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lerobot
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Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
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Remi 2024-05-20 17:43:22 +02:00 committed by GitHub
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2 changed files with 4 additions and 4 deletions
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4
examples/4_calculate_validation_loss.py
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@ -40,7 +40,7 @@ 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], "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.
# - Load full dataset # - Load full dataset
full_dataset = LeRobotDataset("lerobot/pusht", split="train") full_dataset = LeRobotDataset("lerobot/pusht", split="train")
# - Calculate train and val subsets # - Calculate train and val subsets
@ -48,7 +48,7 @@ num_train_episodes = math.floor(full_dataset.num_episodes * 90 / 100)
num_val_episodes = full_dataset.num_episodes - num_train_episodes 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 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 training dataset (90% subset): {num_train_episodes}")
print(f"Number of episodes in validation dataset (10% reset): {num_val_episodes}") print(f"Number of episodes in validation dataset (10% subset): {num_val_episodes}")
# - Get first frame index of the validation set # - Get first frame index of the validation set
first_val_frame_index = full_dataset.episode_data_index["from"][num_train_episodes].item() 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. # - Load frames subset belonging to validation set using the `split` argument.

4
lerobot/common/datasets/utils.py
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@ -84,12 +84,12 @@ def load_hf_dataset(repo_id, version, root, split) -> datasets.Dataset:
hf_dataset = load_from_disk(str(Path(root) / repo_id / "train")) hf_dataset = load_from_disk(str(Path(root) / repo_id / "train"))
# TODO(rcadene): clean this which enables getting a subset of dataset # TODO(rcadene): clean this which enables getting a subset of dataset
if split != "train": if split != "train":
match = match = re.search(r"train\[(\d+):\]", split) match = re.search(r"train\[(\d+):\]", split)
if match: if match:
from_frame_index = int(match.group(1)) from_frame_index = int(match.group(1))
hf_dataset = hf_dataset.select(range(from_frame_index, len(hf_dataset))) hf_dataset = hf_dataset.select(range(from_frame_index, len(hf_dataset)))
else: else:
raise ValueError(split) raise ValueError(split), '`split` should either be "train" or of the form "train[{int}:]"'
else: else:
hf_dataset = load_dataset(repo_id, revision=version, split=split) hf_dataset = load_dataset(repo_id, revision=version, split=split)
hf_dataset.set_transform(hf_transform_to_torch) hf_dataset.set_transform(hf_transform_to_torch)