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https://github.com/huggingface/lerobot.git
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Jade Choghari
67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
from lerobot.datasets.lerobot_dataset import MultiLeRobotDataset
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REPO_A = "lerobot/pusht"
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REPO_B = "lerobot/aloha_mobile_cabinet" # replace with the actual repo id
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feature_keys_mapping = {
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REPO_A: { # pusht (1 camera, 2-dim)
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"action": "actions",
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"observation.state": "obs_state",
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"observation.image": "obs_image.cam_high",
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},
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REPO_B: { # dual arm (3 cameras, 14-dim)
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"action": "actions",
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"observation.state": "obs_state",
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"observation.images.cam_high": "obs_image.cam_high",
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"observation.images.cam_left_wrist": "obs_image.cam_left_wrist",
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"observation.images.cam_right_wrist": "obs_image.cam_right_wrist",
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},
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}
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from torchvision.transforms.v2 import Compose, ToImage, Resize
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image_tf = Compose([
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ToImage(), # converts to tensor if needed
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Resize((224, 224)), # unify sizes across datasets (96x96 vs 480x640)
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])
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from torch.utils.data import DataLoader
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dataset = MultiLeRobotDataset(
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repo_ids=[REPO_A, REPO_B],
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image_transforms=image_tf, # ensures same HxW
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feature_keys_mapping=feature_keys_mapping,
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train_on_all_features=True, # keep union of cameras; zero-fill missing
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# optional: override if you want fixed maxima; else inferred:
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# max_action_dim=14,
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# max_state_dim=14,
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max_action_dim=14,
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max_state_dim=14,
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max_image_dim=224,
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ignore_keys=[
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"next.*", # drop reward/done/success
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"index",
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"timestamp",
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"videos/*", # drop all video metadata
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"observation.effort", # 👈 drop effort everywhere
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],
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)
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breakpoint()
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loader = DataLoader(dataset, batch_size=8, shuffle=True, num_workers=0, pin_memory=True)
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for _ in range(100):
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batch = next(iter(loader))
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breakpoint()
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# vectors padded to maxima (pusht:2 -> 14; dual-arm:14 -> 14)
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assert batch["actions"].shape[-1] == 14
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assert batch["obs_state"].shape[-1] == 14
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assert batch["actions_padding_mask"].shape[-1] == 14
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assert batch["obs_state_padding_mask"].shape[-1] == 14
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# cameras: all canonical keys exist; pusht will have wrists zero-filled
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for cam in ["obs_image.cam_high", "obs_image.cam_left_wrist", "obs_image.cam_right_wrist"]:
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assert cam in batch
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assert f"{cam}_is_pad" in batch
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# images should all be 3x224x224 (or your transform’s size)
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img = batch[cam]
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assert img.ndim in (4, 5) # (B,C,H,W) or (B,T,C,H,W) depending on your loader
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