Commandline running process inside Docker

.dockerignore

@@ -0,0 +1,26 @@
+# Ignore the following files and directories when building the Docker image
+*.pyc
+__pycache__/
+*.ipynb_checkpoints
+*.log
+*.csv
+*.tsv
+*.h5
+*.pth
+*.pt
+*.zip
+*.tar.gz
+*.egg-info/
+dist/
+build/
+.env
+venv/
+.env.local
+*.DS_Store
+*.egg
+*.whl
+*.pkl
+*.json
+*.yaml
+*.yml
+submodules/

.gitignore

@@ -158,3 +158,6 @@ dmypy.json
 # Pyre type checker
 .pyre/
 learnableearthparser/fast_sampler/_sampler.c
+
+# data
+data/

Dockerfile

@@ -0,0 +1,60 @@
+FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
+
+# Set the working directory
+WORKDIR /EDGS
+
+# Install system dependencies first, including git, build-essential, and cmake
+RUN apt-get update && apt-get install -y \
+  git \
+  wget \
+  build-essential \
+  cmake \
+  ninja-build \
+  libgl1-mesa-glx \
+  libglib2.0-0 \
+  && rm -rf /var/lib/apt/lists/*
+
+# Copy only essential files for cloning submodules first (e.g., .gitmodules)
+# Or, if submodules are public, you might not need to copy anything specific for this step
+# For simplicity, we'll copy everything, but this could be optimized
+COPY . .
+
+# Initialize and update submodules
+RUN git submodule init && git submodule update --recursive
+
+# Install Miniconda
+RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda.sh && \
+  bash /tmp/miniconda.sh -b -p /opt/conda && \
+  rm /tmp/miniconda.sh
+ENV PATH="/opt/conda/bin:${PATH}"
+
+# Create the conda environment and install dependencies
+# Accept Anaconda TOS before using conda
+RUN conda init bash && \
+  conda config --set always_yes yes --set changeps1 no && \
+  conda config --add channels defaults && \
+  conda config --set channel_priority strict && \
+  conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main && \
+  conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
+# Now you can safely create your environment
+RUN conda create -y -n edgs python=3.10 pip && \
+  conda clean -afy && \
+  echo "source activate edgs" > ~/.bashrc
+
+# Set CUDA architectures to compile for
+ENV TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9;9.0+PTX"
+
+# Activate the environment and install Python dependencies
+RUN /bin/bash -c "source activate edgs && \
+  pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 && \
+  pip install -e ./submodules/gaussian-splatting/submodules/diff-gaussian-rasterization && \
+  pip install -e ./submodules/gaussian-splatting/submodules/simple-knn && \
+  pip install pycolmap wandb hydra-core tqdm torchmetrics lpips matplotlib rich plyfile imageio imageio-ffmpeg && \
+  pip install -e ./submodules/RoMa && \
+  pip install gradio plotly scikit-learn moviepy==2.1.1 ffmpeg open3d jupyterlab matplotlib"
+
+# Expose the port for Gradio
+EXPOSE 7862
+
+# Keep the container running in detached mode
+CMD ["tail", "-f", "/dev/null"]

README.md

@@ -69,45 +69,14 @@ Alternatively, check our [Colab notebook](https://colab.research.google.com/gith
 <a id="sec-install"></a>
 ## 🛠️ Installation
 
-You can either run `install.sh` or manually install using the following:
+You can install it just:
 
 ```bash
-git clone [email protected]:CompVis/EDGS.git --recursive
-cd EDGS
-git submodule update --init --recursive 
-
-conda create -y -n edgs python=3.10 pip
-conda activate edgs
-
-# Set up path to your CUDA. In our experience similar versions like 12.2 also work well 
-export CUDA_HOME=/usr/local/cuda-12.1
-export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
-export PATH=$CUDA_HOME/bin:$PATH
-
-conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia -y
-conda install nvidia/label/cuda-12.1.0::cuda-toolkit -y
-
-pip install -e submodules/gaussian-splatting/submodules/diff-gaussian-rasterization
-pip install -e submodules/gaussian-splatting/submodules/simple-knn
-
-# For COLMAP and pycolmap
-# Optionally install original colmap but probably pycolmap suffices
-# conda install conda-forge/label/colmap_dev::colmap
-pip install pycolmap
-
-
-pip install wandb hydra-core tqdm torchmetrics lpips matplotlib rich plyfile imageio imageio-ffmpeg
-conda install numpy=1.26.4 -y -c conda-forge --override-channels
-
-pip install -e submodules/RoMa
-conda install anaconda::jupyter --yes
-
-# Stuff necessary for gradio and visualizations
-pip install gradio 
-pip install plotly scikit-learn moviepy==2.1.1 ffmpeg
-pip install open3d 
+docker compose up -d
 ```
 
+or you can install with running `script/install.sh`.
+
 <a id="sec-data"></a>
 ## 📦 Data
 
@@ -118,6 +87,36 @@ We evaluated on the following datasets:
 
 ### Using Your Own Dataset
 
+#### Option A
+Use gradle demo.
+After running `docker compose up -d`,
+```
+docker compose exec edgs-app bash
+python script/gradio_demo.py --port 7862
+```
+
+#### Option B
+From command line.
+```
+docker compose exec edgs-app bash
+python script/fit_model_to_scene_full.py --video_path <your mp4 video> [--processed_scenes_dir <output directory>]
+```
+
+#### Option C
+Using Jupyter lab.
+```
+docker compose exec edgs-app bash
+```
+And in the terminal in the docker container,
+```
+jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --notebook-dir=notebooks
+```
+After JupyterLab starts, it will print URLs to the terminal. Look for a URL containing a token, like:
+    `http://127.0.0.1:8888/lab?token=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx`
+Open `http://localhost:8888` (or `http://127.0.0.1:8888`) in your host browser.
+When prompted for a "Password or token", paste the `xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx` part from the URL in step 4 into the field and log in. Alternatively, you can paste the full URL from step 4 directly into your browser.
+
+#### Option D
 You can use the same data format as the [3DGS project](https://github.com/graphdeco-inria/gaussian-splatting?tab=readme-ov-file#processing-your-own-scenes). Please follow their guide to prepare your scene.
 
 Expected folder structure:
@@ -134,6 +133,11 @@ scene_folder
         |---points3D.bin
 ```
 
+```
+docker compose exec edgs-app bash
+```
+Then run training command as described below section.
+
 Nerf synthetic format is also acceptable. 
 
 You can also use functions provided in our code to convert a collection of images or a sinlge video into a desired format. However, this may requre tweaking and processing time can be large for large collection of images with little overlap.
@@ -144,7 +148,7 @@ You can also use functions provided in our code to convert a collection of image
 
 To optimize on a single scene in COLMAP format use this code.  
 ```bash
-python train.py \
+python script/train.py \
   train.gs_epochs=30000 \
   train.no_densify=True \
   gs.dataset.source_path=<scene folder> \

configs/train.yaml

@@ -1,22 +1,22 @@
 defaults:
   - gs: base
-  - _self_ 
+  - _self_
 
 seed: 228
 
 wandb:
-  mode: "online" # "disabled" for no logging
+  mode: "disabled" # "online" or "disabled"
   entity: "3dcorrespondence"
   project: "Adv3DGS"
   group: null
   name: null
   tag: "debug"
-    
+
 train:
-  gs_epochs: 0 # number of 3dgs iterations
-  reduce_opacity: True 
+  gs_epochs: 1000 # number of 3dgs iterations
+  reduce_opacity: True
   no_densify: False # if True, the model will not be densified
-  max_lr: True 
+  max_lr: True
 
 load:
   gs: null #path to 3dgs checkpoint
@@ -28,11 +28,9 @@ verbose: true
 init_wC:
   use: True # use EDGS
   matches_per_ref: 15_000 # number of matches per reference
-  num_refs: 180 # number of reference images
+  num_refs: 18 # number of reference images
   nns_per_ref: 3 # number of nearest neighbors per reference
   scaling_factor: 0.001
   proj_err_tolerance: 0.01
   roma_model: "outdoors" # you can change this to "indoors" or "outdoors"
-  add_SfM_init : False
-
-
+  add_SfM_init: False

docker-compose.yml

@@ -0,0 +1,25 @@
+services:
+  edgs-app:
+    build: . # Instructs Docker Compose to build using the Dockerfile in the current directory
+    image: edgs-app # This is the name of the image you built
+    ports:
+      - "7862:7862" # For Gradio, if you still use it
+      - "8888:8888" # Map port 8888 for JupyterLab
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all # Use all available GPUs
+              capabilities: [gpu] # Request GPU capabilities
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=all
+    volumes:
+      - ./data:/EDGS/data # Example: map a local 'data' folder to '/EDGS/data' in the container
+      - ./outputs:/EDGS/outputs # Example: map a local 'output' folder
+      - ./script:/EDGS/script # Example: map a local 'scripts' folder
+      - ./source:/EDGS/source # Example: map a local 'sources' folder
+      - ./configs:/EDGS/configs # Example: map a local 'config' folder
+      - ./notebooks:/EDGS/notebooks # Map a local 'notebooks' folder for JupyterLab
+    stdin_open: true # Keep STDIN open for interactive processes
+    tty: true        # Allocate a TTY