diff --git a/lerobot/__init__.py b/lerobot/__init__.py index 3963fe61..022f9cbe 100644 --- a/lerobot/__init__.py +++ b/lerobot/__init__.py @@ -70,6 +70,8 @@ available_datasets_per_env = { "lerobot/aloha_sim_transfer_cube_human_image", "lerobot/aloha_sim_transfer_cube_scripted_image", ], + # TODO(alexander-soare): Add "lerobot/pusht_keypoints". Right now we can't because this is too tightly + # coupled with tests. "pusht": ["lerobot/pusht", "lerobot/pusht_image"], "xarm": [ "lerobot/xarm_lift_medium", diff --git a/lerobot/common/datasets/lerobot_dataset.py b/lerobot/common/datasets/lerobot_dataset.py index 61c35aa4..1bf336e0 100644 --- a/lerobot/common/datasets/lerobot_dataset.py +++ b/lerobot/common/datasets/lerobot_dataset.py @@ -36,7 +36,7 @@ from lerobot.common.datasets.utils import ( from lerobot.common.datasets.video_utils import VideoFrame, load_from_videos DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None -CODEBASE_VERSION = "v1.4" +CODEBASE_VERSION = "v1.5" class LeRobotDataset(torch.utils.data.Dataset): diff --git a/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py b/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py index d9c7eb65..69b23a47 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py +++ b/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py @@ -54,7 +54,14 @@ def check_format(raw_dir): 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): +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, +): try: import pymunk from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely @@ -105,10 +112,11 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod assert (episode_ids[from_idx:to_idx] == ep_idx).all() # get image - image = imgs[from_idx:to_idx] - assert image.min() >= 0.0 - assert image.max() <= 255.0 - image = image.type(torch.uint8) + 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] @@ -116,9 +124,11 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod block_pos = state[:, 2:4] block_angle = state[:, 4] - # get reward, success, done + # 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() @@ -134,7 +144,7 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod ] space.add(*walls) - block_body = PushTEnv.add_tee(space, block_pos[i].tolist(), block_angle[i].item()) + 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 @@ -142,33 +152,40 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod 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 = {} - 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) + 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) + # 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) - # clean temporary images directory - shutil.rmtree(tmp_imgs_dir) + # 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] + # 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) @@ -180,7 +197,6 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod 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] @@ -188,17 +204,23 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod return data_dict -def to_hf_dataset(data_dict, video): +def to_hf_dataset(data_dict, video, keypoints_instead_of_image: bool = False): features = {} - if video: - features["observation.image"] = VideoFrame() - else: - features["observation.image"] = Image() + 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) ) @@ -222,17 +244,21 @@ def from_raw_to_lerobot_format( video: bool = True, episodes: list[int] | 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) - hf_dataset = to_hf_dataset(data_dict, video) + data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, keypoints_instead_of_image) + hf_dataset = to_hf_dataset(data_dict, video, keypoints_instead_of_image) episode_data_index = calculate_episode_data_index(hf_dataset) info = { "fps": fps, - "video": video, + "video": video if not keypoints_instead_of_image else 0, } return hf_dataset, episode_data_index, info diff --git a/lerobot/scripts/push_dataset_to_hub.py b/lerobot/scripts/push_dataset_to_hub.py index e471d5bd..012de94f 100644 --- a/lerobot/scripts/push_dataset_to_hub.py +++ b/lerobot/scripts/push_dataset_to_hub.py @@ -40,6 +40,60 @@ python lerobot/scripts/push_dataset_to_hub.py \ --raw-format umi_zarr \ --repo-id lerobot/umi_cup_in_the_wild ``` + +**WARNING: Updating an existing dataset** + +If you want to update an existing dataset, you need to change the `CODEBASE_VERSION` from `lerobot_dataset.py` +before running `push_dataset_to_hub.py`. This is especially useful if you introduce a breaking change +intentionally or not (i.e. something not backward compatible such as modifying the reward functions used, +deleting some frames at the end of an episode, etc.). That way, people running a previous version of the +codebase won't be affected by your change and backward compatibility is maintained. + +For instance, Pusht has many versions to maintain backward compatibility between LeRobot codebase versions: +- [v1.0](https://huggingface.co/datasets/lerobot/pusht/tree/v1.0) +- [v1.1](https://huggingface.co/datasets/lerobot/pusht/tree/v1.1) +- [v1.2](https://huggingface.co/datasets/lerobot/pusht/tree/v1.2) +- [v1.3](https://huggingface.co/datasets/lerobot/pusht/tree/v1.3) +- [v1.4](https://huggingface.co/datasets/lerobot/pusht/tree/v1.4) +- [v1.5](https://huggingface.co/datasets/lerobot/pusht/tree/v1.5) <-- last version +- [main](https://huggingface.co/datasets/lerobot/pusht/tree/main) <-- points to the last version + +However, you will need to update the version of ALL the other datasets so that they have the new +`CODEBASE_VERSION` as a branch in their hugging face dataset repository. Don't worry, there is an easy way +that doesn't require to run `push_dataset_to_hub.py`. You can just "branch-out" from the `main` branch on HF +dataset repo by running this script which corresponds to a `git checkout -b` (so no copy or upload needed): + +```python +import os + +from huggingface_hub import create_branch, hf_hub_download +from huggingface_hub.utils._errors import RepositoryNotFoundError + +from lerobot import available_datasets +from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION + +os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1" # makes it easier to see the print-out below + +NEW_CODEBASE_VERSION = "v1.5" # REPLACE THIS WITH YOUR DESIRED VERSION + +for repo_id in available_datasets: + # First check if the newer version already exists. + try: + hf_hub_download( + repo_id=repo_id, repo_type="dataset", filename=".gitattributes", revision=NEW_CODEBASE_VERSION + ) + print(f"Found existing branch for {repo_id}. Please contact a member of the core LeRobot team.") + print("Exiting early") + break + except RepositoryNotFoundError: + # Now create a branch. + create_branch(repo_id, repo_type="dataset", branch=NEW_CODEBASE_VERSION, revision=CODEBASE_VERSION) + print(f"{repo_id} successfully updated") + +``` + +On the other hand, if you are pushing a new dataset, you don't need to worry about any of the instructions +above, nor to be compatible with previous codebase versions. """ import argparse @@ -317,7 +371,10 @@ def main(): parser.add_argument( "--tests-data-dir", type=Path, - help="When provided, save tests artifacts into the given directory for (e.g. `--tests-data-dir tests/data/lerobot/pusht`).", + 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() diff --git a/poetry.lock b/poetry.lock index 48c6c057..437a1cdc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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