Add pusht_zarr.py

Author: Cadene

examples/port_datasets/pusht_zarr.py

@@ -0,0 +1,232 @@
+import shutil
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, LeRobotDataset
+from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw
+
+
+def create_empty_dataset(repo_id, mode):
+    features = {}
+
+    if mode == "keypoints":
+        state_dim = 16
+    else:
+        state_dim = 2
+        features["observation.image"] = {
+            "dtype": mode,
+            "shape": (3, 96, 96),
+            "names": [
+                "channel",
+                "height",
+                "width",
+            ],
+        }
+
+    features.update(
+        {
+            "observation.state": {
+                "dtype": "float32",
+                "shape": (state_dim,),
+                "names": [
+                    ["x", "y"],
+                ],
+            },
+            "action": {
+                "dtype": "float32",
+                "shape": (2,),
+                "names": [
+                    ["x", "y"],
+                ],
+            },
+            "next.reward": {
+                "dtype": "float32",
+                "shape": (1,),
+                "names": None,
+            },
+            "next.success": {
+                "dtype": "bool",
+                "shape": (1,),
+                "names": None,
+            },
+        }
+    )
+
+    dataset = LeRobotDataset.create(
+        repo_id=repo_id,
+        fps=10,
+        robot_type="2d pointer",
+        features=features,
+    )
+    return dataset
+
+
+def load_raw_dataset(zarr_path, load_images=True):
+    try:
+        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
+
+    zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path)
+
+    env_state = zarr_data["state"][:]
+    agent_pos = env_state[:, :2]
+    block_pos = env_state[:, 2:4]
+    block_angle = env_state[:, 4]
+
+    action = zarr_data["action"][:]
+
+    image = None
+    if load_images:
+        # b h w c
+        image = zarr_data["img"]
+
+    episode_data_index = {
+        "from": np.array([0] + zarr_data.meta["episode_ends"][:-1].tolist()),
+        "to": zarr_data.meta["episode_ends"],
+    }
+
+    return image, agent_pos, block_pos, block_angle, action, episode_data_index
+
+
+def calculate_coverage(block_pos, block_angle):
+    try:
+        import pymunk
+        from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely
+    except ModuleNotFoundError as e:
+        print("`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`")
+        raise e
+
+    num_frames = len(block_pos)
+
+    coverage = np.zeros((num_frames,))
+    # 8 keypoints with 2 coords each
+    keypoints = np.zeros((num_frames, 16))
+
+    # Set x, y, theta (in radians)
+    goal_pos_angle = np.array([256, 256, np.pi / 4])
+    goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle)
+
+    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[i] = intersection_area / goal_area
+        keypoints[i] = torch.from_numpy(PushTEnv.get_keypoints(block_shapes).flatten())
+
+    return coverage, keypoints
+
+
+def calculate_success(coverage, success_threshold):
+    return coverage > success_threshold
+
+
+def calculate_reward(coverage, success_threshold):
+    return np.clip(coverage / success_threshold, 0, 1)
+
+
+def populate_dataset(dataset, episode_data_index, episodes, image, state, action, reward, success):
+    if episodes is None:
+        episodes = range(len(episode_data_index["from"]))
+
+    for ep_idx in episodes:
+        from_idx = episode_data_index["from"][ep_idx]
+        to_idx = episode_data_index["to"][ep_idx]
+        num_frames = to_idx - from_idx
+
+        for frame_idx in range(num_frames):
+            i = from_idx + frame_idx
+
+            frame = {
+                "action": torch.from_numpy(action[i]),
+                "timestamp": frame_idx / dataset.fps,
+                # Shift reward and success by +1 until the last item of the episode
+                "next.reward": reward[i + (frame_idx < num_frames - 1)],
+                "next.success": success[i + (frame_idx < num_frames - 1)],
+            }
+
+            frame["observation.state"] = torch.from_numpy(state[i])
+            if image is not None:
+                frame["observation.image"] = torch.from_numpy(image[i])
+
+            # TODO(rcadene): add_frame_to_buffer, add_episode_from_buffer
+            dataset.add_frame(frame)
+
+        dataset.add_episode(task="Push the T-shaped blue block onto the T-shaped green target surface.")
+
+    return dataset
+
+
+def port_pusht(raw_dir, repo_id, episodes=None, mode="video", push_to_hub=True):
+    if mode not in ["video", "image", "keypoints"]:
+        raise ValueError(mode)
+
+    if (LEROBOT_HOME / repo_id).exists():
+        shutil.rmtree(LEROBOT_HOME / repo_id)
+
+    raw_dir = Path(raw_dir)
+    if not raw_dir.exists():
+        download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw")
+
+    image, agent_pos, block_pos, block_angle, action, episode_data_index = load_raw_dataset(
+        zarr_path=raw_dir / "pusht_cchi_v7_replay.zarr"
+    )
+
+    # Calculate success and reward based on the overlapping area
+    # of the T-object and the T-area.
+    coverage, keypoints = calculate_coverage(block_pos, block_angle)
+    success = calculate_success(coverage, success_threshold=0.95)
+    reward = calculate_reward(coverage, success_threshold=0.95)
+
+    dataset = create_empty_dataset(repo_id, mode)
+    dataset = populate_dataset(
+        dataset,
+        episode_data_index,
+        episodes,
+        image=None if mode == "keypoints" else image,
+        state=keypoints if mode == "keypoints" else agent_pos,
+        action=action,
+        reward=reward,
+        success=success,
+    )
+    dataset.consolidate()
+
+    if push_to_hub:
+        dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    episodes = [0, 1]
+    # episodes = None
+
+    # for mode in ["video"]:
+    for mode in ["image"]:
+        # for mode in ["keypoints"]:
+        # for mode in ["video", "image", "keypoints"]:
+        repo_id = "cadene/pusht_v2"
+        if mode in ["image", "keypoints"]:
+            repo_id += f"_{mode}"
+        port_pusht("data/lerobot-raw/pusht_raw", repo_id=repo_id, mode=mode, episodes=episodes)
+
+    # dataset = LeRobotDataset(repo_id="cadene/pusht_v2", local_files_only=True)
+    # dataset_old = LeRobotDataset(repo_id="lerobot/pusht")