image dtype.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Test wrong dtype image = np.random.rand(64, 64, 3).astype(np.float32) @@ -134,7 +119,7 @@ def test_invalid_image_dtype(): def test_no_pixels_in_observation(): """Test processor when no pixels are in observation.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() observation = {"other_data": np.array([1, 2, 3])} transition = create_transition(observation=observation) @@ -149,9 +134,9 @@ def test_no_pixels_in_observation(): def test_none_observation(): """Test processor with None observation.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() - transition = create_transition() + transition = create_transition(observation={}) result = processor(transition) assert result == transition @@ -159,7 +144,7 @@ def test_none_observation(): def test_serialization_methods(): """Test serialization methods.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Test get_config config = processor.get_config() @@ -178,7 +163,7 @@ def test_serialization_methods(): def test_process_environment_state(): """Test processing environment_state.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32) observation = {"environment_state": env_state} @@ -188,10 +173,10 @@ def test_process_environment_state(): processed_obs = result[TransitionKey.OBSERVATION] # Check that environment_state was renamed and processed - assert "observation.environment_state" in processed_obs + assert OBS_ENV_STATE in processed_obs assert "environment_state" not in processed_obs - processed_state = processed_obs["observation.environment_state"] + processed_state = processed_obs[OBS_ENV_STATE] assert processed_state.shape == (1, 3) # Batch dimension added assert processed_state.dtype == torch.float32 torch.testing.assert_close(processed_state, torch.tensor([[1.0, 2.0, 3.0]])) @@ -199,7 +184,7 @@ def test_process_environment_state(): def test_process_agent_pos(): """Test processing agent_pos.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32) observation = {"agent_pos": agent_pos} @@ -209,10 +194,10 @@ def test_process_agent_pos(): processed_obs = result[TransitionKey.OBSERVATION] # Check that agent_pos was renamed and processed - assert "observation.state" in processed_obs + assert OBS_STATE in processed_obs assert "agent_pos" not in processed_obs - processed_state = processed_obs["observation.state"] + processed_state = processed_obs[OBS_STATE] assert processed_state.shape == (1, 3) # Batch dimension added assert processed_state.dtype == torch.float32 torch.testing.assert_close(processed_state, torch.tensor([[0.5, -0.5, 1.0]])) @@ -220,7 +205,7 @@ def test_process_agent_pos(): def test_process_batched_states(): """Test processing already batched states.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() env_state = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) agent_pos = np.array([[0.5, -0.5], [1.0, -1.0]], dtype=np.float32) @@ -232,13 +217,13 @@ def test_process_batched_states(): processed_obs = result[TransitionKey.OBSERVATION] # Check that batch dimensions are preserved - assert processed_obs["observation.environment_state"].shape == (2, 2) - assert processed_obs["observation.state"].shape == (2, 2) + assert processed_obs[OBS_ENV_STATE].shape == (2, 2) + assert processed_obs[OBS_STATE].shape == (2, 2) def test_process_both_states(): """Test processing both environment_state and agent_pos.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() env_state = np.array([1.0, 2.0], dtype=np.float32) agent_pos = np.array([0.5, -0.5], dtype=np.float32) @@ -250,8 +235,8 @@ def test_process_both_states(): processed_obs = result[TransitionKey.OBSERVATION] # Check that both states were processed - assert "observation.environment_state" in processed_obs - assert "observation.state" in processed_obs + assert OBS_ENV_STATE in processed_obs + assert OBS_STATE in processed_obs # Check that original keys were removed assert "environment_state" not in processed_obs @@ -263,7 +248,7 @@ def test_process_both_states(): def test_no_states_in_observation(): """Test processor when no states are in observation.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() observation = {"other_data": np.array([1, 2, 3])} transition = create_transition(observation=observation) @@ -277,7 +262,7 @@ def test_no_states_in_observation(): def test_complete_observation_processing(): """Test processing a complete observation with both images and states.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Create mock data image = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8) @@ -296,12 +281,12 @@ def test_complete_observation_processing(): processed_obs = result[TransitionKey.OBSERVATION] # Check that image was processed - assert "observation.image" in processed_obs - assert processed_obs["observation.image"].shape == (1, 3, 32, 32) + assert OBS_IMAGE in processed_obs + assert processed_obs[OBS_IMAGE].shape == (1, 3, 32, 32) # Check that states were processed - assert "observation.environment_state" in processed_obs - assert "observation.state" in processed_obs + assert OBS_ENV_STATE in processed_obs + assert OBS_STATE in processed_obs # Check that original keys were removed assert "pixels" not in processed_obs @@ -314,7 +299,7 @@ def test_complete_observation_processing(): def test_image_only_processing(): """Test processing observation with only images.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8) observation = {"pixels": image} @@ -323,13 +308,13 @@ def test_image_only_processing(): result = processor(transition) processed_obs = result[TransitionKey.OBSERVATION] - assert "observation.image" in processed_obs + assert OBS_IMAGE in processed_obs assert len(processed_obs) == 1 def test_state_only_processing(): """Test processing observation with only states.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() agent_pos = np.array([1.0, 2.0], dtype=np.float32) observation = {"agent_pos": agent_pos} @@ -338,13 +323,13 @@ def test_state_only_processing(): result = processor(transition) processed_obs = result[TransitionKey.OBSERVATION] - assert "observation.state" in processed_obs + assert OBS_STATE in processed_obs assert "agent_pos" not in processed_obs def test_empty_observation(): """Test processing empty observation.""" - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() observation = {} transition = create_transition(observation=observation) @@ -360,7 +345,7 @@ def test_equivalent_to_original_function(): # Import the original function for comparison from lerobot.envs.utils import preprocess_observation - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Create test data similar to what the original function expects image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8) @@ -387,7 +372,7 @@ def test_equivalent_with_image_dict(): """Test equivalence with dictionary of images.""" from lerobot.envs.utils import preprocess_observation - processor = VanillaObservationProcessor() + processor = VanillaObservationProcessorStep() # Create test data with multiple cameras image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8) @@ -410,77 +395,133 @@ def test_equivalent_with_image_dict(): torch.testing.assert_close(original_result[key], processor_result[key]) -def test_image_processor_feature_contract_pixels_to_image(policy_feature_factory): - processor = VanillaObservationProcessor() +def test_image_processor_features_pixels_to_image(policy_feature_factory): + processor = VanillaObservationProcessorStep() features = { - "pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), - "keep": policy_feature_factory(FeatureType.ENV, (1,)), + PipelineFeatureType.OBSERVATION: { + "pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), + "keep": policy_feature_factory(FeatureType.ENV, (1,)), + }, } - out = processor.feature_contract(features.copy()) - - assert OBS_IMAGE in out and out[OBS_IMAGE] == features["pixels"] - assert "pixels" not in out - assert out["keep"] == features["keep"] + out = processor.transform_features(features.copy()) + + assert ( + OBS_IMAGE in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][OBS_IMAGE] + == features[PipelineFeatureType.OBSERVATION]["pixels"] + ) + assert "pixels" not in out[PipelineFeatureType.OBSERVATION] + assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"] assert_contract_is_typed(out) -def test_image_processor_feature_contract_observation_pixels_to_image(policy_feature_factory): - processor = VanillaObservationProcessor() +def test_image_processor_features_observation_pixels_to_image(policy_feature_factory): + processor = VanillaObservationProcessorStep() features = { - "observation.pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), - "keep": policy_feature_factory(FeatureType.ENV, (1,)), + PipelineFeatureType.OBSERVATION: { + "observation.pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), + "keep": policy_feature_factory(FeatureType.ENV, (1,)), + }, } - out = processor.feature_contract(features.copy()) - - assert OBS_IMAGE in out and out[OBS_IMAGE] == features["observation.pixels"] - assert "observation.pixels" not in out - assert out["keep"] == features["keep"] + out = processor.transform_features(features.copy()) + + assert ( + OBS_IMAGE in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][OBS_IMAGE] + == features[PipelineFeatureType.OBSERVATION]["observation.pixels"] + ) + assert "observation.pixels" not in out[PipelineFeatureType.OBSERVATION] + assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"] assert_contract_is_typed(out) -def test_image_processor_feature_contract_multi_camera_and_prefixed(policy_feature_factory): - processor = VanillaObservationProcessor() +def test_image_processor_features_multi_camera_and_prefixed(policy_feature_factory): + processor = VanillaObservationProcessorStep() features = { - "pixels.front": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), - "pixels.wrist": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), - "observation.pixels.rear": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), - "keep": policy_feature_factory(FeatureType.ENV, (7,)), + PipelineFeatureType.OBSERVATION: { + "pixels.front": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), + "pixels.wrist": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), + "observation.pixels.rear": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), + "keep": policy_feature_factory(FeatureType.ENV, (7,)), + }, } - out = processor.feature_contract(features.copy()) - - assert f"{OBS_IMAGES}.front" in out and out[f"{OBS_IMAGES}.front"] == features["pixels.front"] - assert f"{OBS_IMAGES}.wrist" in out and out[f"{OBS_IMAGES}.wrist"] == features["pixels.wrist"] - assert f"{OBS_IMAGES}.rear" in out and out[f"{OBS_IMAGES}.rear"] == features["observation.pixels.rear"] - assert "pixels.front" not in out and "pixels.wrist" not in out and "observation.pixels.rear" not in out - assert out["keep"] == features["keep"] + out = processor.transform_features(features.copy()) + + assert ( + f"{OBS_IMAGES}.front" in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.front"] + == features[PipelineFeatureType.OBSERVATION]["pixels.front"] + ) + assert ( + f"{OBS_IMAGES}.wrist" in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.wrist"] + == features[PipelineFeatureType.OBSERVATION]["pixels.wrist"] + ) + assert ( + f"{OBS_IMAGES}.rear" in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.rear"] + == features[PipelineFeatureType.OBSERVATION]["observation.pixels.rear"] + ) + assert ( + "pixels.front" not in out[PipelineFeatureType.OBSERVATION] + and "pixels.wrist" not in out[PipelineFeatureType.OBSERVATION] + and "observation.pixels.rear" not in out[PipelineFeatureType.OBSERVATION] + ) + assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"] assert_contract_is_typed(out) -def test_state_processor_feature_contract_environment_and_agent_pos(policy_feature_factory): - processor = VanillaObservationProcessor() +def test_state_processor_features_environment_and_agent_pos(policy_feature_factory): + processor = VanillaObservationProcessorStep() features = { - "environment_state": policy_feature_factory(FeatureType.STATE, (3,)), - "agent_pos": policy_feature_factory(FeatureType.STATE, (7,)), - "keep": policy_feature_factory(FeatureType.ENV, (1,)), + PipelineFeatureType.OBSERVATION: { + "environment_state": policy_feature_factory(FeatureType.STATE, (3,)), + "agent_pos": policy_feature_factory(FeatureType.STATE, (7,)), + "keep": policy_feature_factory(FeatureType.ENV, (1,)), + }, } - out = processor.feature_contract(features.copy()) - - assert OBS_ENV_STATE in out and out[OBS_ENV_STATE] == features["environment_state"] - assert OBS_STATE in out and out[OBS_STATE] == features["agent_pos"] - assert "environment_state" not in out and "agent_pos" not in out - assert out["keep"] == features["keep"] + out = processor.transform_features(features.copy()) + + assert ( + OBS_ENV_STATE in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][OBS_ENV_STATE] + == features[PipelineFeatureType.OBSERVATION]["environment_state"] + ) + assert ( + OBS_STATE in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][OBS_STATE] + == features[PipelineFeatureType.OBSERVATION]["agent_pos"] + ) + assert ( + "environment_state" not in out[PipelineFeatureType.OBSERVATION] + and "agent_pos" not in out[PipelineFeatureType.OBSERVATION] + ) + assert out[PipelineFeatureType.OBSERVATION]["keep"] == features[PipelineFeatureType.OBSERVATION]["keep"] assert_contract_is_typed(out) -def test_state_processor_feature_contract_prefixed_inputs(policy_feature_factory): - proc = VanillaObservationProcessor() +def test_state_processor_features_prefixed_inputs(policy_feature_factory): + proc = VanillaObservationProcessorStep() features = { - "observation.environment_state": policy_feature_factory(FeatureType.STATE, (2,)), - "observation.agent_pos": policy_feature_factory(FeatureType.STATE, (4,)), + PipelineFeatureType.OBSERVATION: { + OBS_ENV_STATE: policy_feature_factory(FeatureType.STATE, (2,)), + "observation.agent_pos": policy_feature_factory(FeatureType.STATE, (4,)), + }, } - out = proc.feature_contract(features.copy()) - - assert OBS_ENV_STATE in out and out[OBS_ENV_STATE] == features["observation.environment_state"] - assert OBS_STATE in out and out[OBS_STATE] == features["observation.agent_pos"] - assert "environment_state" not in out and "agent_pos" not in out + out = proc.transform_features(features.copy()) + + assert ( + OBS_ENV_STATE in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][OBS_ENV_STATE] + == features[PipelineFeatureType.OBSERVATION][OBS_ENV_STATE] + ) + assert ( + OBS_STATE in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION][OBS_STATE] + == features[PipelineFeatureType.OBSERVATION]["observation.agent_pos"] + ) + assert ( + "environment_state" not in out[PipelineFeatureType.OBSERVATION] + and "agent_pos" not in out[PipelineFeatureType.OBSERVATION] + ) assert_contract_is_typed(out) diff --git a/tests/processor/test_pipeline.py b/tests/processor/test_pipeline.py index 5665d5a7d3f..134228c05ca 100644 --- a/tests/processor/test_pipeline.py +++ b/tests/processor/test_pipeline.py @@ -25,29 +25,22 @@ import torch import torch.nn as nn -from lerobot.configs.types import FeatureType, PolicyFeature -from lerobot.processor import EnvTransition, ProcessorStepRegistry, RobotProcessor -from lerobot.processor.pipeline import TransitionKey +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature +from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features +from lerobot.processor import ( + DataProcessorPipeline, + EnvTransition, + ProcessorStep, + ProcessorStepRegistry, + TransitionKey, +) +from lerobot.processor.converters import create_transition, identity_transition +from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STATE, REWARD, TRUNCATED from tests.conftest import assert_contract_is_typed -def create_transition( - observation=None, action=None, reward=0.0, done=False, truncated=False, info=None, complementary_data=None -): - """Helper to create an EnvTransition dictionary.""" - return { - TransitionKey.OBSERVATION: observation, - TransitionKey.ACTION: action, - TransitionKey.REWARD: reward, - TransitionKey.DONE: done, - TransitionKey.TRUNCATED: truncated, - TransitionKey.INFO: info if info is not None else {}, - TransitionKey.COMPLEMENTARY_DATA: complementary_data if complementary_data is not None else {}, - } - - @dataclass -class MockStep: +class MockStep(ProcessorStep): """Mock pipeline step for testing - demonstrates best practices. This example shows the proper separation: @@ -90,13 +83,15 @@ def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: def reset(self) -> None: self.counter = 0 - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features @dataclass -class MockStepWithoutOptionalMethods: +class MockStepWithoutOptionalMethods(ProcessorStep): """Mock step that only implements the required __call__ method.""" multiplier: float = 2.0 @@ -112,13 +107,15 @@ def __call__(self, transition: EnvTransition) -> EnvTransition: return transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features @dataclass -class MockStepWithTensorState: +class MockStepWithTensorState(ProcessorStep): """Mock step demonstrating mixed JSON attributes and tensor state.""" name: str = "tensor_step" @@ -168,14 +165,16 @@ def reset(self) -> None: self.running_mean.zero_() self.running_count.zero_() - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features def test_empty_pipeline(): """Test pipeline with no steps.""" - pipeline = RobotProcessor() + pipeline = DataProcessorPipeline([], to_transition=identity_transition, to_output=identity_transition) transition = create_transition() result = pipeline(transition) @@ -187,7 +186,7 @@ def test_empty_pipeline(): def test_single_step_pipeline(): """Test pipeline with a single step.""" step = MockStep("test_step") - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step], to_transition=identity_transition, to_output=identity_transition) transition = create_transition() result = pipeline(transition) @@ -204,7 +203,9 @@ def test_multiple_steps_pipeline(): """Test pipeline with multiple steps.""" step1 = MockStep("step1") step2 = MockStep("step2") - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline( + [step1, step2], to_transition=identity_transition, to_output=identity_transition + ) transition = create_transition() result = pipeline(transition) @@ -216,7 +217,7 @@ def test_multiple_steps_pipeline(): def test_invalid_transition_format(): """Test pipeline with invalid transition format.""" - pipeline = RobotProcessor([MockStep()]) + pipeline = DataProcessorPipeline([MockStep()]) # Test with wrong type (tuple instead of dict) with pytest.raises(ValueError, match="EnvTransition must be a dictionary"): @@ -231,7 +232,7 @@ def test_step_through(): """Test step_through method with dict input.""" step1 = MockStep("step1") step2 = MockStep("step2") - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) transition = create_transition() @@ -245,21 +246,21 @@ def test_step_through(): # Ensure all results are dicts (same format as input) for result in results: assert isinstance(result, dict) - assert all(isinstance(k, TransitionKey) for k in result.keys()) + assert all(isinstance(k, TransitionKey) for k in result) def test_step_through_with_dict(): """Test step_through method with dict input.""" step1 = MockStep("step1") step2 = MockStep("step2") - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) batch = { - "observation.image": None, - "action": None, - "next.reward": 0.0, - "next.done": False, - "next.truncated": False, + OBS_IMAGE: None, + ACTION: None, + REWARD: 0.0, + DONE: False, + TRUNCATED: False, "info": {}, } @@ -291,7 +292,7 @@ def test_step_through_with_dict(): def test_step_through_no_hooks(): """Test that step_through doesn't execute hooks.""" step = MockStep("test_step") - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) hook_calls = [] @@ -326,7 +327,7 @@ def test_indexing(): """Test pipeline indexing.""" step1 = MockStep("step1") step2 = MockStep("step2") - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) # Test integer indexing assert pipeline[0] is step1 @@ -334,7 +335,7 @@ def test_indexing(): # Test slice indexing sub_pipeline = pipeline[0:1] - assert isinstance(sub_pipeline, RobotProcessor) + assert isinstance(sub_pipeline, DataProcessorPipeline) assert len(sub_pipeline) == 1 assert sub_pipeline[0] is step1 @@ -342,7 +343,7 @@ def test_indexing(): def test_hooks(): """Test before/after step hooks.""" step = MockStep("test_step") - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) before_calls = [] after_calls = [] @@ -366,7 +367,7 @@ def after_hook(idx: int, transition: EnvTransition): def test_unregister_hooks(): """Test unregistering hooks from the pipeline.""" step = MockStep("test_step") - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) # Test before_step_hook before_calls = [] @@ -405,7 +406,7 @@ def after_hook(idx: int, transition: EnvTransition): def test_unregister_nonexistent_hook(): """Test error handling when unregistering hooks that don't exist.""" - pipeline = RobotProcessor([MockStep()]) + pipeline = DataProcessorPipeline([MockStep()]) def some_hook(idx: int, transition: EnvTransition): pass @@ -423,7 +424,7 @@ def reset_hook(): def test_multiple_hooks_and_selective_unregister(): """Test registering multiple hooks and selectively unregistering them.""" - pipeline = RobotProcessor([MockStep("step1"), MockStep("step2")]) + pipeline = DataProcessorPipeline([MockStep("step1"), MockStep("step2")]) calls_1 = [] calls_2 = [] @@ -469,7 +470,7 @@ def hook3(idx: int, transition: EnvTransition): def test_hook_execution_order_documentation(): """Test and document that hooks are executed sequentially in registration order.""" - pipeline = RobotProcessor([MockStep("step")]) + pipeline = DataProcessorPipeline([MockStep("step")]) execution_order = [] @@ -521,7 +522,7 @@ def test_save_and_load_pretrained(): step1.counter = 5 step2.counter = 10 - pipeline = RobotProcessor([step1, step2], name="TestPipeline") + pipeline = DataProcessorPipeline([step1, step2], name="TestPipeline") with tempfile.TemporaryDirectory() as tmp_dir: # Save pipeline @@ -543,7 +544,7 @@ def test_save_and_load_pretrained(): assert config["steps"][1]["config"]["counter"] == 10 # Load pipeline - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir) + loaded_pipeline = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="testpipeline.json") assert loaded_pipeline.name == "TestPipeline" assert len(loaded_pipeline) == 2 @@ -556,7 +557,9 @@ def test_save_and_load_pretrained(): def test_step_without_optional_methods(): """Test pipeline with steps that don't implement optional methods.""" step = MockStepWithoutOptionalMethods(multiplier=3.0) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline( + [step], to_transition=identity_transition, to_output=identity_transition + ) # Identity for EnvTransition input/output transition = create_transition(reward=2.0) result = pipeline(transition) @@ -569,14 +572,16 @@ def test_step_without_optional_methods(): # Save/load should work even without optional methods with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json" + ) assert len(loaded_pipeline) == 1 def test_mixed_json_and_tensor_state(): """Test step with both JSON attributes and tensor state.""" step = MockStepWithTensorState(name="stats", learning_rate=0.05, window_size=5) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) # Process some transitions with rewards for i in range(10): @@ -592,13 +597,15 @@ def test_mixed_json_and_tensor_state(): pipeline.save_pretrained(tmp_dir) # Check that both config and state files were created - config_path = Path(tmp_dir) / "robotprocessor.json" # Default name is "RobotProcessor" - state_path = Path(tmp_dir) / "robotprocessor_step_0.safetensors" + config_path = Path(tmp_dir) / "dataprocessorpipeline.json" # Default name is "RobotProcessor" + state_path = Path(tmp_dir) / "dataprocessorpipeline_step_0.safetensors" assert config_path.exists() assert state_path.exists() # Load and verify - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json" + ) loaded_step = loaded_pipeline.steps[0] # Check JSON attributes were restored @@ -611,7 +618,7 @@ def test_mixed_json_and_tensor_state(): assert torch.allclose(loaded_step.running_mean, step.running_mean) -class MockModuleStep(nn.Module): +class MockModuleStep(ProcessorStep, nn.Module): """Mock step that inherits from nn.Module to test state_dict handling of module parameters.""" def __init__(self, input_dim: int = 10, hidden_dim: int = 5): @@ -651,23 +658,25 @@ def get_config(self) -> dict[str, Any]: def state_dict(self) -> dict[str, torch.Tensor]: """Override to return all module parameters and buffers.""" # Get the module's state dict (includes all parameters and buffers) - return super().state_dict() + return nn.Module.state_dict(self) def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: """Override to load all module parameters and buffers.""" # Use the module's load_state_dict - super().load_state_dict(state) + nn.Module.load_state_dict(self, state) def reset(self) -> None: self.running_mean.zero_() self.counter = 0 - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features -class MockNonModuleStepWithState: +class MockNonModuleStepWithState(ProcessorStep): """Mock step that explicitly does NOT inherit from nn.Module but has tensor state. This tests the state_dict/load_state_dict path for regular classes. @@ -744,14 +753,16 @@ def reset(self) -> None: self.step_count.zero_() self.history.clear() - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features # Tests for overrides functionality @dataclass -class MockStepWithNonSerializableParam: +class MockStepWithNonSerializableParam(ProcessorStep): """Mock step that requires a non-serializable parameter.""" def __init__(self, name: str = "mock_env_step", multiplier: float = 1.0, env: Any = None): @@ -759,7 +770,7 @@ def __init__(self, name: str = "mock_env_step", multiplier: float = 1.0, env: An # Add type validation for multiplier if isinstance(multiplier, str): raise ValueError(f"multiplier must be a number, got string '{multiplier}'") - if not isinstance(multiplier, (int, float)): + if not isinstance(multiplier, (int | float)): raise TypeError(f"multiplier must be a number, got {type(multiplier).__name__}") self.multiplier = float(multiplier) self.env = env # Non-serializable parameter (like gym.Env) @@ -799,14 +810,16 @@ def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: def reset(self) -> None: pass - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features @ProcessorStepRegistry.register("registered_mock_step") @dataclass -class RegisteredMockStep: +class RegisteredMockStep(ProcessorStep): """Mock step registered in the registry.""" value: int = 42 @@ -838,8 +851,10 @@ def load_state_dict(self, state: dict[str, torch.Tensor]) -> None: def reset(self) -> None: pass - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features @@ -859,7 +874,7 @@ def test_from_pretrained_with_overrides(): env_step = MockStepWithNonSerializableParam(name="env_step", multiplier=2.0) registered_step = RegisteredMockStep(value=100, device="cpu") - pipeline = RobotProcessor([env_step, registered_step], name="TestOverrides") + pipeline = DataProcessorPipeline([env_step, registered_step], name="TestOverrides") with tempfile.TemporaryDirectory() as tmp_dir: # Save the pipeline @@ -877,7 +892,13 @@ def test_from_pretrained_with_overrides(): "registered_mock_step": {"device": "cuda", "value": 200}, } - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="testoverrides.json", + overrides=overrides, + to_transition=identity_transition, + to_output=identity_transition, + ) # Verify the pipeline was loaded correctly assert len(loaded_pipeline) == 2 @@ -903,7 +924,7 @@ def test_from_pretrained_with_partial_overrides(): step1 = MockStepWithNonSerializableParam(name="step1", multiplier=1.0) step2 = MockStepWithNonSerializableParam(name="step2", multiplier=2.0) - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -913,7 +934,13 @@ def test_from_pretrained_with_partial_overrides(): # The current implementation applies overrides to ALL steps with the same class name # Both steps will get the override - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + overrides=overrides, + to_transition=identity_transition, + to_output=identity_transition, + ) transition = create_transition(reward=1.0) result = loaded_pipeline(transition) @@ -927,7 +954,7 @@ def test_from_pretrained_with_partial_overrides(): def test_from_pretrained_invalid_override_key(): """Test that invalid override keys raise KeyError.""" step = MockStepWithNonSerializableParam() - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -936,13 +963,15 @@ def test_from_pretrained_invalid_override_key(): overrides = {"NonExistentStep": {"param": "value"}} with pytest.raises(KeyError, match="Override keys.*do not match any step"): - RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json", overrides=overrides + ) def test_from_pretrained_multiple_invalid_override_keys(): """Test that multiple invalid override keys are reported.""" step = MockStepWithNonSerializableParam() - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -951,7 +980,9 @@ def test_from_pretrained_multiple_invalid_override_keys(): overrides = {"NonExistentStep1": {"param": "value1"}, "NonExistentStep2": {"param": "value2"}} with pytest.raises(KeyError) as exc_info: - RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json", overrides=overrides + ) error_msg = str(exc_info.value) assert "NonExistentStep1" in error_msg @@ -962,7 +993,7 @@ def test_from_pretrained_multiple_invalid_override_keys(): def test_from_pretrained_registered_step_override(): """Test overriding registered steps using registry names.""" registered_step = RegisteredMockStep(value=50, device="cpu") - pipeline = RobotProcessor([registered_step]) + pipeline = DataProcessorPipeline([registered_step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -970,7 +1001,13 @@ def test_from_pretrained_registered_step_override(): # Override using registry name overrides = {"registered_mock_step": {"value": 999, "device": "cuda"}} - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + overrides=overrides, + to_transition=identity_transition, + to_output=identity_transition, + ) # Test that overrides were applied transition = create_transition() @@ -986,7 +1023,7 @@ def test_from_pretrained_mixed_registered_and_unregistered(): unregistered_step = MockStepWithNonSerializableParam(name="unregistered", multiplier=1.0) registered_step = RegisteredMockStep(value=10, device="cpu") - pipeline = RobotProcessor([unregistered_step, registered_step]) + pipeline = DataProcessorPipeline([unregistered_step, registered_step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -998,7 +1035,13 @@ def test_from_pretrained_mixed_registered_and_unregistered(): "registered_mock_step": {"value": 777}, } - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + overrides=overrides, + to_transition=identity_transition, + to_output=identity_transition, + ) # Test both steps transition = create_transition(reward=2.0) @@ -1013,13 +1056,18 @@ def test_from_pretrained_mixed_registered_and_unregistered(): def test_from_pretrained_no_overrides(): """Test that from_pretrained works without overrides (backward compatibility).""" step = MockStepWithNonSerializableParam(name="no_override", multiplier=3.0) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Load without overrides - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + to_transition=identity_transition, + to_output=identity_transition, + ) assert len(loaded_pipeline) == 1 @@ -1033,13 +1081,19 @@ def test_from_pretrained_no_overrides(): def test_from_pretrained_empty_overrides(): """Test that from_pretrained works with empty overrides dict.""" step = MockStepWithNonSerializableParam(multiplier=2.0) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Load with empty overrides - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides={}) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + overrides={}, + to_transition=identity_transition, + to_output=identity_transition, + ) assert len(loaded_pipeline) == 1 @@ -1053,7 +1107,7 @@ def test_from_pretrained_empty_overrides(): def test_from_pretrained_override_instantiation_error(): """Test that instantiation errors with overrides are properly reported.""" step = MockStepWithNonSerializableParam(multiplier=1.0) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -1066,13 +1120,15 @@ def test_from_pretrained_override_instantiation_error(): } with pytest.raises(ValueError, match="Failed to instantiate processor step"): - RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json", overrides=overrides + ) def test_from_pretrained_with_state_and_overrides(): """Test that overrides work correctly with steps that have tensor state.""" step = MockStepWithTensorState(name="tensor_step", learning_rate=0.01, window_size=5) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) # Process some data to create state for i in range(10): @@ -1090,7 +1146,9 @@ def test_from_pretrained_with_state_and_overrides(): } } - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json", overrides=overrides + ) loaded_step = loaded_pipeline.steps[0] # Check that config overrides were applied @@ -1109,7 +1167,7 @@ def test_from_pretrained_override_error_messages(): """Test that error messages for override failures are helpful.""" step1 = MockStepWithNonSerializableParam(name="step1") step2 = RegisteredMockStep() - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -1118,7 +1176,9 @@ def test_from_pretrained_override_error_messages(): overrides = {"WrongStepName": {"param": "value"}} with pytest.raises(KeyError) as exc_info: - RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json", overrides=overrides + ) error_msg = str(exc_info.value) assert "WrongStepName" in error_msg @@ -1129,20 +1189,20 @@ def test_from_pretrained_override_error_messages(): def test_repr_empty_processor(): """Test __repr__ with empty processor.""" - pipeline = RobotProcessor() + pipeline = DataProcessorPipeline() repr_str = repr(pipeline) - expected = "RobotProcessor(name='RobotProcessor', steps=0: [])" + expected = "DataProcessorPipeline(name='DataProcessorPipeline', steps=0: [])" assert repr_str == expected def test_repr_single_step(): """Test __repr__ with single step.""" step = MockStep("test_step") - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) repr_str = repr(pipeline) - expected = "RobotProcessor(name='RobotProcessor', steps=1: [MockStep])" + expected = "DataProcessorPipeline(name='DataProcessorPipeline', steps=1: [MockStep])" assert repr_str == expected @@ -1150,18 +1210,18 @@ def test_repr_multiple_steps_under_limit(): """Test __repr__ with 2-3 steps (all shown).""" step1 = MockStep("step1") step2 = MockStepWithoutOptionalMethods() - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) repr_str = repr(pipeline) - expected = "RobotProcessor(name='RobotProcessor', steps=2: [MockStep, MockStepWithoutOptionalMethods])" + expected = "DataProcessorPipeline(name='DataProcessorPipeline', steps=2: [MockStep, MockStepWithoutOptionalMethods])" assert repr_str == expected # Test with 3 steps (boundary case) step3 = MockStepWithTensorState() - pipeline = RobotProcessor([step1, step2, step3]) + pipeline = DataProcessorPipeline([step1, step2, step3]) repr_str = repr(pipeline) - expected = "RobotProcessor(name='RobotProcessor', steps=3: [MockStep, MockStepWithoutOptionalMethods, MockStepWithTensorState])" + expected = "DataProcessorPipeline(name='DataProcessorPipeline', steps=3: [MockStep, MockStepWithoutOptionalMethods, MockStepWithTensorState])" assert repr_str == expected @@ -1173,30 +1233,30 @@ def test_repr_many_steps_truncated(): step4 = MockModuleStep() step5 = MockNonModuleStepWithState() - pipeline = RobotProcessor([step1, step2, step3, step4, step5]) + pipeline = DataProcessorPipeline([step1, step2, step3, step4, step5]) repr_str = repr(pipeline) - expected = "RobotProcessor(name='RobotProcessor', steps=5: [MockStep, MockStepWithoutOptionalMethods, ..., MockNonModuleStepWithState])" + expected = "DataProcessorPipeline(name='DataProcessorPipeline', steps=5: [MockStep, MockStepWithoutOptionalMethods, ..., MockNonModuleStepWithState])" assert repr_str == expected def test_repr_with_custom_name(): """Test __repr__ with custom processor name.""" step = MockStep("test_step") - pipeline = RobotProcessor([step], name="CustomProcessor") + pipeline = DataProcessorPipeline([step], name="CustomProcessor") repr_str = repr(pipeline) - expected = "RobotProcessor(name='CustomProcessor', steps=1: [MockStep])" + expected = "DataProcessorPipeline(name='CustomProcessor', steps=1: [MockStep])" assert repr_str == expected def test_repr_with_seed(): """Test __repr__ with seed parameter.""" step = MockStep("test_step") - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) repr_str = repr(pipeline) - expected = "RobotProcessor(name='RobotProcessor', steps=1: [MockStep])" + expected = "DataProcessorPipeline(name='DataProcessorPipeline', steps=1: [MockStep])" assert repr_str == expected @@ -1204,20 +1264,22 @@ def test_repr_with_custom_name_and_seed(): """Test __repr__ with both custom name and seed.""" step1 = MockStep("step1") step2 = MockStepWithoutOptionalMethods() - pipeline = RobotProcessor([step1, step2], name="MyProcessor") + pipeline = DataProcessorPipeline([step1, step2], name="MyProcessor") repr_str = repr(pipeline) - expected = "RobotProcessor(name='MyProcessor', steps=2: [MockStep, MockStepWithoutOptionalMethods])" + expected = ( + "DataProcessorPipeline(name='MyProcessor', steps=2: [MockStep, MockStepWithoutOptionalMethods])" + ) assert repr_str == expected def test_repr_without_seed(): """Test __repr__ when seed is explicitly None (should not show seed).""" step = MockStep("test_step") - pipeline = RobotProcessor([step], name="TestProcessor") + pipeline = DataProcessorPipeline([step], name="TestProcessor") repr_str = repr(pipeline) - expected = "RobotProcessor(name='TestProcessor', steps=1: [MockStep])" + expected = "DataProcessorPipeline(name='TestProcessor', steps=1: [MockStep])" assert repr_str == expected @@ -1228,10 +1290,10 @@ def test_repr_various_step_types(): step3 = MockModuleStep() step4 = MockNonModuleStepWithState() - pipeline = RobotProcessor([step1, step2, step3, step4], name="MixedSteps") + pipeline = DataProcessorPipeline([step1, step2, step3, step4], name="MixedSteps") repr_str = repr(pipeline) - expected = "RobotProcessor(name='MixedSteps', steps=4: [MockStep, MockStepWithTensorState, ..., MockNonModuleStepWithState])" + expected = "DataProcessorPipeline(name='MixedSteps', steps=4: [MockStep, MockStepWithTensorState, ..., MockNonModuleStepWithState])" assert repr_str == expected @@ -1242,10 +1304,10 @@ def test_repr_edge_case_long_names(): step3 = MockStepWithTensorState() step4 = MockNonModuleStepWithState() - pipeline = RobotProcessor([step1, step2, step3, step4], name="LongNames") + pipeline = DataProcessorPipeline([step1, step2, step3, step4], name="LongNames") repr_str = repr(pipeline) - expected = "RobotProcessor(name='LongNames', steps=4: [MockStepWithNonSerializableParam, MockStepWithoutOptionalMethods, ..., MockNonModuleStepWithState])" + expected = "DataProcessorPipeline(name='LongNames', steps=4: [MockStepWithNonSerializableParam, MockStepWithoutOptionalMethods, ..., MockNonModuleStepWithState])" assert repr_str == expected @@ -1253,7 +1315,7 @@ def test_repr_edge_case_long_names(): def test_save_with_custom_config_filename(): """Test saving processor with custom config filename.""" step = MockStep("test") - pipeline = RobotProcessor([step], name="TestProcessor") + pipeline = DataProcessorPipeline([step], name="TestProcessor") with tempfile.TemporaryDirectory() as tmp_dir: # Save with custom filename @@ -1269,16 +1331,18 @@ def test_save_with_custom_config_filename(): assert config["name"] == "TestProcessor" # Load with specific filename - loaded = RobotProcessor.from_pretrained(tmp_dir, config_filename="my_custom_config.json") + loaded = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="my_custom_config.json") assert loaded.name == "TestProcessor" def test_multiple_processors_same_directory(): """Test saving multiple processors to the same directory with different config files.""" # Create different processors - preprocessor = RobotProcessor([MockStep("pre1"), MockStep("pre2")], name="preprocessor") + preprocessor = DataProcessorPipeline([MockStep("pre1"), MockStep("pre2")], name="preprocessor") - postprocessor = RobotProcessor([MockStepWithoutOptionalMethods(multiplier=0.5)], name="postprocessor") + postprocessor = DataProcessorPipeline( + [MockStepWithoutOptionalMethods(multiplier=0.5)], name="postprocessor" + ) with tempfile.TemporaryDirectory() as tmp_dir: # Save both to same directory @@ -1290,8 +1354,8 @@ def test_multiple_processors_same_directory(): assert (Path(tmp_dir) / "postprocessor.json").exists() # Load them back - loaded_pre = RobotProcessor.from_pretrained(tmp_dir, config_filename="preprocessor.json") - loaded_post = RobotProcessor.from_pretrained(tmp_dir, config_filename="postprocessor.json") + loaded_pre = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="preprocessor.json") + loaded_post = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="postprocessor.json") assert loaded_pre.name == "preprocessor" assert loaded_post.name == "postprocessor" @@ -1299,31 +1363,34 @@ def test_multiple_processors_same_directory(): assert len(loaded_post) == 1 -def test_auto_detect_single_config(): - """Test automatic config detection when there's only one JSON file.""" +def test_explicit_config_filename_loading(): + """Test explicit config filename loading (no more auto-detection).""" step = MockStepWithTensorState() - pipeline = RobotProcessor([step], name="SingleConfig") + pipeline = DataProcessorPipeline([step], name="SingleConfig") with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) - # Load without specifying config_filename - loaded = RobotProcessor.from_pretrained(tmp_dir) + # Load with explicit config_filename (now required) + loaded = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="singleconfig.json") assert loaded.name == "SingleConfig" -def test_error_multiple_configs_no_filename(): - """Test error when multiple configs exist and no filename specified.""" - proc1 = RobotProcessor([MockStep()], name="processor1") - proc2 = RobotProcessor([MockStep()], name="processor2") +def test_explicit_config_selection_with_multiple_configs(): + """Test explicit config selection when multiple configs exist.""" + proc1 = DataProcessorPipeline([MockStep()], name="processor1") + proc2 = DataProcessorPipeline([MockStep()], name="processor2") with tempfile.TemporaryDirectory() as tmp_dir: proc1.save_pretrained(tmp_dir) proc2.save_pretrained(tmp_dir) - # Should raise error - with pytest.raises(ValueError, match="Multiple .json files found"): - RobotProcessor.from_pretrained(tmp_dir) + # Can load specific configs explicitly + loaded1 = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="processor1.json") + loaded2 = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="processor2.json") + + assert loaded1.name == "processor1" + assert loaded2.name == "processor2" def test_state_file_naming_with_indices(): @@ -1333,7 +1400,7 @@ def test_state_file_naming_with_indices(): step2 = MockStepWithTensorState(name="norm2", window_size=10) step3 = MockModuleStep(input_dim=5) - pipeline = RobotProcessor([step1, step2, step3]) + pipeline = DataProcessorPipeline([step1, step2, step3]) # Process some data to create state for i in range(5): @@ -1349,9 +1416,9 @@ def test_state_file_naming_with_indices(): # Files should be named with pipeline name prefix and indices expected_names = [ - "robotprocessor_step_0.safetensors", - "robotprocessor_step_1.safetensors", - "robotprocessor_step_2.safetensors", + "dataprocessorpipeline_step_0.safetensors", + "dataprocessorpipeline_step_1.safetensors", + "dataprocessorpipeline_step_2.safetensors", ] actual_names = [f.name for f in state_files] assert actual_names == expected_names @@ -1363,7 +1430,7 @@ def test_state_file_naming_with_registry(): # Register a test step @ProcessorStepRegistry.register("test_stateful_step") @dataclass - class TestStatefulStep: + class TestStatefulStep(ProcessorStep): value: int = 0 def __init__(self, value: int = 0): @@ -1382,15 +1449,17 @@ def state_dict(self): def load_state_dict(self, state): self.state_tensor = state["state_tensor"] - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features try: # Create pipeline with registered steps step1 = TestStatefulStep(1) step2 = TestStatefulStep(2) - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -1401,8 +1470,8 @@ def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, Poli # Should include pipeline name, index and registry name expected_names = [ - "robotprocessor_step_0_test_stateful_step.safetensors", - "robotprocessor_step_1_test_stateful_step.safetensors", + "dataprocessorpipeline_step_0_test_stateful_step.safetensors", + "dataprocessorpipeline_step_1_test_stateful_step.safetensors", ] actual_names = [f.name for f in state_files] assert actual_names == expected_names @@ -1418,7 +1487,7 @@ def test_override_with_nested_config(): @ProcessorStepRegistry.register("complex_config_step") @dataclass - class ComplexConfigStep: + class ComplexConfigStep(ProcessorStep): name: str = "complex" simple_param: int = 42 nested_config: dict = None @@ -1439,21 +1508,26 @@ def __call__(self, transition: EnvTransition) -> EnvTransition: def get_config(self): return {"name": self.name, "simple_param": self.simple_param, "nested_config": self.nested_config} - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features try: step = ComplexConfigStep() - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Load with nested override - loaded = RobotProcessor.from_pretrained( + loaded = DataProcessorPipeline.from_pretrained( tmp_dir, + config_filename="dataprocessorpipeline.json", overrides={"complex_config_step": {"nested_config": {"level1": {"level2": "overridden"}}}}, + to_transition=identity_transition, + to_output=identity_transition, ) # Test that override worked @@ -1467,14 +1541,15 @@ def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, Poli def test_override_preserves_defaults(): """Test that overrides only affect specified parameters.""" step = MockStepWithNonSerializableParam(name="test", multiplier=2.0) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Override only one parameter - loaded = RobotProcessor.from_pretrained( + loaded = DataProcessorPipeline.from_pretrained( tmp_dir, + config_filename="dataprocessorpipeline.json", overrides={ "MockStepWithNonSerializableParam": { "multiplier": 5.0 # Only override multiplier @@ -1491,7 +1566,7 @@ def test_override_preserves_defaults(): def test_override_type_validation(): """Test that type errors in overrides are caught properly.""" step = MockStepWithTensorState(learning_rate=0.01) - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -1504,7 +1579,9 @@ def test_override_type_validation(): } with pytest.raises(ValueError, match="Failed to instantiate"): - RobotProcessor.from_pretrained(tmp_dir, overrides=overrides) + DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json", overrides=overrides + ) def test_override_with_callables(): @@ -1512,7 +1589,7 @@ def test_override_with_callables(): @ProcessorStepRegistry.register("callable_step") @dataclass - class CallableStep: + class CallableStep(ProcessorStep): name: str = "callable_step" transform_fn: Any = None @@ -1531,28 +1608,32 @@ def __call__(self, transition: EnvTransition) -> EnvTransition: def get_config(self): return {"name": self.name} - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features try: step = CallableStep() - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Define a transform function def double_values(x): - if isinstance(x, (int, float)): - return x * 2 - elif isinstance(x, torch.Tensor): + if isinstance(x, (int | float | torch.Tensor)): return x * 2 return x # Load with callable override - loaded = RobotProcessor.from_pretrained( - tmp_dir, overrides={"callable_step": {"transform_fn": double_values}} + loaded = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + overrides={"callable_step": {"transform_fn": double_values}}, + to_transition=identity_transition, + to_output=identity_transition, ) # Test it works @@ -1567,14 +1648,16 @@ def test_override_multiple_same_class_warning(): """Test behavior when multiple steps of same class exist.""" step1 = MockStepWithNonSerializableParam(name="step1", multiplier=1.0) step2 = MockStepWithNonSerializableParam(name="step2", multiplier=2.0) - pipeline = RobotProcessor([step1, step2]) + pipeline = DataProcessorPipeline([step1, step2]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Override affects all instances of the class - loaded = RobotProcessor.from_pretrained( - tmp_dir, overrides={"MockStepWithNonSerializableParam": {"multiplier": 10.0}} + loaded = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + overrides={"MockStepWithNonSerializableParam": {"multiplier": 10.0}}, ) # Both steps get the same override @@ -1589,7 +1672,7 @@ def test_override_multiple_same_class_warning(): def test_config_filename_special_characters(): """Test config filenames with special characters are sanitized.""" # Processor name with special characters - pipeline = RobotProcessor([MockStep()], name="My/Processor\\With:Special*Chars") + pipeline = DataProcessorPipeline([MockStep()], name="My/Processor\\With:Special*Chars") with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) @@ -1607,10 +1690,10 @@ def test_state_file_naming_with_multiple_processors(): """Test that state files are properly prefixed with pipeline names to avoid conflicts.""" # Create two processors with state step1 = MockStepWithTensorState(name="norm", window_size=5) - preprocessor = RobotProcessor([step1], name="PreProcessor") + preprocessor = DataProcessorPipeline([step1], name="PreProcessor") step2 = MockStepWithTensorState(name="norm", window_size=10) - postprocessor = RobotProcessor([step2], name="PostProcessor") + postprocessor = DataProcessorPipeline([step2], name="PostProcessor") # Process some data to create state for i in range(3): @@ -1630,8 +1713,8 @@ def test_state_file_naming_with_multiple_processors(): assert (Path(tmp_dir) / "postprocessor_step_0.safetensors").exists() # Load both back and verify they work correctly - loaded_pre = RobotProcessor.from_pretrained(tmp_dir, config_filename="preprocessor.json") - loaded_post = RobotProcessor.from_pretrained(tmp_dir, config_filename="postprocessor.json") + loaded_pre = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="preprocessor.json") + loaded_post = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="postprocessor.json") assert loaded_pre.name == "PreProcessor" assert loaded_post.name == "PostProcessor" @@ -1644,7 +1727,7 @@ def test_override_with_device_strings(): @ProcessorStepRegistry.register("device_aware_step") @dataclass - class DeviceAwareStep: + class DeviceAwareStep(ProcessorStep): device: str = "cpu" def __init__(self, device: str = "cpu"): @@ -1663,21 +1746,25 @@ def state_dict(self): def load_state_dict(self, state): self.buffer = state["buffer"] - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - # We do not test feature_contract here + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # We do not test features here return features try: step = DeviceAwareStep(device="cpu") - pipeline = RobotProcessor([step]) + pipeline = DataProcessorPipeline([step]) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Override device if torch.cuda.is_available(): - loaded = RobotProcessor.from_pretrained( - tmp_dir, overrides={"device_aware_step": {"device": "cuda:0"}} + loaded = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="dataprocessorpipeline.json", + overrides={"device_aware_step": {"device": "cuda:0"}}, ) loaded_step = loaded.steps[0] @@ -1691,20 +1778,26 @@ def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, Poli def test_from_pretrained_nonexistent_path(): """Test error handling when loading from non-existent sources.""" - from huggingface_hub.errors import HfHubHTTPError, HFValidationError + from huggingface_hub.errors import HfHubHTTPError + + # Test with an invalid local path - should raise FileNotFoundError + with pytest.raises(FileNotFoundError): + DataProcessorPipeline.from_pretrained("/path/that/does/not/exist", config_filename="processor.json") - # Test with an invalid repo ID (too many slashes) - caught by HF validation - with pytest.raises(HFValidationError): - RobotProcessor.from_pretrained("/path/that/does/not/exist") + # Test with a path that doesn't exist as a directory + with pytest.raises(FileNotFoundError): + DataProcessorPipeline.from_pretrained("user/repo/extra/path", config_filename="processor.json") - # Test with a non-existent but valid Hub repo format + # Test with a non-existent Hub repo with pytest.raises((FileNotFoundError, HfHubHTTPError)): - RobotProcessor.from_pretrained("nonexistent-user/nonexistent-repo") + DataProcessorPipeline.from_pretrained( + "nonexistent-user/nonexistent-repo", config_filename="processor.json" + ) # Test with a local directory that exists but has no config files - with tempfile.TemporaryDirectory() as tmp_dir: - with pytest.raises(FileNotFoundError, match="No .json configuration files found"): - RobotProcessor.from_pretrained(tmp_dir) + with tempfile.TemporaryDirectory() as tmp_dir, pytest.raises(FileNotFoundError): + # Since the directory exists but has no config, it will raise FileNotFoundError + DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="processor.json") def test_save_load_with_custom_converter_functions(): @@ -1733,55 +1826,61 @@ def custom_to_output(transition): } # Create processor with custom converters - pipeline = RobotProcessor([MockStep()], to_transition=custom_to_transition, to_output=custom_to_output) + pipeline = DataProcessorPipeline( + [MockStep()], to_transition=custom_to_transition, to_output=custom_to_output + ) with tempfile.TemporaryDirectory() as tmp_dir: pipeline.save_pretrained(tmp_dir) # Load - should use default converters - loaded = RobotProcessor.from_pretrained(tmp_dir) + loaded = DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="dataprocessorpipeline.json") # Verify it uses default converters by checking with standard batch format batch = { - "observation.image": torch.randn(1, 3, 32, 32), - "action": torch.randn(1, 7), - "next.reward": torch.tensor([1.0]), - "next.done": torch.tensor([False]), - "next.truncated": torch.tensor([False]), + OBS_IMAGE: torch.randn(1, 3, 32, 32), + ACTION: torch.randn(1, 7), + REWARD: torch.tensor([1.0]), + DONE: torch.tensor([False]), + TRUNCATED: torch.tensor([False]), "info": {}, } # Should work with standard format (wouldn't work with custom converter) result = loaded(batch) - assert "observation.image" in result # Standard format preserved + # With new behavior, default to_output is _default_transition_to_batch, so result is batch dict + assert OBS_IMAGE in result class NonCompliantStep: - """Intentionally non-compliant: missing feature_contract.""" + """Intentionally non-compliant: missing features.""" def __call__(self, transition: EnvTransition) -> EnvTransition: return transition -def test_construction_rejects_step_without_feature_contract(): - with pytest.raises(TypeError, match=r"must define feature_contract\(features\) -> dict\[str, Any\]"): - RobotProcessor([NonCompliantStep()]) - - -class NonCallableStep: +class NonCallableStep(ProcessorStep): """Intentionally non-compliant: missing __call__.""" - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: return features def test_construction_rejects_step_without_call(): - with pytest.raises(TypeError, match=r"must define __call__"): - RobotProcessor([NonCallableStep()]) + """Test that DataProcessorPipeline rejects steps that don't inherit from ProcessorStep.""" + with pytest.raises( + TypeError, match=r"Can't instantiate abstract class NonCallableStep with abstract method __call_" + ): + DataProcessorPipeline([NonCallableStep()]) + + with pytest.raises(TypeError, match=r"must inherit from ProcessorStep"): + DataProcessorPipeline([NonCompliantStep()]) @dataclass -class FeatureContractAddStep: +class FeatureContractAddStep(ProcessorStep): """Adds a PolicyFeature""" key: str = "a" @@ -1790,39 +1889,47 @@ class FeatureContractAddStep: def __call__(self, transition: EnvTransition) -> EnvTransition: return transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - features[self.key] = self.value + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + features[PipelineFeatureType.OBSERVATION][self.key] = self.value return features @dataclass -class FeatureContractMutateStep: +class FeatureContractMutateStep(ProcessorStep): """Mutates a PolicyFeature""" key: str = "a" - fn: Callable[[PolicyFeature | None], PolicyFeature] = lambda x: x # noqa: E731 + fn: Callable[[PolicyFeature | None], PolicyFeature] = identity_transition # noqa: E731 def __call__(self, transition: EnvTransition) -> EnvTransition: return transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - features[self.key] = self.fn(features.get(self.key)) + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + features[PipelineFeatureType.OBSERVATION][self.key] = self.fn( + features[PipelineFeatureType.OBSERVATION].get(self.key) + ) return features @dataclass -class FeatureContractBadReturnStep: +class FeatureContractBadReturnStep(ProcessorStep): """Returns a non-dict""" def __call__(self, transition: EnvTransition) -> EnvTransition: return transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: return ["not-a-dict"] @dataclass -class FeatureContractRemoveStep: +class FeatureContractRemoveStep(ProcessorStep): """Removes a PolicyFeature""" key: str @@ -1830,32 +1937,39 @@ class FeatureContractRemoveStep: def __call__(self, transition: EnvTransition) -> EnvTransition: return transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: - features.pop(self.key, None) + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + features[PipelineFeatureType.OBSERVATION].pop(self.key, None) return features -def test_feature_contract_orders_and_merges(policy_feature_factory): - p = RobotProcessor( +def test_features_orders_and_merges(policy_feature_factory): + p = DataProcessorPipeline( [ FeatureContractAddStep("a", policy_feature_factory(FeatureType.STATE, (1,))), FeatureContractMutateStep("a", lambda v: PolicyFeature(type=v.type, shape=(3,))), FeatureContractAddStep("b", policy_feature_factory(FeatureType.ENV, (2,))), ] ) - out = p.feature_contract({}) - - assert out["a"].type == FeatureType.STATE and out["a"].shape == (3,) - assert out["b"].type == FeatureType.ENV and out["b"].shape == (2,) + out = p.transform_features({PipelineFeatureType.OBSERVATION: {}}) + assert out[PipelineFeatureType.OBSERVATION]["a"].type == FeatureType.STATE and out[ + PipelineFeatureType.OBSERVATION + ]["a"].shape == (3,) + assert out[PipelineFeatureType.OBSERVATION]["b"].type == FeatureType.ENV and out[ + PipelineFeatureType.OBSERVATION + ]["b"].shape == (2,) assert_contract_is_typed(out) -def test_feature_contract_respects_initial_without_mutation(policy_feature_factory): +def test_features_respects_initial_without_mutation(policy_feature_factory): initial = { - "seed": policy_feature_factory(FeatureType.STATE, (7,)), - "nested": policy_feature_factory(FeatureType.ENV, (0,)), + PipelineFeatureType.OBSERVATION: { + "seed": policy_feature_factory(FeatureType.STATE, (7,)), + "nested": policy_feature_factory(FeatureType.ENV, (0,)), + } } - p = RobotProcessor( + p = DataProcessorPipeline( [ FeatureContractMutateStep("seed", lambda v: PolicyFeature(type=v.type, shape=(v.shape[0] + 1,))), FeatureContractMutateStep( @@ -1863,57 +1977,224 @@ def test_feature_contract_respects_initial_without_mutation(policy_feature_facto ), ] ) - out = p.feature_contract(initial_features=initial) + out = p.transform_features(initial_features=initial) - assert out["seed"].shape == (8,) - assert out["nested"].shape == (5,) + assert out[PipelineFeatureType.OBSERVATION]["seed"].shape == (8,) + assert out[PipelineFeatureType.OBSERVATION]["nested"].shape == (5,) # Initial dict must be preserved - assert initial["seed"].shape == (7,) - assert initial["nested"].shape == (0,) + assert initial[PipelineFeatureType.OBSERVATION]["seed"].shape == (7,) + assert initial[PipelineFeatureType.OBSERVATION]["nested"].shape == (0,) assert_contract_is_typed(out) -def test_feature_contract_type_error_on_bad_step(): - p = RobotProcessor([FeatureContractAddStep(), FeatureContractBadReturnStep()]) - with pytest.raises(TypeError, match=r"\w+\.feature_contract must return dict\[str, Any\]"): - _ = p.feature_contract({}) - - -def test_feature_contract_execution_order_tracking(): - class Track: +def test_features_execution_order_tracking(): + class Track(ProcessorStep): def __init__(self, label): self.label = label def __call__(self, transition: EnvTransition) -> EnvTransition: return transition - def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]: + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: code = {"A": 1, "B": 2, "C": 3}[self.label] - pf = features.get("order", PolicyFeature(type=FeatureType.ENV, shape=())) - features["order"] = PolicyFeature(type=pf.type, shape=pf.shape + (code,)) + pf = features[PipelineFeatureType.OBSERVATION].get( + "order", PolicyFeature(type=FeatureType.ENV, shape=()) + ) + features[PipelineFeatureType.OBSERVATION]["order"] = PolicyFeature( + type=pf.type, shape=pf.shape + (code,) + ) return features - out = RobotProcessor([Track("A"), Track("B"), Track("C")]).feature_contract({}) - assert out["order"].shape == (1, 2, 3) + out = DataProcessorPipeline([Track("A"), Track("B"), Track("C")]).transform_features( + initial_features={PipelineFeatureType.OBSERVATION: {}} + ) + assert out[PipelineFeatureType.OBSERVATION]["order"].shape == (1, 2, 3) -def test_feature_contract_remove_key(policy_feature_factory): - p = RobotProcessor( +def test_features_remove_key(policy_feature_factory): + p = DataProcessorPipeline( [ FeatureContractAddStep("a", policy_feature_factory(FeatureType.STATE, (1,))), FeatureContractRemoveStep("a"), ] ) - out = p.feature_contract({}) - assert "a" not in out + out = p.transform_features({PipelineFeatureType.OBSERVATION: {}}) + assert "a" not in out[PipelineFeatureType.OBSERVATION] -def test_feature_contract_remove_from_initial(policy_feature_factory): +def test_features_remove_from_initial(policy_feature_factory): initial = { - "keep": policy_feature_factory(FeatureType.STATE, (1,)), - "drop": policy_feature_factory(FeatureType.STATE, (1,)), + PipelineFeatureType.OBSERVATION: { + "keep": policy_feature_factory(FeatureType.STATE, (1,)), + "drop": policy_feature_factory(FeatureType.STATE, (1,)), + }, } - p = RobotProcessor([FeatureContractRemoveStep("drop")]) - out = p.feature_contract(initial_features=initial) - assert "drop" not in out and out["keep"] == initial["keep"] + p = DataProcessorPipeline([FeatureContractRemoveStep("drop")]) + out = p.transform_features(initial_features=initial) + assert ( + "drop" not in out[PipelineFeatureType.OBSERVATION] + and out[PipelineFeatureType.OBSERVATION]["keep"] == initial[PipelineFeatureType.OBSERVATION]["keep"] + ) + + +@dataclass +class AddActionEEAndJointFeatures(ProcessorStep): + """Adds both EE and JOINT action features.""" + + def __call__(self, tr): + return tr + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # EE features + features[PipelineFeatureType.ACTION]["action.ee.x"] = float + features[PipelineFeatureType.ACTION]["action.ee.y"] = float + # JOINT features + features[PipelineFeatureType.ACTION]["action.j1.pos"] = float + features[PipelineFeatureType.ACTION]["action.j2.pos"] = float + return features + + +@dataclass +class AddObservationStateFeatures(ProcessorStep): + """Adds state features (and optionally an image spec to test precedence).""" + + add_front_image: bool = False + front_image_shape: tuple = (240, 320, 3) + + def __call__(self, tr): + return tr + + def transform_features( + self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]] + ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]: + # State features (mix EE and a joint state) + features[PipelineFeatureType.OBSERVATION][f"{OBS_STATE}.ee.x"] = float + features[PipelineFeatureType.OBSERVATION][f"{OBS_STATE}.j1.pos"] = float + if self.add_front_image: + features[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.front"] = self.front_image_shape + return features + + +def test_aggregate_joint_action_only(): + rp = DataProcessorPipeline([AddActionEEAndJointFeatures()]) + initial = {PipelineFeatureType.OBSERVATION: {"front": (480, 640, 3)}, PipelineFeatureType.ACTION: {}} + + out = aggregate_pipeline_dataset_features( + pipeline=rp, + initial_features=initial, + use_videos=True, + patterns=["action.j1.pos", "action.j2.pos"], + ) + + # Expect only ACTION with joint names + assert ACTION in out and OBS_STATE not in out + assert out[ACTION]["dtype"] == "float32" + assert set(out[ACTION]["names"]) == {"j1.pos", "j2.pos"} + assert out[ACTION]["shape"] == (len(out[ACTION]["names"]),) + + +def test_aggregate_ee_action_and_observation_with_videos(): + rp = DataProcessorPipeline([AddActionEEAndJointFeatures(), AddObservationStateFeatures()]) + initial = {"front": (480, 640, 3), "side": (720, 1280, 3)} + + out = aggregate_pipeline_dataset_features( + pipeline=rp, + initial_features={PipelineFeatureType.OBSERVATION: initial, PipelineFeatureType.ACTION: {}}, + use_videos=True, + patterns=["action.ee", OBS_STATE], + ) + + # Action should pack only EE names + assert ACTION in out + assert set(out[ACTION]["names"]) == {"ee.x", "ee.y"} + assert out[ACTION]["dtype"] == "float32" + + # Observation state should pack both ee.x and j1.pos as a vector + assert OBS_STATE in out + assert set(out[OBS_STATE]["names"]) == {"ee.x", "j1.pos"} + assert out[OBS_STATE]["dtype"] == "float32" + + # Cameras from initial_features appear as videos + for cam in ("front", "side"): + key = f"{OBS_IMAGES}.{cam}" + assert key in out + assert out[key]["dtype"] == "video" + assert out[key]["shape"] == initial[cam] + assert out[key]["names"] == ["height", "width", "channels"] + + +def test_aggregate_both_action_types(): + rp = DataProcessorPipeline([AddActionEEAndJointFeatures()]) + out = aggregate_pipeline_dataset_features( + pipeline=rp, + initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: {}}, + use_videos=True, + patterns=["action.ee", "action.j1", "action.j2.pos"], + ) + + assert ACTION in out + expected = {"ee.x", "ee.y", "j1.pos", "j2.pos"} + assert set(out[ACTION]["names"]) == expected + assert out[ACTION]["shape"] == (len(expected),) + + +def test_aggregate_images_when_use_videos_false(): + rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)]) + initial = {"back": (480, 640, 3)} + + out = aggregate_pipeline_dataset_features( + pipeline=rp, + initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial}, + use_videos=False, # expect "image" dtype + patterns=None, + ) + + key = f"{OBS_IMAGES}.back" + key_front = f"{OBS_IMAGES}.front" + assert key not in out + assert key_front not in out + + +def test_aggregate_images_when_use_videos_true(): + rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)]) + initial = {"back": (480, 640, 3)} + + out = aggregate_pipeline_dataset_features( + pipeline=rp, + initial_features={PipelineFeatureType.OBSERVATION: initial, PipelineFeatureType.ACTION: {}}, + use_videos=True, + patterns=None, + ) + + key = f"{OBS_IMAGES}.front" + key_back = f"{OBS_IMAGES}.back" + assert key in out + assert key_back in out + assert out[key]["dtype"] == "video" + assert out[key_back]["dtype"] == "video" + assert out[key_back]["shape"] == initial["back"] + + +def test_initial_camera_not_overridden_by_step_image(): + # Step explicitly sets a different front image shape; initial has another shape. + # aggregate_pipeline_dataset_features should keep the step's value (setdefault behavior on initial cams). + rp = DataProcessorPipeline( + [AddObservationStateFeatures(add_front_image=True, front_image_shape=(240, 320, 3))] + ) + initial = {"front": (480, 640, 3)} # should NOT override the step-provided (240, 320, 3) + + out = aggregate_pipeline_dataset_features( + pipeline=rp, + initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial}, + use_videos=True, + patterns=[f"{OBS_IMAGES}.front"], + ) + + key = f"{OBS_IMAGES}.front" + assert key in out + assert out[key]["shape"] == (240, 320, 3) # from the step, not from initial diff --git a/tests/processor/test_pipeline_from_pretrained_helpers.py b/tests/processor/test_pipeline_from_pretrained_helpers.py new file mode 100644 index 00000000000..89d45cbaded --- /dev/null +++ b/tests/processor/test_pipeline_from_pretrained_helpers.py @@ -0,0 +1,259 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Tests for DataProcessorPipeline.from_pretrained helper methods. + +These tests focus on the individual private methods that were extracted from +the main from_pretrained method to improve modularity and testability. +""" + +import json +import tempfile +from pathlib import Path + +import pytest + +from lerobot.processor.pipeline import DataProcessorPipeline, ProcessorMigrationError + +# Simplified Config Loading Tests + + +def test_load_config_directory(): + """Test loading config from directory.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create a config file + config_file = tmp_path / "processor.json" + test_config = {"name": "TestProcessor", "steps": []} + config_file.write_text(json.dumps(test_config)) + + # Load from directory + loaded_config, base_path = DataProcessorPipeline._load_config(str(tmp_path), "processor.json", {}) + + assert loaded_config == test_config + assert base_path == tmp_path + + +def test_load_config_single_file(): + """Test loading config from a single file path.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create a config file + config_file = tmp_path / "processor.json" + test_config = {"name": "TestProcessor", "steps": []} + config_file.write_text(json.dumps(test_config)) + + # Load using file path directly + loaded_config, base_path = DataProcessorPipeline._load_config( + str(config_file), "any_filename_ignored", {} + ) + + assert loaded_config == test_config + assert base_path == tmp_path + + +def test_load_config_directory_file_not_found(): + """Test directory loading when config file doesn't exist.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Directory exists but no processor.json + with pytest.raises(FileNotFoundError, match="not found in directory"): + DataProcessorPipeline._load_config(str(tmp_path), "processor.json", {}) + + +def test_load_config_directory_with_migration_detection(): + """Test that missing config triggers migration detection.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create old-style config to trigger migration + (tmp_path / "config.json").write_text(json.dumps({"type": "act"})) + + # Try to load processor.json (doesn't exist), should trigger migration + with pytest.raises(ProcessorMigrationError): + DataProcessorPipeline._load_config(str(tmp_path), "processor.json", {}) + + +def test_load_config_nonexistent_path_tries_hub(): + """Test that nonexistent paths try Hub (simplified logic).""" + # This path doesn't exist locally, should try Hub + with pytest.raises(FileNotFoundError, match="on the HuggingFace Hub"): + DataProcessorPipeline._load_config("nonexistent/path", "processor.json", {}) + + +# Config Validation Tests + + +def test_validate_loaded_config_valid_config(): + """Test validation with valid processor config.""" + valid_config = {"name": "TestProcessor", "steps": []} + + # Should not raise any exception + DataProcessorPipeline._validate_loaded_config("any-path", valid_config, "processor.json") + + +def test_validate_loaded_config_invalid_config(): + """Test validation with invalid processor config.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create non-processor config to trigger migration + (tmp_path / "config.json").write_text(json.dumps({"type": "act"})) + + invalid_config = {"type": "act", "hidden_dim": 256} + + with pytest.raises(ProcessorMigrationError): + DataProcessorPipeline._validate_loaded_config(str(tmp_path), invalid_config, "config.json") + + +def test_validate_loaded_config_invalid_config_no_migration(): + """Test validation with invalid config when no migration is detected.""" + # Non-directory path (Hub repo) - no migration detection + invalid_config = {"type": "act", "hidden_dim": 256} + + with pytest.raises(ValueError, match="not a valid processor configuration"): + DataProcessorPipeline._validate_loaded_config("user/repo", invalid_config, "config.json") + + +# Step Class Resolution Tests + + +def test_resolve_step_class_registry_name(): + """Test resolution using registry name.""" + from lerobot.processor.pipeline import ProcessorStep, ProcessorStepRegistry + + # Register a test step + @ProcessorStepRegistry.register("test_step") + class TestStep(ProcessorStep): + def __call__(self, transition): + return transition + + def transform_features(self, features): + return features + + try: + step_entry = {"registry_name": "test_step"} + step_class, step_key = DataProcessorPipeline._resolve_step_class(step_entry) + + assert step_class is TestStep + assert step_key == "test_step" + finally: + ProcessorStepRegistry.unregister("test_step") + + +def test_resolve_step_class_registry_name_not_found(): + """Test resolution with non-existent registry name.""" + step_entry = {"registry_name": "nonexistent_step"} + + with pytest.raises(ImportError, match="Failed to load processor step from registry"): + DataProcessorPipeline._resolve_step_class(step_entry) + + +def test_resolve_step_class_import_path(): + """Test resolution using full import path.""" + # Use a valid existing class (this should work) + step_entry = {"class": "lerobot.processor.pipeline.ProcessorStep"} + + # This should succeed - ProcessorStep can be imported, just not instantiated + step_class, step_key = DataProcessorPipeline._resolve_step_class(step_entry) + + from lerobot.processor.pipeline import ProcessorStep + + assert step_class is ProcessorStep + assert step_key == "ProcessorStep" + + +def test_resolve_step_class_invalid_import_path(): + """Test resolution with invalid import path.""" + step_entry = {"class": "nonexistent.module.ClassName"} + + with pytest.raises(ImportError, match="Failed to load processor step"): + DataProcessorPipeline._resolve_step_class(step_entry) + + +# Override Validation Tests + + +def test_validate_overrides_used_all_used(): + """Test validation when all overrides are used.""" + # Empty set means all overrides were used + remaining_overrides = set() + config = {"steps": [{"class": "SomeStep"}]} + + # Should not raise + DataProcessorPipeline._validate_overrides_used(remaining_overrides, config) + + +def test_validate_overrides_used_some_unused(): + """Test validation when some overrides are unused.""" + remaining_overrides = {"NonExistentStep", "AnotherMissingStep"} + config = { + "steps": [ + {"registry_name": "normalize_step"}, + {"class": "some.module.TransformStep"}, + ] + } + + with pytest.raises(KeyError, match="Override keys.*do not match any step"): + DataProcessorPipeline._validate_overrides_used(remaining_overrides, config) + + +def test_validate_overrides_used_helpful_error_message(): + """Test that error message includes available step keys.""" + remaining_overrides = {"WrongStep"} + config = { + "steps": [ + {"registry_name": "correct_step"}, + {"class": "module.path.CorrectClass"}, + ] + } + + with pytest.raises(KeyError) as exc_info: + DataProcessorPipeline._validate_overrides_used(remaining_overrides, config) + + error_msg = str(exc_info.value) + assert "Available step keys" in error_msg + assert "correct_step" in error_msg + assert "CorrectClass" in error_msg + + +# Integration Tests for Simplified Logic + + +def test_simplified_three_way_loading(): + """Test that the simplified 3-way loading logic works correctly.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Test 1: Directory loading + config_file = tmp_path / "processor.json" + test_config = {"name": "DirectoryTest", "steps": []} + config_file.write_text(json.dumps(test_config)) + + loaded_config, base_path = DataProcessorPipeline._load_config(str(tmp_path), "processor.json", {}) + assert loaded_config["name"] == "DirectoryTest" + assert base_path == tmp_path + + # Test 2: Single file loading + loaded_config, base_path = DataProcessorPipeline._load_config( + str(config_file), "ignored_filename", {} + ) + assert loaded_config["name"] == "DirectoryTest" + assert base_path == tmp_path diff --git a/tests/processor/test_policy_robot_bridge.py b/tests/processor/test_policy_robot_bridge.py new file mode 100644 index 00000000000..6269c508f9d --- /dev/null +++ b/tests/processor/test_policy_robot_bridge.py @@ -0,0 +1,526 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tempfile +from pathlib import Path + +import pytest +import torch + +from lerobot.configs.types import FeatureType, PipelineFeatureType +from lerobot.processor import ( + DataProcessorPipeline, + PolicyActionToRobotActionProcessorStep, + ProcessorStepRegistry, + RobotActionToPolicyActionProcessorStep, +) +from lerobot.processor.converters import identity_transition +from lerobot.utils.constants import ACTION +from tests.conftest import assert_contract_is_typed + + +def test_robot_to_policy_basic_action_conversion(): + """Test basic robot action to policy action conversion.""" + motor_names = ["joint1", "joint2", "joint3"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + robot_action = { + "joint1.pos": 1.0, + "joint2.pos": 2.0, + "joint3.pos": 3.0, + } + + policy_action = processor.action(robot_action) + + assert isinstance(policy_action, torch.Tensor) + assert policy_action.shape == (3,) + torch.testing.assert_close(policy_action, torch.tensor([1.0, 2.0, 3.0])) + + +def test_robot_to_policy_action_conversion_preserves_order(): + """Test that motor names order is preserved in conversion.""" + motor_names = ["gripper", "arm", "wrist"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + robot_action = { + "arm.pos": 10.0, + "gripper.pos": 5.0, + "wrist.pos": 15.0, + } + + policy_action = processor.action(robot_action) + + expected = torch.tensor([5.0, 10.0, 15.0]) + torch.testing.assert_close(policy_action, expected) + + +def test_robot_to_policy_action_conversion_with_floats_and_tensors(): + """Test conversion with mixed float and tensor values.""" + motor_names = ["joint1", "joint2"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + robot_action = { + "joint1.pos": torch.tensor(1.5), + "joint2.pos": 2.5, # Regular float + } + + policy_action = processor.action(robot_action) + + assert isinstance(policy_action, torch.Tensor) + torch.testing.assert_close(policy_action, torch.tensor([1.5, 2.5])) + + +def test_robot_to_policy_action_length_mismatch_error(): + """Test error when robot action length doesn't match motor names.""" + motor_names = ["joint1", "joint2", "joint3"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + # Too few actions + robot_action = {"joint1.pos": 1.0, "joint2.pos": 2.0} + + with pytest.raises(ValueError, match="Action must have 3 elements, got 2"): + processor.action(robot_action) + + robot_action = { + "joint1.pos": 1.0, + "joint2.pos": 2.0, + "joint3.pos": 3.0, + "extra.pos": 4.0, + } + + with pytest.raises(ValueError, match="Action must have 3 elements, got 4"): + processor.action(robot_action) + + +def test_robot_to_policy_missing_motor_key_error(): + """Test error when robot action is missing expected motor keys.""" + motor_names = ["joint1", "joint2"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + robot_action = { + "joint1.pos": 1.0, + "wrong_key.pos": 2.0, + } + + with pytest.raises(KeyError): + processor.action(robot_action) + + +def test_robot_to_policy_transform_features(): + """Test feature transformation for robot to policy action processor.""" + motor_names = ["joint1", "joint2", "joint3"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + features = { + PipelineFeatureType.ACTION: { + "joint1.pos": {"type": FeatureType.ACTION, "shape": (1,)}, + "joint2.pos": {"type": FeatureType.ACTION, "shape": (1,)}, + "joint3.pos": {"type": FeatureType.ACTION, "shape": (1,)}, + "other_data": {"type": FeatureType.ENV, "shape": (1,)}, + } + } + + transformed = processor.transform_features(features) + + assert ACTION in transformed[PipelineFeatureType.ACTION] + action_feature = transformed[PipelineFeatureType.ACTION][ACTION] + assert action_feature.type == FeatureType.ACTION + assert action_feature.shape == (3,) + + assert "joint1.pos" in transformed[PipelineFeatureType.ACTION] + assert "joint2.pos" in transformed[PipelineFeatureType.ACTION] + assert "joint3.pos" in transformed[PipelineFeatureType.ACTION] + + assert "other_data" in transformed[PipelineFeatureType.ACTION] + + +def test_robot_to_policy_get_config(): + """Test configuration serialization.""" + motor_names = ["motor1", "motor2"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + config = processor.get_config() + assert config == {"motor_names": motor_names} + + +def test_robot_to_policy_state_dict(): + """Test state dict operations.""" + processor = RobotActionToPolicyActionProcessorStep(motor_names=["joint1"]) + + state = processor.state_dict() + assert state == {} + + processor.load_state_dict({}) + + +def test_robot_to_policy_single_motor(): + """Test with single motor.""" + processor = RobotActionToPolicyActionProcessorStep(motor_names=["single_joint"]) + + robot_action = {"single_joint.pos": 42.0} + policy_action = processor.action(robot_action) + + assert policy_action.shape == (1,) + torch.testing.assert_close(policy_action, torch.tensor([42.0])) + + +def test_policy_to_robot_basic_action_conversion(): + """Test basic policy action to robot action conversion.""" + motor_names = ["joint1", "joint2", "joint3"] + processor = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + policy_action = torch.tensor([1.0, 2.0, 3.0]) + robot_action = processor.action(policy_action) + + assert isinstance(robot_action, dict) + assert len(robot_action) == 3 + + expected = { + "joint1.pos": 1.0, + "joint2.pos": 2.0, + "joint3.pos": 3.0, + } + + for key, expected_value in expected.items(): + assert key in robot_action + actual_value = robot_action[key] + if isinstance(actual_value, torch.Tensor): + actual_value = actual_value.item() + assert actual_value == pytest.approx(expected_value) + + +def test_policy_to_robot_action_conversion_preserves_order(): + """Test that motor names order corresponds to tensor indices.""" + motor_names = ["gripper", "arm", "wrist"] + processor = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + policy_action = torch.tensor([5.0, 10.0, 15.0]) + robot_action = processor.action(policy_action) + + assert robot_action["gripper.pos"] == pytest.approx(5.0) + assert robot_action["arm.pos"] == pytest.approx(10.0) + assert robot_action["wrist.pos"] == pytest.approx(15.0) + + +def test_policy_to_robot_action_conversion_with_numpy_input(): + """Test conversion with numpy array input.""" + import numpy as np + + motor_names = ["joint1", "joint2"] + processor = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + policy_action = np.array([1.5, 2.5]) + robot_action = processor.action(policy_action) + + assert robot_action["joint1.pos"] == pytest.approx(1.5) + assert robot_action["joint2.pos"] == pytest.approx(2.5) + + +def test_policy_to_robot_action_length_mismatch_error(): + """Test error when policy action length doesn't match motor names.""" + motor_names = ["joint1", "joint2", "joint3"] + processor = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + policy_action = torch.tensor([1.0, 2.0]) + + with pytest.raises(ValueError, match="Action must have 3 elements, got 2"): + processor.action(policy_action) + + policy_action = torch.tensor([1.0, 2.0, 3.0, 4.0]) + + with pytest.raises(ValueError, match="Action must have 3 elements, got 4"): + processor.action(policy_action) + + +def test_policy_to_robot_transform_features(): + """Test feature transformation for policy to robot action processor.""" + motor_names = ["joint1", "joint2"] + processor = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + features = { + PipelineFeatureType.ACTION: { + ACTION: {"type": FeatureType.ACTION, "shape": (2,)}, + "other_data": {"type": FeatureType.ENV, "shape": (1,)}, + } + } + + transformed = processor.transform_features(features) + + assert "joint1.pos" in transformed[PipelineFeatureType.ACTION] + assert "joint2.pos" in transformed[PipelineFeatureType.ACTION] + + for motor in motor_names: + motor_feature = transformed[PipelineFeatureType.ACTION][f"{motor}.pos"] + assert motor_feature.type == FeatureType.ACTION + assert motor_feature.shape == (1,) + + assert ACTION in transformed[PipelineFeatureType.ACTION] + + assert "other_data" in transformed[PipelineFeatureType.ACTION] + + +def test_policy_to_robot_get_config(): + """Test configuration serialization.""" + motor_names = ["motor1", "motor2"] + processor = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + config = processor.get_config() + assert config == {"motor_names": motor_names} + + +def test_policy_to_robot_state_dict(): + """Test state dict operations.""" + processor = PolicyActionToRobotActionProcessorStep(motor_names=["joint1"]) + + state = processor.state_dict() + assert state == {} + + processor.load_state_dict({}) + + +def test_policy_to_robot_single_motor(): + """Test with single motor.""" + processor = PolicyActionToRobotActionProcessorStep(motor_names=["single_joint"]) + + policy_action = torch.tensor([42.0]) + robot_action = processor.action(policy_action) + + assert len(robot_action) == 1 + assert robot_action["single_joint.pos"] == pytest.approx(42.0) + + +def test_robot_to_policy_registry(): + """Test RobotActionToPolicyActionProcessorStep registry.""" + assert "robot_action_to_policy_action_processor" in ProcessorStepRegistry.list() + + retrieved_class = ProcessorStepRegistry.get("robot_action_to_policy_action_processor") + assert retrieved_class is RobotActionToPolicyActionProcessorStep + + instance = retrieved_class(motor_names=["test"]) + assert isinstance(instance, RobotActionToPolicyActionProcessorStep) + assert instance.motor_names == ["test"] + + +def test_policy_to_robot_registry(): + """Test PolicyActionToRobotActionProcessorStep registry.""" + assert "policy_action_to_robot_action_processor" in ProcessorStepRegistry.list() + + retrieved_class = ProcessorStepRegistry.get("policy_action_to_robot_action_processor") + assert retrieved_class is PolicyActionToRobotActionProcessorStep + + instance = retrieved_class(motor_names=["test"]) + assert isinstance(instance, PolicyActionToRobotActionProcessorStep) + assert instance.motor_names == ["test"] + + +def test_save_and_load_robot_to_policy(): + """Test saving and loading RobotActionToPolicyActionProcessorStep.""" + motor_names = ["joint1", "joint2", "joint3"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + pipeline = DataProcessorPipeline([processor], name="TestRobotToPolicy") + + with tempfile.TemporaryDirectory() as tmp_dir: + # Save pipeline + pipeline.save_pretrained(tmp_dir) + + # Check config file exists + config_path = Path(tmp_dir) / "testrobottopolicy.json" + assert config_path.exists() + + # Load pipeline + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + "testrobottopolicy.json", + to_transition=identity_transition, + to_output=identity_transition, + ) + + assert loaded_pipeline.name == "TestRobotToPolicy" + assert len(loaded_pipeline) == 1 + + # Check loaded processor + loaded_processor = loaded_pipeline.steps[0] + assert isinstance(loaded_processor, RobotActionToPolicyActionProcessorStep) + assert loaded_processor.motor_names == motor_names + + # Test functionality after loading + robot_action = {"joint1.pos": 1.0, "joint2.pos": 2.0, "joint3.pos": 3.0} + policy_action = loaded_processor.action(robot_action) + torch.testing.assert_close(policy_action, torch.tensor([1.0, 2.0, 3.0])) + + +def test_save_and_load_policy_to_robot(): + """Test saving and loading PolicyActionToRobotActionProcessorStep.""" + motor_names = ["motor_a", "motor_b"] + processor = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + pipeline = DataProcessorPipeline([processor], name="TestPolicyToRobot") + + with tempfile.TemporaryDirectory() as tmp_dir: + # Save pipeline + pipeline.save_pretrained(tmp_dir) + + # Load pipeline + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + "testpolicytorobot.json", + to_transition=identity_transition, + to_output=identity_transition, + ) + + loaded_processor = loaded_pipeline.steps[0] + assert isinstance(loaded_processor, PolicyActionToRobotActionProcessorStep) + assert loaded_processor.motor_names == motor_names + + policy_action = torch.tensor([10.0, 20.0]) + robot_action = loaded_processor.action(policy_action) + assert robot_action["motor_a.pos"] == pytest.approx(10.0) + assert robot_action["motor_b.pos"] == pytest.approx(20.0) + + +# Integration and chaining tests + + +def test_round_trip_conversion(): + """Test that robot->policy->robot conversion preserves values.""" + motor_names = ["joint1", "joint2", "joint3"] + robot_to_policy = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + policy_to_robot = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + original_robot_action = { + "joint1.pos": 1.5, + "joint2.pos": -2.3, + "joint3.pos": 0.7, + } + + policy_action = robot_to_policy.action(original_robot_action) + final_robot_action = policy_to_robot.action(policy_action) + + for key in original_robot_action: + original_val = original_robot_action[key] + final_val = final_robot_action[key] + if isinstance(final_val, torch.Tensor): + final_val = final_val.item() + assert final_val == pytest.approx(original_val, abs=1e-6) + + +def test_chained_processors_in_pipeline(): + """Test both processors chained in a pipeline.""" + motor_names = ["joint1", "joint2"] + robot_to_policy = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + policy_to_robot = PolicyActionToRobotActionProcessorStep(motor_names=motor_names) + + pipeline = DataProcessorPipeline( + [robot_to_policy, policy_to_robot], + to_transition=identity_transition, + to_output=identity_transition, + ) + + assert len(pipeline.steps) == 2 + assert isinstance(pipeline.steps[0], RobotActionToPolicyActionProcessorStep) + assert isinstance(pipeline.steps[1], PolicyActionToRobotActionProcessorStep) + + +def test_robot_to_policy_features_contract(policy_feature_factory): + """Test feature transformation maintains proper typing contract.""" + processor = RobotActionToPolicyActionProcessorStep(motor_names=["j1", "j2"]) + features = { + PipelineFeatureType.ACTION: { + "j1.pos": policy_feature_factory(FeatureType.ACTION, (1,)), + "j2.pos": policy_feature_factory(FeatureType.ACTION, (1,)), + "other": policy_feature_factory(FeatureType.ENV, (3,)), + } + } + + out = processor.transform_features(features.copy()) + + assert_contract_is_typed(out) + + assert ACTION in out[PipelineFeatureType.ACTION] + action_feature = out[PipelineFeatureType.ACTION][ACTION] + assert action_feature.type == FeatureType.ACTION + assert action_feature.shape == (2,) + + +def test_policy_to_robot_features_contract(policy_feature_factory): + """Test feature transformation maintains proper typing contract.""" + processor = PolicyActionToRobotActionProcessorStep(motor_names=["m1", "m2", "m3"]) + features = { + PipelineFeatureType.ACTION: { + ACTION: policy_feature_factory(FeatureType.ACTION, (3,)), + "other": policy_feature_factory(FeatureType.ENV, (1,)), + } + } + + out = processor.transform_features(features.copy()) + + assert_contract_is_typed(out) + + for motor in ["m1", "m2", "m3"]: + key = f"{motor}.pos" + assert key in out[PipelineFeatureType.ACTION] + motor_feature = out[PipelineFeatureType.ACTION][key] + assert motor_feature.type == FeatureType.ACTION + assert motor_feature.shape == (1,) + + +def test_empty_motor_names_list(): + """Test behavior with empty motor names list.""" + processor = RobotActionToPolicyActionProcessorStep(motor_names=[]) + + robot_action = {} + policy_action = processor.action(robot_action) + + assert isinstance(policy_action, torch.Tensor) + assert policy_action.shape == (0,) + + +def test_empty_motor_names_list_policy_to_robot(): + """Test PolicyActionToRobotActionProcessorStep with empty motor names.""" + processor = PolicyActionToRobotActionProcessorStep(motor_names=[]) + + policy_action = torch.tensor([]) + robot_action = processor.action(policy_action) + + assert isinstance(robot_action, dict) + assert len(robot_action) == 0 + + +def test_very_long_motor_names(): + """Test with many motor names.""" + motor_names = [f"joint_{i}" for i in range(100)] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + robot_action = {f"joint_{i}.pos": float(i) for i in range(100)} + policy_action = processor.action(robot_action) + + assert policy_action.shape == (100,) + expected = torch.tensor([float(i) for i in range(100)]) + torch.testing.assert_close(policy_action, expected) + + +def test_special_characters_in_motor_names(): + """Test with special characters in motor names.""" + motor_names = ["motor-1", "motor_2", "motor.3"] + processor = RobotActionToPolicyActionProcessorStep(motor_names=motor_names) + + robot_action = { + "motor-1.pos": 1.0, + "motor_2.pos": 2.0, + "motor.3.pos": 3.0, + } + + policy_action = processor.action(robot_action) + torch.testing.assert_close(policy_action, torch.tensor([1.0, 2.0, 3.0])) diff --git a/tests/processor/test_rename_processor.py b/tests/processor/test_rename_processor.py index 229d57f9f25..efb9f932893 100644 --- a/tests/processor/test_rename_processor.py +++ b/tests/processor/test_rename_processor.py @@ -19,33 +19,26 @@ import numpy as np import torch -from lerobot.configs.types import FeatureType -from lerobot.processor import ProcessorStepRegistry, RenameProcessor, RobotProcessor, TransitionKey +from lerobot.configs.types import FeatureType, PipelineFeatureType +from lerobot.processor import ( + DataProcessorPipeline, + ProcessorStepRegistry, + RenameObservationsProcessorStep, + TransitionKey, +) +from lerobot.processor.converters import create_transition, identity_transition +from lerobot.processor.rename_processor import rename_stats +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_IMAGES, OBS_STATE from tests.conftest import assert_contract_is_typed -def create_transition( - observation=None, action=None, reward=None, done=None, truncated=None, info=None, complementary_data=None -): - """Helper to create an EnvTransition dictionary.""" - return { - TransitionKey.OBSERVATION: observation, - TransitionKey.ACTION: action, - TransitionKey.REWARD: reward, - TransitionKey.DONE: done, - TransitionKey.TRUNCATED: truncated, - TransitionKey.INFO: info, - TransitionKey.COMPLEMENTARY_DATA: complementary_data, - } - - def test_basic_renaming(): """Test basic key renaming functionality.""" rename_map = { "old_key1": "new_key1", "old_key2": "new_key2", } - processor = RenameProcessor(rename_map=rename_map) + processor = RenameObservationsProcessorStep(rename_map=rename_map) observation = { "old_key1": torch.tensor([1.0, 2.0]), @@ -73,7 +66,7 @@ def test_basic_renaming(): def test_empty_rename_map(): """Test processor with empty rename map (should pass through unchanged).""" - processor = RenameProcessor(rename_map={}) + processor = RenameObservationsProcessorStep(rename_map={}) observation = { "key1": torch.tensor([1.0]), @@ -92,9 +85,9 @@ def test_empty_rename_map(): def test_none_observation(): """Test processor with None observation.""" - processor = RenameProcessor(rename_map={"old": "new"}) + processor = RenameObservationsProcessorStep(rename_map={"old": "new"}) - transition = create_transition() + transition = create_transition(observation={}) result = processor(transition) # Should return transition unchanged @@ -107,7 +100,7 @@ def test_overlapping_rename(): "a": "b", "b": "c", # This creates a potential conflict } - processor = RenameProcessor(rename_map=rename_map) + processor = RenameObservationsProcessorStep(rename_map=rename_map) observation = { "a": 1, @@ -129,13 +122,13 @@ def test_overlapping_rename(): def test_partial_rename(): """Test renaming only some keys.""" rename_map = { - "observation.state": "observation.proprio_state", - "pixels": "observation.image", + OBS_STATE: "observation.proprio_state", + "pixels": OBS_IMAGE, } - processor = RenameProcessor(rename_map=rename_map) + processor = RenameObservationsProcessorStep(rename_map=rename_map) observation = { - "observation.state": torch.randn(10), + OBS_STATE: torch.randn(10), "pixels": np.random.randint(0, 256, (64, 64, 3), dtype=np.uint8), "reward": 1.0, "info": {"episode": 1}, @@ -147,8 +140,8 @@ def test_partial_rename(): # Check renamed keys assert "observation.proprio_state" in processed_obs - assert "observation.image" in processed_obs - assert "observation.state" not in processed_obs + assert OBS_IMAGE in processed_obs + assert OBS_STATE not in processed_obs assert "pixels" not in processed_obs # Check unchanged keys @@ -162,15 +155,15 @@ def test_get_config(): "old1": "new1", "old2": "new2", } - processor = RenameProcessor(rename_map=rename_map) + processor = RenameObservationsProcessorStep(rename_map=rename_map) config = processor.get_config() assert config == {"rename_map": rename_map} def test_state_dict(): - """Test state dict (should be empty for RenameProcessor).""" - processor = RenameProcessor(rename_map={"old": "new"}) + """Test state dict (should be empty for RenameProcessorStep).""" + processor = RenameObservationsProcessorStep(rename_map={"old": "new"}) state = processor.state_dict() assert state == {} @@ -182,12 +175,14 @@ def test_state_dict(): def test_integration_with_robot_processor(): """Test integration with RobotProcessor pipeline.""" rename_map = { - "agent_pos": "observation.state", - "pixels": "observation.image", + "agent_pos": OBS_STATE, + "pixels": OBS_IMAGE, } - rename_processor = RenameProcessor(rename_map=rename_map) + rename_processor = RenameObservationsProcessorStep(rename_map=rename_map) - pipeline = RobotProcessor([rename_processor]) + pipeline = DataProcessorPipeline( + [rename_processor], to_transition=identity_transition, to_output=identity_transition + ) observation = { "agent_pos": np.array([1.0, 2.0, 3.0]), @@ -202,8 +197,8 @@ def test_integration_with_robot_processor(): processed_obs = result[TransitionKey.OBSERVATION] # Check renaming worked through pipeline - assert "observation.state" in processed_obs - assert "observation.image" in processed_obs + assert OBS_STATE in processed_obs + assert OBS_IMAGE in processed_obs assert "agent_pos" not in processed_obs assert "pixels" not in processed_obs assert processed_obs["other_data"] == "preserve_me" @@ -216,33 +211,40 @@ def test_integration_with_robot_processor(): def test_save_and_load_pretrained(): """Test saving and loading processor with RobotProcessor.""" rename_map = { - "old_state": "observation.state", - "old_image": "observation.image", + "old_state": OBS_STATE, + "old_image": OBS_IMAGE, } - processor = RenameProcessor(rename_map=rename_map) - pipeline = RobotProcessor([processor], name="TestRenameProcessor") + processor = RenameObservationsProcessorStep(rename_map=rename_map) + pipeline = DataProcessorPipeline([processor], name="TestRenameProcessorStep") with tempfile.TemporaryDirectory() as tmp_dir: # Save pipeline pipeline.save_pretrained(tmp_dir) # Check files were created - config_path = Path(tmp_dir) / "testrenameprocessor.json" # Based on name="TestRenameProcessor" + config_path = ( + Path(tmp_dir) / "testrenameprocessorstep.json" + ) # Based on name="TestRenameProcessorStep" assert config_path.exists() - # No state files should be created for RenameProcessor + # No state files should be created for RenameProcessorStep state_files = list(Path(tmp_dir).glob("*.safetensors")) assert len(state_files) == 0 # Load pipeline - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir) - - assert loaded_pipeline.name == "TestRenameProcessor" + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, + config_filename="testrenameprocessorstep.json", + to_transition=identity_transition, + to_output=identity_transition, + ) + + assert loaded_pipeline.name == "TestRenameProcessorStep" assert len(loaded_pipeline) == 1 # Check that loaded processor works correctly loaded_processor = loaded_pipeline.steps[0] - assert isinstance(loaded_processor, RenameProcessor) + assert isinstance(loaded_processor, RenameObservationsProcessorStep) assert loaded_processor.rename_map == rename_map # Test functionality after loading @@ -252,31 +254,33 @@ def test_save_and_load_pretrained(): result = loaded_pipeline(transition) processed_obs = result[TransitionKey.OBSERVATION] - assert "observation.state" in processed_obs - assert "observation.image" in processed_obs - assert processed_obs["observation.state"] == [1, 2, 3] - assert processed_obs["observation.image"] == "image_data" + assert OBS_STATE in processed_obs + assert OBS_IMAGE in processed_obs + assert processed_obs[OBS_STATE] == [1, 2, 3] + assert processed_obs[OBS_IMAGE] == "image_data" def test_registry_functionality(): - """Test that RenameProcessor is properly registered.""" + """Test that RenameProcessorStep is properly registered.""" # Check that it's registered - assert "rename_processor" in ProcessorStepRegistry.list() + assert "rename_observations_processor" in ProcessorStepRegistry.list() # Get from registry - retrieved_class = ProcessorStepRegistry.get("rename_processor") - assert retrieved_class is RenameProcessor + retrieved_class = ProcessorStepRegistry.get("rename_observations_processor") + assert retrieved_class is RenameObservationsProcessorStep # Create instance from registry instance = retrieved_class(rename_map={"old": "new"}) - assert isinstance(instance, RenameProcessor) + assert isinstance(instance, RenameObservationsProcessorStep) assert instance.rename_map == {"old": "new"} def test_registry_based_save_load(): """Test save/load using registry name instead of module path.""" - processor = RenameProcessor(rename_map={"key1": "renamed_key1"}) - pipeline = RobotProcessor([processor]) + processor = RenameObservationsProcessorStep(rename_map={"key1": "renamed_key1"}) + pipeline = DataProcessorPipeline( + [processor], to_transition=identity_transition, to_output=identity_transition + ) with tempfile.TemporaryDirectory() as tmp_dir: # Save and load @@ -285,24 +289,26 @@ def test_registry_based_save_load(): # Verify config uses registry name import json - with open(Path(tmp_dir) / "robotprocessor.json") as f: # Default name is "RobotProcessor" + with open(Path(tmp_dir) / "dataprocessorpipeline.json") as f: # Default name is "RobotProcessor" config = json.load(f) assert "registry_name" in config["steps"][0] - assert config["steps"][0]["registry_name"] == "rename_processor" + assert config["steps"][0]["registry_name"] == "rename_observations_processor" assert "class" not in config["steps"][0] # Should use registry, not module path # Load should work - loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir) + loaded_pipeline = DataProcessorPipeline.from_pretrained( + tmp_dir, config_filename="dataprocessorpipeline.json" + ) loaded_processor = loaded_pipeline.steps[0] - assert isinstance(loaded_processor, RenameProcessor) + assert isinstance(loaded_processor, RenameObservationsProcessorStep) assert loaded_processor.rename_map == {"key1": "renamed_key1"} def test_chained_rename_processors(): - """Test multiple RenameProcessors in a pipeline.""" + """Test multiple RenameProcessorSteps in a pipeline.""" # First processor: rename raw keys to intermediate format - processor1 = RenameProcessor( + processor1 = RenameObservationsProcessorStep( rename_map={ "pos": "agent_position", "img": "camera_image", @@ -310,14 +316,16 @@ def test_chained_rename_processors(): ) # Second processor: rename to final format - processor2 = RenameProcessor( + processor2 = RenameObservationsProcessorStep( rename_map={ - "agent_position": "observation.state", - "camera_image": "observation.image", + "agent_position": OBS_STATE, + "camera_image": OBS_IMAGE, } ) - pipeline = RobotProcessor([processor1, processor2]) + pipeline = DataProcessorPipeline( + [processor1, processor2], to_transition=identity_transition, to_output=identity_transition + ) observation = { "pos": np.array([1.0, 2.0]), @@ -335,8 +343,8 @@ def test_chained_rename_processors(): # After second processor final_obs = results[2][TransitionKey.OBSERVATION] - assert "observation.state" in final_obs - assert "observation.image" in final_obs + assert OBS_STATE in final_obs + assert OBS_IMAGE in final_obs assert final_obs["extra"] == "keep_me" # Original keys should be gone @@ -349,15 +357,15 @@ def test_chained_rename_processors(): def test_nested_observation_rename(): """Test renaming with nested observation structures.""" rename_map = { - "observation.images.left": "observation.camera.left_view", - "observation.images.right": "observation.camera.right_view", + f"{OBS_IMAGES}.left": "observation.camera.left_view", + f"{OBS_IMAGES}.right": "observation.camera.right_view", "observation.proprio": "observation.proprioception", } - processor = RenameProcessor(rename_map=rename_map) + processor = RenameObservationsProcessorStep(rename_map=rename_map) observation = { - "observation.images.left": torch.randn(3, 64, 64), - "observation.images.right": torch.randn(3, 64, 64), + f"{OBS_IMAGES}.left": torch.randn(3, 64, 64), + f"{OBS_IMAGES}.right": torch.randn(3, 64, 64), "observation.proprio": torch.randn(7), "observation.gripper": torch.tensor([0.0]), # Not renamed } @@ -375,15 +383,15 @@ def test_nested_observation_rename(): assert "observation.gripper" in processed_obs # Check old keys removed - assert "observation.images.left" not in processed_obs - assert "observation.images.right" not in processed_obs + assert f"{OBS_IMAGES}.left" not in processed_obs + assert f"{OBS_IMAGES}.right" not in processed_obs assert "observation.proprio" not in processed_obs def test_value_types_preserved(): """Test that various value types are preserved during renaming.""" rename_map = {"old_tensor": "new_tensor", "old_array": "new_array", "old_scalar": "new_scalar"} - processor = RenameProcessor(rename_map=rename_map) + processor = RenameObservationsProcessorStep(rename_map=rename_map) tensor_value = torch.randn(3, 3) array_value = np.random.rand(2, 2) @@ -410,58 +418,81 @@ def test_value_types_preserved(): assert processed_obs["old_list"] == [1, 2, 3] -def test_feature_contract_basic_renaming(policy_feature_factory): - processor = RenameProcessor(rename_map={"a": "x", "b": "y"}) +def test_features_basic_renaming(policy_feature_factory): + processor = RenameObservationsProcessorStep(rename_map={"a": "x", "b": "y"}) features = { - "a": policy_feature_factory(FeatureType.STATE, (2,)), - "b": policy_feature_factory(FeatureType.ACTION, (3,)), - "c": policy_feature_factory(FeatureType.ENV, (1,)), + PipelineFeatureType.OBSERVATION: { + "a": policy_feature_factory(FeatureType.VISUAL, (2,)), + "b": policy_feature_factory(FeatureType.VISUAL, (3,)), + "c": policy_feature_factory(FeatureType.VISUAL, (1,)), + }, } - out = processor.feature_contract(features.copy()) + out = processor.transform_features(features.copy()) # Values preserved and typed - assert out["x"] == features["a"] - assert out["y"] == features["b"] - assert out["c"] == features["c"] + assert out[PipelineFeatureType.OBSERVATION]["x"] == features[PipelineFeatureType.OBSERVATION]["a"] + assert out[PipelineFeatureType.OBSERVATION]["y"] == features[PipelineFeatureType.OBSERVATION]["b"] + assert out[PipelineFeatureType.OBSERVATION]["c"] == features[PipelineFeatureType.OBSERVATION]["c"] assert_contract_is_typed(out) # Input not mutated - assert set(features) == {"a", "b", "c"} + assert set(features[PipelineFeatureType.OBSERVATION]) == {"a", "b", "c"} -def test_feature_contract_overlapping_keys(policy_feature_factory): +def test_features_overlapping_keys(policy_feature_factory): # Overlapping renames: both 'a' and 'b' exist. 'a'->'b', 'b'->'c' - processor = RenameProcessor(rename_map={"a": "b", "b": "c"}) + processor = RenameObservationsProcessorStep(rename_map={"a": "b", "b": "c"}) features = { - "a": policy_feature_factory(FeatureType.STATE, (1,)), - "b": policy_feature_factory(FeatureType.STATE, (2,)), + PipelineFeatureType.OBSERVATION: { + "a": policy_feature_factory(FeatureType.VISUAL, (1,)), + "b": policy_feature_factory(FeatureType.VISUAL, (2,)), + }, } - out = processor.feature_contract(features) - - assert set(out) == {"b", "c"} - assert out["b"] == features["a"] # 'a' renamed to'b' - assert out["c"] == features["b"] # 'b' renamed to 'c' + out = processor.transform_features(features) + + assert set(out[PipelineFeatureType.OBSERVATION]) == {"b", "c"} + assert ( + out[PipelineFeatureType.OBSERVATION]["b"] == features[PipelineFeatureType.OBSERVATION]["a"] + ) # 'a' renamed to'b' + assert ( + out[PipelineFeatureType.OBSERVATION]["c"] == features[PipelineFeatureType.OBSERVATION]["b"] + ) # 'b' renamed to 'c' assert_contract_is_typed(out) -def test_feature_contract_chained_processors(policy_feature_factory): +def test_features_chained_processors(policy_feature_factory): # Chain two rename processors at the contract level - processor1 = RenameProcessor(rename_map={"pos": "agent_position", "img": "camera_image"}) - processor2 = RenameProcessor( - rename_map={"agent_position": "observation.state", "camera_image": "observation.image"} + processor1 = RenameObservationsProcessorStep(rename_map={"pos": "agent_position", "img": "camera_image"}) + processor2 = RenameObservationsProcessorStep( + rename_map={"agent_position": OBS_STATE, "camera_image": OBS_IMAGE} ) - pipeline = RobotProcessor([processor1, processor2]) + pipeline = DataProcessorPipeline([processor1, processor2]) spec = { - "pos": policy_feature_factory(FeatureType.STATE, (7,)), - "img": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), - "extra": policy_feature_factory(FeatureType.ENV, (1,)), + PipelineFeatureType.OBSERVATION: { + "pos": policy_feature_factory(FeatureType.VISUAL, (7,)), + "img": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)), + "extra": policy_feature_factory(FeatureType.VISUAL, (1,)), + }, } - out = pipeline.feature_contract(initial_features=spec) + out = pipeline.transform_features(initial_features=spec) - assert set(out) == {"observation.state", "observation.image", "extra"} - assert out["observation.state"] == spec["pos"] - assert out["observation.image"] == spec["img"] - assert out["extra"] == spec["extra"] + assert set(out[PipelineFeatureType.OBSERVATION]) == {OBS_STATE, OBS_IMAGE, "extra"} + assert out[PipelineFeatureType.OBSERVATION][OBS_STATE] == spec[PipelineFeatureType.OBSERVATION]["pos"] + assert out[PipelineFeatureType.OBSERVATION][OBS_IMAGE] == spec[PipelineFeatureType.OBSERVATION]["img"] + assert out[PipelineFeatureType.OBSERVATION]["extra"] == spec[PipelineFeatureType.OBSERVATION]["extra"] assert_contract_is_typed(out) + + +def test_rename_stats_basic(): + orig = { + OBS_STATE: {"mean": np.array([0.0]), "std": np.array([1.0])}, + ACTION: {"mean": np.array([0.0])}, + } + mapping = {OBS_STATE: "observation.robot_state"} + renamed = rename_stats(orig, mapping) + assert "observation.robot_state" in renamed and OBS_STATE not in renamed + # Ensure deep copy: mutate original and verify renamed unaffected + orig[OBS_STATE]["mean"][0] = 42.0 + assert renamed["observation.robot_state"]["mean"][0] != 42.0 diff --git a/tests/processor/test_sac_processor.py b/tests/processor/test_sac_processor.py new file mode 100644 index 00000000000..a1a4b285d5e --- /dev/null +++ b/tests/processor/test_sac_processor.py @@ -0,0 +1,414 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for SAC policy processor.""" + +import tempfile + +import pytest +import torch + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.policies.sac.configuration_sac import SACConfig +from lerobot.policies.sac.processor_sac import make_sac_pre_post_processors +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DataProcessorPipeline, + DeviceProcessorStep, + NormalizerProcessorStep, + RenameObservationsProcessorStep, + TransitionKey, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import create_transition, transition_to_batch +from lerobot.utils.constants import ACTION, OBS_STATE + + +def create_default_config(): + """Create a default SAC configuration for testing.""" + config = SACConfig() + config.input_features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)), + } + config.output_features = { + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(5,)), + } + config.normalization_mapping = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.ACTION: NormalizationMode.MIN_MAX, + } + config.device = "cpu" + return config + + +def create_default_stats(): + """Create default dataset statistics for testing.""" + return { + OBS_STATE: {"mean": torch.zeros(10), "std": torch.ones(10)}, + ACTION: {"min": torch.full((5,), -1.0), "max": torch.ones(5)}, + } + + +def test_make_sac_processor_basic(): + """Test basic creation of SAC processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Check processor names + assert preprocessor.name == "policy_preprocessor" + assert postprocessor.name == "policy_postprocessor" + + # Check steps in preprocessor + assert len(preprocessor.steps) == 4 + assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep) + assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep) + assert isinstance(preprocessor.steps[2], DeviceProcessorStep) + assert isinstance(preprocessor.steps[3], NormalizerProcessorStep) + + # Check steps in postprocessor + assert len(postprocessor.steps) == 2 + assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep) + assert isinstance(postprocessor.steps[1], DeviceProcessorStep) + + +def test_sac_processor_normalization_modes(): + """Test that SAC processor correctly handles different normalization modes.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Create test data + observation = {OBS_STATE: torch.randn(10) * 2} # Larger values to test normalization + action = torch.rand(5) * 2 - 1 # Range [-1, 1] + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data is normalized and batched + # State should be mean-std normalized + # Action should be min-max normalized to [-1, 1] + assert processed[OBS_STATE].shape == (1, 10) + assert processed[TransitionKey.ACTION.value].shape == (1, 5) + + # Process action through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is unnormalized (but still batched) + assert postprocessed.shape == (1, 5) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_sac_processor_cuda(): + """Test SAC processor with CUDA device.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Create CPU data + observation = {OBS_STATE: torch.randn(10)} + action = torch.randn(5) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data is on CUDA + assert processed[OBS_STATE].device.type == "cuda" + assert processed[TransitionKey.ACTION.value].device.type == "cuda" + + # Process through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is back on CPU + assert postprocessed.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_sac_processor_accelerate_scenario(): + """Test SAC processor in simulated Accelerate scenario.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Simulate Accelerate: data already on GPU + device = torch.device("cuda:0") + observation = {OBS_STATE: torch.randn(10).to(device)} + action = torch.randn(5).to(device) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data stays on same GPU + assert processed[OBS_STATE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_sac_processor_multi_gpu(): + """Test SAC processor with multi-GPU setup.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Simulate data on different GPU + device = torch.device("cuda:1") + observation = {OBS_STATE: torch.randn(10).to(device)} + action = torch.randn(5).to(device) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data stays on cuda:1 + assert processed[OBS_STATE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +def test_sac_processor_without_stats(): + """Test SAC processor creation without dataset statistics.""" + config = create_default_config() + + preprocessor, postprocessor = make_sac_pre_post_processors(config, dataset_stats=None) + + # Should still create processors + assert preprocessor is not None + assert postprocessor is not None + + # Process should still work + observation = {OBS_STATE: torch.randn(10)} + action = torch.randn(5) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed is not None + + +def test_sac_processor_save_and_load(): + """Test saving and loading SAC processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + # Save preprocessor + preprocessor.save_pretrained(tmpdir) + + # Load preprocessor + loaded_preprocessor = DataProcessorPipeline.from_pretrained( + tmpdir, config_filename="policy_preprocessor.json" + ) + + # Test that loaded processor works + observation = {OBS_STATE: torch.randn(10)} + action = torch.randn(5) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = loaded_preprocessor(batch) + assert processed[OBS_STATE].shape == (1, 10) + assert processed[TransitionKey.ACTION.value].shape == (1, 5) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_sac_processor_mixed_precision(): + """Test SAC processor with mixed precision.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + # Create processor + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Replace DeviceProcessorStep with one that uses float16 + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="float16")) + elif isinstance(step, NormalizerProcessorStep): + # Update normalizer to use the same device as the device processor + norm_step = step # Now type checker knows this is NormalizerProcessorStep + modified_steps.append( + NormalizerProcessorStep( + features=norm_step.features, + norm_map=norm_step.norm_map, + stats=norm_step.stats, + device=config.device, + dtype=torch.float16, # Match the float16 dtype + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Create test data + observation = {OBS_STATE: torch.randn(10, dtype=torch.float32)} + action = torch.randn(5, dtype=torch.float32) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that data is converted to float16 + assert processed[OBS_STATE].dtype == torch.float16 + assert processed[TransitionKey.ACTION.value].dtype == torch.float16 + + +def test_sac_processor_batch_data(): + """Test SAC processor with batched data.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Test with batched data + batch_size = 32 + observation = {OBS_STATE: torch.randn(batch_size, 10)} + action = torch.randn(batch_size, 5) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through preprocessor + processed = preprocessor(batch) + + # Check that batch dimension is preserved + assert processed[OBS_STATE].shape == (batch_size, 10) + assert processed[TransitionKey.ACTION.value].shape == (batch_size, 5) + + +def test_sac_processor_edge_cases(): + """Test SAC processor with edge cases.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_sac_pre_post_processors( + config, + stats, + ) + + # Test with observation that has no state key but still exists + observation = {"observation.dummy": torch.randn(1)} # Some dummy observation to pass validation + action = torch.randn(5) + batch = {TransitionKey.ACTION.value: action, **observation} + processed = preprocessor(batch) + # observation.state wasn't in original, so it won't be in processed + assert OBS_STATE not in processed + assert processed[TransitionKey.ACTION.value].shape == (1, 5) + + # Test with zero action (representing "null" action) + transition = create_transition(observation={OBS_STATE: torch.randn(10)}, action=torch.zeros(5)) + batch = transition_to_batch(transition) + processed = preprocessor(batch) + assert processed[OBS_STATE].shape == (1, 10) + # Action should be present and batched, even if it's zeros + assert processed[TransitionKey.ACTION.value].shape == (1, 5) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_sac_processor_bfloat16_device_float32_normalizer(): + """Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, _ = make_sac_pre_post_processors( + config, + stats, + ) + + # Modify the pipeline to use bfloat16 device processor with float32 normalizer + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + # Device processor converts to bfloat16 + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16")) + elif isinstance(step, NormalizerProcessorStep): + # Normalizer stays configured as float32 (will auto-adapt to bfloat16) + norm_step = step # Now type checker knows this is NormalizerProcessorStep + modified_steps.append( + NormalizerProcessorStep( + features=norm_step.features, + norm_map=norm_step.norm_map, + stats=norm_step.stats, + device=config.device, + dtype=torch.float32, # Deliberately configured as float32 + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Verify initial normalizer configuration + normalizer_step = preprocessor.steps[3] # NormalizerProcessorStep + assert normalizer_step.dtype == torch.float32 + + # Create test data + observation = {OBS_STATE: torch.randn(10, dtype=torch.float32)} # Start with float32 + action = torch.randn(5, dtype=torch.float32) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + # Process through full pipeline + processed = preprocessor(batch) + + # Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16 + assert processed[OBS_STATE].dtype == torch.bfloat16 + assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16 + + # Verify normalizer automatically adapted its internal state + assert normalizer_step.dtype == torch.bfloat16 + for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 diff --git a/tests/processor/test_smolvla_processor.py b/tests/processor/test_smolvla_processor.py new file mode 100644 index 00000000000..227b1dc3598 --- /dev/null +++ b/tests/processor/test_smolvla_processor.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for SmolVLA policy processor.""" + +from unittest.mock import patch + +import pytest +import torch + +from lerobot.configs.types import FeatureType, NormalizationMode, PipelineFeatureType, PolicyFeature +from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig +from lerobot.policies.smolvla.processor_smolvla import ( + SmolVLANewLineProcessor, + make_smolvla_pre_post_processors, +) +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DeviceProcessorStep, + EnvTransition, + NormalizerProcessorStep, + ProcessorStep, + RenameObservationsProcessorStep, + TransitionKey, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import create_transition, transition_to_batch +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE + + +class MockTokenizerProcessorStep(ProcessorStep): + """Mock tokenizer processor step for testing.""" + + def __init__(self, *args, **kwargs): + # Accept any arguments to mimic the real TokenizerProcessorStep interface + pass + + def __call__(self, transition: EnvTransition) -> EnvTransition: + # Pass through transition unchanged + return transition + + def transform_features(self, features): + # Pass through features unchanged + return features + + +def create_default_config(): + """Create a default SmolVLA configuration for testing.""" + config = SmolVLAConfig() + config.input_features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(8,)), + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), + } + config.output_features = { + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)), + } + config.normalization_mapping = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.MIN_MAX, + } + config.device = "cpu" + config.vlm_model_name = "HuggingFaceTB/SmolVLM-Instruct" + config.pad_language_to = "max_length" + config.tokenizer_max_length = 100 + return config + + +def create_default_stats(): + """Create default dataset statistics for testing.""" + return { + OBS_STATE: {"mean": torch.zeros(8), "std": torch.ones(8)}, + OBS_IMAGE: {}, # No normalization for images + ACTION: {"min": torch.full((7,), -1.0), "max": torch.ones(7)}, + } + + +def test_make_smolvla_processor_basic(): + """Test basic creation of SmolVLA processor.""" + config = create_default_config() + stats = create_default_stats() + + with patch( + "lerobot.policies.smolvla.processor_smolvla.TokenizerProcessorStep", MockTokenizerProcessorStep + ): + preprocessor, postprocessor = make_smolvla_pre_post_processors( + config, + stats, + ) + + # Check processor names + assert preprocessor.name == "policy_preprocessor" + assert postprocessor.name == "policy_postprocessor" + + # Check steps in preprocessor + assert len(preprocessor.steps) == 6 + assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep) + assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep) + assert isinstance(preprocessor.steps[2], SmolVLANewLineProcessor) + # Step 3 would be TokenizerProcessorStep but it's mocked + assert isinstance(preprocessor.steps[4], DeviceProcessorStep) + assert isinstance(preprocessor.steps[5], NormalizerProcessorStep) + + # Check steps in postprocessor + assert len(postprocessor.steps) == 2 + assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep) + assert isinstance(postprocessor.steps[1], DeviceProcessorStep) + + +def test_smolvla_newline_processor_single_task(): + """Test SmolVLANewLineProcessor with single task string.""" + processor = SmolVLANewLineProcessor() + + # Test with task that doesn't have newline + transition = create_transition(complementary_data={"task": "test task"}) + result = processor(transition) + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "test task\n" + + # Test with task that already has newline + transition = create_transition(complementary_data={"task": "test task\n"}) + result = processor(transition) + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "test task\n" + + +def test_smolvla_newline_processor_list_of_tasks(): + """Test SmolVLANewLineProcessor with list of task strings.""" + processor = SmolVLANewLineProcessor() + + # Test with list of tasks + tasks = ["task1", "task2\n", "task3"] + transition = create_transition(complementary_data={"task": tasks}) + result = processor(transition) + expected = ["task1\n", "task2\n", "task3\n"] + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == expected + + +def test_smolvla_newline_processor_empty_transition(): + """Test SmolVLANewLineProcessor with empty transition.""" + processor = SmolVLANewLineProcessor() + + # Test with no complementary_data + transition = create_transition() + result = processor(transition) + assert result == transition + + # Test with complementary_data but no task + transition = create_transition(complementary_data={"other": "data"}) + result = processor(transition) + assert result == transition + + # Test with None task + transition = create_transition(complementary_data={"task": None}) + result = processor(transition) + assert result == transition + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_smolvla_processor_cuda(): + """Test SmolVLA processor with CUDA device.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + # Mock the tokenizer processor to act as pass-through + class MockTokenizerProcessorStep(ProcessorStep): + def __init__(self, *args, **kwargs): + pass + + def __call__(self, transition): + return transition + + def state_dict(self): + return {} + + def load_state_dict(self, state): + pass + + def reset(self): + pass + + def get_config(self): + return {"tokenizer_name": "HuggingFaceTB/SmolVLM-Instruct"} + + def transform_features(self, features): + return features + + with patch( + "lerobot.policies.smolvla.processor_smolvla.TokenizerProcessorStep", MockTokenizerProcessorStep + ): + preprocessor, postprocessor = make_smolvla_pre_post_processors( + config, + stats, + ) + + # Create CPU data + observation = { + OBS_STATE: torch.randn(8), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(7) + transition = create_transition(observation, action, complementary_data={"task": "test task"}) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is on CUDA + assert processed[OBS_STATE].device.type == "cuda" + assert processed[OBS_IMAGE].device.type == "cuda" + assert processed[TransitionKey.ACTION.value].device.type == "cuda" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_smolvla_processor_accelerate_scenario(): + """Test SmolVLA processor in simulated Accelerate scenario.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + # Mock the tokenizer processor to act as pass-through + class MockTokenizerProcessorStep(ProcessorStep): + def __init__(self, *args, **kwargs): + pass + + def __call__(self, transition): + return transition + + def state_dict(self): + return {} + + def load_state_dict(self, state): + pass + + def reset(self): + pass + + def get_config(self): + return {"tokenizer_name": "HuggingFaceTB/SmolVLM-Instruct"} + + def transform_features(self, features): + return features + + with patch( + "lerobot.policies.smolvla.processor_smolvla.TokenizerProcessorStep", MockTokenizerProcessorStep + ): + preprocessor, postprocessor = make_smolvla_pre_post_processors( + config, + stats, + ) + + # Simulate Accelerate: data already on GPU and batched + device = torch.device("cuda:0") + observation = { + OBS_STATE: torch.randn(1, 8).to(device), + OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device), + } + action = torch.randn(1, 7).to(device) + transition = create_transition(observation, action, complementary_data={"task": ["test task"]}) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on same GPU + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_smolvla_processor_multi_gpu(): + """Test SmolVLA processor with multi-GPU setup.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + # Mock the tokenizer processor to act as pass-through + class MockTokenizerProcessorStep(ProcessorStep): + def __init__(self, *args, **kwargs): + pass + + def __call__(self, transition): + return transition + + def state_dict(self): + return {} + + def load_state_dict(self, state): + pass + + def reset(self): + pass + + def get_config(self): + return {"tokenizer_name": "HuggingFaceTB/SmolVLM-Instruct"} + + def transform_features(self, features): + return features + + with patch( + "lerobot.policies.smolvla.processor_smolvla.TokenizerProcessorStep", MockTokenizerProcessorStep + ): + preprocessor, postprocessor = make_smolvla_pre_post_processors( + config, + stats, + ) + + # Simulate data on different GPU + device = torch.device("cuda:1") + observation = { + OBS_STATE: torch.randn(1, 8).to(device), + OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device), + } + action = torch.randn(1, 7).to(device) + transition = create_transition(observation, action, complementary_data={"task": ["test task"]}) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on cuda:1 + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +def test_smolvla_processor_without_stats(): + """Test SmolVLA processor creation without dataset statistics.""" + config = create_default_config() + + # Mock the tokenizer processor + with patch( + "lerobot.policies.smolvla.processor_smolvla.TokenizerProcessorStep", MockTokenizerProcessorStep + ): + preprocessor, postprocessor = make_smolvla_pre_post_processors( + config, + dataset_stats=None, + ) + + # Should still create processors + assert preprocessor is not None + assert postprocessor is not None + + +def test_smolvla_newline_processor_state_dict(): + """Test SmolVLANewLineProcessor state dict methods.""" + processor = SmolVLANewLineProcessor() + + # Test state_dict (should be empty) + state = processor.state_dict() + assert state == {} + + # Test load_state_dict (should do nothing) + processor.load_state_dict({}) + + # Test reset (should do nothing) + processor.reset() + + # Test get_config + config = processor.get_config() + assert config == {} + + +def test_smolvla_newline_processor_transform_features(): + """Test SmolVLANewLineProcessor transform_features method.""" + processor = SmolVLANewLineProcessor() + + # Test transform_features + features = { + PipelineFeatureType.OBSERVATION: {OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))}, + } + result = processor.transform_features(features) + assert result == features # Should return unchanged + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_smolvla_processor_bfloat16_device_float32_normalizer(): + """Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + with patch( + "lerobot.policies.smolvla.processor_smolvla.TokenizerProcessorStep", MockTokenizerProcessorStep + ): + preprocessor, _ = make_smolvla_pre_post_processors( + config, + stats, + ) + + # Modify the pipeline to use bfloat16 device processor with float32 normalizer + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + # Device processor converts to bfloat16 + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16")) + elif isinstance(step, NormalizerProcessorStep): + # Normalizer stays configured as float32 (will auto-adapt to bfloat16) + modified_steps.append( + NormalizerProcessorStep( + features=step.features, + norm_map=step.norm_map, + stats=step.stats, + device=config.device, + dtype=torch.float32, # Deliberately configured as float32 + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Verify initial normalizer configuration (SmolVLA has NormalizerProcessorStep at index 5) + normalizer_step = preprocessor.steps[5] # NormalizerProcessorStep + assert normalizer_step.dtype == torch.float32 + + # Create test data with both state and visual observations + observation = { + OBS_STATE: torch.randn(8, dtype=torch.float32), + OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32), + } + action = torch.randn(7, dtype=torch.float32) + transition = create_transition( + observation, action, complementary_data={"task": "test bfloat16 adaptation"} + ) + + batch = transition_to_batch(transition) + + # Process through full pipeline + processed = preprocessor(batch) + + # Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16 + assert processed[OBS_STATE].dtype == torch.bfloat16 + assert processed[OBS_IMAGE].dtype == torch.bfloat16 # IDENTITY normalization still gets dtype conversion + assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16 + + # Verify normalizer automatically adapted its internal state + assert normalizer_step.dtype == torch.bfloat16 + # Check state stats (has normalization) + for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 + # OBS_IMAGE uses IDENTITY normalization, so no stats to check diff --git a/tests/processor/test_tdmpc_processor.py b/tests/processor/test_tdmpc_processor.py new file mode 100644 index 00000000000..edbc25ae3e1 --- /dev/null +++ b/tests/processor/test_tdmpc_processor.py @@ -0,0 +1,467 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for TDMPC policy processor.""" + +import tempfile + +import pytest +import torch + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig +from lerobot.policies.tdmpc.processor_tdmpc import make_tdmpc_pre_post_processors +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DataProcessorPipeline, + DeviceProcessorStep, + NormalizerProcessorStep, + RenameObservationsProcessorStep, + TransitionKey, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import create_transition, transition_to_batch +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE + + +def create_default_config(): + """Create a default TDMPC configuration for testing.""" + config = TDMPCConfig() + config.input_features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(12,)), + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), + } + config.output_features = { + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(6,)), + } + config.normalization_mapping = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.MIN_MAX, + } + config.device = "cpu" + return config + + +def create_default_stats(): + """Create default dataset statistics for testing.""" + return { + OBS_STATE: {"mean": torch.zeros(12), "std": torch.ones(12)}, + OBS_IMAGE: {}, # No normalization for images + ACTION: {"min": torch.full((6,), -1.0), "max": torch.ones(6)}, + } + + +def test_make_tdmpc_processor_basic(): + """Test basic creation of TDMPC processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Check processor names + assert preprocessor.name == "policy_preprocessor" + assert postprocessor.name == "policy_postprocessor" + + # Check steps in preprocessor + assert len(preprocessor.steps) == 4 + assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep) + assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep) + assert isinstance(preprocessor.steps[2], DeviceProcessorStep) + assert isinstance(preprocessor.steps[3], NormalizerProcessorStep) + + # Check steps in postprocessor + assert len(postprocessor.steps) == 2 + assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep) + assert isinstance(postprocessor.steps[1], DeviceProcessorStep) + + +def test_tdmpc_processor_normalization(): + """Test that TDMPC processor correctly normalizes and unnormalizes data.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Create test data + observation = { + OBS_STATE: torch.randn(12), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is processed and batched + assert processed[OBS_STATE].shape == (1, 12) + assert processed[OBS_IMAGE].shape == (1, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1, 6) + + # Process action through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is unnormalized (but still batched) + assert postprocessed.shape == (1, 6) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_tdmpc_processor_cuda(): + """Test TDMPC processor with CUDA device.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Create CPU data + observation = { + OBS_STATE: torch.randn(12), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is on CUDA + assert processed[OBS_STATE].device.type == "cuda" + assert processed[OBS_IMAGE].device.type == "cuda" + assert processed[TransitionKey.ACTION.value].device.type == "cuda" + + # Process through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is back on CPU + assert postprocessed.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_tdmpc_processor_accelerate_scenario(): + """Test TDMPC processor in simulated Accelerate scenario.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Simulate Accelerate: data already on GPU + device = torch.device("cuda:0") + observation = { + OBS_STATE: torch.randn(12).to(device), + OBS_IMAGE: torch.randn(3, 224, 224).to(device), + } + action = torch.randn(6).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on same GPU + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_tdmpc_processor_multi_gpu(): + """Test TDMPC processor with multi-GPU setup.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Simulate data on different GPU + device = torch.device("cuda:1") + observation = { + OBS_STATE: torch.randn(12).to(device), + OBS_IMAGE: torch.randn(3, 224, 224).to(device), + } + action = torch.randn(6).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on cuda:1 + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +def test_tdmpc_processor_without_stats(): + """Test TDMPC processor creation without dataset statistics.""" + config = create_default_config() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors(config, dataset_stats=None) + + # Should still create processors + assert preprocessor is not None + assert postprocessor is not None + + # Process should still work + observation = { + OBS_STATE: torch.randn(12), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed is not None + + +def test_tdmpc_processor_save_and_load(): + """Test saving and loading TDMPC processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + # Save preprocessor + preprocessor.save_pretrained(tmpdir) + + # Load preprocessor + loaded_preprocessor = DataProcessorPipeline.from_pretrained( + tmpdir, config_filename="policy_preprocessor.json" + ) + + # Test that loaded processor works + observation = { + OBS_STATE: torch.randn(12), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + processed = loaded_preprocessor(batch) + assert processed[OBS_STATE].shape == (1, 12) + assert processed[OBS_IMAGE].shape == (1, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1, 6) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_tdmpc_processor_mixed_precision(): + """Test TDMPC processor with mixed precision.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + # Create processor + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Replace DeviceProcessorStep with one that uses float16 + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="float16")) + elif isinstance(step, NormalizerProcessorStep): + # Update normalizer to use the same device as the device processor + modified_steps.append( + NormalizerProcessorStep( + features=step.features, + norm_map=step.norm_map, + stats=step.stats, + device=config.device, + dtype=torch.float16, # Match the float16 dtype + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Create test data + observation = { + OBS_STATE: torch.randn(12, dtype=torch.float32), + OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32), + } + action = torch.randn(6, dtype=torch.float32) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is converted to float16 + assert processed[OBS_STATE].dtype == torch.float16 + assert processed[OBS_IMAGE].dtype == torch.float16 + assert processed[TransitionKey.ACTION.value].dtype == torch.float16 + + +def test_tdmpc_processor_batch_data(): + """Test TDMPC processor with batched data.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Test with batched data + batch_size = 64 + observation = { + OBS_STATE: torch.randn(batch_size, 12), + OBS_IMAGE: torch.randn(batch_size, 3, 224, 224), + } + action = torch.randn(batch_size, 6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that batch dimension is preserved + assert processed[OBS_STATE].shape == (batch_size, 12) + assert processed[OBS_IMAGE].shape == (batch_size, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (batch_size, 6) + + +def test_tdmpc_processor_edge_cases(): + """Test TDMPC processor with edge cases.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Test with only state observation (no image) + observation = {OBS_STATE: torch.randn(12)} + action = torch.randn(6) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed[OBS_STATE].shape == (1, 12) + assert OBS_IMAGE not in processed + + # Test with only image observation (no state) + observation = {OBS_IMAGE: torch.randn(3, 224, 224)} + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed[OBS_IMAGE].shape == (1, 3, 224, 224) + assert OBS_STATE not in processed + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_tdmpc_processor_bfloat16_device_float32_normalizer(): + """Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, _ = make_tdmpc_pre_post_processors( + config, + stats, + ) + + # Modify the pipeline to use bfloat16 device processor with float32 normalizer + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + # Device processor converts to bfloat16 + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16")) + elif isinstance(step, NormalizerProcessorStep): + # Normalizer stays configured as float32 (will auto-adapt to bfloat16) + modified_steps.append( + NormalizerProcessorStep( + features=step.features, + norm_map=step.norm_map, + stats=step.stats, + device=config.device, + dtype=torch.float32, # Deliberately configured as float32 + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Verify initial normalizer configuration + normalizer_step = preprocessor.steps[3] # NormalizerProcessorStep + assert normalizer_step.dtype == torch.float32 + + # Create test data with both state and visual observations + observation = { + OBS_STATE: torch.randn(12, dtype=torch.float32), + OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32), + } + action = torch.randn(6, dtype=torch.float32) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through full pipeline + processed = preprocessor(batch) + + # Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16 + assert processed[OBS_STATE].dtype == torch.bfloat16 + assert processed[OBS_IMAGE].dtype == torch.bfloat16 # IDENTITY normalization still gets dtype conversion + assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16 + + # Verify normalizer automatically adapted its internal state + assert normalizer_step.dtype == torch.bfloat16 + # Check state stats (has normalization) + for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 + # OBS_IMAGE uses IDENTITY normalization, so no stats to check diff --git a/tests/processor/test_tokenizer_processor.py b/tests/processor/test_tokenizer_processor.py new file mode 100644 index 00000000000..d6f87f56796 --- /dev/null +++ b/tests/processor/test_tokenizer_processor.py @@ -0,0 +1,1040 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Tests for the TokenizerProcessorStep class. +""" + +import tempfile +from unittest.mock import patch + +import pytest +import torch + +from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature +from lerobot.processor import DataProcessorPipeline, TokenizerProcessorStep, TransitionKey +from lerobot.processor.converters import create_transition, identity_transition +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_LANGUAGE, OBS_STATE +from tests.utils import require_package + + +class MockTokenizer: + """Mock tokenizer for testing that mimics transformers tokenizer interface.""" + + def __init__(self, vocab_size: int = 1000): + self.vocab_size = vocab_size + + def __call__( + self, + text: str | list[str], + max_length: int = 512, + truncation: bool = True, + padding: str = "max_length", + padding_side: str = "right", + return_tensors: str = "pt", + **kwargs, + ) -> dict[str, torch.Tensor]: + """Mock tokenization that returns deterministic tokens based on text.""" + texts = [text] if isinstance(text, str) else text + + batch_size = len(texts) + + # Create mock input_ids and attention_mask + input_ids = torch.zeros(batch_size, max_length, dtype=torch.long) + attention_mask = torch.zeros(batch_size, max_length, dtype=torch.long) + + for i, txt in enumerate(texts): + # Simple mock: use hash of text to generate deterministic tokens + text_hash = hash(txt) % self.vocab_size + seq_len = min(len(txt.split()), max_length) + + # Fill input_ids with simple pattern based on text + for j in range(seq_len): + input_ids[i, j] = (text_hash + j) % self.vocab_size + + # Set attention mask for non-padded positions + attention_mask[i, :seq_len] = 1 + + result = { + "input_ids": input_ids, + "attention_mask": attention_mask, + } + + # Return single sequence for single input to match transformers behavior + if len(texts) == 1: + result = {k: v.squeeze(0) for k, v in result.items()} + + return result + + +@pytest.fixture +def mock_tokenizer(): + """Provide a mock tokenizer for testing.""" + return MockTokenizer(vocab_size=100) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_basic_tokenization(mock_auto_tokenizer): + """Test basic string tokenization functionality.""" + # Mock AutoTokenizer.from_pretrained to return our mock tokenizer + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "pick up the red cube"}, + ) + + result = processor(transition) + + # Check that original task is preserved + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "pick up the red cube" + + # Check that tokens were added to observation + observation = result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + + # Check token structure + tokens = observation[f"{OBS_LANGUAGE}.tokens"] + attention_mask = observation[f"{OBS_LANGUAGE}.attention_mask"] + assert isinstance(tokens, torch.Tensor) + assert isinstance(attention_mask, torch.Tensor) + assert tokens.shape == (10,) + assert attention_mask.shape == (10,) + + +@require_package("transformers") +def test_basic_tokenization_with_tokenizer_object(): + """Test basic string tokenization functionality using tokenizer object directly.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "pick up the red cube"}, + ) + + result = processor(transition) + + # Check that original task is preserved + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == "pick up the red cube" + + # Check that tokens were added to observation + observation = result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + + # Check token structure + tokens = observation[f"{OBS_LANGUAGE}.tokens"] + attention_mask = observation[f"{OBS_LANGUAGE}.attention_mask"] + assert isinstance(tokens, torch.Tensor) + assert isinstance(attention_mask, torch.Tensor) + assert tokens.shape == (10,) + assert attention_mask.shape == (10,) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_list_of_strings_tokenization(mock_auto_tokenizer): + """Test tokenization of a list of strings.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=8) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": ["pick up cube", "place on table"]}, + ) + + result = processor(transition) + + # Check that original task is preserved + assert result[TransitionKey.COMPLEMENTARY_DATA]["task"] == ["pick up cube", "place on table"] + + # Check that tokens were added to observation + observation = result[TransitionKey.OBSERVATION] + tokens = observation[f"{OBS_LANGUAGE}.tokens"] + attention_mask = observation[f"{OBS_LANGUAGE}.attention_mask"] + assert tokens.shape == (2, 8) # batch_size=2, seq_len=8 + assert attention_mask.shape == (2, 8) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_custom_keys(mock_auto_tokenizer): + """Test using custom task_key.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", task_key="instruction", max_length=5) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"instruction": "move forward"}, + ) + + result = processor(transition) + + # Check that tokens are stored in observation regardless of task_key + observation = result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + + tokens = observation[f"{OBS_LANGUAGE}.tokens"] + assert tokens.shape == (5,) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_none_complementary_data(mock_auto_tokenizer): + """Test handling of None complementary_data.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer") + + transition = create_transition(observation={}, complementary_data=None) + + # create_transition converts None complementary_data to empty dict, so task key is missing + with pytest.raises(KeyError, match="task"): + processor(transition) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_missing_task_key(mock_auto_tokenizer): + """Test handling when task key is missing.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer") + + transition = create_transition(observation={}, complementary_data={"other_field": "some value"}) + + with pytest.raises(KeyError, match="task"): + processor(transition) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_none_task_value(mock_auto_tokenizer): + """Test handling when task value is None.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer") + + transition = create_transition(observation={}, complementary_data={"task": None}) + + with pytest.raises(ValueError, match="Task extracted from Complementary data is None"): + processor(transition) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_unsupported_task_type(mock_auto_tokenizer): + """Test handling of unsupported task types.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer") + + # Test with integer task - get_task returns None, observation raises ValueError + transition = create_transition(observation={}, complementary_data={"task": 123}) + + with pytest.raises(ValueError, match="Task cannot be None"): + processor(transition) + + # Test with mixed list - get_task returns None, observation raises ValueError + transition = create_transition(observation={}, complementary_data={"task": ["text", 123, "more text"]}) + + with pytest.raises(ValueError, match="Task cannot be None"): + processor(transition) + + +@require_package("transformers") +def test_no_tokenizer_error(): + """Test that ValueError is raised when neither tokenizer nor tokenizer_name is provided.""" + with pytest.raises(ValueError, match="Either 'tokenizer' or 'tokenizer_name' must be provided"): + TokenizerProcessorStep() + + +@require_package("transformers") +def test_invalid_tokenizer_name_error(): + """Test that error is raised when invalid tokenizer_name is provided.""" + with patch("lerobot.processor.tokenizer_processor.AutoTokenizer") as mock_auto_tokenizer: + # Mock import error + mock_auto_tokenizer.from_pretrained.side_effect = Exception("Model not found") + + with pytest.raises(Exception, match="Model not found"): + TokenizerProcessorStep(tokenizer_name="invalid-tokenizer") + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_get_config_with_tokenizer_name(mock_auto_tokenizer): + """Test configuration serialization when using tokenizer_name.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep( + tokenizer_name="test-tokenizer", + max_length=256, + task_key="instruction", + padding="longest", + truncation=False, + ) + + config = processor.get_config() + + expected = { + "tokenizer_name": "test-tokenizer", + "max_length": 256, + "task_key": "instruction", + "padding_side": "right", + "padding": "longest", + "truncation": False, + } + + assert config == expected + + +@require_package("transformers") +def test_get_config_with_tokenizer_object(): + """Test configuration serialization when using tokenizer object.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + + processor = TokenizerProcessorStep( + tokenizer=mock_tokenizer, + max_length=256, + task_key="instruction", + padding="longest", + truncation=False, + ) + + config = processor.get_config() + + # tokenizer_name should not be in config when tokenizer object is used + expected = { + "max_length": 256, + "task_key": "instruction", + "padding_side": "right", + "padding": "longest", + "truncation": False, + } + + assert config == expected + assert "tokenizer_name" not in config + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_state_dict_methods(mock_auto_tokenizer): + """Test state_dict and load_state_dict methods.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer") + + # Should return empty dict + state = processor.state_dict() + assert state == {} + + # load_state_dict should not raise error + processor.load_state_dict({}) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_reset_method(mock_auto_tokenizer): + """Test reset method.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer") + + # Should not raise error + processor.reset() + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_integration_with_robot_processor(mock_auto_tokenizer): + """Test integration with RobotProcessor.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + tokenizer_processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=6) + robot_processor = DataProcessorPipeline( + [tokenizer_processor], to_transition=identity_transition, to_output=identity_transition + ) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "test task"}, + ) + + result = robot_processor(transition) + + # Check that observation exists and tokenization was applied + assert TransitionKey.OBSERVATION in result + observation = result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + tokens = observation[f"{OBS_LANGUAGE}.tokens"] + attention_mask = observation[f"{OBS_LANGUAGE}.attention_mask"] + assert tokens.shape == (6,) + assert attention_mask.shape == (6,) + + # Check that other data is preserved + assert torch.equal( + result[TransitionKey.OBSERVATION]["state"], transition[TransitionKey.OBSERVATION]["state"] + ) + assert torch.equal(result[TransitionKey.ACTION], transition[TransitionKey.ACTION]) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_save_and_load_pretrained_with_tokenizer_name(mock_auto_tokenizer): + """Test saving and loading processor with tokenizer_name.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + original_processor = TokenizerProcessorStep( + tokenizer_name="test-tokenizer", max_length=32, task_key="instruction" + ) + + robot_processor = DataProcessorPipeline( + [original_processor], to_transition=identity_transition, to_output=identity_transition + ) + + with tempfile.TemporaryDirectory() as temp_dir: + # Save processor + robot_processor.save_pretrained(temp_dir) + + # Load processor - tokenizer will be recreated from saved config + loaded_processor = DataProcessorPipeline.from_pretrained( + temp_dir, + config_filename="dataprocessorpipeline.json", + to_transition=identity_transition, + to_output=identity_transition, + ) + + # Test that loaded processor works + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"instruction": "test instruction"}, + ) + + result = loaded_processor(transition) + assert TransitionKey.OBSERVATION in result + assert f"{OBS_LANGUAGE}.tokens" in result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.attention_mask" in result[TransitionKey.OBSERVATION] + + +@require_package("transformers") +def test_save_and_load_pretrained_with_tokenizer_object(): + """Test saving and loading processor with tokenizer object using overrides.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + + original_processor = TokenizerProcessorStep( + tokenizer=mock_tokenizer, max_length=32, task_key="instruction" + ) + + robot_processor = DataProcessorPipeline( + [original_processor], to_transition=identity_transition, to_output=identity_transition + ) + + with tempfile.TemporaryDirectory() as temp_dir: + # Save processor + robot_processor.save_pretrained(temp_dir) + + # Load processor with tokenizer override (since tokenizer object wasn't saved) + loaded_processor = DataProcessorPipeline.from_pretrained( + temp_dir, + config_filename="dataprocessorpipeline.json", + overrides={"tokenizer_processor": {"tokenizer": mock_tokenizer}}, + to_transition=identity_transition, + to_output=identity_transition, + ) + + # Test that loaded processor works + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"instruction": "test instruction"}, + ) + + result = loaded_processor(transition) + assert TransitionKey.OBSERVATION in result + assert f"{OBS_LANGUAGE}.tokens" in result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.attention_mask" in result[TransitionKey.OBSERVATION] + + +@require_package("transformers") +def test_registry_functionality(): + """Test that the processor is properly registered.""" + from lerobot.processor import ProcessorStepRegistry + + # Check that the processor is registered + assert "tokenizer_processor" in ProcessorStepRegistry.list() + + # Check that we can retrieve it + retrieved_class = ProcessorStepRegistry.get("tokenizer_processor") + assert retrieved_class is TokenizerProcessorStep + + +@require_package("transformers") +def test_features_basic(): + """Test basic feature contract functionality.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=128) + + input_features = { + PipelineFeatureType.OBSERVATION: {OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))}, + PipelineFeatureType.ACTION: {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(5,))}, + } + + output_features = processor.transform_features(input_features) + + # Check that original features are preserved + assert OBS_STATE in output_features[PipelineFeatureType.OBSERVATION] + assert ACTION in output_features[PipelineFeatureType.ACTION] + + # Check that tokenized features are added + assert f"{OBS_LANGUAGE}.tokens" in output_features[PipelineFeatureType.OBSERVATION] + assert f"{OBS_LANGUAGE}.attention_mask" in output_features[PipelineFeatureType.OBSERVATION] + + # Check feature properties + tokens_feature = output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask_feature = output_features[PipelineFeatureType.OBSERVATION][ + f"{OBS_LANGUAGE}.attention_mask" + ] + + assert tokens_feature.type == FeatureType.LANGUAGE + assert tokens_feature.shape == (128,) + assert attention_mask_feature.type == FeatureType.LANGUAGE + assert attention_mask_feature.shape == (128,) + + +@require_package("transformers") +def test_features_with_custom_max_length(): + """Test feature contract with custom max_length.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=64) + + input_features = {PipelineFeatureType.OBSERVATION: {}} + output_features = processor.transform_features(input_features) + + # Check that features use correct max_length + assert f"{OBS_LANGUAGE}.tokens" in output_features[PipelineFeatureType.OBSERVATION] + assert f"{OBS_LANGUAGE}.attention_mask" in output_features[PipelineFeatureType.OBSERVATION] + + tokens_feature = output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask_feature = output_features[PipelineFeatureType.OBSERVATION][ + f"{OBS_LANGUAGE}.attention_mask" + ] + + assert tokens_feature.shape == (64,) + assert attention_mask_feature.shape == (64,) + + +@require_package("transformers") +def test_features_existing_features(): + """Test feature contract when tokenized features already exist.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=256) + + input_features = { + PipelineFeatureType.OBSERVATION: { + f"{OBS_LANGUAGE}.tokens": PolicyFeature(type=FeatureType.LANGUAGE, shape=(100,)), + f"{OBS_LANGUAGE}.attention_mask": PolicyFeature(type=FeatureType.LANGUAGE, shape=(100,)), + } + } + + output_features = processor.transform_features(input_features) + + # Should not overwrite existing features + assert output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.tokens"].shape == ( + 100, + ) # Original shape preserved + assert output_features[PipelineFeatureType.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"].shape == (100,) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_tokenization_parameters(mock_auto_tokenizer): + """Test that tokenization parameters are correctly passed to tokenizer.""" + + # Create a custom mock that tracks calls + class TrackingMockTokenizer: + def __init__(self): + self.last_call_args = None + self.last_call_kwargs = None + + def __call__(self, *args, **kwargs): + self.last_call_args = args + self.last_call_kwargs = kwargs + # Return minimal valid output + return { + "input_ids": torch.zeros(16, dtype=torch.long), + "attention_mask": torch.ones(16, dtype=torch.long), + } + + tracking_tokenizer = TrackingMockTokenizer() + mock_auto_tokenizer.from_pretrained.return_value = tracking_tokenizer + + processor = TokenizerProcessorStep( + tokenizer_name="test-tokenizer", + max_length=16, + padding="longest", + truncation=False, + padding_side="left", + ) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "test task"}, + ) + + processor(transition) + + # Check that parameters were passed correctly (task is converted to list) + assert tracking_tokenizer.last_call_args == (["test task"],) + assert tracking_tokenizer.last_call_kwargs["max_length"] == 16 + assert tracking_tokenizer.last_call_kwargs["padding"] == "longest" + assert tracking_tokenizer.last_call_kwargs["padding_side"] == "left" + assert tracking_tokenizer.last_call_kwargs["truncation"] is False + assert tracking_tokenizer.last_call_kwargs["return_tensors"] == "pt" + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_preserves_other_complementary_data(mock_auto_tokenizer): + """Test that other complementary data fields are preserved.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer") + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={ + "task": "test task", + "episode_id": 123, + "timestamp": 456.789, + "other_field": {"nested": "data"}, + }, + ) + + result = processor(transition) + comp_data = result[TransitionKey.COMPLEMENTARY_DATA] + + # Check that all original fields are preserved + assert comp_data["task"] == "test task" + assert comp_data["episode_id"] == 123 + assert comp_data["timestamp"] == 456.789 + assert comp_data["other_field"] == {"nested": "data"} + + # Check that tokens were added to observation + observation = result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_deterministic_tokenization(mock_auto_tokenizer): + """Test that tokenization is deterministic for the same input.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "consistent test"}, + ) + + result1 = processor(transition) + result2 = processor(transition) + + tokens1 = result1[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask1 = result1[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + tokens2 = result2[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask2 = result2[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + # Results should be identical + assert torch.equal(tokens1, tokens2) + assert torch.equal(attention_mask1, attention_mask2) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_empty_string_task(mock_auto_tokenizer): + """Test handling of empty string task.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=8) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": ""}, + ) + + result = processor(transition) + + # Should still tokenize (mock tokenizer handles empty strings) + observation = result[TransitionKey.OBSERVATION] + assert f"{OBS_LANGUAGE}.tokens" in observation + tokens = observation[f"{OBS_LANGUAGE}.tokens"] + assert tokens.shape == (8,) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_very_long_task(mock_auto_tokenizer): + """Test handling of very long task strings.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=5, truncation=True) + + long_task = " ".join(["word"] * 100) # Very long task + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": long_task}, + ) + + result = processor(transition) + + # Should be truncated to max_length + observation = result[TransitionKey.OBSERVATION] + tokens = observation[f"{OBS_LANGUAGE}.tokens"] + attention_mask = observation[f"{OBS_LANGUAGE}.attention_mask"] + assert tokens.shape == (5,) + assert attention_mask.shape == (5,) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_custom_padding_side(mock_auto_tokenizer): + """Test using custom padding_side parameter.""" + + # Create a mock tokenizer that tracks padding_side calls + class PaddingSideTrackingTokenizer: + def __init__(self): + self.padding_side_calls = [] + + def __call__( + self, + text, + max_length=512, + truncation=True, + padding="max_length", + padding_side="right", + return_tensors="pt", + **kwargs, + ): + self.padding_side_calls.append(padding_side) + # Return minimal valid output + return { + "input_ids": torch.zeros(max_length, dtype=torch.long), + "attention_mask": torch.ones(max_length, dtype=torch.long), + } + + tracking_tokenizer = PaddingSideTrackingTokenizer() + mock_auto_tokenizer.from_pretrained.return_value = tracking_tokenizer + + # Test left padding + processor_left = TokenizerProcessorStep( + tokenizer_name="test-tokenizer", max_length=10, padding_side="left" + ) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "test task"}, + ) + processor_left(transition) + + assert tracking_tokenizer.padding_side_calls[-1] == "left" + + # Test right padding (default) + processor_right = TokenizerProcessorStep( + tokenizer_name="test-tokenizer", max_length=10, padding_side="right" + ) + + processor_right(transition) + + assert tracking_tokenizer.padding_side_calls[-1] == "right" + + +@require_package("transformers") +def test_device_detection_cpu(): + """Test that tokenized tensors stay on CPU when other tensors are on CPU.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Create transition with CPU tensors + observation = {OBS_STATE: torch.randn(10)} # CPU tensor + action = torch.randn(5) # CPU tensor + transition = create_transition( + observation=observation, action=action, complementary_data={"task": "test task"} + ) + + result = processor(transition) + + # Check that tokenized tensors are on CPU + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + assert tokens.device.type == "cpu" + assert attention_mask.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@require_package("transformers") +def test_device_detection_cuda(): + """Test that tokenized tensors are moved to CUDA when other tensors are on CUDA.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Create transition with CUDA tensors + observation = {OBS_STATE: torch.randn(10).cuda()} # CUDA tensor + action = torch.randn(5).cuda() # CUDA tensor + transition = create_transition( + observation=observation, action=action, complementary_data={"task": "test task"} + ) + + result = processor(transition) + + # Check that tokenized tensors are on CUDA + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + assert tokens.device.type == "cuda" + assert attention_mask.device.type == "cuda" + assert tokens.device.index == 0 # Should be on same device as input + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +@require_package("transformers") +def test_device_detection_multi_gpu(): + """Test that tokenized tensors match device in multi-GPU setup.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Test with tensors on cuda:1 + device = torch.device("cuda:1") + observation = {OBS_STATE: torch.randn(10).to(device)} + action = torch.randn(5).to(device) + transition = create_transition( + observation=observation, action=action, complementary_data={"task": "multi gpu test"} + ) + + result = processor(transition) + + # Check that tokenized tensors are on cuda:1 + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + assert tokens.device == device + assert attention_mask.device == device + + +@require_package("transformers") +def test_device_detection_no_tensors(): + """Test that tokenized tensors stay on CPU when no other tensors exist.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Create transition with no tensors + transition = create_transition( + observation={"metadata": {"key": "value"}}, # No tensors + complementary_data={"task": "no tensor test"}, + ) + + result = processor(transition) + + # Check that tokenized tensors are on CPU (default) + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + assert tokens.device.type == "cpu" + assert attention_mask.device.type == "cpu" + + +@require_package("transformers") +def test_device_detection_mixed_devices(): + """Test device detection when tensors are on different devices (uses first found).""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + if torch.cuda.is_available(): + # Create transition with mixed devices + observation = { + "observation.cpu": torch.randn(10), # CPU + "observation.cuda": torch.randn(10).cuda(), # CUDA + } + transition = create_transition( + observation=observation, complementary_data={"task": "mixed device test"} + ) + + result = processor(transition) + + # The device detection should use the first tensor found + # (iteration order depends on dict, but result should be consistent) + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + # Both should be on the same device + assert tokens.device == attention_mask.device + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@require_package("transformers") +def test_device_detection_from_action(): + """Test that device is detected from action tensor when no observation tensors exist.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Create transition with action on CUDA but no observation tensors + observation = {"metadata": {"key": "value"}} # No tensors in observation + action = torch.randn(5).cuda() + transition = create_transition( + observation=observation, action=action, complementary_data={"task": "action device test"} + ) + + result = processor(transition) + + # Check that tokenized tensors match action's device + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + assert tokens.device.type == "cuda" + assert attention_mask.device.type == "cuda" + + +@require_package("transformers") +def test_device_detection_preserves_dtype(): + """Test that device detection doesn't affect dtype of tokenized tensors.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Create transition with float tensor (to test dtype isn't affected) + observation = {OBS_STATE: torch.randn(10, dtype=torch.float16)} + transition = create_transition(observation=observation, complementary_data={"task": "dtype test"}) + + result = processor(transition) + + # Check that tokenized tensors have correct dtypes (not affected by input dtype) + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + assert tokens.dtype == torch.long # Should remain long + assert attention_mask.dtype == torch.bool # Should be bool (converted in processor) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_integration_with_device_processor(mock_auto_tokenizer): + """Test that TokenizerProcessorStep works correctly with DeviceProcessorStep in pipeline.""" + from lerobot.processor import DeviceProcessorStep + + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + # Create pipeline with TokenizerProcessorStep then DeviceProcessorStep + tokenizer_processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=6) + device_processor = DeviceProcessorStep(device="cuda:0") + robot_processor = DataProcessorPipeline( + [tokenizer_processor, device_processor], + to_transition=identity_transition, + to_output=identity_transition, + ) + + # Start with CPU tensors + transition = create_transition( + observation={OBS_STATE: torch.randn(10)}, # CPU + action=torch.randn(5), # CPU + complementary_data={"task": "pipeline test"}, + ) + + result = robot_processor(transition) + + # All tensors should end up on CUDA (moved by DeviceProcessorStep) + assert result[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cuda" + assert result[TransitionKey.ACTION].device.type == "cuda" + + # Tokenized tensors should also be on CUDA + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + assert tokens.device.type == "cuda" + assert attention_mask.device.type == "cuda" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@require_package("transformers") +def test_simulated_accelerate_scenario(): + """Test scenario simulating Accelerate with data already on GPU.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Simulate Accelerate scenario: batch already on GPU + device = torch.device("cuda:0") + observation = { + OBS_STATE: torch.randn(1, 10).to(device), # Batched, on GPU + OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device), # Batched, on GPU + } + action = torch.randn(1, 5).to(device) # Batched, on GPU + + transition = create_transition( + observation=observation, + action=action, + complementary_data={"task": ["accelerate test"]}, # List for batched task + ) + + result = processor(transition) + + # Tokenized tensors should match GPU placement + tokens = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.tokens"] + attention_mask = result[TransitionKey.OBSERVATION][f"{OBS_LANGUAGE}.attention_mask"] + + assert tokens.device == device + assert attention_mask.device == device + # MockTokenizer squeezes single-item batches, so shape is (max_length,) not (1, max_length) + assert tokens.shape == (10,) # MockTokenizer behavior for single string in list + assert attention_mask.shape == (10,) diff --git a/tests/processor/test_vqbet_processor.py b/tests/processor/test_vqbet_processor.py new file mode 100644 index 00000000000..47e41dff479 --- /dev/null +++ b/tests/processor/test_vqbet_processor.py @@ -0,0 +1,462 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for VQBeT policy processor.""" + +import tempfile + +import pytest +import torch + +from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature +from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig +from lerobot.policies.vqbet.processor_vqbet import make_vqbet_pre_post_processors +from lerobot.processor import ( + AddBatchDimensionProcessorStep, + DataProcessorPipeline, + DeviceProcessorStep, + NormalizerProcessorStep, + RenameObservationsProcessorStep, + TransitionKey, + UnnormalizerProcessorStep, +) +from lerobot.processor.converters import create_transition, transition_to_batch +from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE + + +def create_default_config(): + """Create a default VQBeT configuration for testing.""" + config = VQBeTConfig() + config.input_features = { + OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(8,)), + OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)), + } + config.output_features = { + ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)), + } + config.normalization_mapping = { + FeatureType.STATE: NormalizationMode.MEAN_STD, + FeatureType.VISUAL: NormalizationMode.IDENTITY, + FeatureType.ACTION: NormalizationMode.MIN_MAX, + } + config.device = "cpu" + return config + + +def create_default_stats(): + """Create default dataset statistics for testing.""" + return { + OBS_STATE: {"mean": torch.zeros(8), "std": torch.ones(8)}, + OBS_IMAGE: {}, # No normalization for images + ACTION: {"min": torch.full((7,), -1.0), "max": torch.ones(7)}, + } + + +def test_make_vqbet_processor_basic(): + """Test basic creation of VQBeT processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Check processor names + assert preprocessor.name == "policy_preprocessor" + assert postprocessor.name == "policy_postprocessor" + + # Check steps in preprocessor + assert len(preprocessor.steps) == 4 + assert isinstance(preprocessor.steps[0], RenameObservationsProcessorStep) + assert isinstance(preprocessor.steps[1], AddBatchDimensionProcessorStep) + assert isinstance(preprocessor.steps[2], DeviceProcessorStep) + assert isinstance(preprocessor.steps[3], NormalizerProcessorStep) + + # Check steps in postprocessor + assert len(postprocessor.steps) == 2 + assert isinstance(postprocessor.steps[0], UnnormalizerProcessorStep) + assert isinstance(postprocessor.steps[1], DeviceProcessorStep) + + +def test_vqbet_processor_with_images(): + """Test VQBeT processor with image and state observations.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Create test data with images and states + observation = { + OBS_STATE: torch.randn(8), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(7) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is batched + assert processed[OBS_STATE].shape == (1, 8) + assert processed[OBS_IMAGE].shape == (1, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1, 7) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_vqbet_processor_cuda(): + """Test VQBeT processor with CUDA device.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Create CPU data + observation = { + OBS_STATE: torch.randn(8), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(7) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is on CUDA + assert processed[OBS_STATE].device.type == "cuda" + assert processed[OBS_IMAGE].device.type == "cuda" + assert processed[TransitionKey.ACTION.value].device.type == "cuda" + + # Process through postprocessor + postprocessed = postprocessor(processed[TransitionKey.ACTION.value]) + + # Check that action is back on CPU + assert postprocessed.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_vqbet_processor_accelerate_scenario(): + """Test VQBeT processor in simulated Accelerate scenario.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Simulate Accelerate: data already on GPU and batched + device = torch.device("cuda:0") + observation = { + OBS_STATE: torch.randn(1, 8).to(device), + OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device), + } + action = torch.randn(1, 7).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on same GPU + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +def test_vqbet_processor_multi_gpu(): + """Test VQBeT processor with multi-GPU setup.""" + config = create_default_config() + config.device = "cuda:0" + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Simulate data on different GPU + device = torch.device("cuda:1") + observation = { + OBS_STATE: torch.randn(1, 8).to(device), + OBS_IMAGE: torch.randn(1, 3, 224, 224).to(device), + } + action = torch.randn(1, 7).to(device) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data stays on cuda:1 + assert processed[OBS_STATE].device == device + assert processed[OBS_IMAGE].device == device + assert processed[TransitionKey.ACTION.value].device == device + + +def test_vqbet_processor_without_stats(): + """Test VQBeT processor creation without dataset statistics.""" + config = create_default_config() + + preprocessor, postprocessor = make_vqbet_pre_post_processors(config, dataset_stats=None) + + # Should still create processors + assert preprocessor is not None + assert postprocessor is not None + + # Process should still work + observation = { + OBS_STATE: torch.randn(8), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(7) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + assert processed is not None + + +def test_vqbet_processor_save_and_load(): + """Test saving and loading VQBeT processor.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + # Save preprocessor + preprocessor.save_pretrained(tmpdir) + + # Load preprocessor + loaded_preprocessor = DataProcessorPipeline.from_pretrained( + tmpdir, config_filename="policy_preprocessor.json" + ) + + # Test that loaded processor works + observation = { + OBS_STATE: torch.randn(8), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(7) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + processed = loaded_preprocessor(batch) + assert processed[OBS_STATE].shape == (1, 8) + assert processed[OBS_IMAGE].shape == (1, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (1, 7) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_vqbet_processor_mixed_precision(): + """Test VQBeT processor with mixed precision.""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + # Create processor + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Replace DeviceProcessorStep with one that uses float16 + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="float16")) + elif isinstance(step, NormalizerProcessorStep): + # Update normalizer to use the same device as the device processor + modified_steps.append( + NormalizerProcessorStep( + features=step.features, + norm_map=step.norm_map, + stats=step.stats, + device=config.device, + dtype=torch.float16, # Match the float16 dtype + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Create test data + observation = { + OBS_STATE: torch.randn(8, dtype=torch.float32), + OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32), + } + action = torch.randn(7, dtype=torch.float32) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that data is converted to float16 + assert processed[OBS_STATE].dtype == torch.float16 + assert processed[OBS_IMAGE].dtype == torch.float16 + assert processed[TransitionKey.ACTION.value].dtype == torch.float16 + + +def test_vqbet_processor_large_batch(): + """Test VQBeT processor with large batch sizes.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Test with large batch + batch_size = 128 + observation = { + OBS_STATE: torch.randn(batch_size, 8), + OBS_IMAGE: torch.randn(batch_size, 3, 224, 224), + } + action = torch.randn(batch_size, 7) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through preprocessor + + processed = preprocessor(batch) + + # Check that batch dimension is preserved + assert processed[OBS_STATE].shape == (batch_size, 8) + assert processed[OBS_IMAGE].shape == (batch_size, 3, 224, 224) + assert processed[TransitionKey.ACTION.value].shape == (batch_size, 7) + + +def test_vqbet_processor_sequential_processing(): + """Test VQBeT processor with sequential data processing.""" + config = create_default_config() + stats = create_default_stats() + + preprocessor, postprocessor = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Process multiple samples sequentially + results = [] + for _ in range(5): + observation = { + OBS_STATE: torch.randn(8), + OBS_IMAGE: torch.randn(3, 224, 224), + } + action = torch.randn(7) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + processed = preprocessor(batch) + results.append(processed) + + # Check that all results are consistent + for result in results: + assert result[OBS_STATE].shape == (1, 8) + assert result[OBS_IMAGE].shape == (1, 3, 224, 224) + assert result[TransitionKey.ACTION.value].shape == (1, 7) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_vqbet_processor_bfloat16_device_float32_normalizer(): + """Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation""" + config = create_default_config() + config.device = "cuda" + stats = create_default_stats() + + preprocessor, _ = make_vqbet_pre_post_processors( + config, + stats, + ) + + # Modify the pipeline to use bfloat16 device processor with float32 normalizer + modified_steps = [] + for step in preprocessor.steps: + if isinstance(step, DeviceProcessorStep): + # Device processor converts to bfloat16 + modified_steps.append(DeviceProcessorStep(device=config.device, float_dtype="bfloat16")) + elif isinstance(step, NormalizerProcessorStep): + # Normalizer stays configured as float32 (will auto-adapt to bfloat16) + modified_steps.append( + NormalizerProcessorStep( + features=step.features, + norm_map=step.norm_map, + stats=step.stats, + device=config.device, + dtype=torch.float32, # Deliberately configured as float32 + ) + ) + else: + modified_steps.append(step) + preprocessor.steps = modified_steps + + # Verify initial normalizer configuration + normalizer_step = preprocessor.steps[3] # NormalizerProcessorStep + assert normalizer_step.dtype == torch.float32 + + # Create test data with both state and visual observations + observation = { + OBS_STATE: torch.randn(8, dtype=torch.float32), + OBS_IMAGE: torch.randn(3, 224, 224, dtype=torch.float32), + } + action = torch.randn(7, dtype=torch.float32) + transition = create_transition(observation, action) + + batch = transition_to_batch(transition) + + # Process through full pipeline + processed = preprocessor(batch) + + # Verify: DeviceProcessor → bfloat16, NormalizerProcessor adapts → final output is bfloat16 + assert processed[OBS_STATE].dtype == torch.bfloat16 + assert processed[OBS_IMAGE].dtype == torch.bfloat16 # IDENTITY normalization still gets dtype conversion + assert processed[TransitionKey.ACTION.value].dtype == torch.bfloat16 + + # Verify normalizer automatically adapted its internal state + assert normalizer_step.dtype == torch.bfloat16 + # Check state stats (has normalization) + for stat_tensor in normalizer_step._tensor_stats[OBS_STATE].values(): + assert stat_tensor.dtype == torch.bfloat16 + # OBS_IMAGE uses IDENTITY normalization, so no stats to check diff --git a/tests/rl/test_actor.py b/tests/rl/test_actor.py index f078b46024e..ec67f1889aa 100644 --- a/tests/rl/test_actor.py +++ b/tests/rl/test_actor.py @@ -21,6 +21,7 @@ import torch from torch.multiprocessing import Event, Queue +from lerobot.utils.constants import OBS_STR from lerobot.utils.transition import Transition from tests.utils import require_package @@ -65,7 +66,7 @@ def close_service_stub(channel, server): @require_package("grpc") def test_establish_learner_connection_success(): - from lerobot.scripts.rl.actor import establish_learner_connection + from lerobot.rl.actor import establish_learner_connection """Test successful connection establishment.""" stub, _servicer, channel, server = create_learner_service_stub() @@ -82,7 +83,7 @@ def test_establish_learner_connection_success(): @require_package("grpc") def test_establish_learner_connection_failure(): - from lerobot.scripts.rl.actor import establish_learner_connection + from lerobot.rl.actor import establish_learner_connection """Test connection failure.""" stub, servicer, channel, server = create_learner_service_stub() @@ -101,7 +102,7 @@ def test_establish_learner_connection_failure(): @require_package("grpc") def test_push_transitions_to_transport_queue(): - from lerobot.scripts.rl.actor import push_transitions_to_transport_queue + from lerobot.rl.actor import push_transitions_to_transport_queue from lerobot.transport.utils import bytes_to_transitions from tests.transport.test_transport_utils import assert_transitions_equal @@ -110,12 +111,12 @@ def test_push_transitions_to_transport_queue(): transitions = [] for i in range(3): transition = Transition( - state={"observation": torch.randn(3, 64, 64), "state": torch.randn(10)}, + state={OBS_STR: torch.randn(3, 64, 64), "state": torch.randn(10)}, action=torch.randn(5), reward=torch.tensor(1.0 + i), done=torch.tensor(False), truncated=torch.tensor(False), - next_state={"observation": torch.randn(3, 64, 64), "state": torch.randn(10)}, + next_state={OBS_STR: torch.randn(3, 64, 64), "state": torch.randn(10)}, complementary_info={"step": torch.tensor(i)}, ) transitions.append(transition) @@ -137,7 +138,7 @@ def test_push_transitions_to_transport_queue(): @require_package("grpc") @pytest.mark.timeout(3) # force cross-platform watchdog def test_transitions_stream(): - from lerobot.scripts.rl.actor import transitions_stream + from lerobot.rl.actor import transitions_stream """Test transitions stream functionality.""" shutdown_event = Event() @@ -169,7 +170,7 @@ def test_transitions_stream(): @require_package("grpc") @pytest.mark.timeout(3) # force cross-platform watchdog def test_interactions_stream(): - from lerobot.scripts.rl.actor import interactions_stream + from lerobot.rl.actor import interactions_stream from lerobot.transport.utils import bytes_to_python_object, python_object_to_bytes """Test interactions stream functionality.""" diff --git a/tests/rl/test_actor_learner.py b/tests/rl/test_actor_learner.py index b2a7a5d5fe7..5d95dee042c 100644 --- a/tests/rl/test_actor_learner.py +++ b/tests/rl/test_actor_learner.py @@ -24,6 +24,7 @@ from lerobot.configs.train import TrainRLServerPipelineConfig from lerobot.policies.sac.configuration_sac import SACConfig +from lerobot.utils.constants import OBS_STR from lerobot.utils.transition import Transition from tests.utils import require_package @@ -33,12 +34,12 @@ def create_test_transitions(count: int = 3) -> list[Transition]: transitions = [] for i in range(count): transition = Transition( - state={"observation": torch.randn(3, 64, 64), "state": torch.randn(10)}, + state={OBS_STR: torch.randn(3, 64, 64), "state": torch.randn(10)}, action=torch.randn(5), reward=torch.tensor(1.0 + i), done=torch.tensor(i == count - 1), # Last transition is done truncated=torch.tensor(False), - next_state={"observation": torch.randn(3, 64, 64), "state": torch.randn(10)}, + next_state={OBS_STR: torch.randn(3, 64, 64), "state": torch.randn(10)}, complementary_info={"step": torch.tensor(i), "episode_id": i // 2}, ) transitions.append(transition) @@ -90,13 +91,13 @@ def cfg(): @require_package("grpc") @pytest.mark.timeout(10) # force cross-platform watchdog def test_end_to_end_transitions_flow(cfg): - from lerobot.scripts.rl.actor import ( + from lerobot.rl.actor import ( establish_learner_connection, learner_service_client, push_transitions_to_transport_queue, send_transitions, ) - from lerobot.scripts.rl.learner import start_learner + from lerobot.rl.learner import start_learner from lerobot.transport.utils import bytes_to_transitions from tests.transport.test_transport_utils import assert_transitions_equal @@ -152,12 +153,12 @@ def test_end_to_end_transitions_flow(cfg): @require_package("grpc") @pytest.mark.timeout(10) def test_end_to_end_interactions_flow(cfg): - from lerobot.scripts.rl.actor import ( + from lerobot.rl.actor import ( establish_learner_connection, learner_service_client, send_interactions, ) - from lerobot.scripts.rl.learner import start_learner + from lerobot.rl.learner import start_learner from lerobot.transport.utils import bytes_to_python_object, python_object_to_bytes """Test complete interactions flow from actor to learner.""" @@ -226,8 +227,8 @@ def test_end_to_end_interactions_flow(cfg): @pytest.mark.parametrize("data_size", ["small", "large"]) @pytest.mark.timeout(10) def test_end_to_end_parameters_flow(cfg, data_size): - from lerobot.scripts.rl.actor import establish_learner_connection, learner_service_client, receive_policy - from lerobot.scripts.rl.learner import start_learner + from lerobot.rl.actor import establish_learner_connection, learner_service_client, receive_policy + from lerobot.rl.learner import start_learner from lerobot.transport.utils import bytes_to_state_dict, state_to_bytes """Test complete parameter flow from learner to actor, with small and large data.""" diff --git a/tests/rl/test_learner_service.py b/tests/rl/test_learner_service.py index f5e1e8d48d9..b0e61165af2 100644 --- a/tests/rl/test_learner_service.py +++ b/tests/rl/test_learner_service.py @@ -50,7 +50,7 @@ def create_learner_service_stub( ): import grpc - from lerobot.scripts.rl.learner_service import LearnerService + from lerobot.rl.learner_service import LearnerService from lerobot.transport import services_pb2_grpc # generated from .proto """Fixture to start a LearnerService gRPC server and provide a connected stub.""" diff --git a/tests/utils/test_queue.py b/tests/rl/test_queue.py similarity index 98% rename from tests/utils/test_queue.py rename to tests/rl/test_queue.py index 6e42acdb7be..b6716fbd642 100644 --- a/tests/utils/test_queue.py +++ b/tests/rl/test_queue.py @@ -20,7 +20,7 @@ from torch.multiprocessing import Queue as TorchMPQueue -from lerobot.utils.queue import get_last_item_from_queue +from lerobot.rl.queue import get_last_item_from_queue def test_get_last_item_single_item(): diff --git a/tests/robots/test_reachy2.py b/tests/robots/test_reachy2.py index c93fbeced28..94152ea38eb 100644 --- a/tests/robots/test_reachy2.py +++ b/tests/robots/test_reachy2.py @@ -245,14 +245,14 @@ def test_get_observation(reachy2): obs = reachy2.get_observation() expected_keys = set(reachy2.joints_dict) - expected_keys.update(f"{v}" for v in REACHY2_VEL.keys() if reachy2.config.with_mobile_base) + expected_keys.update(f"{v}" for v in REACHY2_VEL if reachy2.config.with_mobile_base) expected_keys.update(reachy2.cameras.keys()) assert set(obs.keys()) == expected_keys - for motor in reachy2.joints_dict.keys(): + for motor in reachy2.joints_dict: assert obs[motor] == reachy2.reachy.joints[REACHY2_JOINTS[motor]].present_position if reachy2.config.with_mobile_base: - for vel in REACHY2_VEL.keys(): + for vel in REACHY2_VEL: assert obs[vel] == reachy2.reachy.mobile_base.odometry[REACHY2_VEL[vel]] if reachy2.config.with_left_teleop_camera: assert obs["teleop_left"].shape == ( @@ -282,7 +282,7 @@ def test_send_action(reachy2): action.update({k: i * 0.1 for i, k in enumerate(REACHY2_VEL.keys(), start=1)}) previous_present_position = { - k: reachy2.reachy.joints[REACHY2_JOINTS[k]].present_position for k in reachy2.joints_dict.keys() + k: reachy2.reachy.joints[REACHY2_JOINTS[k]].present_position for k in reachy2.joints_dict } returned = reachy2.send_action(action) @@ -290,7 +290,7 @@ def test_send_action(reachy2): assert returned == action assert reachy2.reachy._goal_position_set_total == len(reachy2.joints_dict) - for motor in reachy2.joints_dict.keys(): + for motor in reachy2.joints_dict: expected_pos = action[motor] real_pos = reachy2.reachy.joints[REACHY2_JOINTS[motor]].goal_position if reachy2.config.max_relative_target is None: diff --git a/tests/teleoperators/test_reachy2_teleoperator.py b/tests/teleoperators/test_reachy2_teleoperator.py index 5130de87da8..dd8c5904c33 100644 --- a/tests/teleoperators/test_reachy2_teleoperator.py +++ b/tests/teleoperators/test_reachy2_teleoperator.py @@ -121,20 +121,20 @@ def test_get_action(reachy2): action = reachy2.get_action() expected_keys = set(reachy2.joints_dict) - expected_keys.update(f"{v}" for v in REACHY2_VEL.keys() if reachy2.config.with_mobile_base) + expected_keys.update(f"{v}" for v in REACHY2_VEL if reachy2.config.with_mobile_base) assert set(action.keys()) == expected_keys - for motor in reachy2.joints_dict.keys(): + for motor in reachy2.joints_dict: if reachy2.config.use_present_position: assert action[motor] == reachy2.reachy.joints[REACHY2_JOINTS[motor]].present_position else: assert action[motor] == reachy2.reachy.joints[REACHY2_JOINTS[motor]].goal_position if reachy2.config.with_mobile_base: if reachy2.config.use_present_position: - for vel in REACHY2_VEL.keys(): + for vel in REACHY2_VEL: assert action[vel] == reachy2.reachy.mobile_base.odometry[REACHY2_VEL[vel]] else: - for vel in REACHY2_VEL.keys(): + for vel in REACHY2_VEL: assert action[vel] == reachy2.reachy.mobile_base.last_cmd_vel[REACHY2_VEL[vel]] diff --git a/tests/test_control_robot.py b/tests/test_control_robot.py index 374f9812989..ace0aea49df 100644 --- a/tests/test_control_robot.py +++ b/tests/test_control_robot.py @@ -16,10 +16,10 @@ from unittest.mock import patch -from lerobot.calibrate import CalibrateConfig, calibrate -from lerobot.record import DatasetRecordConfig, RecordConfig, record -from lerobot.replay import DatasetReplayConfig, ReplayConfig, replay -from lerobot.teleoperate import TeleoperateConfig, teleoperate +from lerobot.scripts.lerobot_calibrate import CalibrateConfig, calibrate +from lerobot.scripts.lerobot_record import DatasetRecordConfig, RecordConfig, record +from lerobot.scripts.lerobot_replay import DatasetReplayConfig, ReplayConfig, replay +from lerobot.scripts.lerobot_teleoperate import TeleoperateConfig, teleoperate from tests.fixtures.constants import DUMMY_REPO_ID from tests.mocks.mock_robot import MockRobotConfig from tests.mocks.mock_teleop import MockTeleopConfig diff --git a/tests/transport/test_transport_utils.py b/tests/transport/test_transport_utils.py index 79edad4e4f8..52825a24e28 100644 --- a/tests/transport/test_transport_utils.py +++ b/tests/transport/test_transport_utils.py @@ -21,6 +21,7 @@ import pytest import torch +from lerobot.utils.constants import ACTION from lerobot.utils.transition import Transition from tests.utils import require_cuda, require_package @@ -512,7 +513,7 @@ def test_transitions_to_bytes_single_transition(): def assert_transitions_equal(t1: Transition, t2: Transition): """Helper to assert two transitions are equal.""" assert_observation_equal(t1["state"], t2["state"]) - assert torch.allclose(t1["action"], t2["action"]) + assert torch.allclose(t1[ACTION], t2[ACTION]) assert torch.allclose(t1["reward"], t2["reward"]) assert torch.equal(t1["done"], t2["done"]) assert_observation_equal(t1["next_state"], t2["next_state"]) diff --git a/tests/utils/test_encoding_utils.py b/tests/utils/test_encoding_utils.py index 81394286254..8a023122102 100644 --- a/tests/utils/test_encoding_utils.py +++ b/tests/utils/test_encoding_utils.py @@ -16,7 +16,7 @@ import pytest -from lerobot.utils.encoding_utils import ( +from lerobot.motors.encoding_utils import ( decode_sign_magnitude, decode_twos_complement, encode_sign_magnitude, diff --git a/tests/utils/test_io_utils.py b/tests/utils/test_io_utils.py index 9768a5ef9d9..0beea639d6a 100644 --- a/tests/utils/test_io_utils.py +++ b/tests/utils/test_io_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + import json from pathlib import Path from typing import Any diff --git a/tests/utils/test_logging_utils.py b/tests/utils/test_logging_utils.py index 927fdc14dbc..560ba570155 100644 --- a/tests/utils/test_logging_utils.py +++ b/tests/utils/test_logging_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + import pytest from lerobot.utils.logging_utils import AverageMeter, MetricsTracker diff --git a/tests/utils/test_process.py b/tests/utils/test_process.py index 61e6e2c735d..e2b00cae9cb 100644 --- a/tests/utils/test_process.py +++ b/tests/utils/test_process.py @@ -22,7 +22,7 @@ import pytest -from lerobot.utils.process import ProcessSignalHandler +from lerobot.rl.process import ProcessSignalHandler # Fixture to reset shutdown_event_counter and original signal handlers before and after each test diff --git a/tests/utils/test_random_utils.py b/tests/utils/test_random_utils.py index 5865361d0b5..e3a5d420f2c 100644 --- a/tests/utils/test_random_utils.py +++ b/tests/utils/test_random_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + import random import numpy as np diff --git a/tests/utils/test_replay_buffer.py b/tests/utils/test_replay_buffer.py index 8781c5c0d23..b9d3a1ac046 100644 --- a/tests/utils/test_replay_buffer.py +++ b/tests/utils/test_replay_buffer.py @@ -21,12 +21,13 @@ import torch from lerobot.datasets.lerobot_dataset import LeRobotDataset -from lerobot.utils.buffer import BatchTransition, ReplayBuffer, random_crop_vectorized +from lerobot.rl.buffer import BatchTransition, ReplayBuffer, random_crop_vectorized +from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_STATE, OBS_STR, REWARD from tests.fixtures.constants import DUMMY_REPO_ID def state_dims() -> list[str]: - return ["observation.image", "observation.state"] + return [OBS_IMAGE, OBS_STATE] @pytest.fixture @@ -61,10 +62,10 @@ def create_random_image() -> torch.Tensor: def create_dummy_transition() -> dict: return { - "observation.image": create_random_image(), - "action": torch.randn(4), + OBS_IMAGE: create_random_image(), + ACTION: torch.randn(4), "reward": torch.tensor(1.0), - "observation.state": torch.randn( + OBS_STATE: torch.randn( 10, ), "done": torch.tensor(False), @@ -98,8 +99,8 @@ def create_dataset_from_replay_buffer(tmp_path) -> tuple[LeRobotDataset, ReplayB def create_dummy_state() -> dict: return { - "observation.image": create_random_image(), - "observation.state": torch.randn( + OBS_IMAGE: create_random_image(), + OBS_STATE: torch.randn( 10, ), } @@ -120,7 +121,7 @@ def get_tensors_memory_consumption(obj, visited_addresses): if isinstance(obj, torch.Tensor): return get_tensor_memory_consumption(obj) - elif isinstance(obj, (list, tuple)): + elif isinstance(obj, (list | tuple)): for item in obj: total_size += get_tensors_memory_consumption(item, visited_addresses) elif isinstance(obj, dict): @@ -180,7 +181,7 @@ def test_empty_buffer_sample_raises_error(replay_buffer): def test_zero_capacity_buffer_raises_error(): with pytest.raises(ValueError, match="Capacity must be greater than 0."): - ReplayBuffer(0, "cpu", ["observation", "next_observation"]) + ReplayBuffer(0, "cpu", [OBS_STR, "next_observation"]) def test_add_transition(replay_buffer, dummy_state, dummy_action): @@ -203,7 +204,7 @@ def test_add_transition(replay_buffer, dummy_state, dummy_action): def test_add_over_capacity(): - replay_buffer = ReplayBuffer(2, "cpu", ["observation", "next_observation"]) + replay_buffer = ReplayBuffer(2, "cpu", [OBS_STR, "next_observation"]) dummy_state_1 = create_dummy_state() dummy_action_1 = create_dummy_action() @@ -340,7 +341,7 @@ def test_sample_batch(replay_buffer): f"{k} should be equal to one of the dummy states." ) - for got_action_item in got_batch_transition["action"]: + for got_action_item in got_batch_transition[ACTION]: assert any(torch.equal(got_action_item, dummy_action) for dummy_action in dummy_actions), ( "Actions should be equal to the dummy actions." ) @@ -373,22 +374,22 @@ def test_to_lerobot_dataset(tmp_path): assert ds.num_frames == 4 for j, value in enumerate(ds): - print(torch.equal(value["observation.image"], buffer.next_states["observation.image"][j])) + print(torch.equal(value[OBS_IMAGE], buffer.next_states[OBS_IMAGE][j])) for i in range(len(ds)): for feature, value in ds[i].items(): - if feature == "action": + if feature == ACTION: assert torch.equal(value, buffer.actions[i]) - elif feature == "next.reward": + elif feature == REWARD: assert torch.equal(value, buffer.rewards[i]) - elif feature == "next.done": + elif feature == DONE: assert torch.equal(value, buffer.dones[i]) - elif feature == "observation.image": + elif feature == OBS_IMAGE: # Tensor -> numpy is not precise, so we have some diff there # TODO: Check and fix it - torch.testing.assert_close(value, buffer.states["observation.image"][i], rtol=0.3, atol=0.003) - elif feature == "observation.state": - assert torch.equal(value, buffer.states["observation.state"][i]) + torch.testing.assert_close(value, buffer.states[OBS_IMAGE][i], rtol=0.3, atol=0.003) + elif feature == OBS_STATE: + assert torch.equal(value, buffer.states[OBS_STATE][i]) def test_from_lerobot_dataset(tmp_path): @@ -436,14 +437,14 @@ def test_from_lerobot_dataset(tmp_path): ) assert torch.equal( - replay_buffer.states["observation.state"][: len(replay_buffer)], - reconverted_buffer.states["observation.state"][: len(replay_buffer)], + replay_buffer.states[OBS_STATE][: len(replay_buffer)], + reconverted_buffer.states[OBS_STATE][: len(replay_buffer)], ), "State should be the same after converting to dataset and return back" for i in range(4): torch.testing.assert_close( - replay_buffer.states["observation.image"][i], - reconverted_buffer.states["observation.image"][i], + replay_buffer.states[OBS_IMAGE][i], + reconverted_buffer.states[OBS_IMAGE][i], rtol=0.4, atol=0.004, ) @@ -454,16 +455,16 @@ def test_from_lerobot_dataset(tmp_path): next_index = (i + 1) % 4 torch.testing.assert_close( - replay_buffer.states["observation.image"][next_index], - reconverted_buffer.next_states["observation.image"][i], + replay_buffer.states[OBS_IMAGE][next_index], + reconverted_buffer.next_states[OBS_IMAGE][i], rtol=0.4, atol=0.004, ) for i in range(2, 4): assert torch.equal( - replay_buffer.states["observation.state"][i], - reconverted_buffer.next_states["observation.state"][i], + replay_buffer.states[OBS_STATE][i], + reconverted_buffer.next_states[OBS_STATE][i], ) @@ -494,7 +495,7 @@ def test_buffer_sample_alignment(): for i in range(50): state_sig = batch["state"]["state_value"][i].item() - action_val = batch["action"][i].item() + action_val = batch[ACTION][i].item() reward_val = batch["reward"][i].item() next_state_sig = batch["next_state"]["state_value"][i].item() is_done = batch["done"][i].item() > 0.5 @@ -563,10 +564,8 @@ def dummy_image_augmentation_function(x): replay_buffer.add(dummy_state, dummy_action, 1.0, dummy_state, False, False) sampled_transitions = replay_buffer.sample(1) - assert torch.all(sampled_transitions["state"]["observation.image"] == 10), ( - "Image augmentations should be applied" - ) - assert torch.all(sampled_transitions["next_state"]["observation.image"] == 10), ( + assert torch.all(sampled_transitions["state"][OBS_IMAGE] == 10), "Image augmentations should be applied" + assert torch.all(sampled_transitions["next_state"][OBS_IMAGE] == 10), ( "Image augmentations should be applied" ) @@ -580,8 +579,8 @@ def test_check_image_augmentations_with_drq_and_default_image_augmentation_funct # Let's check that it doesn't fail and shapes are correct sampled_transitions = replay_buffer.sample(1) - assert sampled_transitions["state"]["observation.image"].shape == (1, 3, 84, 84) - assert sampled_transitions["next_state"]["observation.image"].shape == (1, 3, 84, 84) + assert sampled_transitions["state"][OBS_IMAGE].shape == (1, 3, 84, 84) + assert sampled_transitions["next_state"][OBS_IMAGE].shape == (1, 3, 84, 84) def test_random_crop_vectorized_basic(): @@ -620,7 +619,7 @@ def _populate_buffer_for_async_test(capacity: int = 10) -> ReplayBuffer: buffer = ReplayBuffer( capacity=capacity, device="cpu", - state_keys=["observation.image", "observation.state"], + state_keys=[OBS_IMAGE, OBS_STATE], storage_device="cpu", ) @@ -628,8 +627,8 @@ def _populate_buffer_for_async_test(capacity: int = 10) -> ReplayBuffer: img = torch.ones(3, 128, 128) * i state_vec = torch.arange(11).float() + i state = { - "observation.image": img, - "observation.state": state_vec, + OBS_IMAGE: img, + OBS_STATE: state_vec, } buffer.add( state=state, @@ -648,14 +647,14 @@ def test_async_iterator_shapes_basic(): iterator = buffer.get_iterator(batch_size=batch_size, async_prefetch=True, queue_size=1) batch = next(iterator) - images = batch["state"]["observation.image"] - states = batch["state"]["observation.state"] + images = batch["state"][OBS_IMAGE] + states = batch["state"][OBS_STATE] assert images.shape == (batch_size, 3, 128, 128) assert states.shape == (batch_size, 11) - next_images = batch["next_state"]["observation.image"] - next_states = batch["next_state"]["observation.state"] + next_images = batch["next_state"][OBS_IMAGE] + next_states = batch["next_state"][OBS_STATE] assert next_images.shape == (batch_size, 3, 128, 128) assert next_states.shape == (batch_size, 11) @@ -668,13 +667,13 @@ def test_async_iterator_multiple_iterations(): for _ in range(5): batch = next(iterator) - images = batch["state"]["observation.image"] - states = batch["state"]["observation.state"] + images = batch["state"][OBS_IMAGE] + states = batch["state"][OBS_STATE] assert images.shape == (batch_size, 3, 128, 128) assert states.shape == (batch_size, 11) - next_images = batch["next_state"]["observation.image"] - next_states = batch["next_state"]["observation.state"] + next_images = batch["next_state"][OBS_IMAGE] + next_states = batch["next_state"][OBS_STATE] assert next_images.shape == (batch_size, 3, 128, 128) assert next_states.shape == (batch_size, 11) diff --git a/tests/utils/test_train_utils.py b/tests/utils/test_train_utils.py index 2d963d7aea2..892503e9772 100644 --- a/tests/utils/test_train_utils.py +++ b/tests/utils/test_train_utils.py @@ -1,4 +1,6 @@ -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,10 +13,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + from pathlib import Path from unittest.mock import Mock, patch -from lerobot.constants import ( +from lerobot.utils.constants import ( CHECKPOINTS_DIR, LAST_CHECKPOINT_LINK, OPTIMIZER_PARAM_GROUPS, diff --git a/tests/utils/test_visualization_utils.py b/tests/utils/test_visualization_utils.py new file mode 100644 index 00000000000..08a82757080 --- /dev/null +++ b/tests/utils/test_visualization_utils.py @@ -0,0 +1,226 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import importlib +import sys +from types import SimpleNamespace + +import numpy as np +import pytest + +from lerobot.processor import TransitionKey +from lerobot.utils.constants import OBS_STATE + + +@pytest.fixture +def mock_rerun(monkeypatch): + """ + Provide a mock `rerun` module so tests don't depend on the real library. + Also reload the module-under-test so it binds to this mock `rr`. + """ + calls = [] + + class DummyScalar: + def __init__(self, value): + self.value = float(value) + + class DummyImage: + def __init__(self, arr): + self.arr = arr + + def dummy_log(key, obj, **kwargs): + calls.append((key, obj, kwargs)) + + dummy_rr = SimpleNamespace( + Scalar=DummyScalar, + Image=DummyImage, + log=dummy_log, + init=lambda *a, **k: None, + spawn=lambda *a, **k: None, + ) + + # Inject fake module into sys.modules + monkeypatch.setitem(sys.modules, "rerun", dummy_rr) + + # Now import and reload the module under test, to bind to our rerun mock + import lerobot.utils.visualization_utils as vu + + importlib.reload(vu) + + # Expose both the reloaded module and the call recorder + yield vu, calls + + +def _keys(calls): + """Helper to extract just the keys logged to rr.log""" + return [k for (k, _obj, _kw) in calls] + + +def _obj_for(calls, key): + """Find the first object logged under a given key.""" + for k, obj, _kw in calls: + if k == key: + return obj + raise KeyError(f"Key {key} not found in calls: {calls}") + + +def _kwargs_for(calls, key): + for k, _obj, kw in calls: + if k == key: + return kw + raise KeyError(f"Key {key} not found in calls: {calls}") + + +def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun): + vu, calls = mock_rerun + + # Build EnvTransition dict + obs = { + f"{OBS_STATE}.temperature": np.float32(25.0), + # CHW image should be converted to HWC for rr.Image + "observation.camera": np.zeros((3, 10, 20), dtype=np.uint8), + } + act = { + "action.throttle": 0.7, + # 1D array should log individual Scalars with suffix _i + "action.vector": np.array([1.0, 2.0], dtype=np.float32), + } + transition = { + TransitionKey.OBSERVATION: obs, + TransitionKey.ACTION: act, + } + + # Extract observation and action data from transition like in the real call sites + obs_data = transition.get(TransitionKey.OBSERVATION, {}) + action_data = transition.get(TransitionKey.ACTION, {}) + vu.log_rerun_data(observation=obs_data, action=action_data) + + # We expect: + # - observation.state.temperature -> Scalar + # - observation.camera -> Image (HWC) with static=True + # - action.throttle -> Scalar + # - action.vector_0, action.vector_1 -> Scalars + expected_keys = { + f"{OBS_STATE}.temperature", + "observation.camera", + "action.throttle", + "action.vector_0", + "action.vector_1", + } + assert set(_keys(calls)) == expected_keys + + # Check scalar types and values + temp_obj = _obj_for(calls, f"{OBS_STATE}.temperature") + assert type(temp_obj).__name__ == "DummyScalar" + assert temp_obj.value == pytest.approx(25.0) + + throttle_obj = _obj_for(calls, "action.throttle") + assert type(throttle_obj).__name__ == "DummyScalar" + assert throttle_obj.value == pytest.approx(0.7) + + v0 = _obj_for(calls, "action.vector_0") + v1 = _obj_for(calls, "action.vector_1") + assert type(v0).__name__ == "DummyScalar" + assert type(v1).__name__ == "DummyScalar" + assert v0.value == pytest.approx(1.0) + assert v1.value == pytest.approx(2.0) + + # Check image handling: CHW -> HWC + img_obj = _obj_for(calls, "observation.camera") + assert type(img_obj).__name__ == "DummyImage" + assert img_obj.arr.shape == (10, 20, 3) # transposed + assert _kwargs_for(calls, "observation.camera").get("static", False) is True # static=True for images + + +def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun): + vu, calls = mock_rerun + + # First dict without prefixes treated as observation + # Second dict without prefixes treated as action + obs_plain = { + "temp": 1.5, + # Already HWC image => should stay as-is + "img": np.zeros((5, 6, 3), dtype=np.uint8), + "none": None, # should be skipped + } + act_plain = { + "throttle": 0.3, + "vec": np.array([9, 8, 7], dtype=np.float32), + } + + # Extract observation and action data from list like the old function logic did + # First dict was treated as observation, second as action + vu.log_rerun_data(observation=obs_plain, action=act_plain) + + # Expected keys with auto-prefixes + expected = { + "observation.temp", + "observation.img", + "action.throttle", + "action.vec_0", + "action.vec_1", + "action.vec_2", + } + logged = set(_keys(calls)) + assert logged == expected + + # Scalars + t = _obj_for(calls, "observation.temp") + assert type(t).__name__ == "DummyScalar" + assert t.value == pytest.approx(1.5) + + throttle = _obj_for(calls, "action.throttle") + assert type(throttle).__name__ == "DummyScalar" + assert throttle.value == pytest.approx(0.3) + + # Image stays HWC + img = _obj_for(calls, "observation.img") + assert type(img).__name__ == "DummyImage" + assert img.arr.shape == (5, 6, 3) + assert _kwargs_for(calls, "observation.img").get("static", False) is True + + # Vectors + for i, val in enumerate([9, 8, 7]): + o = _obj_for(calls, f"action.vec_{i}") + assert type(o).__name__ == "DummyScalar" + assert o.value == pytest.approx(val) + + +def test_log_rerun_data_kwargs_only(mock_rerun): + vu, calls = mock_rerun + + vu.log_rerun_data( + observation={"observation.temp": 10.0, "observation.gray": np.zeros((8, 8, 1), dtype=np.uint8)}, + action={"action.a": 1.0}, + ) + + keys = set(_keys(calls)) + assert "observation.temp" in keys + assert "observation.gray" in keys + assert "action.a" in keys + + temp = _obj_for(calls, "observation.temp") + assert type(temp).__name__ == "DummyScalar" + assert temp.value == pytest.approx(10.0) + + img = _obj_for(calls, "observation.gray") + assert type(img).__name__ == "DummyImage" + assert img.arr.shape == (8, 8, 1) # remains HWC + assert _kwargs_for(calls, "observation.gray").get("static", False) is True + + a = _obj_for(calls, "action.a") + assert type(a).__name__ == "DummyScalar" + assert a.value == pytest.approx(1.0)