229 lines
6.8 KiB
Python
229 lines
6.8 KiB
Python
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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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import torch
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from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, 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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"channel",
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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(
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"`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`"
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)
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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(
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"`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`"
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)
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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,))
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# 8 keypoints with 2 coords each
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keypoints = np.zeros((num_frames, 16))
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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(
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space, block_pos[i].tolist(), block_angle[i].item()
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)
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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] = torch.from_numpy(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 (LEROBOT_HOME / repo_id).exists():
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shutil.rmtree(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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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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frame = {
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"action": torch.from_numpy(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[i + (frame_idx < num_frames - 1)],
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"next.success": success[i + (frame_idx < num_frames - 1)],
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}
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frame["observation.state"] = torch.from_numpy(agent_pos[i])
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if mode == "keypoints":
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frame["observation.environment_state"] = torch.from_numpy(keypoints[i])
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else:
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frame["observation.image"] = torch.from_numpy(image[i])
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dataset.add_frame(frame)
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dataset.save_episode(task=PUSHT_TASK)
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dataset.consolidate()
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if push_to_hub:
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dataset.push_to_hub()
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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, local_files_only=True)
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# breakpoint()
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