Merge pull request #36 from Cadene/user/rcadene/2024_03_19_replay_buffer_folder

Add replay_buffer directory and dataset versioning

README.md

@@ -138,7 +138,7 @@ git lfs pull
 
 When adding a new dataset, mock it with
 ```
-python tests/scripts/mock_dataset.py --in-data-dir data/<dataset_id> --out-data-dir tests/data/<dataset_id>
+python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
 ```
 
 Run tests
@@ -148,22 +148,65 @@ DATA_DIR="tests/data" pytest -sx tests
 
 **Datasets**
 
-To add a pytorch rl dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
+To add a dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
 ```
-huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```
 
 Then you can upload it to the hub with:
 ```
-HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload --repo-type dataset $HF_USER/$DATASET data/$DATASET
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
+--repo-type dataset  \
+--revision v1.0
 ```
 
+You will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.0",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
+
 For instance, for [cadene/pusht](https://huggingface.co/datasets/cadene/pusht), we used:
 ```
 HF_USER=cadene
 DATASET=pusht
 ```
 
+If you want to improve an existing dataset, you can download it locally with:
+```
+mkdir -p data/$DATASET
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ${HF_USER}/$DATASET \
+--repo-type dataset \
+--local-dir data/$DATASET \
+--local-dir-use-symlinks=False \
+--revision v1.0
+```
+
+Iterate on your code and dataset with:
+```
+DATA_DIR=data python train.py
+```
+
+Upload a new version (v2.0 or v1.1 if the changes are respectively more or less significant):
+```
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
+--repo-type dataset \
+--revision v1.1 \
+--delete "*"
+```
+
+Then you will need to set the corresponding version as a default argument in your dataset class:
+```python
+  version: str | None = "v1.1",
+```
+See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
+
+
+Finally, you might want to mock the dataset if you need to update the unit tests as well:
+```
+python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
+```
+
 
 ## Acknowledgment
 - Our Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)

lerobot/common/datasets/abstract.py

@@ -19,6 +19,7 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
     def __init__(
         self,
         dataset_id: str,
+        version: str | None = None,
         batch_size: int = None,
         *,
         shuffle: bool = True,
@@ -31,8 +32,15 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
         transform: "torchrl.envs.Transform" = None,
     ):
         self.dataset_id = dataset_id
+        self.version = version
         self.shuffle = shuffle
         self.root = root
+
+        if self.root is not None and self.version is not None:
+            logging.warning(
+                f"The version of the dataset ({self.version}) is not enforced when root is provided ({self.root})."
+            )
+
         storage = self._download_or_load_dataset()
 
         super().__init__(
@@ -96,10 +104,14 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
 
     def _download_or_load_dataset(self) -> torch.StorageBase:
         if self.root is None:
-            self.data_dir = Path(snapshot_download(repo_id=f"cadene/{self.dataset_id}", repo_type="dataset"))
+            self.data_dir = Path(
+                snapshot_download(
+                    repo_id=f"cadene/{self.dataset_id}", repo_type="dataset", revision=self.version
+                )
+            )
         else:
             self.data_dir = self.root / self.dataset_id
-        return TensorStorage(TensorDict.load_memmap(self.data_dir))
+        return TensorStorage(TensorDict.load_memmap(self.data_dir / "replay_buffer"))
 
     def _compute_stats(self, num_batch=100, batch_size=32):
         rb = TensorDictReplayBuffer(

lerobot/common/datasets/aloha.py

@@ -84,6 +84,7 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
     def __init__(
         self,
         dataset_id: str,
+        version: str | None = "v1.0",
         batch_size: int = None,
         *,
         shuffle: bool = True,
@@ -99,6 +100,7 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
 
         super().__init__(
             dataset_id,
+            version,
             batch_size,
             shuffle=shuffle,
             root=root,

lerobot/common/datasets/pusht.py

@@ -87,6 +87,7 @@ class PushtExperienceReplay(AbstractExperienceReplay):
     def __init__(
         self,
         dataset_id: str,
+        version: str | None = "v1.0",
         batch_size: int = None,
         *,
         shuffle: bool = True,
@@ -100,6 +101,7 @@ class PushtExperienceReplay(AbstractExperienceReplay):
     ):
         super().__init__(
             dataset_id,
+            version,
             batch_size,
             shuffle=shuffle,
             root=root,

lerobot/common/datasets/simxarm.py

@@ -40,6 +40,7 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
     def __init__(
         self,
         dataset_id: str,
+        version: str | None = None,
         batch_size: int = None,
         *,
         shuffle: bool = True,
@@ -53,6 +54,7 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
     ):
         super().__init__(
             dataset_id,
+            version,
             batch_size,
             shuffle=shuffle,
             root=root,

tests/scripts/mock_dataset.py

@@ -1,5 +1,18 @@
 """
-    usage: `python tests/scripts/mock_dataset.py --in-data-dir data/pusht --out-data-dir tests/data/pusht`
+This script is designed to facilitate the creation of a subset of an existing dataset by selecting a specific number of frames from the original dataset.
+This subset can then be used for running quick unit tests.
+The script takes an input directory containing the original dataset and an output directory where the subset of the dataset will be saved.
+Additionally, the number of frames to include in the subset can be specified.
+The script ensures that the subset is a representative sample of the original dataset by copying the specified number of frames and retaining the structure and format of the data.
+
+Usage:
+    Run the script with the following command, specifying the path to the input data directory,
+    the path to the output data directory, and optionally the number of frames to include in the subset dataset:
+
+    `python tests/scripts/mock_dataset.py --in-data-dir path/to/input_data --out-data-dir path/to/output_data`
+
+Example:
+    `python tests/scripts/mock_dataset.py --in-data-dir data/pusht --out-data-dir tests/data/pusht`
 """
 
 import argparse
@@ -9,13 +22,16 @@ from tensordict import TensorDict
 from pathlib import Path
 
 
-def mock_dataset(in_data_dir, out_data_dir, num_frames=50):
+def mock_dataset(in_data_dir, out_data_dir, num_frames):
+    in_data_dir = Path(in_data_dir)
+    out_data_dir = Path(out_data_dir)
+
     # load full dataset as a tensor dict
-    in_td_data = TensorDict.load_memmap(in_data_dir)
+    in_td_data = TensorDict.load_memmap(in_data_dir / "replay_buffer")
 
     # use 1 frame to know the specification of the dataset
     # and copy it over `n` frames in the test artifact directory
-    out_td_data = in_td_data[0].expand(num_frames).memmap_like(out_data_dir)
+    out_td_data = in_td_data[0].expand(num_frames).memmap_like(out_data_dir / "replay_buffer")
 
     # copy the first `n` frames so that we have real data
     out_td_data[:num_frames] = in_td_data[:num_frames].clone()
@@ -24,18 +40,19 @@ def mock_dataset(in_data_dir, out_data_dir, num_frames=50):
     out_td_data.lock_()
 
     # copy the full statistics of dataset since it's pretty small
-    in_stats_path = Path(in_data_dir) / "stats.pth"
+    in_stats_path = in_data_dir / "stats.pth"
-    out_stats_path = Path(out_data_dir) / "stats.pth"
+    out_stats_path = out_data_dir / "stats.pth"
     shutil.copy(in_stats_path, out_stats_path)
 
 
 if __name__ == "__main__":
 
-    parser = argparse.ArgumentParser(description="Create dataset")
+    parser = argparse.ArgumentParser(description="Create a dataset with a subset of frames for quick testing.")
 
     parser.add_argument("--in-data-dir", type=str, help="Path to input data")
     parser.add_argument("--out-data-dir", type=str, help="Path to save the output data")
+    parser.add_argument("--num-frames", type=int, default=50, help="Number of frames to copy over")
 
     args = parser.parse_args()
 
-    mock_dataset(args.in_data_dir, args.out_data_dir)
+    mock_dataset(args.in_data_dir, args.out_data_dir, args.num_frames)