pusht_zarr_format.py

#!/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:https://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
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"""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.push_dataset_to_hub.utils import concatenate_episodes, 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, out_dir, fps, video, debug):
    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())
    num_episodes = zarr_data.meta["episode_ends"].shape[0]
    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"])

    ep_dicts = []
    episode_data_index = {"from": [], "to": []}

    id_from = 0
    for ep_idx in tqdm.tqdm(range(num_episodes)):
        id_to = zarr_data.meta["episode_ends"][ep_idx]
        num_frames = id_to - id_from

        # sanity check
        assert (episode_ids[id_from:id_to] == ep_idx).all()

        # get image
        image = imgs[id_from:id_to]
        assert image.min() >= 0.0
        assert image.max() <= 255.0
        image = image.type(torch.uint8)

        # get state
        state = states[id_from:id_to]
        agent_pos = state[:, :2]
        block_pos = state[:, 2:4]
        block_angle = state[:, 4]

        # get reward, success, done
        reward = torch.zeros(num_frames)
        success = torch.zeros(num_frames, dtype=torch.bool)
        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 = 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

        # last step of demonstration is considered done
        done[-1] = True

        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 = out_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 = out_dir / "videos" / fname
            encode_video_frames(tmp_imgs_dir, video_path, fps)

            # 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
        ep_dict["action"] = actions[id_from:id_to]
        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)

        episode_data_index["from"].append(id_from)
        episode_data_index["to"].append(id_from + num_frames)

        id_from += num_frames

        # process first episode only
        if debug:
            break

    data_dict = concatenate_episodes(ep_dicts)
    return data_dict, episode_data_index


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["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, out_dir: Path, fps=None, video=True, debug=False):
    # sanity check
    check_format(raw_dir)

    if fps is None:
        fps = 10

    data_dict, episode_data_index = load_from_raw(raw_dir, out_dir, fps, video, debug)
    hf_dataset = to_hf_dataset(data_dict, video)

    info = {
        "fps": fps,
        "video": video,
    }
    return hf_dataset, episode_data_index, info