From ee200e86cbbcdf2d7c5e18b844827966002533de Mon Sep 17 00:00:00 2001 From: Simon Alibert <75076266+aliberts@users.noreply.github.com> Date: Thu, 18 Jul 2024 12:07:15 +0200 Subject: [PATCH 1/7] Ensure no upper bound constraints on dependencies (#327) --- .github/workflows/quality.yml | 28 ++ .github/workflows/trufflehog.yml | 2 + poetry.lock | 433 +++++++++++++++---------------- pyproject.toml | 18 +- 4 files changed, 253 insertions(+), 228 deletions(-) diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml index 5747891b..851869a0 100644 --- a/.github/workflows/quality.yml +++ b/.github/workflows/quality.yml @@ -54,3 +54,31 @@ jobs: - name: Poetry check run: poetry check + + + poetry_relax: + name: Poetry relax + runs-on: ubuntu-latest + steps: + - name: Checkout Repository + uses: actions/checkout@v3 + + - name: Install poetry + run: pipx install poetry + + - name: Install poetry-relax + run: poetry self add poetry-relax + + - name: Poetry relax + id: poetry_relax + run: | + output=$(poetry relax --check 2>&1) + if echo "$output" | grep -q "Proposing updates"; then + echo "$output" + echo "" + echo "Some dependencies have caret '^' version requirement added by poetry by default." + echo "Please replace them with '>='. You can do this by hand or use poetry-relax to do this." + exit 1 + else + echo "$output" + fi diff --git a/.github/workflows/trufflehog.yml b/.github/workflows/trufflehog.yml index b406d43b..d1dddab7 100644 --- a/.github/workflows/trufflehog.yml +++ b/.github/workflows/trufflehog.yml @@ -16,3 +16,5 @@ jobs: fetch-depth: 0 - name: Secret Scanning uses: trufflesecurity/trufflehog@main + with: + extra_args: --only-verified diff --git a/poetry.lock b/poetry.lock index abbed81f..51dd7385 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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python-versions = ">=3.8" files = [ - {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"}, - {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"}, + {file = "sympy-1.13.0-py3-none-any.whl", hash = "sha256:6b0b32a4673fb91bd3cac3b55406c8e01d53ae22780be467301cc452f6680c92"}, + {file = "sympy-1.13.0.tar.gz", hash = "sha256:3b6af8f4d008b9a1a6a4268b335b984b23835f26d1d60b0526ebc71d48a25f57"}, ] [package.dependencies] -mpmath = ">=1.1.0,<1.4.0" +mpmath = ">=1.1.0,<1.4" + +[package.extras] +dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"] [[package]] name = "tbb" @@ -4507,4 +4502,4 @@ xarm = ["gym-xarm"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "2c59d869c6b1f2132070387f3d371b5b004765ae853501bbd522eb400738f2d0" +content-hash = "882b44dada0890dd4e1c727d3363d95cbe1a4adf1d80aa5263080597d80be42c" diff --git a/pyproject.toml b/pyproject.toml index 3d3b375e..706bcb7e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,12 +38,12 @@ einops = ">=0.8.0" pymunk = ">=6.6.0" zarr = ">=2.17.0" numba = ">=0.59.0" -torch = "^2.2.1" +torch = ">=2.2.1" opencv-python = ">=4.9.0" -diffusers = "^0.27.2" +diffusers = ">=0.27.2" torchvision = ">=0.17.1" h5py = ">=3.10.0" -huggingface-hub = {extras = ["hf-transfer"], version = "^0.23.0"} +huggingface-hub = {extras = ["hf-transfer"], version = ">=0.23.0"} gymnasium = ">=0.29.1" cmake = ">=3.29.0.1" gym-dora = { git = "https://github.com/dora-rs/dora-lerobot.git", subdirectory = "gym_dora", optional = true } @@ -54,17 +54,17 @@ pre-commit = {version = ">=3.7.0", optional = true} debugpy = {version = ">=1.8.1", optional = true} pytest = {version = ">=8.1.0", optional = true} pytest-cov = {version = ">=5.0.0", optional = true} -datasets = "^2.19.0" +datasets = ">=2.19.0" imagecodecs = { version = ">=2024.1.1", optional = true } pyav = ">=12.0.5" moviepy = ">=1.0.3" rerun-sdk = ">=0.15.1" deepdiff = ">=7.0.1" -scikit-image = {version = "^0.23.2", optional = true} -pandas = {version = "^2.2.2", optional = true} -pytest-mock = {version = "^3.14.0", optional = true} -dynamixel-sdk = {version = "^3.7.31", optional = true} -pynput = {version = "^1.7.7", optional = true} +scikit-image = {version = ">=0.23.2", optional = true} +pandas = {version = ">=2.2.2", optional = true} +pytest-mock = {version = ">=3.14.0", optional = true} +dynamixel-sdk = {version = ">=3.7.31", optional = true} +pynput = {version = ">=1.7.7", optional = true} From c75ea789a8fb6bbbdb6f3b2c3d07d81b6a295286 Mon Sep 17 00:00:00 2001 From: Simon Alibert <75076266+aliberts@users.noreply.github.com> Date: Thu, 18 Jul 2024 19:39:15 +0200 Subject: [PATCH 2/7] Detect secrets in pre-commit (#332) --- .pre-commit-config.yaml | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 913fcb5d..1637151b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -14,11 +14,11 @@ repos: - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/asottile/pyupgrade - rev: v3.15.2 + rev: v3.16.0 hooks: - id: pyupgrade - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.4.3 + rev: v0.5.2 hooks: - id: ruff args: [--fix] @@ -31,3 +31,7 @@ repos: args: - "--check" - "--no-update" + - repo: https://github.com/gitleaks/gitleaks + rev: v8.18.4 + hooks: + - id: gitleaks From 461d5472d329a6a461f56a85175d4744b8e78e61 Mon Sep 17 00:00:00 2001 From: Simon Alibert <75076266+aliberts@users.noreply.github.com> Date: Thu, 18 Jul 2024 22:26:00 +0200 Subject: [PATCH 3/7] Fix visualize_image_transforms (#333) --- lerobot/scripts/visualize_image_transforms.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/lerobot/scripts/visualize_image_transforms.py b/lerobot/scripts/visualize_image_transforms.py index b4558dc8..e7cd3582 100644 --- a/lerobot/scripts/visualize_image_transforms.py +++ b/lerobot/scripts/visualize_image_transforms.py @@ -25,7 +25,7 @@ Increase hue jitter ``` python lerobot/scripts/visualize_image_transforms.py \ dataset_repo_id=lerobot/aloha_mobile_shrimp \ - training.image_transforms.hue.min_max=[-0.25,0.25] + training.image_transforms.hue.min_max="[-0.25,0.25]" ``` Increase brightness & brightness weight @@ -33,7 +33,7 @@ Increase brightness & brightness weight python lerobot/scripts/visualize_image_transforms.py \ dataset_repo_id=lerobot/aloha_mobile_shrimp \ training.image_transforms.brightness.weight=10.0 \ - training.image_transforms.brightness.min_max=[1.0,2.0] + training.image_transforms.brightness.min_max="[1.0,2.0]" ``` Blur images and disable saturation & hue @@ -41,7 +41,7 @@ Blur images and disable saturation & hue python lerobot/scripts/visualize_image_transforms.py \ dataset_repo_id=lerobot/aloha_mobile_shrimp \ training.image_transforms.sharpness.weight=10.0 \ - training.image_transforms.sharpness.min_max=[0.0,1.0] \ + training.image_transforms.sharpness.min_max="[0.0,1.0]" \ training.image_transforms.saturation.weight=0.0 \ training.image_transforms.hue.weight=0.0 ``` @@ -172,4 +172,4 @@ def visualize_transforms_cli(cfg): if __name__ == "__main__": - visualize_transforms() + visualize_transforms_cli() From 0b21210d7212677c1680423d410850ca40cd6c9a Mon Sep 17 00:00:00 2001 From: Simon Alibert <75076266+aliberts@users.noreply.github.com> Date: Mon, 22 Jul 2024 20:08:59 +0200 Subject: [PATCH 4/7] Convert datasets to av1 encoding (#302) --- README.md | 4 +- benchmarks/video/run_video_benchmark.py | 8 +- lerobot/__init__.py | 4 + lerobot/common/datasets/lerobot_dataset.py | 5 +- .../push_dataset_to_hub/CODEBASE_VERSION.md | 11 +- .../push_dataset_to_hub/_download_raw.py | 105 +++++----- .../push_dataset_to_hub/_encode_datasets.py | 184 ++++++++++++++++++ .../push_dataset_to_hub/aloha_hdf5_format.py | 23 ++- .../push_dataset_to_hub/cam_png_format.py 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mode 100644 tests/data/save_dataset_to_safetensors/lerobot/xarm_push_medium_replay_image/frame_13.safetensors delete mode 100644 tests/data/save_dataset_to_safetensors/lerobot/xarm_push_medium_replay_image/frame_23.safetensors delete mode 100644 tests/data/save_dataset_to_safetensors/lerobot/xarm_push_medium_replay_image/frame_24.safetensors diff --git a/README.md b/README.md index 518aaafc..e4d3f942 100644 --- a/README.md +++ b/README.md @@ -180,8 +180,10 @@ dataset attributes: │ ├ observation.images.cam_high: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.} │ ... ├ info: a dictionary of metadata on the dataset + │ ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with │ ├ fps (float): frame per second the dataset is recorded/synchronized to - │ └ video (bool): indicates if frames are encoded in mp4 video files to save space or stored as png files + │ ├ video (bool): indicates if frames are encoded in mp4 video files to save space or stored as png files + │ └ encoding (dict): if video, this documents the main options that were used with ffmpeg to encode the videos ├ videos_dir (Path): where the mp4 videos or png images are stored/accessed └ camera_keys (list of string): the keys to access camera features in the item returned by the dataset (e.g. `["observation.images.cam_high", ...]`) ``` diff --git a/benchmarks/video/run_video_benchmark.py b/benchmarks/video/run_video_benchmark.py index ec67f552..46806c07 100644 --- a/benchmarks/video/run_video_benchmark.py +++ b/benchmarks/video/run_video_benchmark.py @@ -257,10 +257,10 @@ def benchmark_encoding_decoding( imgs_dir=imgs_dir, video_path=video_path, fps=fps, - video_codec=encoding_cfg["vcodec"], - pixel_format=encoding_cfg["pix_fmt"], - group_of_pictures_size=encoding_cfg.get("g"), - constant_rate_factor=encoding_cfg.get("crf"), + vcodec=encoding_cfg["vcodec"], + pix_fmt=encoding_cfg["pix_fmt"], + g=encoding_cfg.get("g"), + crf=encoding_cfg.get("crf"), # fast_decode=encoding_cfg.get("fastdecode"), overwrite=True, ) diff --git a/lerobot/__init__.py b/lerobot/__init__.py index 022f9cbe..8e06435c 100644 --- a/lerobot/__init__.py +++ b/lerobot/__init__.py @@ -125,6 +125,10 @@ available_real_world_datasets = [ "lerobot/aloha_static_vinh_cup_left", "lerobot/aloha_static_ziploc_slide", "lerobot/umi_cup_in_the_wild", + "lerobot/unitreeh1_fold_clothes", + "lerobot/unitreeh1_rearrange_objects", + "lerobot/unitreeh1_two_robot_greeting", + "lerobot/unitreeh1_warehouse", ] available_datasets = list( diff --git a/lerobot/common/datasets/lerobot_dataset.py b/lerobot/common/datasets/lerobot_dataset.py index 29800c5c..eb76f78d 100644 --- a/lerobot/common/datasets/lerobot_dataset.py +++ b/lerobot/common/datasets/lerobot_dataset.py @@ -35,9 +35,8 @@ from lerobot.common.datasets.utils import ( ) from lerobot.common.datasets.video_utils import VideoFrame, load_from_videos -# For maintainers, see lerobot/common/datasets/push_dataset_to_hub/codebase_version.md -CODEBASE_VERSION = "v1.5" - +# For maintainers, see lerobot/common/datasets/push_dataset_to_hub/CODEBASE_VERSION.md +CODEBASE_VERSION = "v1.6" DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None diff --git a/lerobot/common/datasets/push_dataset_to_hub/CODEBASE_VERSION.md b/lerobot/common/datasets/push_dataset_to_hub/CODEBASE_VERSION.md index 77948b02..8fcc8bbe 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/CODEBASE_VERSION.md +++ b/lerobot/common/datasets/push_dataset_to_hub/CODEBASE_VERSION.md @@ -10,7 +10,8 @@ For instance, [`lerobot/pusht`](https://huggingface.co/datasets/lerobot/pusht) h - [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 +- [v1.5](https://huggingface.co/datasets/lerobot/pusht/tree/v1.5) +- [v1.6](https://huggingface.co/datasets/lerobot/pusht/tree/v1.6) <-- last version - [main](https://huggingface.co/datasets/lerobot/pusht/tree/main) <-- points to the last version Starting with v1.6, every dataset pushed to the hub or saved locally also have this version number in their @@ -45,13 +46,11 @@ for repo_id in available_datasets: dataset_info = api.list_repo_refs(repo_id, repo_type="dataset") branches = [b.name for b in dataset_info.branches] if CODEBASE_VERSION in branches: - # First check if the newer version already exists. - print(f"Found existing branch for {repo_id}. Please contact a member of the core LeRobot team.") - print("Exiting early") - break + print(f"{repo_id} already @{CODEBASE_VERSION}, skipping.") + continue else: # Now create a branch named after the new version by branching out from "main" # which is expected to be the preceding version api.create_branch(repo_id, repo_type="dataset", branch=CODEBASE_VERSION, revision="main") - print(f"{repo_id} successfully updated") + print(f"{repo_id} successfully updated @{CODEBASE_VERSION}") ``` diff --git a/lerobot/common/datasets/push_dataset_to_hub/_download_raw.py b/lerobot/common/datasets/push_dataset_to_hub/_download_raw.py index b630bbca..36474dd1 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/_download_raw.py +++ b/lerobot/common/datasets/push_dataset_to_hub/_download_raw.py @@ -19,8 +19,8 @@ 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/cadene/pusht_raw \ ---repo-id cadene/pusht_raw +--raw-dir data/lerobot-raw/pusht_raw \ +--repo-id lerobot-raw/pusht_raw ``` """ @@ -31,63 +31,65 @@ from pathlib import Path from huggingface_hub import snapshot_download -AVAILABLE_RAW_REPO_IDS = [ - "lerobot-raw/aloha_mobile_cabinet_raw", - "lerobot-raw/aloha_mobile_chair_raw", - "lerobot-raw/aloha_mobile_elevator_raw", - "lerobot-raw/aloha_mobile_shrimp_raw", - "lerobot-raw/aloha_mobile_wash_pan_raw", - "lerobot-raw/aloha_mobile_wipe_wine_raw", - "lerobot-raw/aloha_sim_insertion_human_raw", - "lerobot-raw/aloha_sim_insertion_scripted_raw", - "lerobot-raw/aloha_sim_transfer_cube_human_raw", - "lerobot-raw/aloha_sim_transfer_cube_scripted_raw", - "lerobot-raw/aloha_static_battery_raw", - "lerobot-raw/aloha_static_candy_raw", - "lerobot-raw/aloha_static_coffee_new_raw", - "lerobot-raw/aloha_static_coffee_raw", - "lerobot-raw/aloha_static_cups_open_raw", - "lerobot-raw/aloha_static_fork_pick_up_raw", - "lerobot-raw/aloha_static_pingpong_test_raw", - "lerobot-raw/aloha_static_pro_pencil_raw", - "lerobot-raw/aloha_static_screw_driver_raw", - "lerobot-raw/aloha_static_tape_raw", - "lerobot-raw/aloha_static_thread_velcro_raw", - "lerobot-raw/aloha_static_towel_raw", - "lerobot-raw/aloha_static_vinh_cup_left_raw", - "lerobot-raw/aloha_static_vinh_cup_raw", - "lerobot-raw/aloha_static_ziploc_slide_raw", - "lerobot-raw/pusht_raw", - "lerobot-raw/umi_cup_in_the_wild_raw", - "lerobot-raw/unitreeh1_fold_clothes_raw", - "lerobot-raw/unitreeh1_rearrange_objects_raw", - "lerobot-raw/unitreeh1_two_robot_greeting_raw", - "lerobot-raw/unitreeh1_warehouse_raw", - "lerobot-raw/xarm_lift_medium_raw", - "lerobot-raw/xarm_lift_medium_replay_raw", - "lerobot-raw/xarm_push_medium_raw", - "lerobot-raw/xarm_push_medium_replay_raw", -] +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/pusht_raw": "pusht_zarr", + "lerobot-raw/umi_cup_in_the_wild_raw": "umi_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", +} def download_raw(raw_dir: Path, repo_id: str): - # Check repo_id is well formated - if len(repo_id.split("/")) != 2: - raise ValueError( - f"`repo_id` is expected to contain a community or user id `/` the name of the dataset (e.g. 'lerobot/pusht'), but contains '{repo_id}'." - ) + 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.", + 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 formated 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.", + 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) @@ -97,8 +99,9 @@ def download_raw(raw_dir: Path, repo_id: str): logging.info(f"Finish downloading from huggingface.co/{user_id} for {dataset_id}") -def download_all_raw_datasets(): - data_dir = Path("data") +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) @@ -106,7 +109,8 @@ def download_all_raw_datasets(): 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`: {AVAILABLE_RAW_REPO_IDS}", + 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`: {AVAILABLE_RAW_REPO_IDS}""", ) parser.add_argument( @@ -119,7 +123,8 @@ def main(): "--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`).", + 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)) diff --git a/lerobot/common/datasets/push_dataset_to_hub/_encode_datasets.py b/lerobot/common/datasets/push_dataset_to_hub/_encode_datasets.py new file mode 100644 index 00000000..184d79fb --- /dev/null +++ b/lerobot/common/datasets/push_dataset_to_hub/_encode_datasets.py @@ -0,0 +1,184 @@ +#!/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() diff --git a/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py b/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py index 24873ca2..52c4bba3 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py +++ b/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py @@ -29,7 +29,11 @@ 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 concatenate_episodes, save_images_concurrently +from lerobot.common.datasets.push_dataset_to_hub.utils import ( + concatenate_episodes, + get_default_encoding, + save_images_concurrently, +) from lerobot.common.datasets.utils import ( calculate_episode_data_index, hf_transform_to_torch, @@ -72,7 +76,14 @@ def check_format(raw_dir) -> bool: 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): +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 @@ -123,7 +134,7 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod # 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_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {})) # clean temporary images directory shutil.rmtree(tmp_imgs_dir) @@ -200,6 +211,7 @@ def from_raw_to_lerobot_format( fps: int | None = None, video: bool = True, episodes: list[int] | None = None, + encoding: dict | None = None, ): # sanity check check_format(raw_dir) @@ -207,7 +219,7 @@ def from_raw_to_lerobot_format( if fps is None: fps = 50 - data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes) + 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 = { @@ -215,4 +227,7 @@ def from_raw_to_lerobot_format( "fps": fps, "video": video, } + if video: + info["encoding"] = get_default_encoding() + return hf_dataset, episode_data_index, info diff --git a/lerobot/common/datasets/push_dataset_to_hub/cam_png_format.py b/lerobot/common/datasets/push_dataset_to_hub/cam_png_format.py index 52eabd99..be20c92c 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/cam_png_format.py +++ b/lerobot/common/datasets/push_dataset_to_hub/cam_png_format.py @@ -81,8 +81,9 @@ def from_raw_to_lerobot_format( fps: int | None = None, video: bool = True, episodes: list[int] | None = None, + encoding: dict | None = None, ): - if video or episodes is not None: + if video or episodes or encoding is not None: # TODO(aliberts): support this raise NotImplementedError diff --git a/lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py b/lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py index 832f3af2..72be130e 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py +++ b/lerobot/common/datasets/push_dataset_to_hub/dora_parquet_format.py @@ -18,6 +18,7 @@ Contains utilities to process raw data format from dora-record """ import re +import warnings from pathlib import Path import pandas as pd @@ -199,6 +200,7 @@ def from_raw_to_lerobot_format( fps: int | None = None, video: bool = True, episodes: list[int] | None = None, + encoding: dict | None = None, ): # sanity check check_format(raw_dir) @@ -211,6 +213,12 @@ def from_raw_to_lerobot_format( 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) @@ -219,4 +227,7 @@ def from_raw_to_lerobot_format( "fps": fps, "video": video, } + if video: + info["encoding"] = "unknown" + return hf_dataset, episode_data_index, info 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 54043eee..13d6c837 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 @@ -26,7 +26,11 @@ 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 concatenate_episodes, save_images_concurrently +from lerobot.common.datasets.push_dataset_to_hub.utils import ( + concatenate_episodes, + get_default_encoding, + save_images_concurrently, +) from lerobot.common.datasets.utils import ( calculate_episode_data_index, hf_transform_to_torch, @@ -62,6 +66,7 @@ def load_from_raw( video: bool, episodes: list[int] | None = None, keypoints_instead_of_image: bool = False, + encoding: dict | None = None, ): try: import pymunk @@ -172,7 +177,7 @@ def load_from_raw( # 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_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {})) # clean temporary images directory shutil.rmtree(tmp_imgs_dir) @@ -244,6 +249,7 @@ def from_raw_to_lerobot_format( 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. @@ -255,7 +261,7 @@ def from_raw_to_lerobot_format( if fps is None: fps = 10 - data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes, keypoints_instead_of_image) + 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 = { @@ -263,4 +269,7 @@ def from_raw_to_lerobot_format( "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 diff --git a/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py b/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py index f9ac849c..d724cf33 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py +++ b/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py @@ -27,7 +27,11 @@ 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 concatenate_episodes, save_images_concurrently +from lerobot.common.datasets.push_dataset_to_hub.utils import ( + concatenate_episodes, + get_default_encoding, + save_images_concurrently, +) from lerobot.common.datasets.utils import ( calculate_episode_data_index, hf_transform_to_torch, @@ -60,7 +64,14 @@ def check_format(raw_dir) -> bool: 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, + encoding: dict | None = None, +): zarr_path = raw_dir / "cup_in_the_wild.zarr" zarr_data = zarr.open(zarr_path, mode="r") @@ -88,49 +99,61 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod 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)): - from_idx = from_ids[selected_ep_idx] - to_idx = to_ids[selected_ep_idx] - num_frames = to_idx - from_idx + 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? + # TODO(rcadene): save temporary images of the episode? - state = states[from_idx:to_idx] + state = states[from_idx:to_idx] - ep_dict = {} + 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: - # save png images in temporary directory - tmp_imgs_dir = videos_dir / "tmp_images" - save_images_concurrently(imgs_array, tmp_imgs_dir) + # 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 - 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 + encode_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {})) - # 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)] + # 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[img_key] = [PILImage.fromarray(x) for x in imgs_array] + ep_dict = torch.load(ep_dict_path) - 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] ep_dicts.append(ep_dict) data_dict = concatenate_episodes(ep_dicts) @@ -183,6 +206,7 @@ def from_raw_to_lerobot_format( fps: int | None = None, video: bool = True, episodes: list[int] | None = None, + encoding: dict | None = None, ): # sanity check check_format(raw_dir) @@ -196,7 +220,7 @@ def from_raw_to_lerobot_format( "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) + 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 = { @@ -204,4 +228,7 @@ def from_raw_to_lerobot_format( "fps": fps, "video": video, } + if video: + info["encoding"] = get_default_encoding() + return hf_dataset, episode_data_index, info diff --git a/lerobot/common/datasets/push_dataset_to_hub/utils.py b/lerobot/common/datasets/push_dataset_to_hub/utils.py index 4feb1dcf..97b54e45 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/utils.py +++ b/lerobot/common/datasets/push_dataset_to_hub/utils.py @@ -13,6 +13,7 @@ # 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. +import inspect from concurrent.futures import ThreadPoolExecutor from pathlib import Path @@ -20,6 +21,8 @@ import numpy import PIL import torch +from lerobot.common.datasets.video_utils import encode_video_frames + def concatenate_episodes(ep_dicts): data_dict = {} @@ -51,3 +54,21 @@ def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers num_images = len(imgs_array) with ThreadPoolExecutor(max_workers=max_workers) as executor: [executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)] + + +def get_default_encoding() -> dict: + """Returns the default ffmpeg encoding parameters used by `encode_video_frames`.""" + signature = inspect.signature(encode_video_frames) + return { + k: v.default + for k, v in signature.parameters.items() + if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"] + } + + +def check_repo_id(repo_id: str) -> None: + if len(repo_id.split("/")) != 2: + raise ValueError( + f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset + (e.g. 'lerobot/pusht'), but contains '{repo_id}'.""" + ) diff --git a/lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py b/lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py index d6ffbea1..ad1cb560 100644 --- a/lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py +++ b/lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py @@ -26,7 +26,11 @@ 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 concatenate_episodes, save_images_concurrently +from lerobot.common.datasets.push_dataset_to_hub.utils import ( + concatenate_episodes, + get_default_encoding, + save_images_concurrently, +) from lerobot.common.datasets.utils import ( calculate_episode_data_index, hf_transform_to_torch, @@ -56,7 +60,14 @@ def check_format(raw_dir): 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): +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: @@ -105,7 +116,7 @@ def load_from_raw(raw_dir: Path, videos_dir: Path, fps: int, video: bool, episod # 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_video_frames(tmp_imgs_dir, video_path, fps, **(encoding or {})) # clean temporary images directory shutil.rmtree(tmp_imgs_dir) @@ -167,6 +178,7 @@ def from_raw_to_lerobot_format( fps: int | None = None, video: bool = True, episodes: list[int] | None = None, + encoding: dict | None = None, ): # sanity check check_format(raw_dir) @@ -174,7 +186,7 @@ def from_raw_to_lerobot_format( if fps is None: fps = 15 - data_dict = load_from_raw(raw_dir, videos_dir, fps, video, episodes) + 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 = { @@ -182,4 +194,7 @@ def from_raw_to_lerobot_format( "fps": fps, "video": video, } + if video: + info["encoding"] = get_default_encoding() + return hf_dataset, episode_data_index, info diff --git a/lerobot/common/datasets/video_utils.py b/lerobot/common/datasets/video_utils.py index bcc29481..d2cc5f83 100644 --- a/lerobot/common/datasets/video_utils.py +++ b/lerobot/common/datasets/video_utils.py @@ -166,10 +166,10 @@ def encode_video_frames( imgs_dir: Path, video_path: Path, fps: int, - video_codec: str = "libsvtav1", - pixel_format: str = "yuv420p", - group_of_pictures_size: int | None = 2, - constant_rate_factor: int | None = 30, + vcodec: str = "libsvtav1", + pix_fmt: str = "yuv420p", + g: int | None = 2, + crf: int | None = 30, fast_decode: int = 0, log_level: str | None = "error", overwrite: bool = False, @@ -183,20 +183,20 @@ def encode_video_frames( ("-f", "image2"), ("-r", str(fps)), ("-i", str(imgs_dir / "frame_%06d.png")), - ("-vcodec", video_codec), - ("-pix_fmt", pixel_format), + ("-vcodec", vcodec), + ("-pix_fmt", pix_fmt), ] ) - if group_of_pictures_size is not None: - ffmpeg_args["-g"] = str(group_of_pictures_size) + if g is not None: + ffmpeg_args["-g"] = str(g) - if constant_rate_factor is not None: - ffmpeg_args["-crf"] = str(constant_rate_factor) + if crf is not None: + ffmpeg_args["-crf"] = str(crf) if fast_decode: - key = "-svtav1-params" if video_codec == "libsvtav1" else "-tune" - value = f"fast-decode={fast_decode}" if video_codec == "libsvtav1" else "fastdecode" + key = "-svtav1-params" if vcodec == "libsvtav1" else "-tune" + value = f"fast-decode={fast_decode}" if vcodec == "libsvtav1" else "fastdecode" ffmpeg_args[key] = value if log_level is not None: diff --git a/lerobot/scripts/control_robot.py b/lerobot/scripts/control_robot.py index 98d04d3f..078e3067 100644 --- a/lerobot/scripts/control_robot.py +++ b/lerobot/scripts/control_robot.py @@ -101,7 +101,7 @@ from termcolor import colored 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.aloha_hdf5_format import to_hf_dataset -from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes +from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes, get_default_encoding from lerobot.common.datasets.utils import calculate_episode_data_index from lerobot.common.datasets.video_utils import encode_video_frames from lerobot.common.policies.factory import make_policy @@ -479,6 +479,8 @@ def record_dataset( "fps": fps, "video": video, } + if video: + info["encoding"] = get_default_encoding() lerobot_dataset = LeRobotDataset.from_preloaded( repo_id=repo_id, diff --git a/lerobot/scripts/push_dataset_to_hub.py b/lerobot/scripts/push_dataset_to_hub.py index ce1a06f7..7969b61e 100644 --- a/lerobot/scripts/push_dataset_to_hub.py +++ b/lerobot/scripts/push_dataset_to_hub.py @@ -55,6 +55,7 @@ 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 flatten_dict @@ -140,14 +141,12 @@ def push_dataset_to_hub( 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 is well formated - if len(repo_id.split("/")) != 2: - raise ValueError( - f"`repo_id` is expected to contain a community or user id `/` the name of the dataset (e.g. 'lerobot/pusht'), but instead contains '{repo_id}'." - ) + check_repo_id(repo_id) user_id, dataset_id = repo_id.split("/") # Robustify when `raw_dir` is str instead of Path @@ -173,7 +172,7 @@ def push_dataset_to_hub( if local_dir.exists(): if force_override: shutil.rmtree(local_dir) - else: + elif not resume: raise ValueError(f"`local_dir` already exists ({local_dir}). Use `--force-override 1`.") meta_data_dir = local_dir / "meta_data" @@ -191,7 +190,7 @@ def push_dataset_to_hub( # 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 + raw_dir, videos_dir, fps, video, episodes, encoding ) lerobot_dataset = LeRobotDataset.from_preloaded( @@ -315,6 +314,12 @@ def main(): 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( "--tests-data-dir", type=Path, diff --git a/poetry.lock b/poetry.lock index 51dd7385..ae500299 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3212,23 +3212,6 @@ pytest = ">=4.6" [package.extras] testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"] -[[package]] -name = "pytest-mock" -version = "3.14.0" -description = "Thin-wrapper around the mock package for easier use with pytest" -optional = true -python-versions = ">=3.8" -files = [ - {file = "pytest-mock-3.14.0.tar.gz", hash = "sha256:2719255a1efeceadbc056d6bf3df3d1c5015530fb40cf347c0f9afac88410bd0"}, - {file = "pytest_mock-3.14.0-py3-none-any.whl", hash = "sha256:0b72c38033392a5f4621342fe11e9219ac11ec9d375f8e2a0c164539e0d70f6f"}, -] - -[package.dependencies] -pytest = ">=6.2.5" - -[package.extras] -dev = ["pre-commit", "pytest-asyncio", "tox"] - [[package]] name = "python-dateutil" version = "2.9.0.post0" @@ -4494,7 +4477,7 @@ dev = ["debugpy", "pre-commit"] dora = ["gym-dora"] koch = ["dynamixel-sdk", "pynput"] pusht = ["gym-pusht"] -test = ["pytest", "pytest-cov", "pytest-mock"] +test = ["pytest", "pytest-cov"] umi = ["imagecodecs"] video-benchmark = ["pandas", "scikit-image"] xarm = ["gym-xarm"] @@ -4502,4 +4485,4 @@ xarm = ["gym-xarm"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "882b44dada0890dd4e1c727d3363d95cbe1a4adf1d80aa5263080597d80be42c" +content-hash = "dfe9c6a54e0382156e62e7bd2c7aab1be6372da76d30c61b06d27232276638cb" diff --git a/pyproject.toml b/pyproject.toml index 706bcb7e..787984a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,7 +62,6 @@ rerun-sdk = ">=0.15.1" deepdiff = ">=7.0.1" scikit-image = {version = ">=0.23.2", optional = true} pandas = {version = ">=2.2.2", optional = true} -pytest-mock = {version = ">=3.14.0", optional = true} dynamixel-sdk = {version = ">=3.7.31", optional = true} pynput = {version = ">=1.7.7", optional = true} @@ -74,7 +73,7 @@ pusht = ["gym-pusht"] xarm = ["gym-xarm"] aloha = ["gym-aloha"] dev = ["pre-commit", "debugpy"] -test = ["pytest", "pytest-cov", "pytest-mock"] +test = ["pytest", "pytest-cov"] umi = ["imagecodecs"] video_benchmark = ["scikit-image", "pandas"] koch = ["dynamixel-sdk", "pynput"] @@ -110,7 +109,6 @@ exclude = [ [tool.ruff.lint] select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"] -ignore-init-module-imports = true [build-system] diff --git a/tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors b/tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors index 767dadb0..2a11302f 100644 --- a/tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors +++ b/tests/data/lerobot/aloha_mobile_cabinet/meta_data/episode_data_index.safetensors @@ -1,3 +1,3 @@ version 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sha256:071a6465638e35f31fb24eba1095546ee608fadf91d53b0aa87310fe3adbba35 +oid sha256:9ff50ee7b750022e17d867ff20307259da8d99a48fff440499e8ca9b4cf42a4a size 36312 diff --git a/tests/scripts/save_dataset_to_safetensors.py b/tests/scripts/save_dataset_to_safetensors.py index 4aa8131f..36707bd0 100644 --- a/tests/scripts/save_dataset_to_safetensors.py +++ b/tests/scripts/save_dataset_to_safetensors.py @@ -31,7 +31,6 @@ from pathlib import Path from safetensors.torch import save_file -from lerobot import available_datasets from lerobot.common.datasets.lerobot_dataset import LeRobotDataset @@ -81,5 +80,9 @@ def save_dataset_to_safetensors(output_dir, repo_id="lerobot/pusht"): if __name__ == "__main__": - for dataset in available_datasets: + for dataset in [ + "lerobot/pusht", + "lerobot/aloha_sim_insertion_human", + "lerobot/xarm_lift_medium", + ]: save_dataset_to_safetensors("tests/data/save_dataset_to_safetensors", repo_id=dataset) diff --git a/tests/scripts/save_policy_to_safetensors.py b/tests/scripts/save_policy_to_safetensors.py index 67308bb3..52c1c520 100644 --- a/tests/scripts/save_policy_to_safetensors.py +++ b/tests/scripts/save_policy_to_safetensors.py @@ -119,9 +119,10 @@ if __name__ == "__main__": # ], # "", # ), + # ("aloha", "act", ["policy.n_action_steps=10"], ""), # ("aloha", "act", ["policy.n_action_steps=1000", "policy.chunk_size=1000"], "_1000_steps"), - # ("dora_aloha_real", "act_real", ["policy.n_action_steps=10"]), - # ("dora_aloha_real", "act_real_no_state", ["policy.n_action_steps=10"]), + # ("dora_aloha_real", "act_real", ["policy.n_action_steps=10"], ""), + # ("dora_aloha_real", "act_real_no_state", ["policy.n_action_steps=10"], ""), ] if len(env_policies) == 0: raise RuntimeError("No policies were provided!") diff --git a/tests/test_push_dataset_to_hub.py b/tests/test_push_dataset_to_hub.py index df46e005..f6725f87 100644 --- a/tests/test_push_dataset_to_hub.py +++ b/tests/test_push_dataset_to_hub.py @@ -211,7 +211,7 @@ def _mock_download_raw_dora(raw_dir, num_frames=6, num_episodes=3, fps=30): fname = f"{cam_key}_episode_{ep_idx:06d}.mp4" video_path = raw_dir / "videos" / fname - encode_video_frames(tmp_imgs_dir, video_path, fps, video_codec="libx264") + encode_video_frames(tmp_imgs_dir, video_path, fps, vcodec="libx264") def _mock_download_raw(raw_dir, repo_id): @@ -229,23 +229,6 @@ def _mock_download_raw(raw_dir, repo_id): raise ValueError(repo_id) -def _mock_encode_video_frames(*args, **kwargs): - kwargs["video_codec"] = "libx264" - return encode_video_frames(*args, **kwargs) - - -def patch_encoder(raw_format, mocker): - format_module_map = { - "aloha_hdf5": "lerobot.common.datasets.push_dataset_to_hub.aloha_hdf5_format.encode_video_frames", - "pusht_zarr": "lerobot.common.datasets.push_dataset_to_hub.pusht_zarr_format.encode_video_frames", - "xarm_pkl": "lerobot.common.datasets.push_dataset_to_hub.xarm_pkl_format.encode_video_frames", - "umi_zarr": "lerobot.common.datasets.push_dataset_to_hub.umi_zarr_format.encode_video_frames", - } - - if raw_format in format_module_map: - mocker.patch(format_module_map[raw_format], side_effect=_mock_encode_video_frames) - - def test_push_dataset_to_hub_invalid_repo_id(tmpdir): with pytest.raises(ValueError): push_dataset_to_hub(Path(tmpdir), "raw_format", "invalid_repo_id") @@ -279,10 +262,7 @@ def test_push_dataset_to_hub_out_dir_force_override_false(tmpdir): ], ) @require_package_arg -def test_push_dataset_to_hub_format(required_packages, tmpdir, raw_format, repo_id, make_test_data, mocker): - # Patch `encode_video_frames` so that it uses 'libx264' instead of 'libsvtav1' for testing - patch_encoder(raw_format, mocker) - +def test_push_dataset_to_hub_format(required_packages, tmpdir, raw_format, repo_id, make_test_data): num_episodes = 3 tmpdir = Path(tmpdir) @@ -300,6 +280,7 @@ def test_push_dataset_to_hub_format(required_packages, tmpdir, raw_format, repo_ force_override=False, cache_dir=tmpdir / "cache", tests_data_dir=tmpdir / "tests/data" if make_test_data else None, + encoding={"vcodec": "libx264"}, ) # minimal generic tests on the local directory containing LeRobotDataset From abbb1d2367eaf5e13268f8112e53e43c52038206 Mon Sep 17 00:00:00 2001 From: Alexander Soare Date: Mon, 22 Jul 2024 20:38:33 +0100 Subject: [PATCH 5/7] Make sure policies don't mutate the batch (#323) --- lerobot/common/policies/act/modeling_act.py | 7 ++++--- .../policies/diffusion/modeling_diffusion.py | 2 ++ lerobot/common/policies/normalize.py | 2 ++ lerobot/common/policies/tdmpc/modeling_tdmpc.py | 2 ++ lerobot/common/policies/vqbet/modeling_vqbet.py | 2 ++ tests/test_policies.py | 17 +++++++++++++++-- 6 files changed, 27 insertions(+), 5 deletions(-) diff --git a/lerobot/common/policies/act/modeling_act.py b/lerobot/common/policies/act/modeling_act.py index c072c31e..02691701 100644 --- a/lerobot/common/policies/act/modeling_act.py +++ b/lerobot/common/policies/act/modeling_act.py @@ -101,6 +101,7 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin): batch = self.normalize_inputs(batch) if len(self.expected_image_keys) > 0: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4) # If we are doing temporal ensembling, do online updates where we keep track of the number of actions @@ -128,6 +129,7 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin): """Run the batch through the model and compute the loss for training or validation.""" batch = self.normalize_inputs(batch) if len(self.expected_image_keys) > 0: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4) batch = self.normalize_targets(batch) actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch) @@ -467,10 +469,9 @@ class ACT(nn.Module): if self.use_images: all_cam_features = [] all_cam_pos_embeds = [] - images = batch["observation.images"] - for cam_index in range(images.shape[-4]): - cam_features = self.backbone(images[:, cam_index])["feature_map"] + for cam_index in range(batch["observation.images"].shape[-4]): + cam_features = self.backbone(batch["observation.images"][:, cam_index])["feature_map"] # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use # buffer cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype) diff --git a/lerobot/common/policies/diffusion/modeling_diffusion.py b/lerobot/common/policies/diffusion/modeling_diffusion.py index ec4039cc..0d7bab95 100644 --- a/lerobot/common/policies/diffusion/modeling_diffusion.py +++ b/lerobot/common/policies/diffusion/modeling_diffusion.py @@ -122,6 +122,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin): """ batch = self.normalize_inputs(batch) if len(self.expected_image_keys) > 0: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4) # Note: It's important that this happens after stacking the images into a single key. self._queues = populate_queues(self._queues, batch) @@ -143,6 +144,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin): """Run the batch through the model and compute the loss for training or validation.""" batch = self.normalize_inputs(batch) if len(self.expected_image_keys) > 0: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4) batch = self.normalize_targets(batch) loss = self.diffusion.compute_loss(batch) diff --git a/lerobot/common/policies/normalize.py b/lerobot/common/policies/normalize.py index 9b055f7e..f2e1179c 100644 --- a/lerobot/common/policies/normalize.py +++ b/lerobot/common/policies/normalize.py @@ -132,6 +132,7 @@ class Normalize(nn.Module): # TODO(rcadene): should we remove torch.no_grad? @torch.no_grad def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: + batch = dict(batch) # shallow copy avoids mutating the input batch for key, mode in self.modes.items(): buffer = getattr(self, "buffer_" + key.replace(".", "_")) @@ -197,6 +198,7 @@ class Unnormalize(nn.Module): # TODO(rcadene): should we remove torch.no_grad? @torch.no_grad def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: + batch = dict(batch) # shallow copy avoids mutating the input batch for key, mode in self.modes.items(): buffer = getattr(self, "buffer_" + key.replace(".", "_")) diff --git a/lerobot/common/policies/tdmpc/modeling_tdmpc.py b/lerobot/common/policies/tdmpc/modeling_tdmpc.py index de9658e9..020e48a2 100644 --- a/lerobot/common/policies/tdmpc/modeling_tdmpc.py +++ b/lerobot/common/policies/tdmpc/modeling_tdmpc.py @@ -137,6 +137,7 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): def select_action(self, batch: dict[str, Tensor]) -> Tensor: """Select a single action given environment observations.""" batch = self.normalize_inputs(batch) + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.image"] = batch[self.input_image_key] self._queues = populate_queues(self._queues, batch) @@ -316,6 +317,7 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): device = get_device_from_parameters(self) batch = self.normalize_inputs(batch) + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.image"] = batch[self.input_image_key] batch = self.normalize_targets(batch) diff --git a/lerobot/common/policies/vqbet/modeling_vqbet.py b/lerobot/common/policies/vqbet/modeling_vqbet.py index 058c177e..bc12dfa2 100644 --- a/lerobot/common/policies/vqbet/modeling_vqbet.py +++ b/lerobot/common/policies/vqbet/modeling_vqbet.py @@ -98,6 +98,7 @@ class VQBeTPolicy(nn.Module, PyTorchModelHubMixin): """ batch = self.normalize_inputs(batch) + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4) # Note: It's important that this happens after stacking the images into a single key. self._queues = populate_queues(self._queues, batch) @@ -123,6 +124,7 @@ class VQBeTPolicy(nn.Module, PyTorchModelHubMixin): def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: """Run the batch through the model and compute the loss for training or validation.""" batch = self.normalize_inputs(batch) + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4) batch = self.normalize_targets(batch) # VQ-BeT discretizes action using VQ-VAE before training BeT (please refer to section 3.2 in the VQ-BeT paper https://arxiv.org/pdf/2403.03181) diff --git a/tests/test_policies.py b/tests/test_policies.py index 63f394e9..d9b946ab 100644 --- a/tests/test_policies.py +++ b/tests/test_policies.py @@ -14,6 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import inspect +from copy import deepcopy from pathlib import Path import einops @@ -161,8 +162,13 @@ def test_policy(env_name, policy_name, extra_overrides): for key in batch: batch[key] = batch[key].to(DEVICE, non_blocking=True) - # Test updating the policy + # Test updating the policy (and test that it does not mutate the batch) + batch_ = deepcopy(batch) policy.forward(batch) + assert set(batch) == set(batch_), "Batch keys are not the same after a forward pass." + assert all( + torch.equal(batch[k], batch_[k]) for k in batch + ), "Batch values are not the same after a forward pass." # reset the policy and environment policy.reset() @@ -174,9 +180,16 @@ def test_policy(env_name, policy_name, extra_overrides): # send observation to device/gpu observation = {key: observation[key].to(DEVICE, non_blocking=True) for key in observation} - # get the next action for the environment + # get the next action for the environment (also check that the observation batch is not modified) + observation_ = deepcopy(observation) with torch.inference_mode(): action = policy.select_action(observation).cpu().numpy() + assert set(observation) == set( + observation_ + ), "Observation batch keys are not the same after a forward pass." + assert all( + torch.equal(observation[k], observation_[k]) for k in observation + ), "Observation batch values are not the same after a forward pass." # Test step through policy env.step(action) From f8a6574698023872acf47f387ac7d40e6067fa8c Mon Sep 17 00:00:00 2001 From: Alexander Soare Date: Thu, 25 Jul 2024 11:16:38 +0100 Subject: [PATCH 6/7] Add online training with TD-MPC as proof of concept (#338) --- Makefile | 24 +- lerobot/common/datasets/online_buffer.py | 384 ++++++++++++++++++ .../policies/tdmpc/configuration_tdmpc.py | 35 +- .../common/policies/tdmpc/modeling_tdmpc.py | 177 ++++---- lerobot/configs/default.yaml | 51 ++- lerobot/configs/env/xarm.yaml | 2 +- lerobot/configs/policy/tdmpc.yaml | 28 +- .../configs/policy/tdmpc_pusht_keypoints.yaml | 105 +++++ lerobot/scripts/eval.py | 125 ++---- lerobot/scripts/train.py | 231 ++++++++++- .../xarm_tdmpc/actions.safetensors | 3 - .../xarm_tdmpc/grad_stats.safetensors | 3 - .../xarm_tdmpc/output_dict.safetensors | 3 - .../xarm_tdmpc/param_stats.safetensors | 3 - .../xarm_tdmpcuse_mpc/actions.safetensors | 3 + .../xarm_tdmpcuse_mpc/grad_stats.safetensors | 3 + .../xarm_tdmpcuse_mpc/output_dict.safetensors | 3 + .../xarm_tdmpcuse_mpc/param_stats.safetensors | 3 + .../xarm_tdmpcuse_policy/actions.safetensors | 3 + .../grad_stats.safetensors | 3 + .../output_dict.safetensors | 3 + .../param_stats.safetensors | 3 + tests/scripts/save_policy_to_safetensors.py | 3 +- tests/test_online_buffer.py | 320 +++++++++++++++ tests/test_policies.py | 3 +- 25 files changed, 1291 insertions(+), 233 deletions(-) create mode 100644 lerobot/common/datasets/online_buffer.py create mode 100644 lerobot/configs/policy/tdmpc_pusht_keypoints.yaml delete mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpc/actions.safetensors delete mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpc/grad_stats.safetensors delete mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpc/output_dict.safetensors delete mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpc/param_stats.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_mpc/actions.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_mpc/grad_stats.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_mpc/output_dict.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_mpc/param_stats.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/actions.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/grad_stats.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/output_dict.safetensors create mode 100644 tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/param_stats.safetensors create mode 100644 tests/test_online_buffer.py diff --git a/Makefile b/Makefile index 9bac437d..f6517497 100644 --- a/Makefile +++ b/Makefile @@ -26,6 +26,7 @@ test-end-to-end: ${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-train ${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval ${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train + ${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train-with-online ${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval ${MAKE} DEVICE=$(DEVICE) test-default-ete-eval ${MAKE} DEVICE=$(DEVICE) test-act-pusht-tutorial @@ -113,7 +114,6 @@ test-diffusion-ete-eval: env.episode_length=8 \ device=$(DEVICE) \ -# TODO(alexander-soare): Restore online_steps to 2 when it is reinstated. test-tdmpc-ete-train: python lerobot/scripts/train.py \ policy=tdmpc \ @@ -133,6 +133,28 @@ test-tdmpc-ete-train: training.image_transforms.enable=true \ hydra.run.dir=tests/outputs/tdmpc/ +test-tdmpc-ete-train-with-online: + python lerobot/scripts/train.py \ + env=pusht \ + env.gym.obs_type=environment_state_agent_pos \ + policy=tdmpc_pusht_keypoints \ + eval.n_episodes=1 \ + eval.batch_size=1 \ + env.episode_length=10 \ + device=$(DEVICE) \ + training.offline_steps=2 \ + training.online_steps=20 \ + training.save_checkpoint=false \ + training.save_freq=10 \ + training.batch_size=2 \ + training.online_rollout_n_episodes=2 \ + training.online_rollout_batch_size=2 \ + training.online_steps_between_rollouts=10 \ + training.online_buffer_capacity=15 \ + eval.use_async_envs=true \ + hydra.run.dir=tests/outputs/tdmpc_online/ + + test-tdmpc-ete-eval: python lerobot/scripts/eval.py \ -p tests/outputs/tdmpc/checkpoints/000002/pretrained_model \ diff --git a/lerobot/common/datasets/online_buffer.py b/lerobot/common/datasets/online_buffer.py new file mode 100644 index 00000000..6b093cda --- /dev/null +++ b/lerobot/common/datasets/online_buffer.py @@ -0,0 +1,384 @@ +#!/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. +"""An online buffer for the online training loop in train.py + +Note to maintainers: This duplicates some logic from LeRobotDataset and EpisodeAwareSampler. We should +consider converging to one approach. Here we have opted to use numpy.memmap to back the data buffer. It's much +faster than using HuggingFace Datasets as there's no conversion to an intermediate non-python object. Also it +supports in-place slicing and mutation which is very handy for a dynamic buffer. +""" + +import os +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + + +def _make_memmap_safe(**kwargs) -> np.memmap: + """Make a numpy memmap with checks on available disk space first. + + Expected kwargs are: "filename", "dtype" (must by np.dtype), "mode" and "shape" + + For information on dtypes: + https://numpy.org/doc/stable/reference/arrays.dtypes.html#arrays-dtypes-constructing + """ + if kwargs["mode"].startswith("w"): + required_space = kwargs["dtype"].itemsize * np.prod(kwargs["shape"]) # bytes + stats = os.statvfs(Path(kwargs["filename"]).parent) + available_space = stats.f_bavail * stats.f_frsize # bytes + if required_space >= available_space * 0.8: + raise RuntimeError( + f"You're about to take up {required_space} of {available_space} bytes available." + ) + return np.memmap(**kwargs) + + +class OnlineBuffer(torch.utils.data.Dataset): + """FIFO data buffer for the online training loop in train.py. + + Follows the protocol of LeRobotDataset as much as is required to have it be used by the online training + loop in the same way that a LeRobotDataset would be used. + + The underlying data structure will have data inserted in a circular fashion. Always insert after the + last index, and when you reach the end, wrap around to the start. + + The data is stored in a numpy memmap. + """ + + NEXT_INDEX_KEY = "_next_index" + OCCUPANCY_MASK_KEY = "_occupancy_mask" + INDEX_KEY = "index" + FRAME_INDEX_KEY = "frame_index" + EPISODE_INDEX_KEY = "episode_index" + TIMESTAMP_KEY = "timestamp" + IS_PAD_POSTFIX = "_is_pad" + + def __init__( + self, + write_dir: str | Path, + data_spec: dict[str, Any] | None, + buffer_capacity: int | None, + fps: float | None = None, + delta_timestamps: dict[str, list[float]] | dict[str, np.ndarray] | None = None, + ): + """ + The online buffer can be provided from scratch or you can load an existing online buffer by passing + a `write_dir` associated with an existing buffer. + + Args: + write_dir: Where to keep the numpy memmap files. One memmap file will be stored for each data key. + Note that if the files already exist, they are opened in read-write mode (used for training + resumption.) + data_spec: A mapping from data key to data specification, like {data_key: {"shape": tuple[int], + "dtype": np.dtype}}. This should include all the data that you wish to record into the buffer, + but note that "index", "frame_index" and "episode_index" are already accounted for by this + class, so you don't need to include them. + buffer_capacity: How many frames should be stored in the buffer as a maximum. Be aware of your + system's available disk space when choosing this. + fps: Same as the fps concept in LeRobot dataset. Here it needs to be provided for the + delta_timestamps logic. You can pass None if you are not using delta_timestamps. + delta_timestamps: Same as the delta_timestamps concept in LeRobotDataset. This is internally + converted to dict[str, np.ndarray] for optimization purposes. + + """ + self.set_delta_timestamps(delta_timestamps) + self._fps = fps + # Tolerance in seconds used to discard loaded frames when their timestamps are not close enough from + # the requested frames. It is only used when `delta_timestamps` is provided. + # minus 1e-4 to account for possible numerical error + self.tolerance_s = 1 / self.fps - 1e-4 if fps is not None else None + self._buffer_capacity = buffer_capacity + data_spec = self._make_data_spec(data_spec, buffer_capacity) + Path(write_dir).mkdir(parents=True, exist_ok=True) + self._data = {} + for k, v in data_spec.items(): + self._data[k] = _make_memmap_safe( + filename=Path(write_dir) / k, + dtype=v["dtype"] if v is not None else None, + mode="r+" if (Path(write_dir) / k).exists() else "w+", + shape=tuple(v["shape"]) if v is not None else None, + ) + + @property + def delta_timestamps(self) -> dict[str, np.ndarray] | None: + return self._delta_timestamps + + def set_delta_timestamps(self, value: dict[str, list[float]] | None): + """Set delta_timestamps converting the values to numpy arrays. + + The conversion is for an optimization in the __getitem__. The loop is much slower if the arrays + need to be converted into numpy arrays. + """ + if value is not None: + self._delta_timestamps = {k: np.array(v) for k, v in value.items()} + else: + self._delta_timestamps = None + + def _make_data_spec(self, data_spec: dict[str, Any], buffer_capacity: int) -> dict[str, dict[str, Any]]: + """Makes the data spec for np.memmap.""" + if any(k.startswith("_") for k in data_spec): + raise ValueError( + "data_spec keys should not start with '_'. This prefix is reserved for internal logic." + ) + preset_keys = { + OnlineBuffer.INDEX_KEY, + OnlineBuffer.FRAME_INDEX_KEY, + OnlineBuffer.EPISODE_INDEX_KEY, + OnlineBuffer.TIMESTAMP_KEY, + } + if len(intersection := set(data_spec).intersection(preset_keys)) > 0: + raise ValueError( + f"data_spec should not contain any of {preset_keys} as these are handled internally. " + f"The provided data_spec has {intersection}." + ) + complete_data_spec = { + # _next_index will be a pointer to the next index that we should start filling from when we add + # more data. + OnlineBuffer.NEXT_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": ()}, + # Since the memmap is initialized with all-zeros, this keeps track of which indices are occupied + # with real data rather than the dummy initialization. + OnlineBuffer.OCCUPANCY_MASK_KEY: {"dtype": np.dtype("?"), "shape": (buffer_capacity,)}, + OnlineBuffer.INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)}, + OnlineBuffer.FRAME_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)}, + OnlineBuffer.EPISODE_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)}, + OnlineBuffer.TIMESTAMP_KEY: {"dtype": np.dtype("float64"), "shape": (buffer_capacity,)}, + } + for k, v in data_spec.items(): + complete_data_spec[k] = {"dtype": v["dtype"], "shape": (buffer_capacity, *v["shape"])} + return complete_data_spec + + def add_data(self, data: dict[str, np.ndarray]): + """Add new data to the buffer, which could potentially mean shifting old data out. + + The new data should contain all the frames (in order) of any number of episodes. The indices should + start from 0 (note to the developer: this can easily be generalized). See the `rollout` and + `eval_policy` functions in `eval.py` for more information on how the data is constructed. + + Shift the incoming data index and episode_index to continue on from the last frame. Note that this + will be done in place! + """ + if len(missing_keys := (set(self.data_keys).difference(set(data)))) > 0: + raise ValueError(f"Missing data keys: {missing_keys}") + new_data_length = len(data[self.data_keys[0]]) + if not all(len(data[k]) == new_data_length for k in self.data_keys): + raise ValueError("All data items should have the same length") + + next_index = self._data[OnlineBuffer.NEXT_INDEX_KEY] + + # Sanity check to make sure that the new data indices start from 0. + assert data[OnlineBuffer.EPISODE_INDEX_KEY][0].item() == 0 + assert data[OnlineBuffer.INDEX_KEY][0].item() == 0 + + # Shift the incoming indices if necessary. + if self.num_samples > 0: + last_episode_index = self._data[OnlineBuffer.EPISODE_INDEX_KEY][next_index - 1] + last_data_index = self._data[OnlineBuffer.INDEX_KEY][next_index - 1] + data[OnlineBuffer.EPISODE_INDEX_KEY] += last_episode_index + 1 + data[OnlineBuffer.INDEX_KEY] += last_data_index + 1 + + # Insert the new data starting from next_index. It may be necessary to wrap around to the start. + n_surplus = max(0, new_data_length - (self._buffer_capacity - next_index)) + for k in self.data_keys: + if n_surplus == 0: + slc = slice(next_index, next_index + new_data_length) + self._data[k][slc] = data[k] + self._data[OnlineBuffer.OCCUPANCY_MASK_KEY][slc] = True + else: + self._data[k][next_index:] = data[k][:-n_surplus] + self._data[OnlineBuffer.OCCUPANCY_MASK_KEY][next_index:] = True + self._data[k][:n_surplus] = data[k][-n_surplus:] + if n_surplus == 0: + self._data[OnlineBuffer.NEXT_INDEX_KEY] = next_index + new_data_length + else: + self._data[OnlineBuffer.NEXT_INDEX_KEY] = n_surplus + + @property + def data_keys(self) -> list[str]: + keys = set(self._data) + keys.remove(OnlineBuffer.OCCUPANCY_MASK_KEY) + keys.remove(OnlineBuffer.NEXT_INDEX_KEY) + return sorted(keys) + + @property + def fps(self) -> float | None: + return self._fps + + @property + def num_episodes(self) -> int: + return len( + np.unique(self._data[OnlineBuffer.EPISODE_INDEX_KEY][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]]) + ) + + @property + def num_samples(self) -> int: + return np.count_nonzero(self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]) + + def __len__(self): + return self.num_samples + + def _item_to_tensors(self, item: dict) -> dict: + item_ = {} + for k, v in item.items(): + if isinstance(v, torch.Tensor): + item_[k] = v + elif isinstance(v, np.ndarray): + item_[k] = torch.from_numpy(v) + else: + item_[k] = torch.tensor(v) + return item_ + + def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: + if idx >= len(self) or idx < -len(self): + raise IndexError + + item = {k: v[idx] for k, v in self._data.items() if not k.startswith("_")} + + if self.delta_timestamps is None: + return self._item_to_tensors(item) + + episode_index = item[OnlineBuffer.EPISODE_INDEX_KEY] + current_ts = item[OnlineBuffer.TIMESTAMP_KEY] + episode_data_indices = np.where( + np.bitwise_and( + self._data[OnlineBuffer.EPISODE_INDEX_KEY] == episode_index, + self._data[OnlineBuffer.OCCUPANCY_MASK_KEY], + ) + )[0] + episode_timestamps = self._data[OnlineBuffer.TIMESTAMP_KEY][episode_data_indices] + + for data_key in self.delta_timestamps: + # Note: The logic in this loop is copied from `load_previous_and_future_frames`. + # Get timestamps used as query to retrieve data of previous/future frames. + query_ts = current_ts + self.delta_timestamps[data_key] + + # Compute distances between each query timestamp and all timestamps of all the frames belonging to + # the episode. + dist = np.abs(query_ts[:, None] - episode_timestamps[None, :]) + argmin_ = np.argmin(dist, axis=1) + min_ = dist[np.arange(dist.shape[0]), argmin_] + + is_pad = min_ > self.tolerance_s + + # Check violated query timestamps are all outside the episode range. + assert ( + (query_ts[is_pad] < episode_timestamps[0]) | (episode_timestamps[-1] < query_ts[is_pad]) + ).all(), ( + f"One or several timestamps unexpectedly violate the tolerance ({min_} > {self.tolerance_s=}" + ") inside the episode range." + ) + + # Load frames for this data key. + item[data_key] = self._data[data_key][episode_data_indices[argmin_]] + + item[f"{data_key}{OnlineBuffer.IS_PAD_POSTFIX}"] = is_pad + + return self._item_to_tensors(item) + + def get_data_by_key(self, key: str) -> torch.Tensor: + """Returns all data for a given data key as a Tensor.""" + return torch.from_numpy(self._data[key][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]]) + + +def compute_sampler_weights( + offline_dataset: LeRobotDataset, + offline_drop_n_last_frames: int = 0, + online_dataset: OnlineBuffer | None = None, + online_sampling_ratio: float | None = None, + online_drop_n_last_frames: int = 0, +) -> torch.Tensor: + """Compute the sampling weights for the online training dataloader in train.py. + + Args: + offline_dataset: The LeRobotDataset used for offline pre-training. + online_drop_n_last_frames: Number of frames to drop from the end of each offline dataset episode. + online_dataset: The OnlineBuffer used in online training. + online_sampling_ratio: The proportion of data that should be sampled from the online dataset. If an + online dataset is provided, this value must also be provided. + online_drop_n_first_frames: See `offline_drop_n_last_frames`. This is the same, but for the online + dataset. + Returns: + Tensor of weights for [offline_dataset; online_dataset], normalized to 1. + + Notes to maintainers: + - This duplicates some logic from EpisodeAwareSampler. We should consider converging to one approach. + - When used with `torch.utils.data.WeightedRandomSampler`, it could completely replace + `EpisodeAwareSampler` as the online dataset related arguments are optional. The only missing feature + is the ability to turn shuffling off. + - Options `drop_first_n_frames` and `episode_indices_to_use` can be added easily. They were not + included here to avoid adding complexity. + """ + if len(offline_dataset) == 0 and (online_dataset is None or len(online_dataset) == 0): + raise ValueError("At least one of `offline_dataset` or `online_dataset` should be contain data.") + if (online_dataset is None) ^ (online_sampling_ratio is None): + raise ValueError( + "`online_dataset` and `online_sampling_ratio` must be provided together or not at all." + ) + offline_sampling_ratio = 0 if online_sampling_ratio is None else 1 - online_sampling_ratio + + weights = [] + + if len(offline_dataset) > 0: + offline_data_mask_indices = [] + for start_index, end_index in zip( + offline_dataset.episode_data_index["from"], + offline_dataset.episode_data_index["to"], + strict=True, + ): + offline_data_mask_indices.extend( + range(start_index.item(), end_index.item() - offline_drop_n_last_frames) + ) + offline_data_mask = torch.zeros(len(offline_dataset), dtype=torch.bool) + offline_data_mask[torch.tensor(offline_data_mask_indices)] = True + weights.append( + torch.full( + size=(len(offline_dataset),), + fill_value=offline_sampling_ratio / offline_data_mask.sum(), + ) + * offline_data_mask + ) + + if online_dataset is not None and len(online_dataset) > 0: + online_data_mask_indices = [] + episode_indices = online_dataset.get_data_by_key("episode_index") + for episode_idx in torch.unique(episode_indices): + where_episode = torch.where(episode_indices == episode_idx) + start_index = where_episode[0][0] + end_index = where_episode[0][-1] + 1 + online_data_mask_indices.extend( + range(start_index.item(), end_index.item() - online_drop_n_last_frames) + ) + online_data_mask = torch.zeros(len(online_dataset), dtype=torch.bool) + online_data_mask[torch.tensor(online_data_mask_indices)] = True + weights.append( + torch.full( + size=(len(online_dataset),), + fill_value=online_sampling_ratio / online_data_mask.sum(), + ) + * online_data_mask + ) + + weights = torch.cat(weights) + + if weights.sum() == 0: + weights += 1 / len(weights) + else: + weights /= weights.sum() + + return weights diff --git a/lerobot/common/policies/tdmpc/configuration_tdmpc.py b/lerobot/common/policies/tdmpc/configuration_tdmpc.py index 49485c39..4a5415a1 100644 --- a/lerobot/common/policies/tdmpc/configuration_tdmpc.py +++ b/lerobot/common/policies/tdmpc/configuration_tdmpc.py @@ -25,12 +25,16 @@ class TDMPCConfig: camera observations. The parameters you will most likely need to change are the ones which depend on the environment / sensors. - Those are: `input_shapes`, `output_shapes`, and perhaps `max_random_shift`. + Those are: `input_shapes`, `output_shapes`, and perhaps `max_random_shift_ratio`. Args: n_action_repeats: The number of times to repeat the action returned by the planning. (hint: Google action repeats in Q-learning or ask your favorite chatbot) horizon: Horizon for model predictive control. + n_action_steps: Number of action steps to take from the plan given by model predictive control. This + is an alternative to using action repeats. If this is set to more than 1, then we require + `n_action_repeats == 1`, `use_mpc == True` and `n_action_steps <= horizon`. Note that this + approach of using multiple steps from the plan is not in the original implementation. input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents the input data name, and the value is a list indicating the dimensions of the corresponding data. For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96], @@ -100,6 +104,7 @@ class TDMPCConfig: # Input / output structure. n_action_repeats: int = 2 horizon: int = 5 + n_action_steps: int = 1 input_shapes: dict[str, list[int]] = field( default_factory=lambda: { @@ -158,17 +163,18 @@ class TDMPCConfig: """Input validation (not exhaustive).""" # There should only be one image key. image_keys = {k for k in self.input_shapes if k.startswith("observation.image")} - if len(image_keys) != 1: + if len(image_keys) > 1: raise ValueError( - f"{self.__class__.__name__} only handles one image for now. Got image keys {image_keys}." - ) - image_key = next(iter(image_keys)) - if self.input_shapes[image_key][-2] != self.input_shapes[image_key][-1]: - # TODO(alexander-soare): This limitation is solely because of code in the random shift - # augmentation. It should be able to be removed. - raise ValueError( - f"Only square images are handled now. Got image shape {self.input_shapes[image_key]}." + f"{self.__class__.__name__} handles at most one image for now. Got image keys {image_keys}." ) + if len(image_keys) > 0: + image_key = next(iter(image_keys)) + if self.input_shapes[image_key][-2] != self.input_shapes[image_key][-1]: + # TODO(alexander-soare): This limitation is solely because of code in the random shift + # augmentation. It should be able to be removed. + raise ValueError( + f"Only square images are handled now. Got image shape {self.input_shapes[image_key]}." + ) if self.n_gaussian_samples <= 0: raise ValueError( f"The number of guassian samples for CEM should be non-zero. Got `{self.n_gaussian_samples=}`" @@ -179,3 +185,12 @@ class TDMPCConfig: f"advised that you stick with the default. See {self.__class__.__name__} docstring for more " "information." ) + if self.n_action_steps > 1: + if self.n_action_repeats != 1: + raise ValueError( + "If `n_action_steps > 1`, `n_action_repeats` must be left to its default value of 1." + ) + if not self.use_mpc: + raise ValueError("If `n_action_steps > 1`, `use_mpc` must be set to `True`.") + if self.n_action_steps > self.horizon: + raise ValueError("`n_action_steps` must be less than or equal to `horizon`.") diff --git a/lerobot/common/policies/tdmpc/modeling_tdmpc.py b/lerobot/common/policies/tdmpc/modeling_tdmpc.py index 020e48a2..7dbffcef 100644 --- a/lerobot/common/policies/tdmpc/modeling_tdmpc.py +++ b/lerobot/common/policies/tdmpc/modeling_tdmpc.py @@ -19,14 +19,10 @@ The comments in this code may sometimes refer to these references: TD-MPC paper: Temporal Difference Learning for Model Predictive Control (https://arxiv.org/abs/2203.04955) FOWM paper: Finetuning Offline World Models in the Real World (https://arxiv.org/abs/2310.16029) - -TODO(alexander-soare): Make rollout work for batch sizes larger than 1. -TODO(alexander-soare): Use batch-first throughout. """ # ruff: noqa: N806 -import logging from collections import deque from copy import deepcopy from functools import partial @@ -56,9 +52,11 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): process communication to use the xarm environment from FOWM. This is because our xarm environment uses newer dependencies and does not match the environment in FOWM. See https://github.com/huggingface/lerobot/pull/103 for implementation details. - - We have NOT checked that training on LeRobot reproduces SOTA results. This is a TODO. + - We have NOT checked that training on LeRobot reproduces the results from FOWM. + - Nevertheless, we have verified that we can train TD-MPC for PushT. See + `lerobot/configs/policy/tdmpc_pusht_keypoints.yaml`. - Our current xarm datasets were generated using the environment from FOWM. Therefore they do not - match our xarm environment. + match our xarm environment. """ name = "tdmpc" @@ -74,22 +72,6 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): that they will be passed with a call to `load_state_dict` before the policy is used. """ super().__init__() - logging.warning( - """ - Please note several warnings for this policy. - - - Evaluation of pretrained weights created with the original FOWM code - (https://github.com/fyhMer/fowm) works as expected. To be precise: we trained and evaluated a - model with the FOWM code for the xarm_lift_medium_replay dataset. We ported the weights across - to LeRobot, and were able to evaluate with the same success metric. BUT, we had to use inter- - process communication to use the xarm environment from FOWM. This is because our xarm - environment uses newer dependencies and does not match the environment in FOWM. See - https://github.com/huggingface/lerobot/pull/103 for implementation details. - - We have NOT checked that training on LeRobot reproduces SOTA results. This is a TODO. - - Our current xarm datasets were generated using the environment from FOWM. Therefore they do not - match our xarm environment. - """ - ) if config is None: config = TDMPCConfig() @@ -114,8 +96,14 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): image_keys = [k for k in config.input_shapes if k.startswith("observation.image")] # Note: This check is covered in the post-init of the config but have a sanity check just in case. - assert len(image_keys) == 1 - self.input_image_key = image_keys[0] + self._use_image = False + self._use_env_state = False + if len(image_keys) > 0: + assert len(image_keys) == 1 + self._use_image = True + self.input_image_key = image_keys[0] + if "observation.environment_state" in config.input_shapes: + self._use_env_state = True self.reset() @@ -125,10 +113,13 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): called on `env.reset()` """ self._queues = { - "observation.image": deque(maxlen=1), "observation.state": deque(maxlen=1), - "action": deque(maxlen=self.config.n_action_repeats), + "action": deque(maxlen=max(self.config.n_action_steps, self.config.n_action_repeats)), } + if self._use_image: + self._queues["observation.image"] = deque(maxlen=1) + if self._use_env_state: + self._queues["observation.environment_state"] = deque(maxlen=1) # Previous mean obtained from the cross-entropy method (CEM) used during MPC. It is used to warm start # CEM for the next step. self._prev_mean: torch.Tensor | None = None @@ -137,8 +128,9 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): def select_action(self, batch: dict[str, Tensor]) -> Tensor: """Select a single action given environment observations.""" batch = self.normalize_inputs(batch) - batch = dict(batch) # shallow copy so that adding a key doesn't modify the original - batch["observation.image"] = batch[self.input_image_key] + if self._use_image: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.image"] = batch[self.input_image_key] self._queues = populate_queues(self._queues, batch) @@ -152,49 +144,57 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): batch[key] = batch[key][:, 0] # NOTE: Order of observations matters here. - z = self.model.encode({k: batch[k] for k in ["observation.image", "observation.state"]}) - if self.config.use_mpc: - batch_size = batch["observation.image"].shape[0] - # Batch processing is not handled in MPC mode, so process the batch in a loop. - action = [] # will be a batch of actions for one step - for i in range(batch_size): - # Note: self.plan does not handle batches, hence the squeeze. - action.append(self.plan(z[i])) - action = torch.stack(action) + encode_keys = [] + if self._use_image: + encode_keys.append("observation.image") + if self._use_env_state: + encode_keys.append("observation.environment_state") + encode_keys.append("observation.state") + z = self.model.encode({k: batch[k] for k in encode_keys}) + if self.config.use_mpc: # noqa: SIM108 + actions = self.plan(z) # (horizon, batch, action_dim) else: - # Plan with the policy (π) alone. - action = self.model.pi(z) + # Plan with the policy (π) alone. This always returns one action so unsqueeze to get a + # sequence dimension like in the MPC branch. + actions = self.model.pi(z).unsqueeze(0) - self.unnormalize_outputs({"action": action})["action"] + actions = torch.clamp(actions, -1, +1) - for _ in range(self.config.n_action_repeats): - self._queues["action"].append(action) + actions = self.unnormalize_outputs({"action": actions})["action"] + + if self.config.n_action_repeats > 1: + for _ in range(self.config.n_action_repeats): + self._queues["action"].append(actions[0]) + else: + # Action queue is (n_action_steps, batch_size, action_dim), so we transpose the action. + self._queues["action"].extend(actions[: self.config.n_action_steps]) action = self._queues["action"].popleft() - return torch.clamp(action, -1, 1) + return action @torch.no_grad() def plan(self, z: Tensor) -> Tensor: - """Plan next action using TD-MPC inference. + """Plan sequence of actions using TD-MPC inference. Args: - z: (latent_dim,) tensor for the initial state. + z: (batch, latent_dim,) tensor for the initial state. Returns: - (action_dim,) tensor for the next action. - - TODO(alexander-soare) Extend this to be able to work with batches. + (horizon, batch, action_dim,) tensor for the planned trajectory of actions. """ device = get_device_from_parameters(self) + batch_size = z.shape[0] + # Sample Nπ trajectories from the policy. pi_actions = torch.empty( self.config.horizon, self.config.n_pi_samples, + batch_size, self.config.output_shapes["action"][0], device=device, ) if self.config.n_pi_samples > 0: - _z = einops.repeat(z, "d -> n d", n=self.config.n_pi_samples) + _z = einops.repeat(z, "b d -> n b d", n=self.config.n_pi_samples) for t in range(self.config.horizon): # Note: Adding a small amount of noise here doesn't hurt during inference and may even be # helpful for CEM. @@ -203,12 +203,14 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): # In the CEM loop we will need this for a call to estimate_value with the gaussian sampled # trajectories. - z = einops.repeat(z, "d -> n d", n=self.config.n_gaussian_samples + self.config.n_pi_samples) + z = einops.repeat(z, "b d -> n b d", n=self.config.n_gaussian_samples + self.config.n_pi_samples) # Model Predictive Path Integral (MPPI) with the cross-entropy method (CEM) as the optimization # algorithm. # The initial mean and standard deviation for the cross-entropy method (CEM). - mean = torch.zeros(self.config.horizon, self.config.output_shapes["action"][0], device=device) + mean = torch.zeros( + self.config.horizon, batch_size, self.config.output_shapes["action"][0], device=device + ) # Maybe warm start CEM with the mean from the previous step. if self._prev_mean is not None: mean[:-1] = self._prev_mean[1:] @@ -219,6 +221,7 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): std_normal_noise = torch.randn( self.config.horizon, self.config.n_gaussian_samples, + batch_size, self.config.output_shapes["action"][0], device=std.device, ) @@ -227,21 +230,24 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): # Compute elite actions. actions = torch.cat([gaussian_actions, pi_actions], dim=1) value = self.estimate_value(z, actions).nan_to_num_(0) - elite_idxs = torch.topk(value, self.config.n_elites, dim=0).indices - elite_value, elite_actions = value[elite_idxs], actions[:, elite_idxs] + elite_idxs = torch.topk(value, self.config.n_elites, dim=0).indices # (n_elites, batch) + elite_value = value.take_along_dim(elite_idxs, dim=0) # (n_elites, batch) + # (horizon, n_elites, batch, action_dim) + elite_actions = actions.take_along_dim(einops.rearrange(elite_idxs, "n b -> 1 n b 1"), dim=1) - # Update guassian PDF parameters to be the (weighted) mean and standard deviation of the elites. - max_value = elite_value.max(0)[0] + # Update gaussian PDF parameters to be the (weighted) mean and standard deviation of the elites. + max_value = elite_value.max(0, keepdim=True)[0] # (1, batch) # The weighting is a softmax over trajectory values. Note that this is not the same as the usage # of Ω in eqn 4 of the TD-MPC paper. Instead it is the normalized version of it: s = Ω/ΣΩ. This # makes the equations: μ = Σ(s⋅Γ), σ = Σ(s⋅(Γ-μ)²). score = torch.exp(self.config.elite_weighting_temperature * (elite_value - max_value)) - score /= score.sum() - _mean = torch.sum(einops.rearrange(score, "n -> n 1") * elite_actions, dim=1) + score /= score.sum(axis=0, keepdim=True) + # (horizon, batch, action_dim) + _mean = torch.sum(einops.rearrange(score, "n b -> n b 1") * elite_actions, dim=1) _std = torch.sqrt( torch.sum( - einops.rearrange(score, "n -> n 1") - * (elite_actions - einops.rearrange(_mean, "h d -> h 1 d")) ** 2, + einops.rearrange(score, "n b -> n b 1") + * (elite_actions - einops.rearrange(_mean, "h b d -> h 1 b d")) ** 2, dim=1, ) ) @@ -256,11 +262,9 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): # Randomly select one of the elite actions from the last iteration of MPPI/CEM using the softmax # scores from the last iteration. - actions = elite_actions[:, torch.multinomial(score, 1).item()] + actions = elite_actions[:, torch.multinomial(score.T, 1).squeeze(), torch.arange(batch_size)] - # Select only the first action - action = actions[0] - return action + return actions @torch.no_grad() def estimate_value(self, z: Tensor, actions: Tensor): @@ -312,13 +316,17 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): G -= running_discount * self.config.uncertainty_regularizer_coeff * terminal_values.std(0) return G - def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]: - """Run the batch through the model and compute the loss.""" + def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor | float]: + """Run the batch through the model and compute the loss. + + Returns a dictionary with loss as a tensor, and other information as native floats. + """ device = get_device_from_parameters(self) batch = self.normalize_inputs(batch) - batch = dict(batch) # shallow copy so that adding a key doesn't modify the original - batch["observation.image"] = batch[self.input_image_key] + if self._use_image: + batch = dict(batch) # shallow copy so that adding a key doesn't modify the original + batch["observation.image"] = batch[self.input_image_key] batch = self.normalize_targets(batch) info = {} @@ -328,12 +336,12 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): if batch[key].ndim > 1: batch[key] = batch[key].transpose(1, 0) - action = batch["action"] # (t, b) - reward = batch["next.reward"] # (t,) + action = batch["action"] # (t, b, action_dim) + reward = batch["next.reward"] # (t, b) observations = {k: v for k, v in batch.items() if k.startswith("observation.")} # Apply random image augmentations. - if self.config.max_random_shift_ratio > 0: + if self._use_image and self.config.max_random_shift_ratio > 0: observations["observation.image"] = flatten_forward_unflatten( partial(random_shifts_aug, max_random_shift_ratio=self.config.max_random_shift_ratio), observations["observation.image"], @@ -345,7 +353,9 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): for k in observations: current_observation[k] = observations[k][0] next_observations[k] = observations[k][1:] - horizon = next_observations["observation.image"].shape[0] + horizon, batch_size = next_observations[ + "observation.image" if self._use_image else "observation.environment_state" + ].shape[:2] # Run latent rollout using the latent dynamics model and policy model. # Note this has shape `horizon+1` because there are `horizon` actions and a current `z`. Each action @@ -415,7 +425,8 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble. q_value_loss = ( ( - F.mse_loss( + temporal_loss_coeffs + * F.mse_loss( q_preds_ensemble, einops.repeat(q_targets, "t b -> e t b", e=q_preds_ensemble.shape[0]), reduction="none", @@ -464,10 +475,11 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin): action_preds = self.model.pi(z_preds[:-1]) # (t, b, a) # Calculate the MSE between the actions and the action predictions. # Note: FOWM's original code calculates the log probability (wrt to a unit standard deviation - # gaussian) and sums over the action dimension. Computing the log probability amounts to multiplying - # the MSE by 0.5 and adding a constant offset (the log(2*pi) term) . Here we drop the constant offset - # as it doesn't change the optimization step, and we drop the 0.5 as we instead make a configuration - # parameter for it (see below where we compute the total loss). + # gaussian) and sums over the action dimension. Computing the (negative) log probability amounts to + # multiplying the MSE by 0.5 and adding a constant offset (the log(2*pi)/2 term, times the action + # dimension). Here we drop the constant offset as it doesn't change the optimization step, and we drop + # the 0.5 as we instead make a configuration parameter for it (see below where we compute the total + # loss). mse = F.mse_loss(action_preds, action, reduction="none").sum(-1) # (t, b) # NOTE: The original implementation does not take the sum over the temporal dimension like with the # other losses. @@ -728,6 +740,16 @@ class TDMPCObservationEncoder(nn.Module): nn.LayerNorm(config.latent_dim), nn.Sigmoid(), ) + if "observation.environment_state" in config.input_shapes: + self.env_state_enc_layers = nn.Sequential( + nn.Linear( + config.input_shapes["observation.environment_state"][0], config.state_encoder_hidden_dim + ), + nn.ELU(), + nn.Linear(config.state_encoder_hidden_dim, config.latent_dim), + nn.LayerNorm(config.latent_dim), + nn.Sigmoid(), + ) def forward(self, obs_dict: dict[str, Tensor]) -> Tensor: """Encode the image and/or state vector. @@ -736,8 +758,11 @@ class TDMPCObservationEncoder(nn.Module): over all features. """ feat = [] + # NOTE: Order of observations matters here. if "observation.image" in self.config.input_shapes: feat.append(flatten_forward_unflatten(self.image_enc_layers, obs_dict["observation.image"])) + if "observation.environment_state" in self.config.input_shapes: + feat.append(self.env_state_enc_layers(obs_dict["observation.environment_state"])) if "observation.state" in self.config.input_shapes: feat.append(self.state_enc_layers(obs_dict["observation.state"])) return torch.stack(feat, dim=0).mean(0) diff --git a/lerobot/configs/default.yaml b/lerobot/configs/default.yaml index 4bb1508d..a3ff1d41 100644 --- a/lerobot/configs/default.yaml +++ b/lerobot/configs/default.yaml @@ -32,19 +32,54 @@ video_backend: pyav training: offline_steps: ??? - # NOTE: `online_steps` is not implemented yet. It's here as a placeholder. - online_steps: ??? - online_steps_between_rollouts: ??? - online_sampling_ratio: 0.5 - # `online_env_seed` is used for environments for online training data rollouts. - online_env_seed: ??? + + # Number of workers for the offline training dataloader. + num_workers: 4 + + batch_size: ??? + eval_freq: ??? log_freq: 200 save_checkpoint: true # Checkpoint is saved every `save_freq` training iterations and after the last training step. save_freq: ??? - num_workers: 4 - batch_size: ??? + + # Online training. Note that the online training loop adopts most of the options above apart from the + # dataloader options. Unless otherwise specified. + # The online training look looks something like: + # + # for i in range(online_steps): + # do_online_rollout_and_update_online_buffer() + # for j in range(online_steps_between_rollouts): + # batch = next(dataloader_with_offline_and_online_data) + # loss = policy(batch) + # loss.backward() + # optimizer.step() + # + online_steps: ??? + # How many episodes to collect at once when we reach the online rollout part of the training loop. + online_rollout_n_episodes: 1 + # The number of environments to use in the gym.vector.VectorEnv. This ends up also being the batch size for + # the policy. Ideally you should set this to by an even divisor or online_rollout_n_episodes. + online_rollout_batch_size: 1 + # How many optimization steps (forward, backward, optimizer step) to do between running rollouts. + online_steps_between_rollouts: null + # The proportion of online samples (vs offline samples) to include in the online training batches. + online_sampling_ratio: 0.5 + # First seed to use for the online rollout environment. Seeds for subsequent rollouts are incremented by 1. + online_env_seed: null + # Sets the maximum number of frames that are stored in the online buffer for online training. The buffer is + # FIFO. + online_buffer_capacity: null + # The minimum number of frames to have in the online buffer before commencing online training. + # If online_buffer_seed_size > online_rollout_n_episodes, the rollout will be run multiple times until the + # seed size condition is satisfied. + online_buffer_seed_size: 0 + # Whether to run the online rollouts asynchronously. This means we can run the online training steps in + # parallel with the rollouts. This might be advised if your GPU has the bandwidth to handle training + # + eval + environment rendering simultaneously. + do_online_rollout_async: false + image_transforms: # These transforms are all using standard torchvision.transforms.v2 # You can find out how these transformations affect images here: diff --git a/lerobot/configs/env/xarm.yaml b/lerobot/configs/env/xarm.yaml index 9dbb96f5..4320379a 100644 --- a/lerobot/configs/env/xarm.yaml +++ b/lerobot/configs/env/xarm.yaml @@ -9,7 +9,7 @@ env: state_dim: 4 action_dim: 4 fps: ${fps} - episode_length: 25 + episode_length: 200 gym: obs_type: pixels_agent_pos render_mode: rgb_array diff --git a/lerobot/configs/policy/tdmpc.yaml b/lerobot/configs/policy/tdmpc.yaml index 379e9320..40eab35f 100644 --- a/lerobot/configs/policy/tdmpc.yaml +++ b/lerobot/configs/policy/tdmpc.yaml @@ -4,19 +4,30 @@ seed: 1 dataset_repo_id: lerobot/xarm_lift_medium training: - offline_steps: 25000 - # TODO(alexander-soare): uncomment when online training gets reinstated - online_steps: 0 # 25000 not implemented yet - eval_freq: 5000 - online_steps_between_rollouts: 1 - online_sampling_ratio: 0.5 - online_env_seed: 10000 - log_freq: 100 + offline_steps: 50000 + + num_workers: 4 batch_size: 256 grad_clip_norm: 10.0 lr: 3e-4 + eval_freq: 5000 + log_freq: 100 + + online_steps: 50000 + online_rollout_n_episodes: 1 + online_rollout_batch_size: 1 + # Note: in FOWM `online_steps_between_rollouts` is actually dynamically set to match exactly the length of + # the last sampled episode. + online_steps_between_rollouts: 50 + online_sampling_ratio: 0.5 + online_env_seed: 10000 + # FOWM Push uses 10000 for `online_buffer_capacity`. Given that their maximum episode length for this task + # is 25, 10000 is approx 400 of their episodes worth. Since our episodes are about 8 times longer, we'll use + # 80000. + online_buffer_capacity: 80000 + delta_timestamps: observation.image: "[i / ${fps} for i in range(${policy.horizon} + 1)]" observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]" @@ -31,6 +42,7 @@ policy: # Input / output structure. n_action_repeats: 2 horizon: 5 + n_action_steps: 1 input_shapes: # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env? diff --git a/lerobot/configs/policy/tdmpc_pusht_keypoints.yaml b/lerobot/configs/policy/tdmpc_pusht_keypoints.yaml new file mode 100644 index 00000000..1cfc5b52 --- /dev/null +++ b/lerobot/configs/policy/tdmpc_pusht_keypoints.yaml @@ -0,0 +1,105 @@ +# @package _global_ + +# Train with: +# +# python lerobot/scripts/train.py \ +# env=pusht \ +# env.gym.obs_type=environment_state_agent_pos \ +# policy=tdmpc_pusht_keypoints \ +# eval.batch_size=50 \ +# eval.n_episodes=50 \ +# eval.use_async_envs=true \ +# device=cuda \ +# use_amp=true + +seed: 1 +dataset_repo_id: lerobot/pusht_keypoints + +training: + offline_steps: 0 + + # Offline training dataloader + num_workers: 4 + + batch_size: 256 + grad_clip_norm: 10.0 + lr: 3e-4 + + eval_freq: 10000 + log_freq: 500 + save_freq: 50000 + + online_steps: 1000000 + online_rollout_n_episodes: 10 + online_rollout_batch_size: 10 + online_steps_between_rollouts: 1000 + online_sampling_ratio: 1.0 + online_env_seed: 10000 + online_buffer_capacity: 40000 + online_buffer_seed_size: 0 + do_online_rollout_async: false + + delta_timestamps: + observation.environment_state: "[i / ${fps} for i in range(${policy.horizon} + 1)]" + observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]" + action: "[i / ${fps} for i in range(${policy.horizon})]" + next.reward: "[i / ${fps} for i in range(${policy.horizon})]" + +policy: + name: tdmpc + + pretrained_model_path: + + # Input / output structure. + n_action_repeats: 1 + horizon: 5 + n_action_steps: 5 + + input_shapes: + # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env? + observation.environment_state: [16] + observation.state: ["${env.state_dim}"] + output_shapes: + action: ["${env.action_dim}"] + + # Normalization / Unnormalization + input_normalization_modes: + observation.environment_state: min_max + observation.state: min_max + output_normalization_modes: + action: min_max + + # Architecture / modeling. + # Neural networks. + image_encoder_hidden_dim: 32 + state_encoder_hidden_dim: 256 + latent_dim: 50 + q_ensemble_size: 5 + mlp_dim: 512 + # Reinforcement learning. + discount: 0.98 + + # Inference. + use_mpc: true + cem_iterations: 6 + max_std: 2.0 + min_std: 0.05 + n_gaussian_samples: 512 + n_pi_samples: 51 + uncertainty_regularizer_coeff: 1.0 + n_elites: 50 + elite_weighting_temperature: 0.5 + gaussian_mean_momentum: 0.1 + + # Training and loss computation. + max_random_shift_ratio: 0.0476 + # Loss coefficients. + reward_coeff: 0.5 + expectile_weight: 0.9 + value_coeff: 0.1 + consistency_coeff: 20.0 + advantage_scaling: 3.0 + pi_coeff: 0.5 + temporal_decay_coeff: 0.5 + # Target model. + target_model_momentum: 0.995 diff --git a/lerobot/scripts/eval.py b/lerobot/scripts/eval.py index 486b4d2b..a07f3530 100644 --- a/lerobot/scripts/eval.py +++ b/lerobot/scripts/eval.py @@ -56,16 +56,13 @@ import einops import gymnasium as gym import numpy as np import torch -from datasets import Dataset, Features, Image, Sequence, Value, concatenate_datasets from huggingface_hub import snapshot_download from huggingface_hub.utils._errors import RepositoryNotFoundError from huggingface_hub.utils._validators import HFValidationError -from PIL import Image as PILImage from torch import Tensor, nn from tqdm import trange from lerobot.common.datasets.factory import make_dataset -from lerobot.common.datasets.utils import hf_transform_to_torch from lerobot.common.envs.factory import make_env from lerobot.common.envs.utils import preprocess_observation from lerobot.common.logger import log_output_dir @@ -318,41 +315,17 @@ def eval_policy( rollout_data, done_indices, start_episode_index=batch_ix * env.num_envs, - start_data_index=( - 0 if episode_data is None else (episode_data["episode_data_index"]["to"][-1].item()) - ), + start_data_index=(0 if episode_data is None else (episode_data["index"][-1].item() + 1)), fps=env.unwrapped.metadata["render_fps"], ) if episode_data is None: episode_data = this_episode_data else: - # Some sanity checks to make sure we are not correctly compiling the data. - assert ( - episode_data["hf_dataset"]["episode_index"][-1] + 1 - == this_episode_data["hf_dataset"]["episode_index"][0] - ) - assert ( - episode_data["hf_dataset"]["index"][-1] + 1 == this_episode_data["hf_dataset"]["index"][0] - ) - assert torch.equal( - episode_data["episode_data_index"]["to"][-1], - this_episode_data["episode_data_index"]["from"][0], - ) + # Some sanity checks to make sure we are correctly compiling the data. + assert episode_data["episode_index"][-1] + 1 == this_episode_data["episode_index"][0] + assert episode_data["index"][-1] + 1 == this_episode_data["index"][0] # Concatenate the episode data. - episode_data = { - "hf_dataset": concatenate_datasets( - [episode_data["hf_dataset"], this_episode_data["hf_dataset"]] - ), - "episode_data_index": { - k: torch.cat( - [ - episode_data["episode_data_index"][k], - this_episode_data["episode_data_index"][k], - ] - ) - for k in ["from", "to"] - }, - } + episode_data = {k: torch.cat([episode_data[k], this_episode_data[k]]) for k in episode_data} # Maybe render video for visualization. if max_episodes_rendered > 0 and len(ep_frames) > 0: @@ -434,89 +407,39 @@ def _compile_episode_data( Similar logic is implemented when datasets are pushed to hub (see: `push_to_hub`). """ ep_dicts = [] - episode_data_index = {"from": [], "to": []} total_frames = 0 - data_index_from = start_data_index for ep_ix in range(rollout_data["action"].shape[0]): - num_frames = done_indices[ep_ix].item() + 1 # + 1 to include the first done frame + # + 2 to include the first done frame and the last observation frame. + num_frames = done_indices[ep_ix].item() + 2 total_frames += num_frames - # TODO(rcadene): We need to add a missing last frame which is the observation - # of a done state. it is critical to have this frame for tdmpc to predict a "done observation/state" + # Here we do `num_frames - 1` as we don't want to include the last observation frame just yet. ep_dict = { - "action": rollout_data["action"][ep_ix, :num_frames], - "episode_index": torch.tensor([start_episode_index + ep_ix] * num_frames), - "frame_index": torch.arange(0, num_frames, 1), - "timestamp": torch.arange(0, num_frames, 1) / fps, - "next.done": rollout_data["done"][ep_ix, :num_frames], - "next.reward": rollout_data["reward"][ep_ix, :num_frames].type(torch.float32), + "action": rollout_data["action"][ep_ix, : num_frames - 1], + "episode_index": torch.tensor([start_episode_index + ep_ix] * (num_frames - 1)), + "frame_index": torch.arange(0, num_frames - 1, 1), + "timestamp": torch.arange(0, num_frames - 1, 1) / fps, + "next.done": rollout_data["done"][ep_ix, : num_frames - 1], + "next.success": rollout_data["success"][ep_ix, : num_frames - 1], + "next.reward": rollout_data["reward"][ep_ix, : num_frames - 1].type(torch.float32), } + + # For the last observation frame, all other keys will just be copy padded. + for k in ep_dict: + ep_dict[k] = torch.cat([ep_dict[k], ep_dict[k][-1:]]) + for key in rollout_data["observation"]: - ep_dict[key] = rollout_data["observation"][key][ep_ix][:num_frames] + ep_dict[key] = rollout_data["observation"][key][ep_ix, :num_frames] + ep_dicts.append(ep_dict) - episode_data_index["from"].append(data_index_from) - episode_data_index["to"].append(data_index_from + num_frames) - - data_index_from += num_frames - data_dict = {} for key in ep_dicts[0]: - if "image" not in key: - data_dict[key] = torch.cat([x[key] for x in ep_dicts]) - else: - if key not in data_dict: - data_dict[key] = [] - for ep_dict in ep_dicts: - for img in ep_dict[key]: - # sanity check that images are channel first - c, h, w = img.shape - assert c < h and c < w, f"expect channel first images, but instead {img.shape}" - - # sanity check that images are float32 in range [0,1] - assert img.dtype == torch.float32, f"expect torch.float32, but instead {img.dtype=}" - assert img.max() <= 1, f"expect pixels lower than 1, but instead {img.max()=}" - assert img.min() >= 0, f"expect pixels greater than 1, but instead {img.min()=}" - - # from float32 in range [0,1] to uint8 in range [0,255] - img *= 255 - img = img.type(torch.uint8) - - # convert to channel last and numpy as expected by PIL - img = PILImage.fromarray(img.permute(1, 2, 0).numpy()) - - data_dict[key].append(img) + data_dict[key] = torch.cat([x[key] for x in ep_dicts]) data_dict["index"] = torch.arange(start_data_index, start_data_index + total_frames, 1) - episode_data_index["from"] = torch.tensor(episode_data_index["from"]) - episode_data_index["to"] = torch.tensor(episode_data_index["to"]) - # TODO(rcadene): clean this - features = {} - for key in rollout_data["observation"]: - if "image" in key: - features[key] = Image() - else: - features[key] = Sequence(length=data_dict[key].shape[1], feature=Value(dtype="float32", id=None)) - features.update( - { - "action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)), - "episode_index": Value(dtype="int64", id=None), - "frame_index": Value(dtype="int64", id=None), - "timestamp": Value(dtype="float32", id=None), - "next.reward": Value(dtype="float32", id=None), - "next.done": Value(dtype="bool", id=None), - #'next.success': Value(dtype='bool', id=None), - "index": Value(dtype="int64", id=None), - } - ) - features = Features(features) - hf_dataset = Dataset.from_dict(data_dict, features=features) - hf_dataset.set_transform(hf_transform_to_torch) - return { - "hf_dataset": hf_dataset, - "episode_data_index": episode_data_index, - } + return data_dict def main( diff --git a/lerobot/scripts/train.py b/lerobot/scripts/train.py index f707fe12..d8fdfc1f 100644 --- a/lerobot/scripts/train.py +++ b/lerobot/scripts/train.py @@ -15,20 +15,25 @@ # limitations under the License. import logging import time +from concurrent.futures import ThreadPoolExecutor from contextlib import nullcontext +from copy import deepcopy from pathlib import Path from pprint import pformat +from threading import Lock import hydra +import numpy as np import torch from deepdiff import DeepDiff -from omegaconf import DictConfig, OmegaConf +from omegaconf import DictConfig, ListConfig, OmegaConf from termcolor import colored from torch import nn from torch.cuda.amp import GradScaler from lerobot.common.datasets.factory import make_dataset, resolve_delta_timestamps from lerobot.common.datasets.lerobot_dataset import MultiLeRobotDataset +from lerobot.common.datasets.online_buffer import OnlineBuffer, compute_sampler_weights from lerobot.common.datasets.sampler import EpisodeAwareSampler from lerobot.common.datasets.utils import cycle from lerobot.common.envs.factory import make_env @@ -107,6 +112,7 @@ def update_policy( grad_scaler: GradScaler, lr_scheduler=None, use_amp: bool = False, + lock=None, ): """Returns a dictionary of items for logging.""" start_time = time.perf_counter() @@ -129,7 +135,8 @@ def update_policy( # Optimizer's gradients are already unscaled, so scaler.step does not unscale them, # although it still skips optimizer.step() if the gradients contain infs or NaNs. - grad_scaler.step(optimizer) + with lock if lock is not None else nullcontext(): + grad_scaler.step(optimizer) # Updates the scale for next iteration. grad_scaler.update() @@ -149,11 +156,12 @@ def update_policy( "update_s": time.perf_counter() - start_time, **{k: v for k, v in output_dict.items() if k != "loss"}, } + info.update({k: v for k, v in output_dict.items() if k not in info}) return info -def log_train_info(logger: Logger, info, step, cfg, dataset, is_offline): +def log_train_info(logger: Logger, info, step, cfg, dataset, is_online): loss = info["loss"] grad_norm = info["grad_norm"] lr = info["lr"] @@ -187,12 +195,12 @@ def log_train_info(logger: Logger, info, step, cfg, dataset, is_offline): info["num_samples"] = num_samples info["num_episodes"] = num_episodes info["num_epochs"] = num_epochs - info["is_offline"] = is_offline + info["is_online"] = is_online logger.log_dict(info, step, mode="train") -def log_eval_info(logger, info, step, cfg, dataset, is_offline): +def log_eval_info(logger, info, step, cfg, dataset, is_online): eval_s = info["eval_s"] avg_sum_reward = info["avg_sum_reward"] pc_success = info["pc_success"] @@ -221,7 +229,7 @@ def log_eval_info(logger, info, step, cfg, dataset, is_offline): info["num_samples"] = num_samples info["num_episodes"] = num_episodes info["num_epochs"] = num_epochs - info["is_offline"] = is_offline + info["is_online"] = is_online logger.log_dict(info, step, mode="eval") @@ -234,6 +242,9 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No init_logging() + if cfg.training.online_steps > 0 and isinstance(cfg.dataset_repo_id, ListConfig): + raise NotImplementedError("Online training with LeRobotMultiDataset is not implemented.") + # If we are resuming a run, we need to check that a checkpoint exists in the log directory, and we need # to check for any differences between the provided config and the checkpoint's config. if cfg.resume: @@ -279,9 +290,6 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No # log metrics to terminal and wandb logger = Logger(cfg, out_dir, wandb_job_name=job_name) - if cfg.training.online_steps > 0: - raise NotImplementedError("Online training is not implemented yet.") - set_global_seed(cfg.seed) # Check device is available @@ -336,7 +344,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})") # Note: this helper will be used in offline and online training loops. - def evaluate_and_checkpoint_if_needed(step): + def evaluate_and_checkpoint_if_needed(step, is_online): _num_digits = max(6, len(str(cfg.training.offline_steps + cfg.training.online_steps))) step_identifier = f"{step:0{_num_digits}d}" @@ -352,7 +360,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No max_episodes_rendered=4, start_seed=cfg.seed, ) - log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline=True) + log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_online=is_online) if cfg.wandb.enable: logger.log_video(eval_info["video_paths"][0], step, mode="eval") logging.info("Resume training") @@ -396,8 +404,9 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No dl_iter = cycle(dataloader) policy.train() + offline_step = 0 for _ in range(step, cfg.training.offline_steps): - if step == 0: + if offline_step == 0: logging.info("Start offline training on a fixed dataset") start_time = time.perf_counter() @@ -420,13 +429,207 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No train_info["dataloading_s"] = dataloading_s if step % cfg.training.log_freq == 0: - log_train_info(logger, train_info, step, cfg, offline_dataset, is_offline=True) + log_train_info(logger, train_info, step, cfg, offline_dataset, is_online=False) # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed, # so we pass in step + 1. - evaluate_and_checkpoint_if_needed(step + 1) + evaluate_and_checkpoint_if_needed(step + 1, is_online=False) step += 1 + offline_step += 1 # noqa: SIM113 + + if cfg.training.online_steps == 0: + if eval_env: + eval_env.close() + logging.info("End of training") + return + + # Online training. + + # Create an env dedicated to online episodes collection from policy rollout. + online_env = make_env(cfg, n_envs=cfg.training.online_rollout_batch_size) + resolve_delta_timestamps(cfg) + online_buffer_path = logger.log_dir / "online_buffer" + if cfg.resume and not online_buffer_path.exists(): + # If we are resuming a run, we default to the data shapes and buffer capacity from the saved online + # buffer. + logging.warning( + "When online training is resumed, we load the latest online buffer from the prior run, " + "and this might not coincide with the state of the buffer as it was at the moment the checkpoint " + "was made. This is because the online buffer is updated on disk during training, independently " + "of our explicit checkpointing mechanisms." + ) + online_dataset = OnlineBuffer( + online_buffer_path, + data_spec={ + **{k: {"shape": v, "dtype": np.dtype("float32")} for k, v in policy.config.input_shapes.items()}, + **{k: {"shape": v, "dtype": np.dtype("float32")} for k, v in policy.config.output_shapes.items()}, + "next.reward": {"shape": (), "dtype": np.dtype("float32")}, + "next.done": {"shape": (), "dtype": np.dtype("?")}, + "next.success": {"shape": (), "dtype": np.dtype("?")}, + }, + buffer_capacity=cfg.training.online_buffer_capacity, + fps=online_env.unwrapped.metadata["render_fps"], + delta_timestamps=cfg.training.delta_timestamps, + ) + + # If we are doing online rollouts asynchronously, deepcopy the policy to use for online rollouts (this + # makes it possible to do online rollouts in parallel with training updates). + online_rollout_policy = deepcopy(policy) if cfg.training.do_online_rollout_async else policy + + # Create dataloader for online training. + concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset]) + sampler_weights = compute_sampler_weights( + offline_dataset, + offline_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0), + online_dataset=online_dataset, + # +1 because online rollouts return an extra frame for the "final observation". Note: we don't have + # this final observation in the offline datasets, but we might add them in future. + online_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0) + 1, + online_sampling_ratio=cfg.training.online_sampling_ratio, + ) + sampler = torch.utils.data.WeightedRandomSampler( + sampler_weights, + num_samples=len(concat_dataset), + replacement=True, + ) + dataloader = torch.utils.data.DataLoader( + concat_dataset, + batch_size=cfg.training.batch_size, + num_workers=cfg.training.num_workers, + sampler=sampler, + pin_memory=device.type != "cpu", + drop_last=True, + ) + dl_iter = cycle(dataloader) + + # Lock and thread pool executor for asynchronous online rollouts. When asynchronous mode is disabled, + # these are still used but effectively do nothing. + lock = Lock() + # Note: 1 worker because we only ever want to run one set of online rollouts at a time. Batch + # parallelization of rollouts is handled within the job. + executor = ThreadPoolExecutor(max_workers=1) + + online_step = 0 + online_rollout_s = 0 # time take to do online rollout + update_online_buffer_s = 0 # time taken to update the online buffer with the online rollout data + # Time taken waiting for the online buffer to finish being updated. This is relevant when using the async + # online rollout option. + await_update_online_buffer_s = 0 + rollout_start_seed = cfg.training.online_env_seed + + while True: + if online_step == cfg.training.online_steps: + break + + if online_step == 0: + logging.info("Start online training by interacting with environment") + + def sample_trajectory_and_update_buffer(): + nonlocal rollout_start_seed + with lock: + online_rollout_policy.load_state_dict(policy.state_dict()) + online_rollout_policy.eval() + start_rollout_time = time.perf_counter() + with torch.no_grad(): + eval_info = eval_policy( + online_env, + online_rollout_policy, + n_episodes=cfg.training.online_rollout_n_episodes, + max_episodes_rendered=min(10, cfg.training.online_rollout_n_episodes), + videos_dir=logger.log_dir / "online_rollout_videos", + return_episode_data=True, + start_seed=( + rollout_start_seed := (rollout_start_seed + cfg.training.batch_size) % 1000000 + ), + ) + online_rollout_s = time.perf_counter() - start_rollout_time + + with lock: + start_update_buffer_time = time.perf_counter() + online_dataset.add_data(eval_info["episodes"]) + + # Update the concatenated dataset length used during sampling. + concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets) + + # Update the sampling weights. + sampler.weights = compute_sampler_weights( + offline_dataset, + offline_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0), + online_dataset=online_dataset, + # +1 because online rollouts return an extra frame for the "final observation". Note: we don't have + # this final observation in the offline datasets, but we might add them in future. + online_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0) + 1, + online_sampling_ratio=cfg.training.online_sampling_ratio, + ) + sampler.num_samples = len(concat_dataset) + + update_online_buffer_s = time.perf_counter() - start_update_buffer_time + + return online_rollout_s, update_online_buffer_s + + future = executor.submit(sample_trajectory_and_update_buffer) + # If we aren't doing async rollouts, or if we haven't yet gotten enough examples in our buffer, wait + # here until the rollout and buffer update is done, before proceeding to the policy update steps. + if ( + not cfg.training.do_online_rollout_async + or len(online_dataset) <= cfg.training.online_buffer_seed_size + ): + online_rollout_s, update_online_buffer_s = future.result() + + if len(online_dataset) <= cfg.training.online_buffer_seed_size: + logging.info( + f"Seeding online buffer: {len(online_dataset)}/{cfg.training.online_buffer_seed_size}" + ) + continue + + policy.train() + for _ in range(cfg.training.online_steps_between_rollouts): + with lock: + start_time = time.perf_counter() + batch = next(dl_iter) + dataloading_s = time.perf_counter() - start_time + + for key in batch: + batch[key] = batch[key].to(cfg.device, non_blocking=True) + + train_info = update_policy( + policy, + batch, + optimizer, + cfg.training.grad_clip_norm, + grad_scaler=grad_scaler, + lr_scheduler=lr_scheduler, + use_amp=cfg.use_amp, + lock=lock, + ) + + train_info["dataloading_s"] = dataloading_s + train_info["online_rollout_s"] = online_rollout_s + train_info["update_online_buffer_s"] = update_online_buffer_s + train_info["await_update_online_buffer_s"] = await_update_online_buffer_s + with lock: + train_info["online_buffer_size"] = len(online_dataset) + + if step % cfg.training.log_freq == 0: + log_train_info(logger, train_info, step, cfg, online_dataset, is_online=True) + + # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed, + # so we pass in step + 1. + evaluate_and_checkpoint_if_needed(step + 1, is_online=True) + + step += 1 + online_step += 1 + + # If we're doing async rollouts, we should now wait until we've completed them before proceeding + # to do the next batch of rollouts. + if future.running(): + start = time.perf_counter() + online_rollout_s, update_online_buffer_s = future.result() + await_update_online_buffer_s = time.perf_counter() - start + + if online_step >= cfg.training.online_steps: + break if eval_env: eval_env.close() diff --git a/tests/data/save_policy_to_safetensors/xarm_tdmpc/actions.safetensors b/tests/data/save_policy_to_safetensors/xarm_tdmpc/actions.safetensors deleted file mode 100644 index b4fe1140..00000000 --- a/tests/data/save_policy_to_safetensors/xarm_tdmpc/actions.safetensors +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid 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b/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/grad_stats.safetensors new file mode 100644 index 00000000..cf756229 --- /dev/null +++ b/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/grad_stats.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d796577863740e8fd643a056e9eff891e51a858ff66019eba11f0a982cb9e9c0 +size 16904 diff --git a/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/output_dict.safetensors b/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/output_dict.safetensors new file mode 100644 index 00000000..f8863cfb --- /dev/null +++ b/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/output_dict.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4636751d82103a268ac7cf36f1e69f6356f356b9c40561a9fe8557bb9255e2ee +size 240 diff --git a/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/param_stats.safetensors b/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/param_stats.safetensors new file mode 100644 index 00000000..8ce3c4f3 --- /dev/null +++ b/tests/data/save_policy_to_safetensors/xarm_tdmpcuse_policy/param_stats.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7d08c9518f1f15226e4efc6f2a8542d0f3e620c91421c7cacea07d9bd9025d6 +size 36312 diff --git a/tests/scripts/save_policy_to_safetensors.py b/tests/scripts/save_policy_to_safetensors.py index 52c1c520..5236b7ae 100644 --- a/tests/scripts/save_policy_to_safetensors.py +++ b/tests/scripts/save_policy_to_safetensors.py @@ -108,7 +108,8 @@ def save_policy_to_safetensors(output_dir, env_name, policy_name, extra_override if __name__ == "__main__": env_policies = [ - # ("xarm", "tdmpc", ["policy.use_mpc=false"], ""), + # ("xarm", "tdmpc", ["policy.use_mpc=false"], "use_policy"), + # ("xarm", "tdmpc", ["policy.use_mpc=true"], "use_mpc"), # ( # "pusht", # "diffusion", diff --git a/tests/test_online_buffer.py b/tests/test_online_buffer.py new file mode 100644 index 00000000..37000e4f --- /dev/null +++ b/tests/test_online_buffer.py @@ -0,0 +1,320 @@ +#!/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.d +from copy import deepcopy +from uuid import uuid4 + +import numpy as np +import pytest +import torch +from datasets import Dataset + +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset +from lerobot.common.datasets.online_buffer import OnlineBuffer, compute_sampler_weights +from lerobot.common.datasets.utils import hf_transform_to_torch + +# Some constants for OnlineBuffer tests. +data_key = "data" +data_shape = (2, 3) # just some arbitrary > 1D shape +buffer_capacity = 100 +fps = 10 + + +def make_new_buffer( + write_dir: str | None = None, delta_timestamps: dict[str, list[float]] | None = None +) -> tuple[OnlineBuffer, str]: + if write_dir is None: + write_dir = f"/tmp/online_buffer_{uuid4().hex}" + buffer = OnlineBuffer( + write_dir, + data_spec={data_key: {"shape": data_shape, "dtype": np.dtype("float32")}}, + buffer_capacity=buffer_capacity, + fps=fps, + delta_timestamps=delta_timestamps, + ) + return buffer, write_dir + + +def make_spoof_data_frames(n_episodes: int, n_frames_per_episode: int) -> dict[str, np.ndarray]: + new_data = { + data_key: np.arange(n_frames_per_episode * n_episodes * np.prod(data_shape)).reshape(-1, *data_shape), + OnlineBuffer.INDEX_KEY: np.arange(n_frames_per_episode * n_episodes), + OnlineBuffer.EPISODE_INDEX_KEY: np.repeat(np.arange(n_episodes), n_frames_per_episode), + OnlineBuffer.FRAME_INDEX_KEY: np.tile(np.arange(n_frames_per_episode), n_episodes), + OnlineBuffer.TIMESTAMP_KEY: np.tile(np.arange(n_frames_per_episode) / fps, n_episodes), + } + return new_data + + +def test_non_mutate(): + """Checks that the data provided to the add_data method is copied rather than passed by reference. + + This means that mutating the data in the buffer does not mutate the original data. + + NOTE: If this test fails, it means some of the other tests may be compromised. For example, we can't trust + a success case for `test_write_read`. + """ + buffer, _ = make_new_buffer() + new_data = make_spoof_data_frames(2, buffer_capacity // 4) + new_data_copy = deepcopy(new_data) + buffer.add_data(new_data) + buffer._data[data_key][:] += 1 + assert all(np.array_equal(new_data[k], new_data_copy[k]) for k in new_data) + + +def test_index_error_no_data(): + buffer, _ = make_new_buffer() + with pytest.raises(IndexError): + buffer[0] + + +def test_index_error_with_data(): + buffer, _ = make_new_buffer() + n_frames = buffer_capacity // 2 + new_data = make_spoof_data_frames(1, n_frames) + buffer.add_data(new_data) + with pytest.raises(IndexError): + buffer[n_frames] + with pytest.raises(IndexError): + buffer[-n_frames - 1] + + +@pytest.mark.parametrize("do_reload", [False, True]) +def test_write_read(do_reload: bool): + """Checks that data can be added to the buffer and read back. + + If do_reload we delete the buffer object and load the buffer back from disk before reading. + """ + buffer, write_dir = make_new_buffer() + n_episodes = 2 + n_frames_per_episode = buffer_capacity // 4 + new_data = make_spoof_data_frames(n_episodes, n_frames_per_episode) + buffer.add_data(new_data) + + if do_reload: + del buffer + buffer, _ = make_new_buffer(write_dir) + + assert len(buffer) == n_frames_per_episode * n_episodes + for i, item in enumerate(buffer): + assert all(isinstance(item[k], torch.Tensor) for k in item) + assert np.array_equal(item[data_key].numpy(), new_data[data_key][i]) + + +def test_read_data_key(): + """Tests that data can be added to a buffer and all data for a. specific key can be read back.""" + buffer, _ = make_new_buffer() + n_episodes = 2 + n_frames_per_episode = buffer_capacity // 4 + new_data = make_spoof_data_frames(n_episodes, n_frames_per_episode) + buffer.add_data(new_data) + + data_from_buffer = buffer.get_data_by_key(data_key) + assert isinstance(data_from_buffer, torch.Tensor) + assert np.array_equal(data_from_buffer.numpy(), new_data[data_key]) + + +def test_fifo(): + """Checks that if data is added beyond the buffer capacity, we discard the oldest data first.""" + buffer, _ = make_new_buffer() + n_frames_per_episode = buffer_capacity // 4 + n_episodes = 3 + new_data = make_spoof_data_frames(n_episodes, n_frames_per_episode) + buffer.add_data(new_data) + n_more_episodes = 2 + # Developer sanity check (in case someone changes the global `buffer_capacity`). + assert ( + n_episodes + n_more_episodes + ) * n_frames_per_episode > buffer_capacity, "Something went wrong with the test code." + more_new_data = make_spoof_data_frames(n_more_episodes, n_frames_per_episode) + buffer.add_data(more_new_data) + assert len(buffer) == buffer_capacity, "The buffer should be full." + + expected_data = {} + for k in new_data: + # Concatenate, left-truncate, then roll, to imitate the cyclical FIFO pattern in OnlineBuffer. + expected_data[k] = np.roll( + np.concatenate([new_data[k], more_new_data[k]])[-buffer_capacity:], + shift=len(new_data[k]) + len(more_new_data[k]) - buffer_capacity, + axis=0, + ) + + for i, item in enumerate(buffer): + assert all(isinstance(item[k], torch.Tensor) for k in item) + assert np.array_equal(item[data_key].numpy(), expected_data[data_key][i]) + + +def test_delta_timestamps_within_tolerance(): + """Check that getting an item with delta_timestamps within tolerance succeeds. + + Note: Copied from `test_datasets.py::test_load_previous_and_future_frames_within_tolerance`. + """ + # Sanity check on global fps as we are assuming it is 10 here. + assert fps == 10, "This test assumes fps==10" + buffer, _ = make_new_buffer(delta_timestamps={"index": [-0.2, 0, 0.139]}) + new_data = make_spoof_data_frames(n_episodes=1, n_frames_per_episode=5) + buffer.add_data(new_data) + buffer.tolerance_s = 0.04 + item = buffer[2] + data, is_pad = item["index"], item[f"index{OnlineBuffer.IS_PAD_POSTFIX}"] + assert torch.allclose(data, torch.tensor([0, 2, 3])), "Data does not match expected values" + assert not is_pad.any(), "Unexpected padding detected" + + +def test_delta_timestamps_outside_tolerance_inside_episode_range(): + """Check that getting an item with delta_timestamps outside of tolerance fails. + + We expect it to fail if and only if the requested timestamps are within the episode range. + + Note: Copied from + `test_datasets.py::test_load_previous_and_future_frames_outside_tolerance_inside_episode_range` + """ + # Sanity check on global fps as we are assuming it is 10 here. + assert fps == 10, "This test assumes fps==10" + buffer, _ = make_new_buffer(delta_timestamps={"index": [-0.2, 0, 0.141]}) + new_data = make_spoof_data_frames(n_episodes=1, n_frames_per_episode=5) + buffer.add_data(new_data) + buffer.tolerance_s = 0.04 + with pytest.raises(AssertionError): + buffer[2] + + +def test_delta_timestamps_outside_tolerance_outside_episode_range(): + """Check that copy-padding of timestamps outside of the episode range works. + + Note: Copied from + `test_datasets.py::test_load_previous_and_future_frames_outside_tolerance_outside_episode_range` + """ + # Sanity check on global fps as we are assuming it is 10 here. + assert fps == 10, "This test assumes fps==10" + buffer, _ = make_new_buffer(delta_timestamps={"index": [-0.3, -0.24, 0, 0.26, 0.3]}) + new_data = make_spoof_data_frames(n_episodes=1, n_frames_per_episode=5) + buffer.add_data(new_data) + buffer.tolerance_s = 0.04 + item = buffer[2] + data, is_pad = item["index"], item["index_is_pad"] + assert torch.equal(data, torch.tensor([0, 0, 2, 4, 4])), "Data does not match expected values" + assert torch.equal( + is_pad, torch.tensor([True, False, False, True, True]) + ), "Padding does not match expected values" + + +# Arbitrarily set small dataset sizes, making sure to have uneven sizes. +@pytest.mark.parametrize("offline_dataset_size", [0, 6]) +@pytest.mark.parametrize("online_dataset_size", [0, 4]) +@pytest.mark.parametrize("online_sampling_ratio", [0.0, 1.0]) +def test_compute_sampler_weights_trivial( + offline_dataset_size: int, online_dataset_size: int, online_sampling_ratio: float +): + # Pass/skip the test if both datasets sizes are zero. + if offline_dataset_size + online_dataset_size == 0: + return + # Create spoof offline dataset. + offline_dataset = LeRobotDataset.from_preloaded( + hf_dataset=Dataset.from_dict({"data": list(range(offline_dataset_size))}) + ) + offline_dataset.hf_dataset.set_transform(hf_transform_to_torch) + if offline_dataset_size == 0: + offline_dataset.episode_data_index = {} + else: + # Set up an episode_data_index with at least two episodes. + offline_dataset.episode_data_index = { + "from": torch.tensor([0, offline_dataset_size // 2]), + "to": torch.tensor([offline_dataset_size // 2, offline_dataset_size]), + } + # Create spoof online datset. + online_dataset, _ = make_new_buffer() + if online_dataset_size > 0: + online_dataset.add_data( + make_spoof_data_frames(n_episodes=2, n_frames_per_episode=online_dataset_size // 2) + ) + + weights = compute_sampler_weights( + offline_dataset, online_dataset=online_dataset, online_sampling_ratio=online_sampling_ratio + ) + if offline_dataset_size == 0 or online_dataset_size == 0: + expected_weights = torch.ones(offline_dataset_size + online_dataset_size) + elif online_sampling_ratio == 0: + expected_weights = torch.cat([torch.ones(offline_dataset_size), torch.zeros(online_dataset_size)]) + elif online_sampling_ratio == 1: + expected_weights = torch.cat([torch.zeros(offline_dataset_size), torch.ones(online_dataset_size)]) + expected_weights /= expected_weights.sum() + assert torch.allclose(weights, expected_weights) + + +def test_compute_sampler_weights_nontrivial_ratio(): + # Arbitrarily set small dataset sizes, making sure to have uneven sizes. + # Create spoof offline dataset. + offline_dataset = LeRobotDataset.from_preloaded(hf_dataset=Dataset.from_dict({"data": list(range(4))})) + offline_dataset.hf_dataset.set_transform(hf_transform_to_torch) + offline_dataset.episode_data_index = { + "from": torch.tensor([0, 2]), + "to": torch.tensor([2, 4]), + } + # Create spoof online datset. + online_dataset, _ = make_new_buffer() + online_dataset.add_data(make_spoof_data_frames(n_episodes=4, n_frames_per_episode=2)) + online_sampling_ratio = 0.8 + weights = compute_sampler_weights( + offline_dataset, online_dataset=online_dataset, online_sampling_ratio=online_sampling_ratio + ) + assert torch.allclose( + weights, torch.tensor([0.05, 0.05, 0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) + ) + + +def test_compute_sampler_weights_nontrivial_ratio_and_drop_last_n(): + # Arbitrarily set small dataset sizes, making sure to have uneven sizes. + # Create spoof offline dataset. + offline_dataset = LeRobotDataset.from_preloaded(hf_dataset=Dataset.from_dict({"data": list(range(4))})) + offline_dataset.hf_dataset.set_transform(hf_transform_to_torch) + offline_dataset.episode_data_index = { + "from": torch.tensor([0]), + "to": torch.tensor([4]), + } + # Create spoof online datset. + online_dataset, _ = make_new_buffer() + online_dataset.add_data(make_spoof_data_frames(n_episodes=4, n_frames_per_episode=2)) + weights = compute_sampler_weights( + offline_dataset, online_dataset=online_dataset, online_sampling_ratio=0.8, online_drop_n_last_frames=1 + ) + assert torch.allclose( + weights, torch.tensor([0.05, 0.05, 0.05, 0.05, 0.2, 0.0, 0.2, 0.0, 0.2, 0.0, 0.2, 0.0]) + ) + + +def test_compute_sampler_weights_drop_n_last_frames(): + """Note: test copied from test_sampler.""" + data_dict = { + "timestamp": [0, 0.1], + "index": [0, 1], + "episode_index": [0, 0], + "frame_index": [0, 1], + } + offline_dataset = LeRobotDataset.from_preloaded(hf_dataset=Dataset.from_dict(data_dict)) + offline_dataset.hf_dataset.set_transform(hf_transform_to_torch) + offline_dataset.episode_data_index = {"from": torch.tensor([0]), "to": torch.tensor([2])} + + online_dataset, _ = make_new_buffer() + online_dataset.add_data(make_spoof_data_frames(n_episodes=4, n_frames_per_episode=2)) + + weights = compute_sampler_weights( + offline_dataset, + offline_drop_n_last_frames=1, + online_dataset=online_dataset, + online_sampling_ratio=0.5, + online_drop_n_last_frames=1, + ) + assert torch.allclose(weights, torch.tensor([0.5, 0, 0.125, 0, 0.125, 0, 0.125, 0, 0.125, 0])) diff --git a/tests/test_policies.py b/tests/test_policies.py index d9b946ab..d90f0071 100644 --- a/tests/test_policies.py +++ b/tests/test_policies.py @@ -357,7 +357,8 @@ def test_normalize(insert_temporal_dim): # TODO(alexander-soare): `policy.use_mpc=false` was previously the default in the config yaml but it # was changed to true. For some reason, tests would pass locally, but not in CI. So here we override # to test with `policy.use_mpc=false`. - ("xarm", "tdmpc", ["policy.use_mpc=false"], ""), + ("xarm", "tdmpc", ["policy.use_mpc=false"], "use_policy"), + # ("xarm", "tdmpc", ["policy.use_mpc=true"], "use_mpc"), ( "pusht", "diffusion", From 7a3cb1ad349be88b9474f09b27fe83a1ac8cdafe Mon Sep 17 00:00:00 2001 From: Halvard Bariller <96191373+HalvardBariller@users.noreply.github.com> Date: Fri, 26 Jul 2024 13:47:03 +0200 Subject: [PATCH 7/7] Adjust the timestamps' description in Diffusion Policy (#343) Co-authored-by: Alexander Soare --- lerobot/common/policies/diffusion/modeling_diffusion.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/lerobot/common/policies/diffusion/modeling_diffusion.py b/lerobot/common/policies/diffusion/modeling_diffusion.py index 0d7bab95..48608537 100644 --- a/lerobot/common/policies/diffusion/modeling_diffusion.py +++ b/lerobot/common/policies/diffusion/modeling_diffusion.py @@ -111,12 +111,12 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin): Schematically this looks like: ---------------------------------------------------------------------------------------------- (legend: o = n_obs_steps, h = horizon, a = n_action_steps) - |timestep | n-o+1 | n-o+2 | ..... | n | ..... | n+a-1 | n+a | ..... |n-o+1+h| - |observation is used | YES | YES | YES | NO | NO | NO | NO | NO | NO | + |timestep | n-o+1 | n-o+2 | ..... | n | ..... | n+a-1 | n+a | ..... | n-o+h | + |observation is used | YES | YES | YES | YES | NO | NO | NO | NO | NO | |action is generated | YES | YES | YES | YES | YES | YES | YES | YES | YES | |action is used | NO | NO | NO | YES | YES | YES | NO | NO | NO | ---------------------------------------------------------------------------------------------- - Note that this means we require: `n_action_steps < horizon - n_obs_steps + 1`. Also, note that + Note that this means we require: `n_action_steps <= horizon - n_obs_steps + 1`. Also, note that "horizon" may not the best name to describe what the variable actually means, because this period is actually measured from the first observation which (if `n_obs_steps` > 1) happened in the past. """