Add manage_dataset

lerobot/common/datasets/lerobot_dataset.py

@@ -873,7 +873,13 @@ def encode_episode_videos(self, episode_index: int) -> dict:
 
         return video_paths
 
-    def consolidate(self, run_compute_stats: bool = True, keep_image_files: bool = False) -> None:
+    def consolidate(
+        self,
+        run_compute_stats: bool = True,
+        keep_image_files: bool = False,
+        batch_size: int = 8,
+        num_workers: int = 8,
+    ) -> None:
         self.hf_dataset = self.load_hf_dataset()
         self.episode_data_index = get_episode_data_index(self.meta.episodes, self.episodes)
         check_timestamps_sync(self.hf_dataset, self.episode_data_index, self.fps, self.tolerance_s)
@@ -896,7 +902,7 @@ def consolidate(self, run_compute_stats: bool = True, keep_image_files: bool = F
         if run_compute_stats:
             self.stop_image_writer()
             # TODO(aliberts): refactor stats in save_episodes
-            self.meta.stats = compute_stats(self)
+            self.meta.stats = compute_stats(self, batch_size=batch_size, num_workers=num_workers)
             serialized_stats = serialize_dict(self.meta.stats)
             write_json(serialized_stats, self.root / STATS_PATH)
             self.consolidated = True

lerobot/scripts/manage_dataset.py

@@ -0,0 +1,110 @@
+"""
+Utilities to manage a dataset.
+
+Examples of usage:
+
+- Consolidate a dataset, by encoding images into videos and computing statistics:
+```bash
+python lerobot/scripts/manage_dataset.py consolidate \
+    --repo-id $USER/koch_test
+```
+
+- Consolidate a dataset which is not uploaded on the hub yet:
+```bash
+python lerobot/scripts/manage_dataset.py consolidate \
+    --repo-id $USER/koch_test \
+    --local-files-only 1
+```
+
+- Upload a dataset on the hub:
+```bash
+python lerobot/scripts/manage_dataset.py push_to_hub \
+    --repo-id $USER/koch_test
+```
+"""
+
+import argparse
+from pathlib import Path
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    subparsers = parser.add_subparsers(dest="mode", required=True)
+
+    # Set common options for all the subparsers
+    base_parser = argparse.ArgumentParser(add_help=False)
+    base_parser.add_argument(
+        "--root",
+        type=Path,
+        default=None,
+        help="Root directory where the dataset will be stored (e.g. 'dataset/path').",
+    )
+    base_parser.add_argument(
+        "--repo-id",
+        type=str,
+        default="lerobot/test",
+        help="Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).",
+    )
+    base_parser.add_argument(
+        "--local-files-only",
+        type=int,
+        default=0,
+        help="Use local files only. By default, this script will try to fetch the dataset from the hub if it exists.",
+    )
+
+    parser_conso = subparsers.add_parser("consolidate", parents=[base_parser])
+    parser_conso.add_argument(
+        "--batch-size",
+        type=int,
+        default=32,
+        help="Batch size loaded by DataLoader for computing the dataset statistics.",
+    )
+    parser_conso.add_argument(
+        "--num-workers",
+        type=int,
+        default=8,
+        help="Number of processes of Dataloader for computing the dataset statistics.",
+    )
+
+    parser_push = subparsers.add_parser("push_to_hub", parents=[base_parser])
+    parser_push.add_argument(
+        "--tags",
+        type=str,
+        nargs="*",
+        default=None,
+        help="Optional additional tags to categorize the dataset on the Hugging Face Hub. Use space-separated values (e.g. 'so100 indoor'). The tag 'LeRobot' will always be added.",
+    )
+    parser_push.add_argument(
+        "--license",
+        type=str,
+        default="apache-2.0",
+        help="Repo license. Must be one of https://huggingface.co/docs/hub/repositories-licenses. Defaults to mit.",
+    )
+    parser_push.add_argument(
+        "--private",
+        type=int,
+        default=0,
+        help="Create a private dataset repository on the Hugging Face Hub.",
+    )
+
+    args = parser.parse_args()
+    kwargs = vars(args)
+
+    mode = kwargs.pop("mode")
+    repo_id = kwargs.pop("repo_id")
+    root = kwargs.pop("root")
+    local_files_only = kwargs.pop("local_files_only")
+
+    dataset = LeRobotDataset(
+        repo_id=repo_id,
+        root=root,
+        local_files_only=local_files_only,
+    )
+
+    if mode == "consolidate":
+        dataset.consolidate(**kwargs)
+
+    elif mode == "push_to_hub":
+        private = kwargs.pop("private") == 1
+        dataset.push_to_hub(private=private, **kwargs)