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LeRobotDataset v2.1 (#711)

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Co-authored-by: Remi <remi.cadene@huggingface.co>
Co-authored-by: Remi Cadene <re.cadene@gmail.com>
This commit is contained in:
Simon Alibert
2025-02-25 15:27:29 +01:00
committed by GitHub
parent aca464ca72
commit 3354d919fc
43 changed files with 2023 additions and 1322 deletions
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41
examples/port_datasets/pusht_zarr.py
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@@ -2,9 +2,10 @@ import shutil
from pathlib import Path
import numpy as np
import torch
from huggingface_hub import HfApi
from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, LeRobotDataset
from lerobot.common.constants import HF_LEROBOT_HOME
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface."
@@ -89,9 +90,9 @@ def calculate_coverage(zarr_data):
num_frames = len(block_pos)
coverage = np.zeros((num_frames,))
coverage = np.zeros((num_frames,), dtype=np.float32)
# 8 keypoints with 2 coords each
keypoints = np.zeros((num_frames, 16))
keypoints = np.zeros((num_frames, 16), dtype=np.float32)
# Set x, y, theta (in radians)
goal_pos_angle = np.array([256, 256, np.pi / 4])
@@ -117,7 +118,7 @@ def calculate_coverage(zarr_data):
intersection_area = goal_geom.intersection(block_geom).area
goal_area = goal_geom.area
coverage[i] = intersection_area / goal_area
keypoints[i] = torch.from_numpy(PushTEnv.get_keypoints(block_shapes).flatten())
keypoints[i] = PushTEnv.get_keypoints(block_shapes).flatten()
return coverage, keypoints
@@ -134,8 +135,8 @@ def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = T
if mode not in ["video", "image", "keypoints"]:
raise ValueError(mode)
if (LEROBOT_HOME / repo_id).exists():
shutil.rmtree(LEROBOT_HOME / repo_id)
if (HF_LEROBOT_HOME / repo_id).exists():
shutil.rmtree(HF_LEROBOT_HOME / repo_id)
if not raw_dir.exists():
download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw")
@@ -148,6 +149,10 @@ def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = T
action = zarr_data["action"][:]
image = zarr_data["img"] # (b, h, w, c)
if image.dtype == np.float32 and image.max() == np.float32(255):
# HACK: images are loaded as float32 but they actually encode uint8 data
image = image.astype(np.uint8)
episode_data_index = {
"from": np.concatenate(([0], zarr_data.meta["episode_ends"][:-1])),
"to": zarr_data.meta["episode_ends"],
@@ -175,28 +180,30 @@ def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = T
for frame_idx in range(num_frames):
i = from_idx + frame_idx
idx = i + (frame_idx < num_frames - 1)
frame = {
"action": torch.from_numpy(action[i]),
"action": action[i],
# Shift reward and success by +1 until the last item of the episode
"next.reward": reward[i + (frame_idx < num_frames - 1)],
"next.success": success[i + (frame_idx < num_frames - 1)],
"next.reward": reward[idx : idx + 1],
"next.success": success[idx : idx + 1],
"task": PUSHT_TASK,
}
frame["observation.state"] = torch.from_numpy(agent_pos[i])
frame["observation.state"] = agent_pos[i]
if mode == "keypoints":
frame["observation.environment_state"] = torch.from_numpy(keypoints[i])
frame["observation.environment_state"] = keypoints[i]
else:
frame["observation.image"] = torch.from_numpy(image[i])
frame["observation.image"] = image[i]
dataset.add_frame(frame)
dataset.save_episode(task=PUSHT_TASK)
dataset.consolidate()
dataset.save_episode()
if push_to_hub:
dataset.push_to_hub()
hub_api = HfApi()
hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
if __name__ == "__main__":
@@ -218,5 +225,5 @@ if __name__ == "__main__":
main(raw_dir, repo_id=repo_id, mode=mode)
# Uncomment if you want to load the local dataset and explore it
# dataset = LeRobotDataset(repo_id=repo_id, local_files_only=True)
# dataset = LeRobotDataset(repo_id=repo_id)
# breakpoint()