Merge branch 'main' into tutorial_act_pusht

examples/4_calculate_validation_loss.py

@@ -8,6 +8,7 @@ especially in the context of imitation learning. The most reliable approach is t
 on the target environment, whether that be in simulation or the real world.
 """
 
+import math
 from pathlib import Path
 
 import torch
@@ -39,11 +40,29 @@ delta_timestamps = {
     "action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],
 }
 
-# Load the last 10 episodes of the dataset as a validation set.
+# Load the last 10% of episodes of the dataset as a validation set.
-# The `split` argument utilizes the `datasets` library's syntax for slicing datasets.
+# - Load full dataset
-# For more information on the Slice API, please see:
+full_dataset = LeRobotDataset("lerobot/pusht", split="train")
+# - Calculate train and val subsets
+num_train_episodes = math.floor(full_dataset.num_episodes * 90 / 100)
+num_val_episodes = full_dataset.num_episodes - num_train_episodes
+print(f"Number of episodes in full dataset: {full_dataset.num_episodes}")
+print(f"Number of episodes in training dataset (90% subset): {num_train_episodes}")
+print(f"Number of episodes in validation dataset (10% subset): {num_val_episodes}")
+# - Get first frame index of the validation set
+first_val_frame_index = full_dataset.episode_data_index["from"][num_train_episodes].item()
+# - Load frames subset belonging to validation set using the `split` argument.
+#   It utilizes the `datasets` library's syntax for slicing datasets.
+#   For more information on the Slice API, please see:
 #   https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits
-val_dataset = LeRobotDataset("lerobot/pusht", split="train[24342:]", delta_timestamps=delta_timestamps)
+train_dataset = LeRobotDataset(
+    "lerobot/pusht", split=f"train[:{first_val_frame_index}]", delta_timestamps=delta_timestamps
+)
+val_dataset = LeRobotDataset(
+    "lerobot/pusht", split=f"train[{first_val_frame_index}:]", delta_timestamps=delta_timestamps
+)
+print(f"Number of frames in training dataset (90% subset): {len(train_dataset)}")
+print(f"Number of frames in validation dataset (10% subset): {len(val_dataset)}")
 
 # Create dataloader for evaluation.
 val_dataloader = torch.utils.data.DataLoader(

lerobot/common/datasets/utils.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import json
+import re
 from pathlib import Path
 from typing import Dict
 
@@ -80,7 +81,23 @@ def hf_transform_to_torch(items_dict):
 def load_hf_dataset(repo_id, version, root, split) -> datasets.Dataset:
     """hf_dataset contains all the observations, states, actions, rewards, etc."""
     if root is not None:
-        hf_dataset = load_from_disk(str(Path(root) / repo_id / split))
+        hf_dataset = load_from_disk(str(Path(root) / repo_id / "train"))
+        # TODO(rcadene): clean this which enables getting a subset of dataset
+        if split != "train":
+            if "%" in split:
+                raise NotImplementedError(f"We dont support splitting based on percentage for now ({split}).")
+            match_from = re.search(r"train\[(\d+):\]", split)
+            match_to = re.search(r"train\[:(\d+)\]", split)
+            if match_from:
+                from_frame_index = int(match_from.group(1))
+                hf_dataset = hf_dataset.select(range(from_frame_index, len(hf_dataset)))
+            elif match_to:
+                to_frame_index = int(match_to.group(1))
+                hf_dataset = hf_dataset.select(range(to_frame_index))
+            else:
+                raise ValueError(
+                    f'`split` ({split}) should either be "train", "train[INT:]", or "train[:INT]"'
+                )
     else:
         hf_dataset = load_dataset(repo_id, revision=version, split=split)
     hf_dataset.set_transform(hf_transform_to_torch)
@@ -273,6 +290,12 @@ def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torc
             "to": [3, 7, 12]
         }
     """
+    if len(hf_dataset) == 0:
+        episode_data_index = {
+            "from": torch.tensor([]),
+            "to": torch.tensor([]),
+        }
+        return episode_data_index
     for idx, episode_idx in enumerate(hf_dataset["episode_index"]):
         if episode_idx != current_episode:
             # We encountered a new episode, so we append its starting location to the "from" list
@@ -303,6 +326,8 @@ def reset_episode_index(hf_dataset: datasets.Dataset) -> datasets.Dataset:
 
     This brings the `episode_index` to the required format.
     """
+    if len(hf_dataset) == 0:
+        return hf_dataset
     unique_episode_idxs = torch.stack(hf_dataset["episode_index"]).unique().tolist()
     episode_idx_to_reset_idx_mapping = {
         ep_id: reset_ep_id for reset_ep_id, ep_id in enumerate(unique_episode_idxs)

tests/test_examples.py

@@ -111,7 +111,7 @@ def test_examples_2_through_4():
                 '# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")',
                 'pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")',
             ),
-            ('split="train[24342:]"', 'split="train[-1:]"'),
+            ('split=f"train[{first_val_frame_index}:]"', 'split="train[30:]"'),
             ("num_workers=4", "num_workers=0"),
             ('device = torch.device("cuda")', 'device = torch.device("cpu")'),
             ("batch_size=64", "batch_size=1"),