chore(scripts): remove deprecated script (#887)
This commit is contained in:
Steven Palma
GitHub
parent
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commit
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import shutil
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from pathlib import Path
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import numpy as np
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from huggingface_hub import HfApi
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from lerobot.common.constants import HF_LEROBOT_HOME
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from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
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from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
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PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface."
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PUSHT_FEATURES = {
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"observation.state": {
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"dtype": "float32",
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"shape": (2,),
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"names": {
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"axes": ["x", "y"],
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},
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},
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"action": {
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"dtype": "float32",
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"shape": (2,),
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"names": {
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"axes": ["x", "y"],
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},
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},
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"next.reward": {
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"dtype": "float32",
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"shape": (1,),
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"names": None,
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},
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"next.success": {
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"dtype": "bool",
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"shape": (1,),
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"names": None,
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},
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"observation.environment_state": {
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"dtype": "float32",
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"shape": (16,),
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"names": [
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"keypoints",
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],
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},
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"observation.image": {
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"dtype": None,
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"shape": (3, 96, 96),
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"names": [
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"channels",
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"height",
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"width",
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],
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},
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}
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def build_features(mode: str) -> dict:
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features = PUSHT_FEATURES
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if mode == "keypoints":
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features.pop("observation.image")
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else:
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features.pop("observation.environment_state")
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features["observation.image"]["dtype"] = mode
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return features
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def load_raw_dataset(zarr_path: Path):
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try:
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from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import (
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ReplayBuffer as DiffusionPolicyReplayBuffer,
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)
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except ModuleNotFoundError as e:
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print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
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raise e
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zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)
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return zarr_data
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def calculate_coverage(zarr_data):
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try:
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import pymunk
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from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
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except ModuleNotFoundError as e:
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print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
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raise e
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block_pos = zarr_data["state"][:, 2:4]
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block_angle = zarr_data["state"][:, 4]
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num_frames = len(block_pos)
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coverage = np.zeros((num_frames,), dtype=np.float32)
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# 8 keypoints with 2 coords each
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keypoints = np.zeros((num_frames, 16), dtype=np.float32)
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# Set x, y, theta (in radians)
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goal_pos_angle = np.array([256, 256, np.pi / 4])
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goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
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for i in range(num_frames):
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space = pymunk.Space()
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space.gravity = 0, 0
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space.damping = 0
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# Add walls.
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walls = [
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PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
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PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
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PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
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PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
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]
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space.add(*walls)
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block_body, block_shapes = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
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goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
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block_geom = pymunk_to_shapely(block_body, block_body.shapes)
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intersection_area = goal_geom.intersection(block_geom).area
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goal_area = goal_geom.area
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coverage[i] = intersection_area / goal_area
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keypoints[i] = PushTEnv.get_keypoints(block_shapes).flatten()
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return coverage, keypoints
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def calculate_success(coverage: float, success_threshold: float):
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return coverage > success_threshold
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def calculate_reward(coverage: float, success_threshold: float):
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return np.clip(coverage / success_threshold, 0, 1)
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def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = True):
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if mode not in ["video", "image", "keypoints"]:
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raise ValueError(mode)
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if (HF_LEROBOT_HOME / repo_id).exists():
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shutil.rmtree(HF_LEROBOT_HOME / repo_id)
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if not raw_dir.exists():
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download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw")
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zarr_data = load_raw_dataset(zarr_path=raw_dir / "pusht_cchi_v7_replay.zarr")
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env_state = zarr_data["state"][:]
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agent_pos = env_state[:, :2]
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action = zarr_data["action"][:]
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image = zarr_data["img"] # (b, h, w, c)
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if image.dtype == np.float32 and image.max() == np.float32(255):
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# HACK: images are loaded as float32 but they actually encode uint8 data
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image = image.astype(np.uint8)
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episode_data_index = {
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"from": np.concatenate(([0], zarr_data.meta["episode_ends"][:-1])),
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"to": zarr_data.meta["episode_ends"],
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}
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# Calculate success and reward based on the overlapping area
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# of the T-object and the T-area.
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coverage, keypoints = calculate_coverage(zarr_data)
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success = calculate_success(coverage, success_threshold=0.95)
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reward = calculate_reward(coverage, success_threshold=0.95)
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features = build_features(mode)
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dataset = LeRobotDataset.create(
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repo_id=repo_id,
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fps=10,
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robot_type="2d pointer",
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features=features,
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image_writer_threads=4,
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)
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episodes = range(len(episode_data_index["from"]))
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for ep_idx in episodes:
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from_idx = episode_data_index["from"][ep_idx]
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to_idx = episode_data_index["to"][ep_idx]
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num_frames = to_idx - from_idx
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for frame_idx in range(num_frames):
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i = from_idx + frame_idx
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idx = i + (frame_idx < num_frames - 1)
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frame = {
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"action": action[i],
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# Shift reward and success by +1 until the last item of the episode
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"next.reward": reward[idx : idx + 1],
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"next.success": success[idx : idx + 1],
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"task": PUSHT_TASK,
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}
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frame["observation.state"] = agent_pos[i]
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if mode == "keypoints":
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frame["observation.environment_state"] = keypoints[i]
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else:
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frame["observation.image"] = image[i]
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dataset.add_frame(frame)
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dataset.save_episode()
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if push_to_hub:
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dataset.push_to_hub()
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hub_api = HfApi()
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hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
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if __name__ == "__main__":
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# To try this script, modify the repo id with your own HuggingFace user (e.g cadene/pusht)
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repo_id = "lerobot/pusht"
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modes = ["video", "image", "keypoints"]
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# Uncomment if you want to try with a specific mode
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# modes = ["video"]
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# modes = ["image"]
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# modes = ["keypoints"]
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raw_dir = Path("data/lerobot-raw/pusht_raw")
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for mode in modes:
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if mode in ["image", "keypoints"]:
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repo_id += f"_{mode}"
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# download and load raw dataset, create LeRobotDataset, populate it, push to hub
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main(raw_dir, repo_id=repo_id, mode=mode)
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# Uncomment if you want to load the local dataset and explore it
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# dataset = LeRobotDataset(repo_id=repo_id)
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# breakpoint()
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@ -1,85 +0,0 @@
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https://drive.google.com/file/d/1_SOJkgfP5yZyVjMhTt3nwhvyUjcnlI51/view?usp=drive_link
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https://drive.google.com/file/d/1rmgN8UUzph1qwJnzG1d-uOafodn-gLvb/view?usp=drive_link
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https://drive.google.com/file/d/1NYQ-XxsBVinB6dUoZmVWweT83367P3i2/view?usp=drive_link
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https://drive.google.com/file/d/1oAv_j74zxxCJieMG7r5Vl2BeHK1__3s3/view?usp=drive_link
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https://drive.google.com/file/d/1wFUJQROsrTJt64YRuIeExhFjr2wnK5uu/view?usp=drive_link
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https://drive.google.com/file/d/1KzL3Tt0Le7jVl58XVRUcmigmXjyiuhbK/view?usp=drive_link
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https://drive.google.com/file/d/1qy_YBladeHtianSSGtgAPSHtMin7msvf/view?usp=drive_link
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https://drive.google.com/file/d/1rA_F0V_qL_nyuC_0aBKCisF4-0TIkF2Y/view?usp=drive_link
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https://drive.google.com/file/d/1hw-8qMpz9VgSt62XoASqNRuPECpCwJQP/view?usp=drive_link
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https://drive.google.com/file/d/1BpHOl9rKMzdvNGka6js7C0s40hH6vnDA/view?usp=drive_link
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https://drive.google.com/file/d/1PazhkhiDnJ-OUMyDVDFxEZNKQQqHiNWS/view?usp=drive_link
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https://drive.google.com/file/d/1lZ665R6ATl57dypxH4dGJ2NSt6XYnbuz/view?usp=drive_link
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https://drive.google.com/file/d/1V9HzLaf-tlG15wUzT7KrTDCS_z1vi5NV/view?usp=drive_link
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https://drive.google.com/file/d/1aKauWiXoKqbNwn_2xs4MrmLlaNYlVNmO/view?usp=drive_link
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https://drive.google.com/file/d/1WVD5DFhriO1YmmOgiVHhacR6HWoTPxav/view?usp=drive_link
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https://drive.google.com/file/d/1_X43WgeBAsfkhH9EmpyPki8U9joMeAGC/view?usp=drive_link
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https://drive.google.com/file/d/1t8x0GqWoNKWtnBsB7_D40Z34nL9ak4kf/view?usp=drive_link
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https://drive.google.com/file/d/15V_f26WaKOXjKnq2T3HRWAmtQUi4lbu2/view?usp=drive_link
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https://drive.google.com/file/d/11VFIAsiSDsMOBANgrOcZBpKB9AFWnLy7/view?usp=drive_link
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https://drive.google.com/file/d/1M0NS7vVaxJv3FHnuRYtdwTFYF7We4LxP/view?usp=drive_link
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https://drive.google.com/file/d/1mR0OItTNqFnVLoczcyKYlm6drAy778lO/view?usp=drive_link
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https://drive.google.com/file/d/1NbVFWDQAh-z4JJ4D-Zw6Lps9kdvpqh2j/view?usp=drive_link
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https://drive.google.com/file/d/1JQoZGBzl4W3QG26-n39tefcGN0fDRMbB/view?usp=drive_link
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https://drive.google.com/file/d/1VBjHl-TvZpncopvasIP5G9gecbB2a5f6/view?usp=drive_link
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https://drive.google.com/file/d/1VzSf6zaB21nahm7MsPwroXbJ84NIwq0b/view?usp=drive_link
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https://drive.google.com/file/d/1OtNnfMEydNtZOcivs4k6E_uJSpf8PkGy/view?usp=drive_link
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https://drive.google.com/file/d/14nVvpvsrFr_03Pa_N7MKzwnRwibOUYM6/view?usp=drive_link
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https://drive.google.com/file/d/1M8li6duiO2r3lv_9HhF_XJn0oZUIEK5F/view?usp=drive_link
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https://drive.google.com/file/d/1Cpzea6fO14lxAaNfSBifqoa4ekhCiLD1/view?usp=drive_link
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https://drive.google.com/file/d/1mbxRTm5vlbsY9UJ0jfjM6j9D7kPJjBpG/view?usp=drive_link
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https://drive.google.com/file/d/1RXD1i6IfWsHRlCxVmG04h2h5Ycm_WwZN/view?usp=drive_link
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https://drive.google.com/file/d/1QFqFSwDGOk1BkgGmqgCcc2BRWnJ6R3MA/view?usp=drive_link
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https://drive.google.com/file/d/1bFqWR8DQM0ZUxxtS2bl-RANQvukeFLzp/view?usp=drive_link
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https://drive.google.com/file/d/1pR-rH3yNGoyPdD4hJ6-3lXQ-PstBx9du/view?usp=drive_link
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https://drive.google.com/file/d/107OAwLY-hva9HeQLIK7VCh-ytdDabVjr/view?usp=drive_link
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https://drive.google.com/file/d/1Tpl08QOaSZ37GTO4awFWSdD8wBR9xdlT/view?usp=drive_link
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https://drive.google.com/file/d/1MR164AOM-0S1T6RX8xKTV2IHyaCvpqAW/view?usp=drive_link
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https://drive.google.com/file/d/1_wknJfVnStIhJ82lU_QtcrwahsqYIsr8/view?usp=drive_link
|
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https://drive.google.com/file/d/1ZuEktWrbYkTx0l5pj3WiZ2CJrfbDOHNo/view?usp=drive_link
|
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https://drive.google.com/file/d/15G_10hkkkq6yxvyI5NGZirlF-RzduR2F/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1DBKxg3ONqh7dhLuX6oh1Yyo2x383V1Hp/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1B5iDBkTUr5vopDddV_fHud18SqAHhauS/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1acwFV0eenRkki1QcjSKH5xqOtys-P3Pr/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1S47BI83xyrh-FKXsvAQqer98Biu_p8XK/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1JL6DmBZl3uyq9dyLfgSqtGF06e7E9JwM/view?usp=drive_link
|
||||
https://drive.google.com/file/d/16WvRS4Kjog8Pxgr0E3sGGnI01YwL9Uql/view?usp=drive_link
|
||||
https://drive.google.com/file/d/12ttGqL33IPWg0-s1SD44rr22M6LiSQBr/view?usp=drive_link
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||||
https://drive.google.com/file/d/1OyZqqnldTU_DliRbr6x0C4a_iWPwIN7j/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1oYk00IpLnR9fesLfD15Ebe7nVBffEbcS/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1eyE2-MQduCEqCd-5_kl5zsoOEERAzpZD/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1ir1Ya-vO0d97pfvbePlUeuKTTRc0qIMU/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1hOi-JnqlMt47gVnLZHMTqeojyYVErohl/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1NFFw5_PqigQ7xGqsL-MNq2B1r5yAscCf/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1uftq1-Zlh8d2sNLWrlVcKYQUwZTD7o24/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1-ax19dSLPacVgk000T-m3l4flPcg07pM/view?usp=drive_link
|
||||
https://drive.google.com/file/d/126y-lgn86-ZmCz8hooF1THKJGGObw3OB/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1JiDniK0VmDIkk92AbBILb8J2Ba59PWML/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1kr8nPIRljiU0R4J9SMgj80o1FPQxzu9z/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1bbThWRij1pKBh_kFgV8FwK0sXtTHBoLX/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1WenzDW6lxk1xkOFm-OiGFfc0ROskAuKU/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1MiKRzuzUn1yN-k_6kPJJzIGy7dT-nnsD/view?usp=drive_link
|
||||
https://drive.google.com/file/d/17rRg2tcmB-gNhQ0KoZJQmNfyFeoij1jH/view?usp=drive_link
|
||||
https://drive.google.com/file/d/11mokBpvrY3ld6sY5WztREtJ1jgqfQV70/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1Il_6IOx9NDp1bX_KHizJfBwzTufTmn86/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1KswtJGsxJ7eeBDAmNA_aeLjOxcH6MIxa/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1gzMhi5uWu4C3Y6WbQ3L-08V96GxTZrRR/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1nRQFtaBxfUCYc2W90Qibh0kHCt6YQCfc/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1vs-gyW-KheqHbUATwAhA2mmR9GOGw7f_/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1MuxzGOA2fgLaHryq82KkQumtuRJGcUOC/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1IIwxZnGlqrXLUXqG6yMO0r7uhCvhpk9e/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1vE7XPyaFcXP4DtTY5Y9WKIt7zWgmX-Cr/view?usp=drive_link
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
https://drive.google.com/file/d/1NovAP-KVJjqcuvWy3d6G4ptGGAIDqcCx/view?usp=drive_link
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||||
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|||
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||||
https://drive.google.com/file/d/1-tx7SvYYgSvXCvnf_EI2OVdwK-CkFY6S/view?usp=drive_link
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||||
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https://drive.google.com/file/d/1Y-t_4vxdE6NpHO0DLJR8f3mD0Q-Wj5-c/view?usp=drive_link
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||||
https://drive.google.com/file/d/1lylRqbbbB8bgtpsBWMPACmHJreuKmllv/view?usp=drive_link
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||||
https://drive.google.com/file/d/1yliSyMig_NXShWfQx6qyW7Ijf2Y5lFK6/view?usp=drive_link
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||||
https://drive.google.com/file/d/1XXhwJsJbeb7KXAooGvJapnm9bjnGUmxS/view?usp=drive_link
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https://drive.google.com/file/d/1_xs1f3hW2JArKyvfF7UWubWjyROGTLs6/view?usp=drive_link
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https://drive.google.com/file/d/1WVEHpr6EqKCZbkHapQSTXJq4xE4SWFT-/view?usp=drive_link
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||||
https://drive.google.com/file/d/1iic44kZoCsjNsfAz2cMstZ9-WQvAhblF/view?usp=drive_link
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https://drive.google.com/file/d/1yLV1lVX-2WnWQldGlnQZ0x7QBuDiVkL3/view?usp=drive_link
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||||
https://drive.google.com/file/d/1Tybp9ru98TTbGn4eyROpUQwDFuALWXmk/view?usp=drive_link
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https://drive.google.com/file/d/13E9OTMiipVJByDs5-J19oWwAz7l94LTN/view?usp=drive_link
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https://drive.google.com/file/d/1EeTpJQdMSliw4JzSMtJ6CyTvVdexjM4M/view?usp=drive_link
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https://drive.google.com/file/d/1NHyNwoFqzeAu-1_PSpq5JfxaiD_xbpn9/view?usp=drive_link
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||||
https://drive.google.com/file/d/1fJcS0phDp4xm_FyGaJ5wr9Pe4KqtHaxD/view?usp=drive_link
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||||
https://drive.google.com/file/d/12AqrLUaewDPEcFRqPZeZFb_TQ0Lfi3At/view?usp=drive_link
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||||
https://drive.google.com/file/d/1x_hd4Qsq1oJS-aj2t3qM7WbbV7KZj05b/view?usp=drive_link
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||||
https://drive.google.com/file/d/14OUSUArmsB068hs6BuEIXQhI1Cyz8Sf0/view?usp=drive_link
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||||
https://drive.google.com/file/d/16zlzh1T5zeUJQnFf382NXkFEKEnDub4O/view?usp=drive_link
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||||
https://drive.google.com/file/d/1IbDltmN-NEFCNtr1TO4ILxEgQ94rtjWv/view?usp=drive_link
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https://drive.google.com/file/d/15gmlf8Gx9455pZ1AlqcCSwh3nDPxMzSr/view?usp=drive_link
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||||
https://drive.google.com/file/d/1qHpRL1oZfIMo_vxnm8qfwQ-7l0BZIVva/view?usp=drive_link
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||||
https://drive.google.com/file/d/1H1xskIgiFZivkYn23rMzH3xePGOh3VTC/view?usp=drive_link
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||||
https://drive.google.com/file/d/1avls6Pv0kYiCMNVknbc1zQsgy64MUDMM/view?usp=drive_link
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||||
https://drive.google.com/file/d/1MmWVgCj5khc8KMIifmt3EzF1o-CtPyyn/view?usp=drive_link
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https://drive.google.com/file/d/1U0kCc_xqW0WNppf4sbnK14euWKdPZtzB/view?usp=drive_link
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||||
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||||
https://drive.google.com/file/d/1UImqiBaIxCR-1DNJaZhHqeHhaySOtVIr/view?usp=drive_link
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||||
https://drive.google.com/file/d/1VpU2V_leIoRIyv_lAvE7eLHBG8DxCTnp/view?usp=drive_link
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||||
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||||
https://drive.google.com/file/d/1IRIXMJMCBDkBjbaHvAlEiBogSvZ1jK_3/view?usp=drive_link
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||||
https://drive.google.com/file/d/1mAHXKjiFbjwydypW2t5Lv8_H5x6nHegl/view?usp=drive_link
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||||
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||||
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||||
https://drive.google.com/file/d/1RuyDtRE1kk76sw-wP8vx5SgLoPF3PA_H/view?usp=drive_link
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||||
https://drive.google.com/file/d/1c4eoQiBbGuy3CTAQDUSkd84Ponh1roAQ/view?usp=drive_link
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||||
https://drive.google.com/file/d/19PXB9z4Ljq6dsbf9TqcOrrP5SRbw2Tc_/view?usp=drive_link
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||||
https://drive.google.com/file/d/1nn1VVZVoIXWdYDozR7XHXE4mPLQG80PQ/view?usp=drive_link
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||||
https://drive.google.com/file/d/1MBdFGOKPV8GUhwoSsJ_Ky3qAMLM2Bv3K/view?usp=drive_link
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||||
https://drive.google.com/file/d/1of3k_M-7Nh3I1TndcWedxK4ca9dn8Sc5/view?usp=drive_link
|
||||
|
|
@ -1,20 +0,0 @@
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|||
https://drive.google.com/file/d/12ctkOAdkCNGN1JLbZb5ww3XTBn2LFpGI/view?usp=drive_link
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||||
https://drive.google.com/file/d/1G_Vd46_4fq6O64gHHjUbJX5Ld44ZZx0y/view?usp=drive_link
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||||
https://drive.google.com/file/d/1uKgUy73B3xBogQAOUhfZjO0X5qZGsi2c/view?usp=drive_link
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||||
https://drive.google.com/file/d/1fu9cIrfI-fE2LhdGUxbx7-8Ci_PF8Ypm/view?usp=drive_link
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||||
https://drive.google.com/file/d/1Ygk9ZPJzx8xw2A9JF3NHbJ44TqnvSTQR/view?usp=drive_link
|
||||
https://drive.google.com/file/d/18m5xPuccNsEB20WPshm3zhxmXc6k63ED/view?usp=drive_link
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||||
https://drive.google.com/file/d/1DiqqxC44rriviRQpqogcv0-EB-Y6nr9g/view?usp=drive_link
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||||
https://drive.google.com/file/d/1qPdaoTVDizJXkfXLioWU7iJ8hqCXSyOQ/view?usp=drive_link
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||||
https://drive.google.com/file/d/1Fj9kIA_mG7f67WFfACJEaZ7izcHG7vUm/view?usp=drive_link
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||||
https://drive.google.com/file/d/1WpYehZnI2P7dUdJPfkE-ij1rqCnjZEbB/view?usp=drive_link
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||||
https://drive.google.com/file/d/1_zwWkT4jPyzB38STWb6whlzsPzXmfA9r/view?usp=drive_link
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||||
https://drive.google.com/file/d/1U6-J4I_fPlSFFGfhZPxS5_YzKXwXIZYp/view?usp=drive_link
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||||
https://drive.google.com/file/d/1pRhxxcTfZp5tQo_EScvJUwfc3amiS6Vk/view?usp=drive_link
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https://drive.google.com/file/d/1lWLntqra83RlYU_gN7Vostnfydf6gutd/view?usp=drive_link
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||||
https://drive.google.com/file/d/1vIBKo0x-NYEHV1FvRpco1lQMpRdAWAIL/view?usp=drive_link
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||||
https://drive.google.com/file/d/1pdrLV3JTQou_XH0Aap61Ssf60iVKm1jJ/view?usp=drive_link
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||||
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||||
https://drive.google.com/file/d/14LJudNc629NT-i8xreXtzl27ce_DxOFJ/view?usp=drive_link
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||||
https://drive.google.com/file/d/1sBvPCODbzxGAI0S3lgN5cSG9Go3lRi00/view?usp=drive_link
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||||
|
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@ -1,18 +0,0 @@
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|||
https://drive.google.com/file/d/1MJn9GbC8p9lN4gC9KDMLEkTkP_gGpXj0/view?usp=drive_link
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||||
https://drive.google.com/file/d/1-4LXgjl7ZCOgp-8GCJmFRD8OeqN5Jf7-/view?usp=drive_link
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||||
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||||
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https://drive.google.com/file/d/1qjY4hM_IvZ8cq2II_n9MeJbvyeuN4oBP/view?usp=drive_link
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||||
https://drive.google.com/file/d/1rKVhO_f92-7sw13T8hTVrza3B9oAVgoy/view?usp=drive_link
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https://drive.google.com/file/d/1pcLPHO8fBkc1-CRa88tyQtEueE4xiXNi/view?usp=drive_link
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||||
https://drive.google.com/file/d/1Vev_chCsIeEdvQ8poEYNsOJFGy_QU8kZ/view?usp=drive_link
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||||
https://drive.google.com/file/d/1l5G4zpRkxSLCQjvGPYSN4zfCvVRQuzMz/view?usp=drive_link
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||||
https://drive.google.com/file/d/14vgthE1eoakXkr2-DRw50E6lAqYOiUuE/view?usp=drive_link
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||||
https://drive.google.com/file/d/17nPSmKKmgQ2B7zkzWrZYiLM3RBuFod82/view?usp=drive_link
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||||
https://drive.google.com/file/d/1QcDsxplVvb_ID9BVrihl5FvlC-j7waXi/view?usp=drive_link
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||||
https://drive.google.com/file/d/18pEejBpI-eEVaWAAjBCyC0vgbX3T1Esj/view?usp=drive_link
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https://drive.google.com/file/d/1H8eH6_IRODtEFT6WoM77ltR5OoOrqXmI/view?usp=drive_link
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||||
https://drive.google.com/file/d/1IWlpFRZhoxyG4nS13CWK4leZVk5wbNx4/view?usp=drive_link
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||||
https://drive.google.com/file/d/1PbZA8_OCGmMLxNP9xbkLRSChniL4uGxl/view?usp=drive_link
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||||
https://drive.google.com/file/d/1p9XAdmG2f_WeflNO4DIJ_tr1rK6M9B4B/view?usp=drive_link
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||||
https://drive.google.com/file/d/1nS59Et1cNAvKo3Y4SeSGRuZD5TvBbCF3/view?usp=drive_link
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||||
|
|
@ -1 +0,0 @@
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|||
https://drive.google.com/drive/folders/1S8eFg98IaGAIKVZ8QFWG1bx4mHa-O204
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||||
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|
@ -1,4 +0,0 @@
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|||
https://drive.google.com/drive/folders/1tC_g1AJ8lglBLY-fjsQrG6DMBa3Ucp-0
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||||
https://drive.google.com/file/d/1fG_Yi2MJrFjiUVN3XoiWXLtTxHlwwaDv/view?usp=drive_link
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||||
https://drive.google.com/file/d/1WX32VWfzzX3Blmd06DRxLwFbMJfVe7P4/view?usp=drive_link
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||||
https://drive.google.com/file/d/18onsX3vXg3xkFwP5bVUCjdV4n9TRn0C9/view?usp=drive_link
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||||
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|
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|||
https://drive.google.com/drive/folders/1RgyD0JgTX30H4IM5XZn8I3zSV_mr8pyF
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||||
https://drive.google.com/file/d/18Cudl6nikDtgRolea7je8iF_gGKzynOP/view?usp=drive_link
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||||
https://drive.google.com/file/d/1C1kZYyROzs-PrLc0SkDgUgMi4-L3lauE/view?usp=drive_link
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||||
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|
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|||
https://drive.google.com/drive/folders/1TsojQQSXtHEoGnqgJ3gmpPQR2DPLtS2N
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||||
https://drive.google.com/file/d/1wfMSZ24oOh5KR_0aaP3Cnu_c4ZCveduB/view?usp=drive_link
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||||
https://drive.google.com/file/d/17EuCUWS6uCCr6yyNzpXdcdE-_TTNCKtf/view?usp=drive_link
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||||
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|
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|||
https://drive.google.com/drive/folders/1sc-E4QYW7A0o23m1u2VWNGVq5smAsfCo
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||||
https://drive.google.com/file/d/18smMymtr8tIxaNUQ61gW6dG50pt3MvGq/view?usp=drive_link
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||||
https://drive.google.com/file/d/1Nk7l53d9sJoGDBKAOnNrExX5nLacATc6/view?usp=drive_link
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@ -1,3 +0,0 @@
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https://drive.google.com/drive/folders/1aRyoOhQwxhyt1J8XgEig4s6kzaw__LXj
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||||
https://drive.google.com/file/d/1pnGIOd-E4-rhz2P3VxpknMKRZCoKt6eI/view?usp=drive_link
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||||
https://drive.google.com/file/d/1GKReZHrXU73NMiC5zKCq_UtqPVtYq8eo/view?usp=drive_link
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|||
https://drive.google.com/drive/folders/19qS_n7vKgDcPeTMnvDHQ5-n73xEbJz5D
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||||
https://drive.google.com/file/d/1oC31By0A2bsBeHyUwBdQw1z4ng6yi9Za/view?usp=drive_link
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||||
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@ -1,2 +0,0 @@
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|||
https://drive.google.com/drive/folders/1m5rQ6UVH8Q9RQp_6c0CxkQ88-L-ScO7q
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||||
https://drive.google.com/file/d/1wHz2qcmwcVG0C0CZ9MjQDQcmj4OY9_a3/view?usp=drive_link
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||||
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@ -1,2 +0,0 @@
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|||
https://drive.google.com/drive/folders/1seQGay470nGQ-knBI5TjsTr8iL9Qws5q
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||||
https://drive.google.com/file/d/1T89hSX5U99wLGvGTE7yUBaQPOpyj6Sai/view?usp=drive_link
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||||
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|||
https://drive.google.com/drive/folders/1t3eDc5Rg0DveyRe8oTm6Dia_FYU5mXyf
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||||
https://drive.google.com/file/d/1TXFaduTakvS0ZWJqKCX-HIvYglum_5CY/view?usp=drive_link
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|||
https://drive.google.com/drive/folders/1Z9X3DNzd6LS0FFjQemNUMoMA5yk5VQOh
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||||
https://drive.google.com/file/d/1Wlyc0vTkjXuWB6zbaVOWhEfD7BmPgUV_/view?usp=drive_link
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||||
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https://drive.google.com/drive/folders/1DYgB4ifX4uIid9m9jnC0Zdz8Nf7ZC0fc
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https://drive.google.com/file/d/1Eb-NRNk_FmVleCbU_Ng5Y4dfcjTKN7Rv/view?usp=drive_link
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https://drive.google.com/file/d/1dkhjEADakT-44l9jf-nK4x89kr4yG_qb/view?usp=drive_link
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||||
https://drive.google.com/file/d/14hDhgcZkVqNExGb4tIXpSjMshhqZETch/view?usp=drive_link
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https://drive.google.com/file/d/1zVMEHpHbuNyP5A_lYU7RPSLB-4V0yfZw/view?usp=drive_link
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||||
https://drive.google.com/file/d/1JtgDjBvy7FnRpFzrx_foC3quorYQFAR-/view?usp=drive_link
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https://drive.google.com/file/d/1EHdneB6F-PP0dQlX8qPaXbxmKoBy_YwO/view?usp=drive_link
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https://drive.google.com/file/d/17Z0jjVBy1OPKREPu77_n_rQzorDiapji/view?usp=drive_link
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https://drive.google.com/file/d/1F4i23qPJ_qTf5jWjfLo4ARGJChznYWt3/view?usp=drive_link
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||||
https://drive.google.com/file/d/1kZtXWM3uS0-rLblydBfJ0mMcVnMMXw9w/view?usp=drive_link
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||||
https://drive.google.com/file/d/1mNODox87xFfY5Z_o5mcLsr8SHb39jDik/view?usp=drive_link
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|
||||
https://drive.google.com/file/d/1mdmJsDGO-YtJAOF_yPKl6lq4PJOIbQhT/view?usp=drive_link
|
||||
https://drive.google.com/file/d/11m9bwfop_sPmnQr_8amB6EEsrbAeG_z5/view?usp=drive_link
|
||||
https://drive.google.com/file/d/19tyYt5FMn5kru0g9o2nMJhKPnsDqkIZv/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1XvTpUdsVTZ-vydvdYYmynbma--HfUGSl/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1MO3hFu68J6NohTzr9aB_fY02VA6QSOqj/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1Lh-UjwAk__04YOTWINF_QGVU8SjetVaY/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1jkSOUwZV5GJ7rZlVeErjcu0DBQs8Np0d/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1VIN1eLI-93WrVQwCjsv6XQr353DqqBYA/view?usp=drive_link
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
https://drive.google.com/drive/folders/1EgKar7rWBmTIRmeJYZciSwjZx3uP2mHO
|
||||
https://drive.google.com/file/d/12eYWQO15atK2hBjXhynPJd9MKAj_42pz/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1Ul4oEeICJDjgfYTl4H1uaisTzVYIM6wd/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1WSF-OG8lKSe2wVYCv5D1aJNipxpgddk-/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1_ppD5j5sFh26aWW0JmhLzJMeNB-lCArk/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1WUp846dgWXYhu4oJfhHxiU6YL_7N6s4W/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1HRZNAIoAQw_uYiPwnBvtBioQoqiqoXdA/view?usp=drive_link
|
||||
https://drive.google.com/file/d/1hedGq-QDMnIn8GlXXBC3GiEJ_Y-LTxyt/view?usp=drive_link
|
||||
|
|
@ -1,634 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Helper code for loading PushT dataset from Diffusion Policy (https://diffusion-policy.cs.columbia.edu/)
|
||||
|
||||
Copied from the original Diffusion Policy repository and used in our `download_and_upload_dataset.py` script.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
import numbers
|
||||
import os
|
||||
from functools import cached_property
|
||||
|
||||
import numcodecs
|
||||
import numpy as np
|
||||
import zarr
|
||||
|
||||
|
||||
def check_chunks_compatible(chunks: tuple, shape: tuple):
|
||||
assert len(shape) == len(chunks)
|
||||
for c in chunks:
|
||||
assert isinstance(c, numbers.Integral)
|
||||
assert c > 0
|
||||
|
||||
|
||||
def rechunk_recompress_array(group, name, chunks=None, chunk_length=None, compressor=None, tmp_key="_temp"):
|
||||
old_arr = group[name]
|
||||
if chunks is None:
|
||||
chunks = (chunk_length,) + old_arr.chunks[1:] if chunk_length is not None else old_arr.chunks
|
||||
check_chunks_compatible(chunks, old_arr.shape)
|
||||
|
||||
if compressor is None:
|
||||
compressor = old_arr.compressor
|
||||
|
||||
if (chunks == old_arr.chunks) and (compressor == old_arr.compressor):
|
||||
# no change
|
||||
return old_arr
|
||||
|
||||
# rechunk recompress
|
||||
group.move(name, tmp_key)
|
||||
old_arr = group[tmp_key]
|
||||
n_copied, n_skipped, n_bytes_copied = zarr.copy(
|
||||
source=old_arr,
|
||||
dest=group,
|
||||
name=name,
|
||||
chunks=chunks,
|
||||
compressor=compressor,
|
||||
)
|
||||
del group[tmp_key]
|
||||
arr = group[name]
|
||||
return arr
|
||||
|
||||
|
||||
def get_optimal_chunks(shape, dtype, target_chunk_bytes=2e6, max_chunk_length=None):
|
||||
"""
|
||||
Common shapes
|
||||
T,D
|
||||
T,N,D
|
||||
T,H,W,C
|
||||
T,N,H,W,C
|
||||
"""
|
||||
itemsize = np.dtype(dtype).itemsize
|
||||
# reversed
|
||||
rshape = list(shape[::-1])
|
||||
if max_chunk_length is not None:
|
||||
rshape[-1] = int(max_chunk_length)
|
||||
split_idx = len(shape) - 1
|
||||
for i in range(len(shape) - 1):
|
||||
this_chunk_bytes = itemsize * np.prod(rshape[:i])
|
||||
next_chunk_bytes = itemsize * np.prod(rshape[: i + 1])
|
||||
if this_chunk_bytes <= target_chunk_bytes and next_chunk_bytes > target_chunk_bytes:
|
||||
split_idx = i
|
||||
|
||||
rchunks = rshape[:split_idx]
|
||||
item_chunk_bytes = itemsize * np.prod(rshape[:split_idx])
|
||||
this_max_chunk_length = rshape[split_idx]
|
||||
next_chunk_length = min(this_max_chunk_length, math.ceil(target_chunk_bytes / item_chunk_bytes))
|
||||
rchunks.append(next_chunk_length)
|
||||
len_diff = len(shape) - len(rchunks)
|
||||
rchunks.extend([1] * len_diff)
|
||||
chunks = tuple(rchunks[::-1])
|
||||
# print(np.prod(chunks) * itemsize / target_chunk_bytes)
|
||||
return chunks
|
||||
|
||||
|
||||
class ReplayBuffer:
|
||||
"""
|
||||
Zarr-based temporal datastructure.
|
||||
Assumes first dimension to be time. Only chunk in time dimension.
|
||||
"""
|
||||
|
||||
def __init__(self, root: zarr.Group | dict[str, dict]):
|
||||
"""
|
||||
Dummy constructor. Use copy_from* and create_from* class methods instead.
|
||||
"""
|
||||
assert "data" in root
|
||||
assert "meta" in root
|
||||
assert "episode_ends" in root["meta"]
|
||||
for value in root["data"].values():
|
||||
assert value.shape[0] == root["meta"]["episode_ends"][-1]
|
||||
self.root = root
|
||||
|
||||
# ============= create constructors ===============
|
||||
@classmethod
|
||||
def create_empty_zarr(cls, storage=None, root=None):
|
||||
if root is None:
|
||||
if storage is None:
|
||||
storage = zarr.MemoryStore()
|
||||
root = zarr.group(store=storage)
|
||||
root.require_group("data", overwrite=False)
|
||||
meta = root.require_group("meta", overwrite=False)
|
||||
if "episode_ends" not in meta:
|
||||
meta.zeros("episode_ends", shape=(0,), dtype=np.int64, compressor=None, overwrite=False)
|
||||
return cls(root=root)
|
||||
|
||||
@classmethod
|
||||
def create_empty_numpy(cls):
|
||||
root = {"data": {}, "meta": {"episode_ends": np.zeros((0,), dtype=np.int64)}}
|
||||
return cls(root=root)
|
||||
|
||||
@classmethod
|
||||
def create_from_group(cls, group, **kwargs):
|
||||
if "data" not in group:
|
||||
# create from stratch
|
||||
buffer = cls.create_empty_zarr(root=group, **kwargs)
|
||||
else:
|
||||
# already exist
|
||||
buffer = cls(root=group, **kwargs)
|
||||
return buffer
|
||||
|
||||
@classmethod
|
||||
def create_from_path(cls, zarr_path, mode="r", **kwargs):
|
||||
"""
|
||||
Open a on-disk zarr directly (for dataset larger than memory).
|
||||
Slower.
|
||||
"""
|
||||
group = zarr.open(os.path.expanduser(zarr_path), mode)
|
||||
return cls.create_from_group(group, **kwargs)
|
||||
|
||||
# ============= copy constructors ===============
|
||||
@classmethod
|
||||
def copy_from_store(
|
||||
cls,
|
||||
src_store,
|
||||
store=None,
|
||||
keys=None,
|
||||
chunks: dict[str, tuple] | None = None,
|
||||
compressors: dict | str | numcodecs.abc.Codec | None = None,
|
||||
if_exists="replace",
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Load to memory.
|
||||
"""
|
||||
src_root = zarr.group(src_store)
|
||||
if chunks is None:
|
||||
chunks = {}
|
||||
if compressors is None:
|
||||
compressors = {}
|
||||
root = None
|
||||
if store is None:
|
||||
# numpy backend
|
||||
meta = {}
|
||||
for key, value in src_root["meta"].items():
|
||||
if len(value.shape) == 0:
|
||||
meta[key] = np.array(value)
|
||||
else:
|
||||
meta[key] = value[:]
|
||||
|
||||
if keys is None:
|
||||
keys = src_root["data"].keys()
|
||||
data = {}
|
||||
for key in keys:
|
||||
arr = src_root["data"][key]
|
||||
data[key] = arr[:]
|
||||
|
||||
root = {"meta": meta, "data": data}
|
||||
else:
|
||||
root = zarr.group(store=store)
|
||||
# copy without recompression
|
||||
n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
|
||||
source=src_store, dest=store, source_path="/meta", dest_path="/meta", if_exists=if_exists
|
||||
)
|
||||
data_group = root.create_group("data", overwrite=True)
|
||||
if keys is None:
|
||||
keys = src_root["data"].keys()
|
||||
for key in keys:
|
||||
value = src_root["data"][key]
|
||||
cks = cls._resolve_array_chunks(chunks=chunks, key=key, array=value)
|
||||
cpr = cls._resolve_array_compressor(compressors=compressors, key=key, array=value)
|
||||
if cks == value.chunks and cpr == value.compressor:
|
||||
# copy without recompression
|
||||
this_path = "/data/" + key
|
||||
n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
|
||||
source=src_store,
|
||||
dest=store,
|
||||
source_path=this_path,
|
||||
dest_path=this_path,
|
||||
if_exists=if_exists,
|
||||
)
|
||||
else:
|
||||
# copy with recompression
|
||||
n_copied, n_skipped, n_bytes_copied = zarr.copy(
|
||||
source=value,
|
||||
dest=data_group,
|
||||
name=key,
|
||||
chunks=cks,
|
||||
compressor=cpr,
|
||||
if_exists=if_exists,
|
||||
)
|
||||
buffer = cls(root=root)
|
||||
return buffer
|
||||
|
||||
@classmethod
|
||||
def copy_from_path(
|
||||
cls,
|
||||
zarr_path,
|
||||
backend=None,
|
||||
store=None,
|
||||
keys=None,
|
||||
chunks: dict[str, tuple] | None = None,
|
||||
compressors: dict | str | numcodecs.abc.Codec | None = None,
|
||||
if_exists="replace",
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Copy a on-disk zarr to in-memory compressed.
|
||||
Recommended
|
||||
"""
|
||||
if chunks is None:
|
||||
chunks = {}
|
||||
if compressors is None:
|
||||
compressors = {}
|
||||
if backend == "numpy":
|
||||
print("backend argument is deprecated!")
|
||||
store = None
|
||||
group = zarr.open(os.path.expanduser(zarr_path), "r")
|
||||
return cls.copy_from_store(
|
||||
src_store=group.store,
|
||||
store=store,
|
||||
keys=keys,
|
||||
chunks=chunks,
|
||||
compressors=compressors,
|
||||
if_exists=if_exists,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# ============= save methods ===============
|
||||
def save_to_store(
|
||||
self,
|
||||
store,
|
||||
chunks: dict[str, tuple] | None = None,
|
||||
compressors: str | numcodecs.abc.Codec | dict | None = None,
|
||||
if_exists="replace",
|
||||
**kwargs,
|
||||
):
|
||||
root = zarr.group(store)
|
||||
if chunks is None:
|
||||
chunks = {}
|
||||
if compressors is None:
|
||||
compressors = {}
|
||||
if self.backend == "zarr":
|
||||
# recompression free copy
|
||||
n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
|
||||
source=self.root.store,
|
||||
dest=store,
|
||||
source_path="/meta",
|
||||
dest_path="/meta",
|
||||
if_exists=if_exists,
|
||||
)
|
||||
else:
|
||||
meta_group = root.create_group("meta", overwrite=True)
|
||||
# save meta, no chunking
|
||||
for key, value in self.root["meta"].items():
|
||||
_ = meta_group.array(name=key, data=value, shape=value.shape, chunks=value.shape)
|
||||
|
||||
# save data, chunk
|
||||
data_group = root.create_group("data", overwrite=True)
|
||||
for key, value in self.root["data"].items():
|
||||
cks = self._resolve_array_chunks(chunks=chunks, key=key, array=value)
|
||||
cpr = self._resolve_array_compressor(compressors=compressors, key=key, array=value)
|
||||
if isinstance(value, zarr.Array):
|
||||
if cks == value.chunks and cpr == value.compressor:
|
||||
# copy without recompression
|
||||
this_path = "/data/" + key
|
||||
n_copied, n_skipped, n_bytes_copied = zarr.copy_store(
|
||||
source=self.root.store,
|
||||
dest=store,
|
||||
source_path=this_path,
|
||||
dest_path=this_path,
|
||||
if_exists=if_exists,
|
||||
)
|
||||
else:
|
||||
# copy with recompression
|
||||
n_copied, n_skipped, n_bytes_copied = zarr.copy(
|
||||
source=value,
|
||||
dest=data_group,
|
||||
name=key,
|
||||
chunks=cks,
|
||||
compressor=cpr,
|
||||
if_exists=if_exists,
|
||||
)
|
||||
else:
|
||||
# numpy
|
||||
_ = data_group.array(name=key, data=value, chunks=cks, compressor=cpr)
|
||||
return store
|
||||
|
||||
def save_to_path(
|
||||
self,
|
||||
zarr_path,
|
||||
chunks: dict[str, tuple] | None = None,
|
||||
compressors: str | numcodecs.abc.Codec | dict | None = None,
|
||||
if_exists="replace",
|
||||
**kwargs,
|
||||
):
|
||||
if chunks is None:
|
||||
chunks = {}
|
||||
if compressors is None:
|
||||
compressors = {}
|
||||
store = zarr.DirectoryStore(os.path.expanduser(zarr_path))
|
||||
return self.save_to_store(
|
||||
store, chunks=chunks, compressors=compressors, if_exists=if_exists, **kwargs
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def resolve_compressor(compressor="default"):
|
||||
if compressor == "default":
|
||||
compressor = numcodecs.Blosc(cname="lz4", clevel=5, shuffle=numcodecs.Blosc.NOSHUFFLE)
|
||||
elif compressor == "disk":
|
||||
compressor = numcodecs.Blosc("zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
|
||||
return compressor
|
||||
|
||||
@classmethod
|
||||
def _resolve_array_compressor(cls, compressors: dict | str | numcodecs.abc.Codec, key, array):
|
||||
# allows compressor to be explicitly set to None
|
||||
cpr = "nil"
|
||||
if isinstance(compressors, dict):
|
||||
if key in compressors:
|
||||
cpr = cls.resolve_compressor(compressors[key])
|
||||
elif isinstance(array, zarr.Array):
|
||||
cpr = array.compressor
|
||||
else:
|
||||
cpr = cls.resolve_compressor(compressors)
|
||||
# backup default
|
||||
if cpr == "nil":
|
||||
cpr = cls.resolve_compressor("default")
|
||||
return cpr
|
||||
|
||||
@classmethod
|
||||
def _resolve_array_chunks(cls, chunks: dict | tuple, key, array):
|
||||
cks = None
|
||||
if isinstance(chunks, dict):
|
||||
if key in chunks:
|
||||
cks = chunks[key]
|
||||
elif isinstance(array, zarr.Array):
|
||||
cks = array.chunks
|
||||
elif isinstance(chunks, tuple):
|
||||
cks = chunks
|
||||
else:
|
||||
raise TypeError(f"Unsupported chunks type {type(chunks)}")
|
||||
# backup default
|
||||
if cks is None:
|
||||
cks = get_optimal_chunks(shape=array.shape, dtype=array.dtype)
|
||||
# check
|
||||
check_chunks_compatible(chunks=cks, shape=array.shape)
|
||||
return cks
|
||||
|
||||
# ============= properties =================
|
||||
@cached_property
|
||||
def data(self):
|
||||
return self.root["data"]
|
||||
|
||||
@cached_property
|
||||
def meta(self):
|
||||
return self.root["meta"]
|
||||
|
||||
def update_meta(self, data):
|
||||
# sanitize data
|
||||
np_data = {}
|
||||
for key, value in data.items():
|
||||
if isinstance(value, np.ndarray):
|
||||
np_data[key] = value
|
||||
else:
|
||||
arr = np.array(value)
|
||||
if arr.dtype == object:
|
||||
raise TypeError(f"Invalid value type {type(value)}")
|
||||
np_data[key] = arr
|
||||
|
||||
meta_group = self.meta
|
||||
if self.backend == "zarr":
|
||||
for key, value in np_data.items():
|
||||
_ = meta_group.array(
|
||||
name=key, data=value, shape=value.shape, chunks=value.shape, overwrite=True
|
||||
)
|
||||
else:
|
||||
meta_group.update(np_data)
|
||||
|
||||
return meta_group
|
||||
|
||||
@property
|
||||
def episode_ends(self):
|
||||
return self.meta["episode_ends"]
|
||||
|
||||
def get_episode_idxs(self):
|
||||
import numba
|
||||
|
||||
numba.jit(nopython=True)
|
||||
|
||||
def _get_episode_idxs(episode_ends):
|
||||
result = np.zeros((episode_ends[-1],), dtype=np.int64)
|
||||
for i in range(len(episode_ends)):
|
||||
start = 0
|
||||
if i > 0:
|
||||
start = episode_ends[i - 1]
|
||||
end = episode_ends[i]
|
||||
for idx in range(start, end):
|
||||
result[idx] = i
|
||||
return result
|
||||
|
||||
return _get_episode_idxs(self.episode_ends)
|
||||
|
||||
@property
|
||||
def backend(self):
|
||||
backend = "numpy"
|
||||
if isinstance(self.root, zarr.Group):
|
||||
backend = "zarr"
|
||||
return backend
|
||||
|
||||
# =========== dict-like API ==============
|
||||
def __repr__(self) -> str:
|
||||
if self.backend == "zarr":
|
||||
return str(self.root.tree())
|
||||
else:
|
||||
return super().__repr__()
|
||||
|
||||
def keys(self):
|
||||
return self.data.keys()
|
||||
|
||||
def values(self):
|
||||
return self.data.values()
|
||||
|
||||
def items(self):
|
||||
return self.data.items()
|
||||
|
||||
def __getitem__(self, key):
|
||||
return self.data[key]
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self.data
|
||||
|
||||
# =========== our API ==============
|
||||
@property
|
||||
def n_steps(self):
|
||||
if len(self.episode_ends) == 0:
|
||||
return 0
|
||||
return self.episode_ends[-1]
|
||||
|
||||
@property
|
||||
def n_episodes(self):
|
||||
return len(self.episode_ends)
|
||||
|
||||
@property
|
||||
def chunk_size(self):
|
||||
if self.backend == "zarr":
|
||||
return next(iter(self.data.arrays()))[-1].chunks[0]
|
||||
return None
|
||||
|
||||
@property
|
||||
def episode_lengths(self):
|
||||
ends = self.episode_ends[:]
|
||||
ends = np.insert(ends, 0, 0)
|
||||
lengths = np.diff(ends)
|
||||
return lengths
|
||||
|
||||
def add_episode(
|
||||
self,
|
||||
data: dict[str, np.ndarray],
|
||||
chunks: dict[str, tuple] | None = None,
|
||||
compressors: str | numcodecs.abc.Codec | dict | None = None,
|
||||
):
|
||||
if chunks is None:
|
||||
chunks = {}
|
||||
if compressors is None:
|
||||
compressors = {}
|
||||
assert len(data) > 0
|
||||
is_zarr = self.backend == "zarr"
|
||||
|
||||
curr_len = self.n_steps
|
||||
episode_length = None
|
||||
for value in data.values():
|
||||
assert len(value.shape) >= 1
|
||||
if episode_length is None:
|
||||
episode_length = len(value)
|
||||
else:
|
||||
assert episode_length == len(value)
|
||||
new_len = curr_len + episode_length
|
||||
|
||||
for key, value in data.items():
|
||||
new_shape = (new_len,) + value.shape[1:]
|
||||
# create array
|
||||
if key not in self.data:
|
||||
if is_zarr:
|
||||
cks = self._resolve_array_chunks(chunks=chunks, key=key, array=value)
|
||||
cpr = self._resolve_array_compressor(compressors=compressors, key=key, array=value)
|
||||
arr = self.data.zeros(
|
||||
name=key, shape=new_shape, chunks=cks, dtype=value.dtype, compressor=cpr
|
||||
)
|
||||
else:
|
||||
# copy data to prevent modify
|
||||
arr = np.zeros(shape=new_shape, dtype=value.dtype)
|
||||
self.data[key] = arr
|
||||
else:
|
||||
arr = self.data[key]
|
||||
assert value.shape[1:] == arr.shape[1:]
|
||||
# same method for both zarr and numpy
|
||||
if is_zarr:
|
||||
arr.resize(new_shape)
|
||||
else:
|
||||
arr.resize(new_shape, refcheck=False)
|
||||
# copy data
|
||||
arr[-value.shape[0] :] = value
|
||||
|
||||
# append to episode ends
|
||||
episode_ends = self.episode_ends
|
||||
if is_zarr:
|
||||
episode_ends.resize(episode_ends.shape[0] + 1)
|
||||
else:
|
||||
episode_ends.resize(episode_ends.shape[0] + 1, refcheck=False)
|
||||
episode_ends[-1] = new_len
|
||||
|
||||
# rechunk
|
||||
if is_zarr and episode_ends.chunks[0] < episode_ends.shape[0]:
|
||||
rechunk_recompress_array(self.meta, "episode_ends", chunk_length=int(episode_ends.shape[0] * 1.5))
|
||||
|
||||
def drop_episode(self):
|
||||
is_zarr = self.backend == "zarr"
|
||||
episode_ends = self.episode_ends[:].copy()
|
||||
assert len(episode_ends) > 0
|
||||
start_idx = 0
|
||||
if len(episode_ends) > 1:
|
||||
start_idx = episode_ends[-2]
|
||||
for value in self.data.values():
|
||||
new_shape = (start_idx,) + value.shape[1:]
|
||||
if is_zarr:
|
||||
value.resize(new_shape)
|
||||
else:
|
||||
value.resize(new_shape, refcheck=False)
|
||||
if is_zarr:
|
||||
self.episode_ends.resize(len(episode_ends) - 1)
|
||||
else:
|
||||
self.episode_ends.resize(len(episode_ends) - 1, refcheck=False)
|
||||
|
||||
def pop_episode(self):
|
||||
assert self.n_episodes > 0
|
||||
episode = self.get_episode(self.n_episodes - 1, copy=True)
|
||||
self.drop_episode()
|
||||
return episode
|
||||
|
||||
def extend(self, data):
|
||||
self.add_episode(data)
|
||||
|
||||
def get_episode(self, idx, copy=False):
|
||||
idx = list(range(len(self.episode_ends)))[idx]
|
||||
start_idx = 0
|
||||
if idx > 0:
|
||||
start_idx = self.episode_ends[idx - 1]
|
||||
end_idx = self.episode_ends[idx]
|
||||
result = self.get_steps_slice(start_idx, end_idx, copy=copy)
|
||||
return result
|
||||
|
||||
def get_episode_slice(self, idx):
|
||||
start_idx = 0
|
||||
if idx > 0:
|
||||
start_idx = self.episode_ends[idx - 1]
|
||||
end_idx = self.episode_ends[idx]
|
||||
return slice(start_idx, end_idx)
|
||||
|
||||
def get_steps_slice(self, start, stop, step=None, copy=False):
|
||||
_slice = slice(start, stop, step)
|
||||
|
||||
result = {}
|
||||
for key, value in self.data.items():
|
||||
x = value[_slice]
|
||||
if copy and isinstance(value, np.ndarray):
|
||||
x = x.copy()
|
||||
result[key] = x
|
||||
return result
|
||||
|
||||
# =========== chunking =============
|
||||
def get_chunks(self) -> dict:
|
||||
assert self.backend == "zarr"
|
||||
chunks = {}
|
||||
for key, value in self.data.items():
|
||||
chunks[key] = value.chunks
|
||||
return chunks
|
||||
|
||||
def set_chunks(self, chunks: dict):
|
||||
assert self.backend == "zarr"
|
||||
for key, value in chunks.items():
|
||||
if key in self.data:
|
||||
arr = self.data[key]
|
||||
if value != arr.chunks:
|
||||
check_chunks_compatible(chunks=value, shape=arr.shape)
|
||||
rechunk_recompress_array(self.data, key, chunks=value)
|
||||
|
||||
def get_compressors(self) -> dict:
|
||||
assert self.backend == "zarr"
|
||||
compressors = {}
|
||||
for key, value in self.data.items():
|
||||
compressors[key] = value.compressor
|
||||
return compressors
|
||||
|
||||
def set_compressors(self, compressors: dict):
|
||||
assert self.backend == "zarr"
|
||||
for key, value in compressors.items():
|
||||
if key in self.data:
|
||||
arr = self.data[key]
|
||||
compressor = self.resolve_compressor(value)
|
||||
if compressor != arr.compressor:
|
||||
rechunk_recompress_array(self.data, key, compressor=compressor)
|
||||
|
|
@ -1,202 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
This file contains download scripts for raw datasets.
|
||||
|
||||
Example of usage:
|
||||
```
|
||||
python lerobot/common/datasets/push_dataset_to_hub/_download_raw.py \
|
||||
--raw-dir data/lerobot-raw/pusht_raw \
|
||||
--repo-id lerobot-raw/pusht_raw
|
||||
```
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
from huggingface_hub import snapshot_download
|
||||
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import check_repo_id
|
||||
|
||||
# {raw_repo_id: raw_format}
|
||||
AVAILABLE_RAW_REPO_IDS = {
|
||||
"lerobot-raw/aloha_mobile_cabinet_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_mobile_chair_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_mobile_elevator_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_mobile_shrimp_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_mobile_wash_pan_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_mobile_wipe_wine_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_sim_insertion_human_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_sim_insertion_scripted_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_sim_transfer_cube_human_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_sim_transfer_cube_scripted_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_battery_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_candy_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_coffee_new_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_coffee_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_cups_open_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_fork_pick_up_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_pingpong_test_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_pro_pencil_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_screw_driver_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_tape_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_thread_velcro_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_towel_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_vinh_cup_left_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_vinh_cup_raw": "aloha_hdf5",
|
||||
"lerobot-raw/aloha_static_ziploc_slide_raw": "aloha_hdf5",
|
||||
"lerobot-raw/umi_cup_in_the_wild_raw": "umi_zarr",
|
||||
"lerobot-raw/pusht_raw": "pusht_zarr",
|
||||
"lerobot-raw/unitreeh1_fold_clothes_raw": "aloha_hdf5",
|
||||
"lerobot-raw/unitreeh1_rearrange_objects_raw": "aloha_hdf5",
|
||||
"lerobot-raw/unitreeh1_two_robot_greeting_raw": "aloha_hdf5",
|
||||
"lerobot-raw/unitreeh1_warehouse_raw": "aloha_hdf5",
|
||||
"lerobot-raw/xarm_lift_medium_raw": "xarm_pkl",
|
||||
"lerobot-raw/xarm_lift_medium_replay_raw": "xarm_pkl",
|
||||
"lerobot-raw/xarm_push_medium_raw": "xarm_pkl",
|
||||
"lerobot-raw/xarm_push_medium_replay_raw": "xarm_pkl",
|
||||
"lerobot-raw/fractal20220817_data_raw": "openx_rlds.fractal20220817_data",
|
||||
"lerobot-raw/kuka_raw": "openx_rlds.kuka",
|
||||
"lerobot-raw/bridge_openx_raw": "openx_rlds.bridge_openx",
|
||||
"lerobot-raw/taco_play_raw": "openx_rlds.taco_play",
|
||||
"lerobot-raw/jaco_play_raw": "openx_rlds.jaco_play",
|
||||
"lerobot-raw/berkeley_cable_routing_raw": "openx_rlds.berkeley_cable_routing",
|
||||
"lerobot-raw/roboturk_raw": "openx_rlds.roboturk",
|
||||
"lerobot-raw/nyu_door_opening_surprising_effectiveness_raw": "openx_rlds.nyu_door_opening_surprising_effectiveness",
|
||||
"lerobot-raw/viola_raw": "openx_rlds.viola",
|
||||
"lerobot-raw/berkeley_autolab_ur5_raw": "openx_rlds.berkeley_autolab_ur5",
|
||||
"lerobot-raw/toto_raw": "openx_rlds.toto",
|
||||
"lerobot-raw/language_table_raw": "openx_rlds.language_table",
|
||||
"lerobot-raw/columbia_cairlab_pusht_real_raw": "openx_rlds.columbia_cairlab_pusht_real",
|
||||
"lerobot-raw/stanford_kuka_multimodal_dataset_raw": "openx_rlds.stanford_kuka_multimodal_dataset",
|
||||
"lerobot-raw/nyu_rot_dataset_raw": "openx_rlds.nyu_rot_dataset",
|
||||
"lerobot-raw/io_ai_tech_raw": "openx_rlds.io_ai_tech",
|
||||
"lerobot-raw/stanford_hydra_dataset_raw": "openx_rlds.stanford_hydra_dataset",
|
||||
"lerobot-raw/austin_buds_dataset_raw": "openx_rlds.austin_buds_dataset",
|
||||
"lerobot-raw/nyu_franka_play_dataset_raw": "openx_rlds.nyu_franka_play_dataset",
|
||||
"lerobot-raw/maniskill_dataset_raw": "openx_rlds.maniskill_dataset",
|
||||
"lerobot-raw/furniture_bench_dataset_raw": "openx_rlds.furniture_bench_dataset",
|
||||
"lerobot-raw/cmu_franka_exploration_dataset_raw": "openx_rlds.cmu_franka_exploration_dataset",
|
||||
"lerobot-raw/ucsd_kitchen_dataset_raw": "openx_rlds.ucsd_kitchen_dataset",
|
||||
"lerobot-raw/ucsd_pick_and_place_dataset_raw": "openx_rlds.ucsd_pick_and_place_dataset",
|
||||
"lerobot-raw/spoc_raw": "openx_rlds.spoc",
|
||||
"lerobot-raw/austin_sailor_dataset_raw": "openx_rlds.austin_sailor_dataset",
|
||||
"lerobot-raw/austin_sirius_dataset_raw": "openx_rlds.austin_sirius_dataset",
|
||||
"lerobot-raw/bc_z_raw": "openx_rlds.bc_z",
|
||||
"lerobot-raw/utokyo_pr2_opening_fridge_raw": "openx_rlds.utokyo_pr2_opening_fridge",
|
||||
"lerobot-raw/utokyo_pr2_tabletop_manipulation_raw": "openx_rlds.utokyo_pr2_tabletop_manipulation",
|
||||
"lerobot-raw/utokyo_xarm_pick_and_place_raw": "openx_rlds.utokyo_xarm_pick_and_place",
|
||||
"lerobot-raw/utokyo_xarm_bimanual_raw": "openx_rlds.utokyo_xarm_bimanual",
|
||||
"lerobot-raw/utokyo_saytap_raw": "openx_rlds.utokyo_saytap",
|
||||
"lerobot-raw/robo_net_raw": "openx_rlds.robo_net",
|
||||
"lerobot-raw/robo_set_raw": "openx_rlds.robo_set",
|
||||
"lerobot-raw/berkeley_mvp_raw": "openx_rlds.berkeley_mvp",
|
||||
"lerobot-raw/berkeley_rpt_raw": "openx_rlds.berkeley_rpt",
|
||||
"lerobot-raw/kaist_nonprehensile_raw": "openx_rlds.kaist_nonprehensile",
|
||||
"lerobot-raw/stanford_mask_vit_raw": "openx_rlds.stanford_mask_vit",
|
||||
"lerobot-raw/tokyo_u_lsmo_raw": "openx_rlds.tokyo_u_lsmo",
|
||||
"lerobot-raw/dlr_sara_pour_raw": "openx_rlds.dlr_sara_pour",
|
||||
"lerobot-raw/dlr_sara_grid_clamp_raw": "openx_rlds.dlr_sara_grid_clamp",
|
||||
"lerobot-raw/dlr_edan_shared_control_raw": "openx_rlds.dlr_edan_shared_control",
|
||||
"lerobot-raw/asu_table_top_raw": "openx_rlds.asu_table_top",
|
||||
"lerobot-raw/stanford_robocook_raw": "openx_rlds.stanford_robocook",
|
||||
"lerobot-raw/imperialcollege_sawyer_wrist_cam_raw": "openx_rlds.imperialcollege_sawyer_wrist_cam",
|
||||
"lerobot-raw/iamlab_cmu_pickup_insert_raw": "openx_rlds.iamlab_cmu_pickup_insert",
|
||||
"lerobot-raw/uiuc_d3field_raw": "openx_rlds.uiuc_d3field",
|
||||
"lerobot-raw/utaustin_mutex_raw": "openx_rlds.utaustin_mutex",
|
||||
"lerobot-raw/berkeley_fanuc_manipulation_raw": "openx_rlds.berkeley_fanuc_manipulation",
|
||||
"lerobot-raw/cmu_playing_with_food_raw": "openx_rlds.cmu_playing_with_food",
|
||||
"lerobot-raw/cmu_play_fusion_raw": "openx_rlds.cmu_play_fusion",
|
||||
"lerobot-raw/cmu_stretch_raw": "openx_rlds.cmu_stretch",
|
||||
"lerobot-raw/berkeley_gnm_recon_raw": "openx_rlds.berkeley_gnm_recon",
|
||||
"lerobot-raw/berkeley_gnm_cory_hall_raw": "openx_rlds.berkeley_gnm_cory_hall",
|
||||
"lerobot-raw/berkeley_gnm_sac_son_raw": "openx_rlds.berkeley_gnm_sac_son",
|
||||
"lerobot-raw/droid_raw": "openx_rlds.droid",
|
||||
"lerobot-raw/droid_100_raw": "openx_rlds.droid100",
|
||||
"lerobot-raw/fmb_raw": "openx_rlds.fmb",
|
||||
"lerobot-raw/dobbe_raw": "openx_rlds.dobbe",
|
||||
"lerobot-raw/usc_cloth_sim_raw": "openx_rlds.usc_cloth_sim",
|
||||
"lerobot-raw/plex_robosuite_raw": "openx_rlds.plex_robosuite",
|
||||
"lerobot-raw/conq_hose_manipulation_raw": "openx_rlds.conq_hose_manipulation",
|
||||
"lerobot-raw/vima_raw": "openx_rlds.vima",
|
||||
"lerobot-raw/robot_vqa_raw": "openx_rlds.robot_vqa",
|
||||
"lerobot-raw/mimic_play_raw": "openx_rlds.mimic_play",
|
||||
"lerobot-raw/tidybot_raw": "openx_rlds.tidybot",
|
||||
"lerobot-raw/eth_agent_affordances_raw": "openx_rlds.eth_agent_affordances",
|
||||
}
|
||||
|
||||
|
||||
def download_raw(raw_dir: Path, repo_id: str):
|
||||
check_repo_id(repo_id)
|
||||
user_id, dataset_id = repo_id.split("/")
|
||||
|
||||
if not dataset_id.endswith("_raw"):
|
||||
warnings.warn(
|
||||
f"""`dataset_id` ({dataset_id}) doesn't end with '_raw' (e.g. 'lerobot/pusht_raw'). Following this
|
||||
naming convention by renaming your repository is advised, but not mandatory.""",
|
||||
stacklevel=1,
|
||||
)
|
||||
|
||||
# Send warning if raw_dir isn't well formatted
|
||||
if raw_dir.parts[-2] != user_id or raw_dir.parts[-1] != dataset_id:
|
||||
warnings.warn(
|
||||
f"""`raw_dir` ({raw_dir}) doesn't contain a community or user id `/` the name of the dataset that
|
||||
match the `repo_id` (e.g. 'data/lerobot/pusht_raw'). Following this naming convention is advised,
|
||||
but not mandatory.""",
|
||||
stacklevel=1,
|
||||
)
|
||||
raw_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
logging.info(f"Start downloading from huggingface.co/{user_id} for {dataset_id}")
|
||||
snapshot_download(repo_id, repo_type="dataset", local_dir=raw_dir)
|
||||
logging.info(f"Finish downloading from huggingface.co/{user_id} for {dataset_id}")
|
||||
|
||||
|
||||
def download_all_raw_datasets(data_dir: Path | None = None):
|
||||
if data_dir is None:
|
||||
data_dir = Path("data")
|
||||
for repo_id in AVAILABLE_RAW_REPO_IDS:
|
||||
raw_dir = data_dir / repo_id
|
||||
download_raw(raw_dir, repo_id)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description=f"""A script to download raw datasets from Hugging Face hub to a local directory. Here is a
|
||||
non exhaustive list of available repositories to use in `--repo-id`: {list(AVAILABLE_RAW_REPO_IDS.keys())}""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--raw-dir",
|
||||
type=Path,
|
||||
required=True,
|
||||
help="Directory containing input raw datasets (e.g. `data/aloha_mobile_chair_raw` or `data/pusht_raw).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--repo-id",
|
||||
type=str,
|
||||
required=True,
|
||||
help="""Repositery identifier on Hugging Face: a community or a user name `/` the name of
|
||||
the dataset (e.g. `lerobot/pusht_raw`, `cadene/aloha_sim_insertion_human_raw`).""",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
download_raw(**vars(args))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,184 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Use this script to batch encode lerobot dataset from their raw format to LeRobotDataset and push their updated
|
||||
version to the hub. Under the hood, this script reuses 'push_dataset_to_hub.py'. It assumes that you already
|
||||
downloaded raw datasets, which you can do with the related '_download_raw.py' script.
|
||||
|
||||
For instance, for codebase_version = 'v1.6', the following command was run, assuming raw datasets from
|
||||
lerobot-raw were downloaded in 'raw/datasets/directory':
|
||||
```bash
|
||||
python lerobot/common/datasets/push_dataset_to_hub/_encode_datasets.py \
|
||||
--raw-dir raw/datasets/directory \
|
||||
--raw-repo-ids lerobot-raw \
|
||||
--local-dir push/datasets/directory \
|
||||
--tests-data-dir tests/data \
|
||||
--push-repo lerobot \
|
||||
--vcodec libsvtav1 \
|
||||
--pix-fmt yuv420p \
|
||||
--g 2 \
|
||||
--crf 30
|
||||
```
|
||||
"""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub._download_raw import AVAILABLE_RAW_REPO_IDS
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import check_repo_id
|
||||
from lerobot.scripts.push_dataset_to_hub import push_dataset_to_hub
|
||||
|
||||
|
||||
def get_push_repo_id_from_raw(raw_repo_id: str, push_repo: str) -> str:
|
||||
dataset_id_raw = raw_repo_id.split("/")[1]
|
||||
dataset_id = dataset_id_raw.removesuffix("_raw")
|
||||
return f"{push_repo}/{dataset_id}"
|
||||
|
||||
|
||||
def encode_datasets(
|
||||
raw_dir: Path,
|
||||
raw_repo_ids: list[str],
|
||||
push_repo: str,
|
||||
vcodec: str,
|
||||
pix_fmt: str,
|
||||
g: int,
|
||||
crf: int,
|
||||
local_dir: Path | None = None,
|
||||
tests_data_dir: Path | None = None,
|
||||
raw_format: str | None = None,
|
||||
dry_run: bool = False,
|
||||
) -> None:
|
||||
if len(raw_repo_ids) == 1 and raw_repo_ids[0].lower() == "lerobot-raw":
|
||||
raw_repo_ids_format = AVAILABLE_RAW_REPO_IDS
|
||||
else:
|
||||
if raw_format is None:
|
||||
raise ValueError(raw_format)
|
||||
raw_repo_ids_format = {id_: raw_format for id_ in raw_repo_ids}
|
||||
|
||||
for raw_repo_id, repo_raw_format in raw_repo_ids_format.items():
|
||||
check_repo_id(raw_repo_id)
|
||||
dataset_repo_id_push = get_push_repo_id_from_raw(raw_repo_id, push_repo)
|
||||
dataset_raw_dir = raw_dir / raw_repo_id
|
||||
dataset_dir = local_dir / dataset_repo_id_push if local_dir is not None else None
|
||||
encoding = {
|
||||
"vcodec": vcodec,
|
||||
"pix_fmt": pix_fmt,
|
||||
"g": g,
|
||||
"crf": crf,
|
||||
}
|
||||
|
||||
if not (dataset_raw_dir).is_dir():
|
||||
raise NotADirectoryError(dataset_raw_dir)
|
||||
|
||||
if not dry_run:
|
||||
push_dataset_to_hub(
|
||||
dataset_raw_dir,
|
||||
raw_format=repo_raw_format,
|
||||
repo_id=dataset_repo_id_push,
|
||||
local_dir=dataset_dir,
|
||||
resume=True,
|
||||
encoding=encoding,
|
||||
tests_data_dir=tests_data_dir,
|
||||
)
|
||||
else:
|
||||
print(
|
||||
f"DRY RUN: {dataset_raw_dir} --> {dataset_dir} --> {dataset_repo_id_push}@{CODEBASE_VERSION}"
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--raw-dir",
|
||||
type=Path,
|
||||
default=Path("data"),
|
||||
help="Directory where raw datasets are located.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--raw-repo-ids",
|
||||
type=str,
|
||||
nargs="*",
|
||||
default=["lerobot-raw"],
|
||||
help="""Raw dataset repo ids. if 'lerobot-raw', the keys from `AVAILABLE_RAW_REPO_IDS` will be
|
||||
used and raw datasets will be fetched from the 'lerobot-raw/' repo and pushed with their
|
||||
associated format. It is assumed that each dataset is located at `raw_dir / raw_repo_id` """,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--raw-format",
|
||||
type=str,
|
||||
default=None,
|
||||
help="""Raw format to use for the raw repo-ids. Must be specified if --raw-repo-ids is not
|
||||
'lerobot-raw'""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--local-dir",
|
||||
type=Path,
|
||||
default=None,
|
||||
help="""When provided, writes the dataset converted to LeRobotDataset format in this directory
|
||||
(e.g. `data/lerobot/aloha_mobile_chair`).""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--push-repo",
|
||||
type=str,
|
||||
default="lerobot",
|
||||
help="Repo to upload datasets to",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--vcodec",
|
||||
type=str,
|
||||
default="libsvtav1",
|
||||
help="Codec to use for encoding videos",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pix-fmt",
|
||||
type=str,
|
||||
default="yuv420p",
|
||||
help="Pixel formats (chroma subsampling) to be used for encoding",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--g",
|
||||
type=int,
|
||||
default=2,
|
||||
help="Group of pictures sizes to be used for encoding.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--crf",
|
||||
type=int,
|
||||
default=30,
|
||||
help="Constant rate factors to be used for encoding.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tests-data-dir",
|
||||
type=Path,
|
||||
default=None,
|
||||
help=(
|
||||
"When provided, save tests artifacts into the given directory "
|
||||
"(e.g. `--tests-data-dir tests/data` will save to tests/data/{--repo-id})."
|
||||
),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dry-run",
|
||||
type=int,
|
||||
default=0,
|
||||
help="If not set to 0, this script won't download or upload anything.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
encode_datasets(**vars(args))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,326 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# imagecodecs/numcodecs.py
|
||||
|
||||
# Copyright (c) 2021-2022, Christoph Gohlke
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright notice,
|
||||
# this list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
# this list of conditions and the following disclaimer in the documentation
|
||||
# and/or other materials provided with the distribution.
|
||||
#
|
||||
# 3. Neither the name of the copyright holder nor the names of its
|
||||
# contributors may be used to endorse or promote products derived from
|
||||
# this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
|
||||
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
||||
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||||
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
||||
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
# POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
# Copied from: https://github.com/real-stanford/universal_manipulation_interface/blob/298776ce251f33b6b3185a98d6e7d1f9ad49168b/diffusion_policy/codecs/imagecodecs_numcodecs.py#L1
|
||||
"""Additional numcodecs implemented using imagecodecs."""
|
||||
|
||||
__version__ = "2022.9.26"
|
||||
|
||||
__all__ = ("register_codecs",)
|
||||
|
||||
import imagecodecs
|
||||
import numpy
|
||||
from numcodecs.abc import Codec
|
||||
from numcodecs.registry import get_codec, register_codec
|
||||
|
||||
# TODO (azouitine): Remove useless codecs
|
||||
|
||||
|
||||
def protective_squeeze(x: numpy.ndarray):
|
||||
"""
|
||||
Squeeze dim only if it's not the last dim.
|
||||
Image dim expected to be *, H, W, C
|
||||
"""
|
||||
img_shape = x.shape[-3:]
|
||||
if len(x.shape) > 3:
|
||||
n_imgs = numpy.prod(x.shape[:-3])
|
||||
if n_imgs > 1:
|
||||
img_shape = (-1,) + img_shape
|
||||
return x.reshape(img_shape)
|
||||
|
||||
|
||||
def get_default_image_compressor(**kwargs):
|
||||
if imagecodecs.JPEGXL:
|
||||
# has JPEGXL
|
||||
this_kwargs = {
|
||||
"effort": 3,
|
||||
"distance": 0.3,
|
||||
# bug in libjxl, invalid codestream for non-lossless
|
||||
# when decoding speed > 1
|
||||
"decodingspeed": 1,
|
||||
}
|
||||
this_kwargs.update(kwargs)
|
||||
return JpegXl(**this_kwargs)
|
||||
else:
|
||||
this_kwargs = {"level": 50}
|
||||
this_kwargs.update(kwargs)
|
||||
return Jpeg2k(**this_kwargs)
|
||||
|
||||
|
||||
class Jpeg2k(Codec):
|
||||
"""JPEG 2000 codec for numcodecs."""
|
||||
|
||||
codec_id = "imagecodecs_jpeg2k"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
level=None,
|
||||
codecformat=None,
|
||||
colorspace=None,
|
||||
tile=None,
|
||||
reversible=None,
|
||||
bitspersample=None,
|
||||
resolutions=None,
|
||||
numthreads=None,
|
||||
verbose=0,
|
||||
):
|
||||
self.level = level
|
||||
self.codecformat = codecformat
|
||||
self.colorspace = colorspace
|
||||
self.tile = None if tile is None else tuple(tile)
|
||||
self.reversible = reversible
|
||||
self.bitspersample = bitspersample
|
||||
self.resolutions = resolutions
|
||||
self.numthreads = numthreads
|
||||
self.verbose = verbose
|
||||
|
||||
def encode(self, buf):
|
||||
buf = protective_squeeze(numpy.asarray(buf))
|
||||
return imagecodecs.jpeg2k_encode(
|
||||
buf,
|
||||
level=self.level,
|
||||
codecformat=self.codecformat,
|
||||
colorspace=self.colorspace,
|
||||
tile=self.tile,
|
||||
reversible=self.reversible,
|
||||
bitspersample=self.bitspersample,
|
||||
resolutions=self.resolutions,
|
||||
numthreads=self.numthreads,
|
||||
verbose=self.verbose,
|
||||
)
|
||||
|
||||
def decode(self, buf, out=None):
|
||||
return imagecodecs.jpeg2k_decode(buf, verbose=self.verbose, numthreads=self.numthreads, out=out)
|
||||
|
||||
|
||||
class JpegXl(Codec):
|
||||
"""JPEG XL codec for numcodecs."""
|
||||
|
||||
codec_id = "imagecodecs_jpegxl"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
# encode
|
||||
level=None,
|
||||
effort=None,
|
||||
distance=None,
|
||||
lossless=None,
|
||||
decodingspeed=None,
|
||||
photometric=None,
|
||||
planar=None,
|
||||
usecontainer=None,
|
||||
# decode
|
||||
index=None,
|
||||
keeporientation=None,
|
||||
# both
|
||||
numthreads=None,
|
||||
):
|
||||
"""
|
||||
Return JPEG XL image from numpy array.
|
||||
Float must be in nominal range 0..1.
|
||||
|
||||
Currently L, LA, RGB, RGBA images are supported in contig mode.
|
||||
Extra channels are only supported for grayscale images in planar mode.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
level : Default to None, i.e. not overwriting lossess and decodingspeed options.
|
||||
When < 0: Use lossless compression
|
||||
When in [0,1,2,3,4]: Sets the decoding speed tier for the provided options.
|
||||
Minimum is 0 (slowest to decode, best quality/density), and maximum
|
||||
is 4 (fastest to decode, at the cost of some quality/density).
|
||||
effort : Default to 3.
|
||||
Sets encoder effort/speed level without affecting decoding speed.
|
||||
Valid values are, from faster to slower speed: 1:lightning 2:thunder
|
||||
3:falcon 4:cheetah 5:hare 6:wombat 7:squirrel 8:kitten 9:tortoise.
|
||||
Speed: lightning, thunder, falcon, cheetah, hare, wombat, squirrel, kitten, tortoise
|
||||
control the encoder effort in ascending order.
|
||||
This also affects memory usage: using lower effort will typically reduce memory
|
||||
consumption during encoding.
|
||||
lightning and thunder are fast modes useful for lossless mode (modular).
|
||||
falcon disables all of the following tools.
|
||||
cheetah enables coefficient reordering, context clustering, and heuristics for selecting DCT sizes and quantization steps.
|
||||
hare enables Gaborish filtering, chroma from luma, and an initial estimate of quantization steps.
|
||||
wombat enables error diffusion quantization and full DCT size selection heuristics.
|
||||
squirrel (default) enables dots, patches, and spline detection, and full context clustering.
|
||||
kitten optimizes the adaptive quantization for a psychovisual metric.
|
||||
tortoise enables a more thorough adaptive quantization search.
|
||||
distance : Default to 1.0
|
||||
Sets the distance level for lossy compression: target max butteraugli distance,
|
||||
lower = higher quality. Range: 0 .. 15. 0.0 = mathematically lossless
|
||||
(however, use JxlEncoderSetFrameLossless instead to use true lossless,
|
||||
as setting distance to 0 alone is not the only requirement).
|
||||
1.0 = visually lossless. Recommended range: 0.5 .. 3.0.
|
||||
lossess : Default to False.
|
||||
Use lossess encoding.
|
||||
decodingspeed : Default to 0.
|
||||
Duplicate to level. [0,4]
|
||||
photometric : Return JxlColorSpace value.
|
||||
Default logic is quite complicated but works most of the time.
|
||||
Accepted value:
|
||||
int: [-1,3]
|
||||
str: ['RGB',
|
||||
'WHITEISZERO', 'MINISWHITE',
|
||||
'BLACKISZERO', 'MINISBLACK', 'GRAY',
|
||||
'XYB', 'KNOWN']
|
||||
planar : Enable multi-channel mode.
|
||||
Default to false.
|
||||
usecontainer :
|
||||
Forces the encoder to use the box-based container format (BMFF)
|
||||
even when not necessary.
|
||||
When using JxlEncoderUseBoxes, JxlEncoderStoreJPEGMetadata or
|
||||
JxlEncoderSetCodestreamLevel with level 10, the encoder will
|
||||
automatically also use the container format, it is not necessary
|
||||
to use JxlEncoderUseContainer for those use cases.
|
||||
By default this setting is disabled.
|
||||
index : Selectively decode frames for animation.
|
||||
Default to 0, decode all frames.
|
||||
When set to > 0, decode that frame index only.
|
||||
keeporientation :
|
||||
Enables or disables preserving of as-in-bitstream pixeldata orientation.
|
||||
Some images are encoded with an Orientation tag indicating that the
|
||||
decoder must perform a rotation and/or mirroring to the encoded image data.
|
||||
|
||||
If skip_reorientation is JXL_FALSE (the default): the decoder will apply
|
||||
the transformation from the orientation setting, hence rendering the image
|
||||
according to its specified intent. When producing a JxlBasicInfo, the decoder
|
||||
will always set the orientation field to JXL_ORIENT_IDENTITY (matching the
|
||||
returned pixel data) and also align xsize and ysize so that they correspond
|
||||
to the width and the height of the returned pixel data.
|
||||
|
||||
If skip_reorientation is JXL_TRUE: the decoder will skip applying the
|
||||
transformation from the orientation setting, returning the image in
|
||||
the as-in-bitstream pixeldata orientation. This may be faster to decode
|
||||
since the decoder doesnt have to apply the transformation, but can
|
||||
cause wrong display of the image if the orientation tag is not correctly
|
||||
taken into account by the user.
|
||||
|
||||
By default, this option is disabled, and the returned pixel data is
|
||||
re-oriented according to the images Orientation setting.
|
||||
threads : Default to 1.
|
||||
If <= 0, use all cores.
|
||||
If > 32, clipped to 32.
|
||||
"""
|
||||
|
||||
self.level = level
|
||||
self.effort = effort
|
||||
self.distance = distance
|
||||
self.lossless = bool(lossless)
|
||||
self.decodingspeed = decodingspeed
|
||||
self.photometric = photometric
|
||||
self.planar = planar
|
||||
self.usecontainer = usecontainer
|
||||
self.index = index
|
||||
self.keeporientation = keeporientation
|
||||
self.numthreads = numthreads
|
||||
|
||||
def encode(self, buf):
|
||||
# TODO: only squeeze all but last dim
|
||||
buf = protective_squeeze(numpy.asarray(buf))
|
||||
return imagecodecs.jpegxl_encode(
|
||||
buf,
|
||||
level=self.level,
|
||||
effort=self.effort,
|
||||
distance=self.distance,
|
||||
lossless=self.lossless,
|
||||
decodingspeed=self.decodingspeed,
|
||||
photometric=self.photometric,
|
||||
planar=self.planar,
|
||||
usecontainer=self.usecontainer,
|
||||
numthreads=self.numthreads,
|
||||
)
|
||||
|
||||
def decode(self, buf, out=None):
|
||||
return imagecodecs.jpegxl_decode(
|
||||
buf,
|
||||
index=self.index,
|
||||
keeporientation=self.keeporientation,
|
||||
numthreads=self.numthreads,
|
||||
out=out,
|
||||
)
|
||||
|
||||
|
||||
def _flat(out):
|
||||
"""Return numpy array as contiguous view of bytes if possible."""
|
||||
if out is None:
|
||||
return None
|
||||
view = memoryview(out)
|
||||
if view.readonly or not view.contiguous:
|
||||
return None
|
||||
return view.cast("B")
|
||||
|
||||
|
||||
def register_codecs(codecs=None, force=False, verbose=True):
|
||||
"""Register codecs in this module with numcodecs."""
|
||||
for name, cls in globals().items():
|
||||
if not hasattr(cls, "codec_id") or name == "Codec":
|
||||
continue
|
||||
if codecs is not None and cls.codec_id not in codecs:
|
||||
continue
|
||||
try:
|
||||
try: # noqa: SIM105
|
||||
get_codec({"id": cls.codec_id})
|
||||
except TypeError:
|
||||
# registered, but failed
|
||||
pass
|
||||
except ValueError:
|
||||
# not registered yet
|
||||
pass
|
||||
else:
|
||||
if not force:
|
||||
if verbose:
|
||||
log_warning(f"numcodec {cls.codec_id!r} already registered")
|
||||
continue
|
||||
if verbose:
|
||||
log_warning(f"replacing registered numcodec {cls.codec_id!r}")
|
||||
register_codec(cls)
|
||||
|
||||
|
||||
def log_warning(msg, *args, **kwargs):
|
||||
"""Log message with level WARNING."""
|
||||
import logging
|
||||
|
||||
logging.getLogger(__name__).warning(msg, *args, **kwargs)
|
||||
|
|
@ -1,233 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Contains utilities to process raw data format of HDF5 files like in: https://github.com/tonyzhaozh/act
|
||||
"""
|
||||
|
||||
import gc
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import h5py
|
||||
import numpy as np
|
||||
import torch
|
||||
import tqdm
|
||||
from datasets import Dataset, Features, Image, Sequence, Value
|
||||
from PIL import Image as PILImage
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import (
|
||||
calculate_episode_data_index,
|
||||
concatenate_episodes,
|
||||
get_default_encoding,
|
||||
save_images_concurrently,
|
||||
)
|
||||
from lerobot.common.datasets.utils import (
|
||||
hf_transform_to_torch,
|
||||
)
|
||||
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
|
||||
|
||||
|
||||
def get_cameras(hdf5_data):
|
||||
# ignore depth channel, not currently handled
|
||||
# TODO(rcadene): add depth
|
||||
rgb_cameras = [key for key in hdf5_data["/observations/images"].keys() if "depth" not in key] # noqa: SIM118
|
||||
return rgb_cameras
|
||||
|
||||
|
||||
def check_format(raw_dir) -> bool:
|
||||
# only frames from simulation are uncompressed
|
||||
compressed_images = "sim" not in raw_dir.name
|
||||
|
||||
hdf5_paths = list(raw_dir.glob("episode_*.hdf5"))
|
||||
assert len(hdf5_paths) != 0
|
||||
for hdf5_path in hdf5_paths:
|
||||
with h5py.File(hdf5_path, "r") as data:
|
||||
assert "/action" in data
|
||||
assert "/observations/qpos" in data
|
||||
|
||||
assert data["/action"].ndim == 2
|
||||
assert data["/observations/qpos"].ndim == 2
|
||||
|
||||
num_frames = data["/action"].shape[0]
|
||||
assert num_frames == data["/observations/qpos"].shape[0]
|
||||
|
||||
for camera in get_cameras(data):
|
||||
assert num_frames == data[f"/observations/images/{camera}"].shape[0]
|
||||
|
||||
if compressed_images:
|
||||
assert data[f"/observations/images/{camera}"].ndim == 2
|
||||
else:
|
||||
assert data[f"/observations/images/{camera}"].ndim == 4
|
||||
b, h, w, c = data[f"/observations/images/{camera}"].shape
|
||||
assert c < h and c < w, f"Expect (h,w,c) image format but ({h=},{w=},{c=}) provided."
|
||||
|
||||
|
||||
def load_from_raw(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int,
|
||||
video: bool,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
# only frames from simulation are uncompressed
|
||||
compressed_images = "sim" not in raw_dir.name
|
||||
|
||||
hdf5_files = sorted(raw_dir.glob("episode_*.hdf5"))
|
||||
num_episodes = len(hdf5_files)
|
||||
|
||||
ep_dicts = []
|
||||
ep_ids = episodes if episodes else range(num_episodes)
|
||||
for ep_idx in tqdm.tqdm(ep_ids):
|
||||
ep_path = hdf5_files[ep_idx]
|
||||
with h5py.File(ep_path, "r") as ep:
|
||||
num_frames = ep["/action"].shape[0]
|
||||
|
||||
# last step of demonstration is considered done
|
||||
done = torch.zeros(num_frames, dtype=torch.bool)
|
||||
done[-1] = True
|
||||
|
||||
state = torch.from_numpy(ep["/observations/qpos"][:])
|
||||
action = torch.from_numpy(ep["/action"][:])
|
||||
if "/observations/qvel" in ep:
|
||||
velocity = torch.from_numpy(ep["/observations/qvel"][:])
|
||||
if "/observations/effort" in ep:
|
||||
effort = torch.from_numpy(ep["/observations/effort"][:])
|
||||
|
||||
ep_dict = {}
|
||||
|
||||
for camera in get_cameras(ep):
|
||||
img_key = f"observation.images.{camera}"
|
||||
|
||||
if compressed_images:
|
||||
import cv2
|
||||
|
||||
# load one compressed image after the other in RAM and uncompress
|
||||
imgs_array = []
|
||||
for data in ep[f"/observations/images/{camera}"]:
|
||||
imgs_array.append(cv2.imdecode(data, 1))
|
||||
imgs_array = np.array(imgs_array)
|
||||
|
||||
else:
|
||||
# load all images in RAM
|
||||
imgs_array = ep[f"/observations/images/{camera}"][:]
|
||||
|
||||
if video:
|
||||
# save png images in temporary directory
|
||||
tmp_imgs_dir = videos_dir / "tmp_images"
|
||||
save_images_concurrently(imgs_array, tmp_imgs_dir)
|
||||
|
||||
# encode images to a mp4 video
|
||||
fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
|
||||
video_path = videos_dir / fname
|
||||
encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
|
||||
|
||||
# clean temporary images directory
|
||||
shutil.rmtree(tmp_imgs_dir)
|
||||
|
||||
# store the reference to the video frame
|
||||
ep_dict[img_key] = [
|
||||
{"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
|
||||
]
|
||||
else:
|
||||
ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
|
||||
|
||||
ep_dict["observation.state"] = state
|
||||
if "/observations/velocity" in ep:
|
||||
ep_dict["observation.velocity"] = velocity
|
||||
if "/observations/effort" in ep:
|
||||
ep_dict["observation.effort"] = effort
|
||||
ep_dict["action"] = action
|
||||
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
|
||||
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
||||
ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
|
||||
ep_dict["next.done"] = done
|
||||
# TODO(rcadene): add reward and success by computing them in sim
|
||||
|
||||
assert isinstance(ep_idx, int)
|
||||
ep_dicts.append(ep_dict)
|
||||
|
||||
gc.collect()
|
||||
|
||||
data_dict = concatenate_episodes(ep_dicts)
|
||||
|
||||
total_frames = data_dict["frame_index"].shape[0]
|
||||
data_dict["index"] = torch.arange(0, total_frames, 1)
|
||||
return data_dict
|
||||
|
||||
|
||||
def to_hf_dataset(data_dict, video) -> Dataset:
|
||||
features = {}
|
||||
|
||||
keys = [key for key in data_dict if "observation.images." in key]
|
||||
for key in keys:
|
||||
if video:
|
||||
features[key] = VideoFrame()
|
||||
else:
|
||||
features[key] = Image()
|
||||
|
||||
features["observation.state"] = Sequence(
|
||||
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
if "observation.velocity" in data_dict:
|
||||
features["observation.velocity"] = Sequence(
|
||||
length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
if "observation.effort" in data_dict:
|
||||
features["observation.effort"] = Sequence(
|
||||
length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["action"] = Sequence(
|
||||
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["episode_index"] = Value(dtype="int64", id=None)
|
||||
features["frame_index"] = Value(dtype="int64", id=None)
|
||||
features["timestamp"] = Value(dtype="float32", id=None)
|
||||
features["next.done"] = Value(dtype="bool", id=None)
|
||||
features["index"] = Value(dtype="int64", id=None)
|
||||
|
||||
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
|
||||
hf_dataset.set_transform(hf_transform_to_torch)
|
||||
return hf_dataset
|
||||
|
||||
|
||||
def from_raw_to_lerobot_format(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
# sanity check
|
||||
check_format(raw_dir)
|
||||
|
||||
if fps is None:
|
||||
fps = 50
|
||||
|
||||
data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
|
||||
hf_dataset = to_hf_dataset(data_dict, video)
|
||||
episode_data_index = calculate_episode_data_index(hf_dataset)
|
||||
info = {
|
||||
"codebase_version": CODEBASE_VERSION,
|
||||
"fps": fps,
|
||||
"video": video,
|
||||
}
|
||||
if video:
|
||||
info["encoding"] = get_default_encoding()
|
||||
|
||||
return hf_dataset, episode_data_index, info
|
||||
|
|
@ -1,107 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Contains utilities to process raw data format of png images files recorded with capture_camera_feed.py
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from datasets import Dataset, Features, Image, Value
|
||||
from PIL import Image as PILImage
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import (
|
||||
calculate_episode_data_index,
|
||||
concatenate_episodes,
|
||||
)
|
||||
from lerobot.common.datasets.utils import hf_transform_to_torch
|
||||
from lerobot.common.datasets.video_utils import VideoFrame
|
||||
|
||||
|
||||
def check_format(raw_dir: Path) -> bool:
|
||||
image_paths = list(raw_dir.glob("frame_*.png"))
|
||||
if len(image_paths) == 0:
|
||||
raise ValueError
|
||||
|
||||
|
||||
def load_from_raw(raw_dir: Path, fps: int, episodes: list[int] | None = None):
|
||||
if episodes is not None:
|
||||
# TODO(aliberts): add support for multi-episodes.
|
||||
raise NotImplementedError()
|
||||
|
||||
ep_dict = {}
|
||||
ep_idx = 0
|
||||
|
||||
image_paths = sorted(raw_dir.glob("frame_*.png"))
|
||||
num_frames = len(image_paths)
|
||||
|
||||
ep_dict["observation.image"] = [PILImage.open(x) for x in image_paths]
|
||||
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
|
||||
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
||||
ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
|
||||
|
||||
ep_dicts = [ep_dict]
|
||||
data_dict = concatenate_episodes(ep_dicts)
|
||||
total_frames = data_dict["frame_index"].shape[0]
|
||||
data_dict["index"] = torch.arange(0, total_frames, 1)
|
||||
return data_dict
|
||||
|
||||
|
||||
def to_hf_dataset(data_dict, video) -> Dataset:
|
||||
features = {}
|
||||
if video:
|
||||
features["observation.image"] = VideoFrame()
|
||||
else:
|
||||
features["observation.image"] = Image()
|
||||
|
||||
features["episode_index"] = Value(dtype="int64", id=None)
|
||||
features["frame_index"] = Value(dtype="int64", id=None)
|
||||
features["timestamp"] = Value(dtype="float32", id=None)
|
||||
features["index"] = Value(dtype="int64", id=None)
|
||||
|
||||
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
|
||||
hf_dataset.set_transform(hf_transform_to_torch)
|
||||
return hf_dataset
|
||||
|
||||
|
||||
def from_raw_to_lerobot_format(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
if video or episodes or encoding is not None:
|
||||
# TODO(aliberts): support this
|
||||
raise NotImplementedError
|
||||
|
||||
# sanity check
|
||||
check_format(raw_dir)
|
||||
|
||||
if fps is None:
|
||||
fps = 30
|
||||
|
||||
data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes)
|
||||
hf_dataset = to_hf_dataset(data_dict, video)
|
||||
episode_data_index = calculate_episode_data_index(hf_dataset)
|
||||
info = {
|
||||
"codebase_version": CODEBASE_VERSION,
|
||||
"fps": fps,
|
||||
"video": video,
|
||||
}
|
||||
return hf_dataset, episode_data_index, info
|
||||
|
|
@ -1,233 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Contains utilities to process raw data format from dora-record
|
||||
"""
|
||||
|
||||
import re
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
import torch
|
||||
from datasets import Dataset, Features, Image, Sequence, Value
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import calculate_episode_data_index
|
||||
from lerobot.common.datasets.utils import (
|
||||
hf_transform_to_torch,
|
||||
)
|
||||
from lerobot.common.datasets.video_utils import VideoFrame
|
||||
|
||||
|
||||
def check_format(raw_dir) -> bool:
|
||||
assert raw_dir.exists()
|
||||
|
||||
leader_file = list(raw_dir.glob("*.parquet"))
|
||||
if len(leader_file) == 0:
|
||||
raise ValueError(f"Missing parquet files in '{raw_dir}'")
|
||||
return True
|
||||
|
||||
|
||||
def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episodes: list[int] | None = None):
|
||||
# Load data stream that will be used as reference for the timestamps synchronization
|
||||
reference_files = list(raw_dir.glob("observation.images.cam_*.parquet"))
|
||||
if len(reference_files) == 0:
|
||||
raise ValueError(f"Missing reference files for camera, starting with in '{raw_dir}'")
|
||||
# select first camera in alphanumeric order
|
||||
reference_key = sorted(reference_files)[0].stem
|
||||
reference_df = pd.read_parquet(raw_dir / f"{reference_key}.parquet")
|
||||
reference_df = reference_df[["timestamp_utc", reference_key]]
|
||||
|
||||
# Merge all data stream using nearest backward strategy
|
||||
df = reference_df
|
||||
for path in raw_dir.glob("*.parquet"):
|
||||
key = path.stem # action or observation.state or ...
|
||||
if key == reference_key:
|
||||
continue
|
||||
if "failed_episode_index" in key:
|
||||
# TODO(rcadene): add support for removing episodes that are tagged as "failed"
|
||||
continue
|
||||
modality_df = pd.read_parquet(path)
|
||||
modality_df = modality_df[["timestamp_utc", key]]
|
||||
df = pd.merge_asof(
|
||||
df,
|
||||
modality_df,
|
||||
on="timestamp_utc",
|
||||
# "nearest" is the best option over "backward", since the latter can desynchronizes camera timestamps by
|
||||
# matching timestamps that are too far apart, in order to fit the backward constraints. It's not the case for "nearest".
|
||||
# However, note that "nearest" might synchronize the reference camera with other cameras on slightly future timestamps.
|
||||
# are too far apart.
|
||||
direction="nearest",
|
||||
tolerance=pd.Timedelta(f"{1 / fps} seconds"),
|
||||
)
|
||||
# Remove rows with episode_index -1 which indicates data that correspond to in-between episodes
|
||||
df = df[df["episode_index"] != -1]
|
||||
|
||||
image_keys = [key for key in df if "observation.images." in key]
|
||||
|
||||
def get_episode_index(row):
|
||||
episode_index_per_cam = {}
|
||||
for key in image_keys:
|
||||
path = row[key][0]["path"]
|
||||
match = re.search(r"_(\d{6}).mp4", path)
|
||||
if not match:
|
||||
raise ValueError(path)
|
||||
episode_index = int(match.group(1))
|
||||
episode_index_per_cam[key] = episode_index
|
||||
if len(set(episode_index_per_cam.values())) != 1:
|
||||
raise ValueError(
|
||||
f"All cameras are expected to belong to the same episode, but getting {episode_index_per_cam}"
|
||||
)
|
||||
return episode_index
|
||||
|
||||
df["episode_index"] = df.apply(get_episode_index, axis=1)
|
||||
|
||||
# dora only use arrays, so single values are encapsulated into a list
|
||||
df["frame_index"] = df.groupby("episode_index").cumcount()
|
||||
df = df.reset_index()
|
||||
df["index"] = df.index
|
||||
|
||||
# set 'next.done' to True for the last frame of each episode
|
||||
df["next.done"] = False
|
||||
df.loc[df.groupby("episode_index").tail(1).index, "next.done"] = True
|
||||
|
||||
df["timestamp"] = df["timestamp_utc"].map(lambda x: x.timestamp())
|
||||
# each episode starts with timestamp 0 to match the ones from the video
|
||||
df["timestamp"] = df.groupby("episode_index")["timestamp"].transform(lambda x: x - x.iloc[0])
|
||||
|
||||
del df["timestamp_utc"]
|
||||
|
||||
# sanity check
|
||||
has_nan = df.isna().any().any()
|
||||
if has_nan:
|
||||
raise ValueError("Dataset contains Nan values.")
|
||||
|
||||
# sanity check episode indices go from 0 to n-1
|
||||
ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
|
||||
expected_ep_ids = list(range(df["episode_index"].max() + 1))
|
||||
if ep_ids != expected_ep_ids:
|
||||
raise ValueError(f"Episodes indices go from {ep_ids} instead of {expected_ep_ids}")
|
||||
|
||||
# Create symlink to raw videos directory (that needs to be absolute not relative)
|
||||
videos_dir.parent.mkdir(parents=True, exist_ok=True)
|
||||
videos_dir.symlink_to((raw_dir / "videos").absolute())
|
||||
|
||||
# sanity check the video paths are well formatted
|
||||
for key in df:
|
||||
if "observation.images." not in key:
|
||||
continue
|
||||
for ep_idx in ep_ids:
|
||||
video_path = videos_dir / f"{key}_episode_{ep_idx:06d}.mp4"
|
||||
if not video_path.exists():
|
||||
raise ValueError(f"Video file not found in {video_path}")
|
||||
|
||||
data_dict = {}
|
||||
for key in df:
|
||||
# is video frame
|
||||
if "observation.images." in key:
|
||||
# we need `[0] because dora only use arrays, so single values are encapsulated into a list.
|
||||
# it is the case for video_frame dictionary = [{"path": ..., "timestamp": ...}]
|
||||
data_dict[key] = [video_frame[0] for video_frame in df[key].values]
|
||||
|
||||
# sanity check the video path is well formatted
|
||||
video_path = videos_dir.parent / data_dict[key][0]["path"]
|
||||
if not video_path.exists():
|
||||
raise ValueError(f"Video file not found in {video_path}")
|
||||
# is number
|
||||
elif df[key].iloc[0].ndim == 0 or df[key].iloc[0].shape[0] == 1:
|
||||
data_dict[key] = torch.from_numpy(df[key].values)
|
||||
# is vector
|
||||
elif df[key].iloc[0].shape[0] > 1:
|
||||
data_dict[key] = torch.stack([torch.from_numpy(x.copy()) for x in df[key].values])
|
||||
else:
|
||||
raise ValueError(key)
|
||||
|
||||
return data_dict
|
||||
|
||||
|
||||
def to_hf_dataset(data_dict, video) -> Dataset:
|
||||
features = {}
|
||||
|
||||
keys = [key for key in data_dict if "observation.images." in key]
|
||||
for key in keys:
|
||||
if video:
|
||||
features[key] = VideoFrame()
|
||||
else:
|
||||
features[key] = Image()
|
||||
|
||||
features["observation.state"] = Sequence(
|
||||
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
if "observation.velocity" in data_dict:
|
||||
features["observation.velocity"] = Sequence(
|
||||
length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
if "observation.effort" in data_dict:
|
||||
features["observation.effort"] = Sequence(
|
||||
length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["action"] = Sequence(
|
||||
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["episode_index"] = Value(dtype="int64", id=None)
|
||||
features["frame_index"] = Value(dtype="int64", id=None)
|
||||
features["timestamp"] = Value(dtype="float32", id=None)
|
||||
features["next.done"] = Value(dtype="bool", id=None)
|
||||
features["index"] = Value(dtype="int64", id=None)
|
||||
|
||||
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
|
||||
hf_dataset.set_transform(hf_transform_to_torch)
|
||||
return hf_dataset
|
||||
|
||||
|
||||
def from_raw_to_lerobot_format(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
# sanity check
|
||||
check_format(raw_dir)
|
||||
|
||||
if fps is None:
|
||||
fps = 30
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
if not video:
|
||||
raise NotImplementedError()
|
||||
|
||||
if encoding is not None:
|
||||
warnings.warn(
|
||||
"Video encoding is currently done outside of LeRobot for the dora_parquet format.",
|
||||
stacklevel=1,
|
||||
)
|
||||
|
||||
data_df = load_from_raw(raw_dir, videos_dir, fps, episodes)
|
||||
hf_dataset = to_hf_dataset(data_df, video)
|
||||
episode_data_index = calculate_episode_data_index(hf_dataset)
|
||||
info = {
|
||||
"codebase_version": CODEBASE_VERSION,
|
||||
"fps": fps,
|
||||
"video": video,
|
||||
}
|
||||
if video:
|
||||
info["encoding"] = "unknown"
|
||||
|
||||
return hf_dataset, episode_data_index, info
|
||||
|
|
@ -1,312 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
For all datasets in the RLDS format.
|
||||
For https://github.com/google-deepmind/open_x_embodiment (OPENX) datasets.
|
||||
|
||||
NOTE: You need to install tensorflow and tensorflow_datasets before running this script.
|
||||
|
||||
Example:
|
||||
python lerobot/scripts/push_dataset_to_hub.py \
|
||||
--raw-dir /path/to/data/bridge_dataset/1.0.0/ \
|
||||
--repo-id your_hub/sampled_bridge_data_v2 \
|
||||
--raw-format rlds \
|
||||
--episodes 3 4 5 8 9
|
||||
|
||||
Exact dataset fps defined in openx/config.py, obtained from:
|
||||
https://docs.google.com/spreadsheets/d/1rPBD77tk60AEIGZrGSODwyyzs5FgCU9Uz3h-3_t2A9g/edit?gid=0#gid=0&range=R:R
|
||||
"""
|
||||
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
import tensorflow_datasets as tfds
|
||||
import torch
|
||||
import tqdm
|
||||
from datasets import Dataset, Features, Image, Sequence, Value
|
||||
from PIL import Image as PILImage
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import (
|
||||
calculate_episode_data_index,
|
||||
concatenate_episodes,
|
||||
get_default_encoding,
|
||||
save_images_concurrently,
|
||||
)
|
||||
from lerobot.common.datasets.utils import (
|
||||
hf_transform_to_torch,
|
||||
)
|
||||
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
|
||||
|
||||
np.set_printoptions(precision=2)
|
||||
|
||||
|
||||
def tf_to_torch(data):
|
||||
return torch.from_numpy(data.numpy())
|
||||
|
||||
|
||||
def tf_img_convert(img):
|
||||
if img.dtype == tf.string:
|
||||
img = tf.io.decode_image(img, expand_animations=False, dtype=tf.uint8)
|
||||
elif img.dtype != tf.uint8:
|
||||
raise ValueError(f"Unsupported image dtype: found with dtype {img.dtype}")
|
||||
return img.numpy()
|
||||
|
||||
|
||||
def _broadcast_metadata_rlds(i: tf.Tensor, traj: dict) -> dict:
|
||||
"""
|
||||
In the RLDS format, each trajectory has some top-level metadata that is explicitly separated out, and a "steps"
|
||||
entry. This function moves the "steps" entry to the top level, broadcasting any metadata to the length of the
|
||||
trajectory. This function also adds the extra metadata fields `_len`, `_traj_index`, and `_frame_index`.
|
||||
|
||||
NOTE: adapted from DLimp library https://github.com/kvablack/dlimp/
|
||||
"""
|
||||
steps = traj.pop("steps")
|
||||
|
||||
traj_len = tf.shape(tf.nest.flatten(steps)[0])[0]
|
||||
|
||||
# broadcast metadata to the length of the trajectory
|
||||
metadata = tf.nest.map_structure(lambda x: tf.repeat(x, traj_len), traj)
|
||||
|
||||
# put steps back in
|
||||
assert "traj_metadata" not in steps
|
||||
traj = {**steps, "traj_metadata": metadata}
|
||||
|
||||
assert "_len" not in traj
|
||||
assert "_traj_index" not in traj
|
||||
assert "_frame_index" not in traj
|
||||
traj["_len"] = tf.repeat(traj_len, traj_len)
|
||||
traj["_traj_index"] = tf.repeat(i, traj_len)
|
||||
traj["_frame_index"] = tf.range(traj_len)
|
||||
|
||||
return traj
|
||||
|
||||
|
||||
def load_from_raw(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int,
|
||||
video: bool,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
raw_dir (Path): _description_
|
||||
videos_dir (Path): _description_
|
||||
fps (int): _description_
|
||||
video (bool): _description_
|
||||
episodes (list[int] | None, optional): _description_. Defaults to None.
|
||||
"""
|
||||
ds_builder = tfds.builder_from_directory(str(raw_dir))
|
||||
dataset = ds_builder.as_dataset(
|
||||
split="all",
|
||||
decoders={"steps": tfds.decode.SkipDecoding()},
|
||||
)
|
||||
|
||||
dataset_info = ds_builder.info
|
||||
print("dataset_info: ", dataset_info)
|
||||
|
||||
ds_length = len(dataset)
|
||||
dataset = dataset.take(ds_length)
|
||||
# "flatten" the dataset as such we can apply trajectory level map() easily
|
||||
# each [obs][key] has a shape of (frame_size, ...)
|
||||
dataset = dataset.enumerate().map(_broadcast_metadata_rlds)
|
||||
|
||||
# we will apply the standardization transform if the dataset_name is provided
|
||||
# if the dataset name is not provided and the goal is to convert any rlds formatted dataset
|
||||
# search for 'image' keys in the observations
|
||||
image_keys = []
|
||||
state_keys = []
|
||||
observation_info = dataset_info.features["steps"]["observation"]
|
||||
for key in observation_info:
|
||||
# check whether the key is for an image or a vector observation
|
||||
if len(observation_info[key].shape) == 3:
|
||||
# only adding uint8 images discards depth images
|
||||
if observation_info[key].dtype == tf.uint8:
|
||||
image_keys.append(key)
|
||||
else:
|
||||
state_keys.append(key)
|
||||
|
||||
lang_key = "language_instruction" if "language_instruction" in dataset.element_spec else None
|
||||
|
||||
print(" - image_keys: ", image_keys)
|
||||
print(" - lang_key: ", lang_key)
|
||||
|
||||
it = iter(dataset)
|
||||
|
||||
ep_dicts = []
|
||||
# Init temp path to save ep_dicts in case of crash
|
||||
tmp_ep_dicts_dir = videos_dir.parent.joinpath("ep_dicts")
|
||||
tmp_ep_dicts_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# check if ep_dicts have already been saved in /tmp
|
||||
starting_ep_idx = 0
|
||||
saved_ep_dicts = [ep.__str__() for ep in tmp_ep_dicts_dir.iterdir()]
|
||||
if len(saved_ep_dicts) > 0:
|
||||
saved_ep_dicts.sort()
|
||||
# get last ep_idx number
|
||||
starting_ep_idx = int(saved_ep_dicts[-1][-13:-3]) + 1
|
||||
for i in range(starting_ep_idx):
|
||||
episode = next(it)
|
||||
ep_dicts.append(torch.load(saved_ep_dicts[i]))
|
||||
|
||||
# if we user specified episodes, skip the ones not in the list
|
||||
if episodes is not None:
|
||||
if ds_length == 0:
|
||||
raise ValueError("No episodes found.")
|
||||
# convert episodes index to sorted list
|
||||
episodes = sorted(episodes)
|
||||
|
||||
for ep_idx in tqdm.tqdm(range(starting_ep_idx, ds_length)):
|
||||
episode = next(it)
|
||||
|
||||
# if user specified episodes, skip the ones not in the list
|
||||
if episodes is not None:
|
||||
if len(episodes) == 0:
|
||||
break
|
||||
if ep_idx == episodes[0]:
|
||||
# process this episode
|
||||
print(" selecting episode idx: ", ep_idx)
|
||||
episodes.pop(0)
|
||||
else:
|
||||
continue # skip
|
||||
|
||||
num_frames = episode["action"].shape[0]
|
||||
|
||||
ep_dict = {}
|
||||
for key in state_keys:
|
||||
ep_dict[f"observation.{key}"] = tf_to_torch(episode["observation"][key])
|
||||
|
||||
ep_dict["action"] = tf_to_torch(episode["action"])
|
||||
ep_dict["next.reward"] = tf_to_torch(episode["reward"]).float()
|
||||
ep_dict["next.done"] = tf_to_torch(episode["is_last"])
|
||||
ep_dict["is_terminal"] = tf_to_torch(episode["is_terminal"])
|
||||
ep_dict["is_first"] = tf_to_torch(episode["is_first"])
|
||||
ep_dict["discount"] = tf_to_torch(episode["discount"])
|
||||
|
||||
# If lang_key is present, convert the entire tensor at once
|
||||
if lang_key is not None:
|
||||
ep_dict["language_instruction"] = [x.numpy().decode("utf-8") for x in episode[lang_key]]
|
||||
|
||||
ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
|
||||
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
|
||||
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
||||
|
||||
image_array_dict = {key: [] for key in image_keys}
|
||||
|
||||
for im_key in image_keys:
|
||||
imgs = episode["observation"][im_key]
|
||||
image_array_dict[im_key] = [tf_img_convert(img) for img in imgs]
|
||||
|
||||
# loop through all cameras
|
||||
for im_key in image_keys:
|
||||
img_key = f"observation.images.{im_key}"
|
||||
imgs_array = image_array_dict[im_key]
|
||||
imgs_array = np.array(imgs_array)
|
||||
if video:
|
||||
# save png images in temporary directory
|
||||
tmp_imgs_dir = videos_dir / "tmp_images"
|
||||
save_images_concurrently(imgs_array, tmp_imgs_dir)
|
||||
|
||||
# encode images to a mp4 video
|
||||
fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
|
||||
video_path = videos_dir / fname
|
||||
encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
|
||||
|
||||
# clean temporary images directory
|
||||
shutil.rmtree(tmp_imgs_dir)
|
||||
|
||||
# store the reference to the video frame
|
||||
ep_dict[img_key] = [
|
||||
{"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
|
||||
]
|
||||
else:
|
||||
ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
|
||||
|
||||
path_ep_dict = tmp_ep_dicts_dir.joinpath(
|
||||
"ep_dict_" + "0" * (10 - len(str(ep_idx))) + str(ep_idx) + ".pt"
|
||||
)
|
||||
torch.save(ep_dict, path_ep_dict)
|
||||
|
||||
ep_dicts.append(ep_dict)
|
||||
|
||||
data_dict = concatenate_episodes(ep_dicts)
|
||||
|
||||
total_frames = data_dict["frame_index"].shape[0]
|
||||
data_dict["index"] = torch.arange(0, total_frames, 1)
|
||||
return data_dict
|
||||
|
||||
|
||||
def to_hf_dataset(data_dict, video) -> Dataset:
|
||||
features = {}
|
||||
|
||||
for key in data_dict:
|
||||
# check if vector state obs
|
||||
if key.startswith("observation.") and "observation.images." not in key:
|
||||
features[key] = Sequence(length=data_dict[key].shape[1], feature=Value(dtype="float32", id=None))
|
||||
# check if image obs
|
||||
elif "observation.images." in key:
|
||||
if video:
|
||||
features[key] = VideoFrame()
|
||||
else:
|
||||
features[key] = Image()
|
||||
|
||||
if "language_instruction" in data_dict:
|
||||
features["language_instruction"] = Value(dtype="string", id=None)
|
||||
|
||||
features["action"] = Sequence(
|
||||
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
|
||||
features["is_terminal"] = Value(dtype="bool", id=None)
|
||||
features["is_first"] = Value(dtype="bool", id=None)
|
||||
features["discount"] = Value(dtype="float32", id=None)
|
||||
|
||||
features["episode_index"] = Value(dtype="int64", id=None)
|
||||
features["frame_index"] = Value(dtype="int64", id=None)
|
||||
features["timestamp"] = Value(dtype="float32", id=None)
|
||||
features["next.reward"] = Value(dtype="float32", id=None)
|
||||
features["next.done"] = Value(dtype="bool", id=None)
|
||||
features["index"] = Value(dtype="int64", id=None)
|
||||
|
||||
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
|
||||
hf_dataset.set_transform(hf_transform_to_torch)
|
||||
return hf_dataset
|
||||
|
||||
|
||||
def from_raw_to_lerobot_format(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
|
||||
hf_dataset = to_hf_dataset(data_dict, video)
|
||||
episode_data_index = calculate_episode_data_index(hf_dataset)
|
||||
info = {
|
||||
"codebase_version": CODEBASE_VERSION,
|
||||
"fps": fps,
|
||||
"video": video,
|
||||
}
|
||||
if video:
|
||||
info["encoding"] = get_default_encoding()
|
||||
|
||||
return hf_dataset, episode_data_index, info
|
||||
|
|
@ -1,275 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Process zarr files formatted like in: https://github.com/real-stanford/diffusion_policy"""
|
||||
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import tqdm
|
||||
import zarr
|
||||
from datasets import Dataset, Features, Image, Sequence, Value
|
||||
from PIL import Image as PILImage
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import (
|
||||
calculate_episode_data_index,
|
||||
concatenate_episodes,
|
||||
get_default_encoding,
|
||||
save_images_concurrently,
|
||||
)
|
||||
from lerobot.common.datasets.utils import (
|
||||
hf_transform_to_torch,
|
||||
)
|
||||
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
|
||||
|
||||
|
||||
def check_format(raw_dir):
|
||||
zarr_path = raw_dir / "pusht_cchi_v7_replay.zarr"
|
||||
zarr_data = zarr.open(zarr_path, mode="r")
|
||||
|
||||
required_datasets = {
|
||||
"data/action",
|
||||
"data/img",
|
||||
"data/keypoint",
|
||||
"data/n_contacts",
|
||||
"data/state",
|
||||
"meta/episode_ends",
|
||||
}
|
||||
for dataset in required_datasets:
|
||||
assert dataset in zarr_data
|
||||
nb_frames = zarr_data["data/img"].shape[0]
|
||||
|
||||
required_datasets.remove("meta/episode_ends")
|
||||
|
||||
assert all(nb_frames == zarr_data[dataset].shape[0] for dataset in required_datasets)
|
||||
|
||||
|
||||
def load_from_raw(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int,
|
||||
video: bool,
|
||||
episodes: list[int] | None = None,
|
||||
keypoints_instead_of_image: bool = False,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
try:
|
||||
import pymunk
|
||||
from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
|
||||
|
||||
from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import (
|
||||
ReplayBuffer as DiffusionPolicyReplayBuffer,
|
||||
)
|
||||
except ModuleNotFoundError as e:
|
||||
print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
|
||||
raise e
|
||||
# as define in gmy-pusht env: https://github.com/huggingface/gym-pusht/blob/e0684ff988d223808c0a9dcfaba9dc4991791370/gym_pusht/envs/pusht.py#L174
|
||||
success_threshold = 0.95 # 95% coverage,
|
||||
|
||||
zarr_path = raw_dir / "pusht_cchi_v7_replay.zarr"
|
||||
zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)
|
||||
|
||||
episode_ids = torch.from_numpy(zarr_data.get_episode_idxs())
|
||||
assert len(
|
||||
{zarr_data[key].shape[0] for key in zarr_data.keys()} # noqa: SIM118
|
||||
), "Some data type dont have the same number of total frames."
|
||||
|
||||
# TODO(rcadene): verify that goal pose is expected to be fixed
|
||||
goal_pos_angle = np.array([256, 256, np.pi / 4]) # x, y, theta (in radians)
|
||||
goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
|
||||
|
||||
imgs = torch.from_numpy(zarr_data["img"]) # b h w c
|
||||
states = torch.from_numpy(zarr_data["state"])
|
||||
actions = torch.from_numpy(zarr_data["action"])
|
||||
|
||||
# load data indices from which each episode starts and ends
|
||||
from_ids, to_ids = [], []
|
||||
from_idx = 0
|
||||
for to_idx in zarr_data.meta["episode_ends"]:
|
||||
from_ids.append(from_idx)
|
||||
to_ids.append(to_idx)
|
||||
from_idx = to_idx
|
||||
|
||||
num_episodes = len(from_ids)
|
||||
|
||||
ep_dicts = []
|
||||
ep_ids = episodes if episodes else range(num_episodes)
|
||||
for ep_idx, selected_ep_idx in tqdm.tqdm(enumerate(ep_ids)):
|
||||
from_idx = from_ids[selected_ep_idx]
|
||||
to_idx = to_ids[selected_ep_idx]
|
||||
num_frames = to_idx - from_idx
|
||||
|
||||
# sanity check
|
||||
assert (episode_ids[from_idx:to_idx] == ep_idx).all()
|
||||
|
||||
# get image
|
||||
if not keypoints_instead_of_image:
|
||||
image = imgs[from_idx:to_idx]
|
||||
assert image.min() >= 0.0
|
||||
assert image.max() <= 255.0
|
||||
image = image.type(torch.uint8)
|
||||
|
||||
# get state
|
||||
state = states[from_idx:to_idx]
|
||||
agent_pos = state[:, :2]
|
||||
block_pos = state[:, 2:4]
|
||||
block_angle = state[:, 4]
|
||||
|
||||
# get reward, success, done, and (maybe) keypoints
|
||||
reward = torch.zeros(num_frames)
|
||||
success = torch.zeros(num_frames, dtype=torch.bool)
|
||||
if keypoints_instead_of_image:
|
||||
keypoints = torch.zeros(num_frames, 16) # 8 keypoints each with 2 coords
|
||||
done = torch.zeros(num_frames, dtype=torch.bool)
|
||||
for i in range(num_frames):
|
||||
space = pymunk.Space()
|
||||
space.gravity = 0, 0
|
||||
space.damping = 0
|
||||
|
||||
# Add walls.
|
||||
walls = [
|
||||
PushTEnv.add_segment(space, (5, 506), (5, 5), 2),
|
||||
PushTEnv.add_segment(space, (5, 5), (506, 5), 2),
|
||||
PushTEnv.add_segment(space, (506, 5), (506, 506), 2),
|
||||
PushTEnv.add_segment(space, (5, 506), (506, 506), 2),
|
||||
]
|
||||
space.add(*walls)
|
||||
|
||||
block_body, block_shapes = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item())
|
||||
goal_geom = pymunk_to_shapely(goal_body, block_body.shapes)
|
||||
block_geom = pymunk_to_shapely(block_body, block_body.shapes)
|
||||
intersection_area = goal_geom.intersection(block_geom).area
|
||||
goal_area = goal_geom.area
|
||||
coverage = intersection_area / goal_area
|
||||
reward[i] = np.clip(coverage / success_threshold, 0, 1)
|
||||
success[i] = coverage > success_threshold
|
||||
if keypoints_instead_of_image:
|
||||
keypoints[i] = torch.from_numpy(PushTEnv.get_keypoints(block_shapes).flatten())
|
||||
|
||||
# last step of demonstration is considered done
|
||||
done[-1] = True
|
||||
|
||||
ep_dict = {}
|
||||
|
||||
if not keypoints_instead_of_image:
|
||||
imgs_array = [x.numpy() for x in image]
|
||||
img_key = "observation.image"
|
||||
if video:
|
||||
# save png images in temporary directory
|
||||
tmp_imgs_dir = videos_dir / "tmp_images"
|
||||
save_images_concurrently(imgs_array, tmp_imgs_dir)
|
||||
|
||||
# encode images to a mp4 video
|
||||
fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
|
||||
video_path = videos_dir / fname
|
||||
encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
|
||||
|
||||
# clean temporary images directory
|
||||
shutil.rmtree(tmp_imgs_dir)
|
||||
|
||||
# store the reference to the video frame
|
||||
ep_dict[img_key] = [
|
||||
{"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
|
||||
]
|
||||
else:
|
||||
ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
|
||||
|
||||
ep_dict["observation.state"] = agent_pos
|
||||
if keypoints_instead_of_image:
|
||||
ep_dict["observation.environment_state"] = keypoints
|
||||
ep_dict["action"] = actions[from_idx:to_idx]
|
||||
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames, dtype=torch.int64)
|
||||
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
||||
ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
|
||||
# ep_dict["next.observation.image"] = image[1:],
|
||||
# ep_dict["next.observation.state"] = agent_pos[1:],
|
||||
# TODO(rcadene)] = verify that reward and done are aligned with image and agent_pos
|
||||
ep_dict["next.reward"] = torch.cat([reward[1:], reward[[-1]]])
|
||||
ep_dict["next.done"] = torch.cat([done[1:], done[[-1]]])
|
||||
ep_dict["next.success"] = torch.cat([success[1:], success[[-1]]])
|
||||
ep_dicts.append(ep_dict)
|
||||
data_dict = concatenate_episodes(ep_dicts)
|
||||
|
||||
total_frames = data_dict["frame_index"].shape[0]
|
||||
data_dict["index"] = torch.arange(0, total_frames, 1)
|
||||
return data_dict
|
||||
|
||||
|
||||
def to_hf_dataset(data_dict, video, keypoints_instead_of_image: bool = False):
|
||||
features = {}
|
||||
|
||||
if not keypoints_instead_of_image:
|
||||
if video:
|
||||
features["observation.image"] = VideoFrame()
|
||||
else:
|
||||
features["observation.image"] = Image()
|
||||
|
||||
features["observation.state"] = Sequence(
|
||||
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
if keypoints_instead_of_image:
|
||||
features["observation.environment_state"] = Sequence(
|
||||
length=data_dict["observation.environment_state"].shape[1],
|
||||
feature=Value(dtype="float32", id=None),
|
||||
)
|
||||
features["action"] = Sequence(
|
||||
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["episode_index"] = Value(dtype="int64", id=None)
|
||||
features["frame_index"] = Value(dtype="int64", id=None)
|
||||
features["timestamp"] = Value(dtype="float32", id=None)
|
||||
features["next.reward"] = Value(dtype="float32", id=None)
|
||||
features["next.done"] = Value(dtype="bool", id=None)
|
||||
features["next.success"] = Value(dtype="bool", id=None)
|
||||
features["index"] = Value(dtype="int64", id=None)
|
||||
|
||||
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
|
||||
hf_dataset.set_transform(hf_transform_to_torch)
|
||||
return hf_dataset
|
||||
|
||||
|
||||
def from_raw_to_lerobot_format(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
# Manually change this to True to use keypoints of the T instead of an image observation (but don't merge
|
||||
# with True). Also make sure to use video = 0 in the `push_dataset_to_hub.py` script.
|
||||
keypoints_instead_of_image = False
|
||||
|
||||
# sanity check
|
||||
check_format(raw_dir)
|
||||
|
||||
if fps is None:
|
||||
fps = 10
|
||||
|
||||
data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, keypoints_instead_of_image, encoding)
|
||||
hf_dataset = to_hf_dataset(data_dict, video, keypoints_instead_of_image)
|
||||
episode_data_index = calculate_episode_data_index(hf_dataset)
|
||||
info = {
|
||||
"codebase_version": CODEBASE_VERSION,
|
||||
"fps": fps,
|
||||
"video": video if not keypoints_instead_of_image else 0,
|
||||
}
|
||||
if video:
|
||||
info["encoding"] = get_default_encoding()
|
||||
|
||||
return hf_dataset, episode_data_index, info
|
||||
|
|
@ -1,234 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Process UMI (Universal Manipulation Interface) data stored in Zarr format like in: https://github.com/real-stanford/universal_manipulation_interface"""
|
||||
|
||||
import logging
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import tqdm
|
||||
import zarr
|
||||
from datasets import Dataset, Features, Image, Sequence, Value
|
||||
from PIL import Image as PILImage
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub._umi_imagecodecs_numcodecs import register_codecs
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import (
|
||||
calculate_episode_data_index,
|
||||
concatenate_episodes,
|
||||
get_default_encoding,
|
||||
save_images_concurrently,
|
||||
)
|
||||
from lerobot.common.datasets.utils import (
|
||||
hf_transform_to_torch,
|
||||
)
|
||||
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
|
||||
|
||||
|
||||
def check_format(raw_dir) -> bool:
|
||||
zarr_path = raw_dir / "cup_in_the_wild.zarr"
|
||||
zarr_data = zarr.open(zarr_path, mode="r")
|
||||
|
||||
required_datasets = {
|
||||
"data/robot0_demo_end_pose",
|
||||
"data/robot0_demo_start_pose",
|
||||
"data/robot0_eef_pos",
|
||||
"data/robot0_eef_rot_axis_angle",
|
||||
"data/robot0_gripper_width",
|
||||
"meta/episode_ends",
|
||||
"data/camera0_rgb",
|
||||
}
|
||||
for dataset in required_datasets:
|
||||
if dataset not in zarr_data:
|
||||
return False
|
||||
|
||||
# mandatory to access zarr_data
|
||||
register_codecs()
|
||||
nb_frames = zarr_data["data/camera0_rgb"].shape[0]
|
||||
|
||||
required_datasets.remove("meta/episode_ends")
|
||||
assert all(nb_frames == zarr_data[dataset].shape[0] for dataset in required_datasets)
|
||||
|
||||
|
||||
def load_from_raw(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int,
|
||||
video: bool,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
zarr_path = raw_dir / "cup_in_the_wild.zarr"
|
||||
zarr_data = zarr.open(zarr_path, mode="r")
|
||||
|
||||
# We process the image data separately because it is too large to fit in memory
|
||||
end_pose = torch.from_numpy(zarr_data["data/robot0_demo_end_pose"][:])
|
||||
start_pos = torch.from_numpy(zarr_data["data/robot0_demo_start_pose"][:])
|
||||
eff_pos = torch.from_numpy(zarr_data["data/robot0_eef_pos"][:])
|
||||
eff_rot_axis_angle = torch.from_numpy(zarr_data["data/robot0_eef_rot_axis_angle"][:])
|
||||
gripper_width = torch.from_numpy(zarr_data["data/robot0_gripper_width"][:])
|
||||
|
||||
states_pos = torch.cat([eff_pos, eff_rot_axis_angle], dim=1)
|
||||
states = torch.cat([states_pos, gripper_width], dim=1)
|
||||
|
||||
episode_ends = zarr_data["meta/episode_ends"][:]
|
||||
num_episodes = episode_ends.shape[0]
|
||||
|
||||
# We convert it in torch tensor later because the jit function does not support torch tensors
|
||||
episode_ends = torch.from_numpy(episode_ends)
|
||||
|
||||
# load data indices from which each episode starts and ends
|
||||
from_ids, to_ids = [], []
|
||||
from_idx = 0
|
||||
for to_idx in episode_ends:
|
||||
from_ids.append(from_idx)
|
||||
to_ids.append(to_idx)
|
||||
from_idx = to_idx
|
||||
|
||||
ep_dicts_dir = videos_dir / "ep_dicts"
|
||||
ep_dicts_dir.mkdir(exist_ok=True, parents=True)
|
||||
ep_dicts = []
|
||||
|
||||
ep_ids = episodes if episodes else range(num_episodes)
|
||||
for ep_idx, selected_ep_idx in tqdm.tqdm(enumerate(ep_ids)):
|
||||
ep_dict_path = ep_dicts_dir / f"{ep_idx}"
|
||||
if not ep_dict_path.is_file():
|
||||
from_idx = from_ids[selected_ep_idx]
|
||||
to_idx = to_ids[selected_ep_idx]
|
||||
num_frames = to_idx - from_idx
|
||||
|
||||
# TODO(rcadene): save temporary images of the episode?
|
||||
|
||||
state = states[from_idx:to_idx]
|
||||
|
||||
ep_dict = {}
|
||||
|
||||
# load 57MB of images in RAM (400x224x224x3 uint8)
|
||||
imgs_array = zarr_data["data/camera0_rgb"][from_idx:to_idx]
|
||||
img_key = "observation.image"
|
||||
if video:
|
||||
fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
|
||||
video_path = videos_dir / fname
|
||||
if not video_path.is_file():
|
||||
# save png images in temporary directory
|
||||
tmp_imgs_dir = videos_dir / "tmp_images"
|
||||
save_images_concurrently(imgs_array, tmp_imgs_dir)
|
||||
|
||||
# encode images to a mp4 video
|
||||
encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
|
||||
|
||||
# clean temporary images directory
|
||||
shutil.rmtree(tmp_imgs_dir)
|
||||
|
||||
# store the reference to the video frame
|
||||
ep_dict[img_key] = [
|
||||
{"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)
|
||||
]
|
||||
else:
|
||||
ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
|
||||
|
||||
ep_dict["observation.state"] = state
|
||||
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames, dtype=torch.int64)
|
||||
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
||||
ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
|
||||
ep_dict["episode_data_index_from"] = torch.tensor([from_idx] * num_frames)
|
||||
ep_dict["episode_data_index_to"] = torch.tensor([from_idx + num_frames] * num_frames)
|
||||
ep_dict["end_pose"] = end_pose[from_idx:to_idx]
|
||||
ep_dict["start_pos"] = start_pos[from_idx:to_idx]
|
||||
ep_dict["gripper_width"] = gripper_width[from_idx:to_idx]
|
||||
torch.save(ep_dict, ep_dict_path)
|
||||
else:
|
||||
ep_dict = torch.load(ep_dict_path)
|
||||
|
||||
ep_dicts.append(ep_dict)
|
||||
|
||||
data_dict = concatenate_episodes(ep_dicts)
|
||||
|
||||
total_frames = data_dict["frame_index"].shape[0]
|
||||
data_dict["index"] = torch.arange(0, total_frames, 1)
|
||||
return data_dict
|
||||
|
||||
|
||||
def to_hf_dataset(data_dict, video):
|
||||
features = {}
|
||||
|
||||
if video:
|
||||
features["observation.image"] = VideoFrame()
|
||||
else:
|
||||
features["observation.image"] = Image()
|
||||
|
||||
features["observation.state"] = Sequence(
|
||||
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["episode_index"] = Value(dtype="int64", id=None)
|
||||
features["frame_index"] = Value(dtype="int64", id=None)
|
||||
features["timestamp"] = Value(dtype="float32", id=None)
|
||||
features["index"] = Value(dtype="int64", id=None)
|
||||
features["episode_data_index_from"] = Value(dtype="int64", id=None)
|
||||
features["episode_data_index_to"] = Value(dtype="int64", id=None)
|
||||
# `start_pos` and `end_pos` respectively represent the positions of the end-effector
|
||||
# at the beginning and the end of the episode.
|
||||
# `gripper_width` indicates the distance between the grippers, and this value is included
|
||||
# in the state vector, which comprises the concatenation of the end-effector position
|
||||
# and gripper width.
|
||||
features["end_pose"] = Sequence(
|
||||
length=data_dict["end_pose"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["start_pos"] = Sequence(
|
||||
length=data_dict["start_pos"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["gripper_width"] = Sequence(
|
||||
length=data_dict["gripper_width"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
|
||||
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
|
||||
hf_dataset.set_transform(hf_transform_to_torch)
|
||||
return hf_dataset
|
||||
|
||||
|
||||
def from_raw_to_lerobot_format(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
# sanity check
|
||||
check_format(raw_dir)
|
||||
|
||||
if fps is None:
|
||||
# For umi cup in the wild: https://arxiv.org/pdf/2402.10329#table.caption.16
|
||||
fps = 10
|
||||
|
||||
if not video:
|
||||
logging.warning(
|
||||
"Generating UMI dataset without `video=True` creates ~150GB on disk and requires ~80GB in RAM."
|
||||
)
|
||||
|
||||
data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
|
||||
hf_dataset = to_hf_dataset(data_dict, video)
|
||||
episode_data_index = calculate_episode_data_index(hf_dataset)
|
||||
info = {
|
||||
"codebase_version": CODEBASE_VERSION,
|
||||
"fps": fps,
|
||||
"video": video,
|
||||
}
|
||||
if video:
|
||||
info["encoding"] = get_default_encoding()
|
||||
|
||||
return hf_dataset, episode_data_index, info
|
||||
|
|
@ -1,200 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Process pickle files formatted like in: https://github.com/fyhMer/fowm"""
|
||||
|
||||
import pickle
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import einops
|
||||
import torch
|
||||
import tqdm
|
||||
from datasets import Dataset, Features, Image, Sequence, Value
|
||||
from PIL import Image as PILImage
|
||||
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import (
|
||||
calculate_episode_data_index,
|
||||
concatenate_episodes,
|
||||
get_default_encoding,
|
||||
save_images_concurrently,
|
||||
)
|
||||
from lerobot.common.datasets.utils import (
|
||||
hf_transform_to_torch,
|
||||
)
|
||||
from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames
|
||||
|
||||
|
||||
def check_format(raw_dir):
|
||||
keys = {"actions", "rewards", "dones"}
|
||||
nested_keys = {"observations": {"rgb", "state"}, "next_observations": {"rgb", "state"}}
|
||||
|
||||
xarm_files = list(raw_dir.glob("*.pkl"))
|
||||
assert len(xarm_files) > 0
|
||||
|
||||
with open(xarm_files[0], "rb") as f:
|
||||
dataset_dict = pickle.load(f)
|
||||
|
||||
assert isinstance(dataset_dict, dict)
|
||||
assert all(k in dataset_dict for k in keys)
|
||||
|
||||
# Check for consistent lengths in nested keys
|
||||
expected_len = len(dataset_dict["actions"])
|
||||
assert all(len(dataset_dict[key]) == expected_len for key in keys if key in dataset_dict)
|
||||
|
||||
for key, subkeys in nested_keys.items():
|
||||
nested_dict = dataset_dict.get(key, {})
|
||||
assert all(len(nested_dict[subkey]) == expected_len for subkey in subkeys if subkey in nested_dict)
|
||||
|
||||
|
||||
def load_from_raw(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int,
|
||||
video: bool,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
pkl_path = raw_dir / "buffer.pkl"
|
||||
|
||||
with open(pkl_path, "rb") as f:
|
||||
pkl_data = pickle.load(f)
|
||||
|
||||
# load data indices from which each episode starts and ends
|
||||
from_ids, to_ids = [], []
|
||||
from_idx, to_idx = 0, 0
|
||||
for done in pkl_data["dones"]:
|
||||
to_idx += 1
|
||||
if not done:
|
||||
continue
|
||||
from_ids.append(from_idx)
|
||||
to_ids.append(to_idx)
|
||||
from_idx = to_idx
|
||||
|
||||
num_episodes = len(from_ids)
|
||||
|
||||
ep_dicts = []
|
||||
ep_ids = episodes if episodes else range(num_episodes)
|
||||
for ep_idx, selected_ep_idx in tqdm.tqdm(enumerate(ep_ids)):
|
||||
from_idx = from_ids[selected_ep_idx]
|
||||
to_idx = to_ids[selected_ep_idx]
|
||||
num_frames = to_idx - from_idx
|
||||
|
||||
image = torch.tensor(pkl_data["observations"]["rgb"][from_idx:to_idx])
|
||||
image = einops.rearrange(image, "b c h w -> b h w c")
|
||||
state = torch.tensor(pkl_data["observations"]["state"][from_idx:to_idx])
|
||||
action = torch.tensor(pkl_data["actions"][from_idx:to_idx])
|
||||
# TODO(rcadene): we have a missing last frame which is the observation when the env is done
|
||||
# it is critical to have this frame for tdmpc to predict a "done observation/state"
|
||||
# next_image = torch.tensor(pkl_data["next_observations"]["rgb"][from_idx:to_idx])
|
||||
# next_state = torch.tensor(pkl_data["next_observations"]["state"][from_idx:to_idx])
|
||||
next_reward = torch.tensor(pkl_data["rewards"][from_idx:to_idx])
|
||||
next_done = torch.tensor(pkl_data["dones"][from_idx:to_idx])
|
||||
|
||||
ep_dict = {}
|
||||
|
||||
imgs_array = [x.numpy() for x in image]
|
||||
img_key = "observation.image"
|
||||
if video:
|
||||
# save png images in temporary directory
|
||||
tmp_imgs_dir = videos_dir / "tmp_images"
|
||||
save_images_concurrently(imgs_array, tmp_imgs_dir)
|
||||
|
||||
# encode images to a mp4 video
|
||||
fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
|
||||
video_path = videos_dir / fname
|
||||
encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {}))
|
||||
|
||||
# clean temporary images directory
|
||||
shutil.rmtree(tmp_imgs_dir)
|
||||
|
||||
# store the reference to the video frame
|
||||
ep_dict[img_key] = [{"path": f"videos/{fname}", "timestamp": i / fps} for i in range(num_frames)]
|
||||
else:
|
||||
ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
|
||||
|
||||
ep_dict["observation.state"] = state
|
||||
ep_dict["action"] = action
|
||||
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames, dtype=torch.int64)
|
||||
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
||||
ep_dict["timestamp"] = torch.arange(0, num_frames, 1) / fps
|
||||
# ep_dict["next.observation.image"] = next_image
|
||||
# ep_dict["next.observation.state"] = next_state
|
||||
ep_dict["next.reward"] = next_reward
|
||||
ep_dict["next.done"] = next_done
|
||||
ep_dicts.append(ep_dict)
|
||||
|
||||
data_dict = concatenate_episodes(ep_dicts)
|
||||
|
||||
total_frames = data_dict["frame_index"].shape[0]
|
||||
data_dict["index"] = torch.arange(0, total_frames, 1)
|
||||
return data_dict
|
||||
|
||||
|
||||
def to_hf_dataset(data_dict, video):
|
||||
features = {}
|
||||
|
||||
if video:
|
||||
features["observation.image"] = VideoFrame()
|
||||
else:
|
||||
features["observation.image"] = Image()
|
||||
|
||||
features["observation.state"] = Sequence(
|
||||
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["action"] = Sequence(
|
||||
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
|
||||
)
|
||||
features["episode_index"] = Value(dtype="int64", id=None)
|
||||
features["frame_index"] = Value(dtype="int64", id=None)
|
||||
features["timestamp"] = Value(dtype="float32", id=None)
|
||||
features["next.reward"] = Value(dtype="float32", id=None)
|
||||
features["next.done"] = Value(dtype="bool", id=None)
|
||||
features["index"] = Value(dtype="int64", id=None)
|
||||
# TODO(rcadene): add success
|
||||
# features["next.success"] = Value(dtype='bool', id=None)
|
||||
|
||||
hf_dataset = Dataset.from_dict(data_dict, features=Features(features))
|
||||
hf_dataset.set_transform(hf_transform_to_torch)
|
||||
return hf_dataset
|
||||
|
||||
|
||||
def from_raw_to_lerobot_format(
|
||||
raw_dir: Path,
|
||||
videos_dir: Path,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
episodes: list[int] | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
# sanity check
|
||||
check_format(raw_dir)
|
||||
|
||||
if fps is None:
|
||||
fps = 15
|
||||
|
||||
data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, encoding)
|
||||
hf_dataset = to_hf_dataset(data_dict, video)
|
||||
episode_data_index = calculate_episode_data_index(hf_dataset)
|
||||
info = {
|
||||
"codebase_version": CODEBASE_VERSION,
|
||||
"fps": fps,
|
||||
"video": video,
|
||||
}
|
||||
if video:
|
||||
info["encoding"] = get_default_encoding()
|
||||
|
||||
return hf_dataset, episode_data_index, info
|
||||
|
|
@ -1,364 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Use this script to convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub,
|
||||
or store it locally. LeRobot dataset format is lightweight, fast to load from, and does not require any
|
||||
installation of neural net specific packages like pytorch, tensorflow, jax.
|
||||
|
||||
Example of how to download raw datasets, convert them into LeRobotDataset format, and push them to the hub:
|
||||
```
|
||||
python lerobot/scripts/push_dataset_to_hub.py \
|
||||
--raw-dir data/pusht_raw \
|
||||
--raw-format pusht_zarr \
|
||||
--repo-id lerobot/pusht
|
||||
|
||||
python lerobot/scripts/push_dataset_to_hub.py \
|
||||
--raw-dir data/xarm_lift_medium_raw \
|
||||
--raw-format xarm_pkl \
|
||||
--repo-id lerobot/xarm_lift_medium
|
||||
|
||||
python lerobot/scripts/push_dataset_to_hub.py \
|
||||
--raw-dir data/aloha_sim_insertion_scripted_raw \
|
||||
--raw-format aloha_hdf5 \
|
||||
--repo-id lerobot/aloha_sim_insertion_scripted
|
||||
|
||||
python lerobot/scripts/push_dataset_to_hub.py \
|
||||
--raw-dir data/umi_cup_in_the_wild_raw \
|
||||
--raw-format umi_zarr \
|
||||
--repo-id lerobot/umi_cup_in_the_wild
|
||||
```
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import shutil
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import torch
|
||||
from huggingface_hub import HfApi
|
||||
from safetensors.torch import save_file
|
||||
|
||||
from lerobot.common.datasets.compute_stats import compute_stats
|
||||
from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
|
||||
from lerobot.common.datasets.push_dataset_to_hub.utils import check_repo_id
|
||||
from lerobot.common.datasets.utils import create_branch, create_lerobot_dataset_card, flatten_dict
|
||||
|
||||
|
||||
def get_from_raw_to_lerobot_format_fn(raw_format: str):
|
||||
if raw_format == "pusht_zarr":
|
||||
from lerobot.common.datasets.push_dataset_to_hub.pusht_zarr_format import from_raw_to_lerobot_format
|
||||
elif raw_format == "umi_zarr":
|
||||
from lerobot.common.datasets.push_dataset_to_hub.umi_zarr_format import from_raw_to_lerobot_format
|
||||
elif raw_format == "aloha_hdf5":
|
||||
from lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format import from_raw_to_lerobot_format
|
||||
elif raw_format in ["rlds", "openx"]:
|
||||
from lerobot.common.datasets.push_dataset_to_hub.openx_rlds_format import from_raw_to_lerobot_format
|
||||
elif raw_format == "dora_parquet":
|
||||
from lerobot.common.datasets.push_dataset_to_hub.dora_parquet_format import from_raw_to_lerobot_format
|
||||
elif raw_format == "xarm_pkl":
|
||||
from lerobot.common.datasets.push_dataset_to_hub.xarm_pkl_format import from_raw_to_lerobot_format
|
||||
elif raw_format == "cam_png":
|
||||
from lerobot.common.datasets.push_dataset_to_hub.cam_png_format import from_raw_to_lerobot_format
|
||||
else:
|
||||
raise ValueError(
|
||||
f"The selected {raw_format} can't be found. Did you add it to `lerobot/scripts/push_dataset_to_hub.py::get_from_raw_to_lerobot_format_fn`?"
|
||||
)
|
||||
|
||||
return from_raw_to_lerobot_format
|
||||
|
||||
|
||||
def save_meta_data(
|
||||
info: dict[str, Any], stats: dict, episode_data_index: dict[str, list], meta_data_dir: Path
|
||||
):
|
||||
meta_data_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# save info
|
||||
info_path = meta_data_dir / "info.json"
|
||||
with open(str(info_path), "w") as f:
|
||||
json.dump(info, f, indent=4)
|
||||
|
||||
# save stats
|
||||
stats_path = meta_data_dir / "stats.safetensors"
|
||||
save_file(flatten_dict(stats), stats_path)
|
||||
|
||||
# save episode_data_index
|
||||
episode_data_index = {key: torch.tensor(episode_data_index[key]) for key in episode_data_index}
|
||||
ep_data_idx_path = meta_data_dir / "episode_data_index.safetensors"
|
||||
save_file(episode_data_index, ep_data_idx_path)
|
||||
|
||||
|
||||
def push_meta_data_to_hub(repo_id: str, meta_data_dir: str | Path, revision: str | None):
|
||||
"""Expect all meta data files to be all stored in a single "meta_data" directory.
|
||||
On the hugging face repositery, they will be uploaded in a "meta_data" directory at the root.
|
||||
"""
|
||||
api = HfApi()
|
||||
api.upload_folder(
|
||||
folder_path=meta_data_dir,
|
||||
path_in_repo="meta_data",
|
||||
repo_id=repo_id,
|
||||
revision=revision,
|
||||
repo_type="dataset",
|
||||
)
|
||||
|
||||
|
||||
def push_dataset_card_to_hub(
|
||||
repo_id: str,
|
||||
revision: str | None,
|
||||
tags: list | None = None,
|
||||
license: str = "apache-2.0",
|
||||
**card_kwargs,
|
||||
):
|
||||
"""Creates and pushes a LeRobotDataset Card with appropriate tags to easily find it on the hub."""
|
||||
card = create_lerobot_dataset_card(tags=tags, license=license, **card_kwargs)
|
||||
card.push_to_hub(repo_id=repo_id, repo_type="dataset", revision=revision)
|
||||
|
||||
|
||||
def push_videos_to_hub(repo_id: str, videos_dir: str | Path, revision: str | None):
|
||||
"""Expect mp4 files to be all stored in a single "videos" directory.
|
||||
On the hugging face repositery, they will be uploaded in a "videos" directory at the root.
|
||||
"""
|
||||
api = HfApi()
|
||||
api.upload_folder(
|
||||
folder_path=videos_dir,
|
||||
path_in_repo="videos",
|
||||
repo_id=repo_id,
|
||||
revision=revision,
|
||||
repo_type="dataset",
|
||||
allow_patterns="*.mp4",
|
||||
)
|
||||
|
||||
|
||||
def push_dataset_to_hub(
|
||||
raw_dir: Path,
|
||||
raw_format: str,
|
||||
repo_id: str,
|
||||
push_to_hub: bool = True,
|
||||
local_dir: Path | None = None,
|
||||
fps: int | None = None,
|
||||
video: bool = True,
|
||||
batch_size: int = 32,
|
||||
num_workers: int = 8,
|
||||
episodes: list[int] | None = None,
|
||||
force_override: bool = False,
|
||||
resume: bool = False,
|
||||
cache_dir: Path = Path("/tmp"),
|
||||
tests_data_dir: Path | None = None,
|
||||
encoding: dict | None = None,
|
||||
):
|
||||
check_repo_id(repo_id)
|
||||
user_id, dataset_id = repo_id.split("/")
|
||||
|
||||
# Robustify when `raw_dir` is str instead of Path
|
||||
raw_dir = Path(raw_dir)
|
||||
if not raw_dir.exists():
|
||||
raise NotADirectoryError(
|
||||
f"{raw_dir} does not exists. Check your paths or run this command to download an existing raw dataset on the hub: "
|
||||
f"`python lerobot/common/datasets/push_dataset_to_hub/_download_raw.py --raw-dir your/raw/dir --repo-id your/repo/id_raw`"
|
||||
)
|
||||
|
||||
if local_dir:
|
||||
# Robustify when `local_dir` is str instead of Path
|
||||
local_dir = Path(local_dir)
|
||||
|
||||
# Send warning if local_dir isn't well formatted
|
||||
if local_dir.parts[-2] != user_id or local_dir.parts[-1] != dataset_id:
|
||||
warnings.warn(
|
||||
f"`local_dir` ({local_dir}) doesn't contain a community or user id `/` the name of the dataset that match the `repo_id` (e.g. 'data/lerobot/pusht'). Following this naming convention is advised, but not mandatory.",
|
||||
stacklevel=1,
|
||||
)
|
||||
|
||||
# Check we don't override an existing `local_dir` by mistake
|
||||
if local_dir.exists():
|
||||
if force_override:
|
||||
shutil.rmtree(local_dir)
|
||||
elif not resume:
|
||||
raise ValueError(f"`local_dir` already exists ({local_dir}). Use `--force-override 1`.")
|
||||
|
||||
meta_data_dir = local_dir / "meta_data"
|
||||
videos_dir = local_dir / "videos"
|
||||
else:
|
||||
# Temporary directory used to store images, videos, meta_data
|
||||
meta_data_dir = Path(cache_dir) / "meta_data"
|
||||
videos_dir = Path(cache_dir) / "videos"
|
||||
|
||||
if raw_format is None:
|
||||
# TODO(rcadene, adilzouitine): implement auto_find_raw_format
|
||||
raise NotImplementedError()
|
||||
# raw_format = auto_find_raw_format(raw_dir)
|
||||
|
||||
# convert dataset from original raw format to LeRobot format
|
||||
from_raw_to_lerobot_format = get_from_raw_to_lerobot_format_fn(raw_format)
|
||||
|
||||
hf_dataset, episode_data_index, info = from_raw_to_lerobot_format(
|
||||
raw_dir,
|
||||
videos_dir,
|
||||
fps,
|
||||
video,
|
||||
episodes,
|
||||
encoding,
|
||||
)
|
||||
|
||||
lerobot_dataset = LeRobotDataset.from_preloaded(
|
||||
repo_id=repo_id,
|
||||
hf_dataset=hf_dataset,
|
||||
episode_data_index=episode_data_index,
|
||||
info=info,
|
||||
videos_dir=videos_dir,
|
||||
)
|
||||
stats = compute_stats(lerobot_dataset, batch_size, num_workers)
|
||||
|
||||
if local_dir:
|
||||
hf_dataset = hf_dataset.with_format(None) # to remove transforms that cant be saved
|
||||
hf_dataset.save_to_disk(str(local_dir / "train"))
|
||||
|
||||
if push_to_hub or local_dir:
|
||||
# mandatory for upload
|
||||
save_meta_data(info, stats, episode_data_index, meta_data_dir)
|
||||
|
||||
if push_to_hub:
|
||||
hf_dataset.push_to_hub(repo_id, revision="main")
|
||||
push_meta_data_to_hub(repo_id, meta_data_dir, revision="main")
|
||||
push_dataset_card_to_hub(repo_id, revision="main")
|
||||
if video:
|
||||
push_videos_to_hub(repo_id, videos_dir, revision="main")
|
||||
create_branch(repo_id, repo_type="dataset", branch=CODEBASE_VERSION)
|
||||
|
||||
if tests_data_dir:
|
||||
# get the first episode
|
||||
num_items_first_ep = episode_data_index["to"][0] - episode_data_index["from"][0]
|
||||
test_hf_dataset = hf_dataset.select(range(num_items_first_ep))
|
||||
episode_data_index = {k: v[:1] for k, v in episode_data_index.items()}
|
||||
|
||||
test_hf_dataset = test_hf_dataset.with_format(None)
|
||||
test_hf_dataset.save_to_disk(str(tests_data_dir / repo_id / "train"))
|
||||
|
||||
tests_meta_data = tests_data_dir / repo_id / "meta_data"
|
||||
save_meta_data(info, stats, episode_data_index, tests_meta_data)
|
||||
|
||||
# copy videos of first episode to tests directory
|
||||
episode_index = 0
|
||||
tests_videos_dir = tests_data_dir / repo_id / "videos"
|
||||
tests_videos_dir.mkdir(parents=True, exist_ok=True)
|
||||
for key in lerobot_dataset.camera_keys:
|
||||
fname = f"{key}_episode_{episode_index:06d}.mp4"
|
||||
shutil.copy(videos_dir / fname, tests_videos_dir / fname)
|
||||
|
||||
if local_dir is None:
|
||||
# clear cache
|
||||
shutil.rmtree(meta_data_dir)
|
||||
shutil.rmtree(videos_dir)
|
||||
|
||||
return lerobot_dataset
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument(
|
||||
"--raw-dir",
|
||||
type=Path,
|
||||
required=True,
|
||||
help="Directory containing input raw datasets (e.g. `data/aloha_mobile_chair_raw` or `data/pusht_raw).",
|
||||
)
|
||||
# TODO(rcadene): add automatic detection of the format
|
||||
parser.add_argument(
|
||||
"--raw-format",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Dataset type (e.g. `pusht_zarr`, `umi_zarr`, `aloha_hdf5`, `xarm_pkl`, `dora_parquet`, `rlds`, `openx`).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--repo-id",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Repositery identifier on Hugging Face: a community or a user name `/` the name of the dataset (e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--local-dir",
|
||||
type=Path,
|
||||
help="When provided, writes the dataset converted to LeRobotDataset format in this directory (e.g. `data/lerobot/aloha_mobile_chair`).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--push-to-hub",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Upload to hub.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--fps",
|
||||
type=int,
|
||||
help="Frame rate used to collect videos. If not provided, use the default one specified in the code.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--video",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Convert each episode of the raw dataset to an mp4 video. This option allows 60 times lower disk space consumption and 25 faster loading time during training.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--batch-size",
|
||||
type=int,
|
||||
default=32,
|
||||
help="Batch size loaded by DataLoader for computing the dataset statistics.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num-workers",
|
||||
type=int,
|
||||
default=8,
|
||||
help="Number of processes of Dataloader for computing the dataset statistics.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--episodes",
|
||||
type=int,
|
||||
nargs="*",
|
||||
help="When provided, only converts the provided episodes (e.g `--episodes 2 3 4`). Useful to test the code on 1 episode.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--force-override",
|
||||
type=int,
|
||||
default=0,
|
||||
help="When set to 1, removes provided output directory if it already exists. By default, raises a ValueError exception.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--resume",
|
||||
type=int,
|
||||
default=0,
|
||||
help="When set to 1, resumes a previous run.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cache-dir",
|
||||
type=Path,
|
||||
required=False,
|
||||
default="/tmp",
|
||||
help="Directory to store the temporary videos and images generated while creating the dataset.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tests-data-dir",
|
||||
type=Path,
|
||||
help=(
|
||||
"When provided, save tests artifacts into the given directory "
|
||||
"(e.g. `--tests-data-dir tests/data` will save to tests/data/{--repo-id})."
|
||||
),
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
push_dataset_to_hub(**vars(args))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue