mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-01 03:11:29 +00:00
[pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot]
committed by
Michel Aractingi
Michel Aractingi
parent
bb69cb3c8c
commit
85fe8a3f4e
@@ -81,7 +81,11 @@ def run_server(
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static_folder: Path,
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template_folder: Path,
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):
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app = Flask(__name__, static_folder=static_folder.resolve(), template_folder=template_folder.resolve())
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app = Flask(
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__name__,
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static_folder=static_folder.resolve(),
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template_folder=template_folder.resolve(),
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)
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app.config["SEND_FILE_MAX_AGE_DEFAULT"] = 0 # specifying not to cache
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@app.route("/")
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@@ -138,8 +142,12 @@ def run_server(
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)
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)
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@app.route("/<string:dataset_namespace>/<string:dataset_name>/episode_<int:episode_id>")
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def show_episode(dataset_namespace, dataset_name, episode_id, dataset=dataset, episodes=episodes):
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@app.route(
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"/<string:dataset_namespace>/<string:dataset_name>/episode_<int:episode_id>"
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)
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def show_episode(
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dataset_namespace, dataset_name, episode_id, dataset=dataset, episodes=episodes
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):
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repo_id = f"{dataset_namespace}/{dataset_name}"
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try:
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if dataset is None:
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@@ -150,7 +158,9 @@ def run_server(
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400,
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)
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dataset_version = (
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str(dataset.meta._version) if isinstance(dataset, LeRobotDataset) else dataset.codebase_version
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str(dataset.meta._version)
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if isinstance(dataset, LeRobotDataset)
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else dataset.codebase_version
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)
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match = re.search(r"v(\d+)\.", dataset_version)
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if match:
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@@ -158,7 +168,9 @@ def run_server(
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if major_version < 2:
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return "Make sure to convert your LeRobotDataset to v2 & above."
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episode_data_csv_str, columns, ignored_columns = get_episode_data(dataset, episode_id)
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episode_data_csv_str, columns, ignored_columns = get_episode_data(
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dataset, episode_id
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)
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dataset_info = {
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"repo_id": f"{dataset_namespace}/{dataset_name}",
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"num_samples": dataset.num_frames
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@@ -171,18 +183,23 @@ def run_server(
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}
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if isinstance(dataset, LeRobotDataset):
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video_paths = [
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dataset.meta.get_video_file_path(episode_id, key) for key in dataset.meta.video_keys
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dataset.meta.get_video_file_path(episode_id, key)
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for key in dataset.meta.video_keys
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]
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videos_info = [
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{
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"url": url_for("static", filename=str(video_path).replace("\\", "/")),
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"url": url_for(
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"static", filename=str(video_path).replace("\\", "/")
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),
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"filename": video_path.parent.name,
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}
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for video_path in video_paths
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]
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tasks = dataset.meta.episodes[episode_id]["tasks"]
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else:
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video_keys = [key for key, ft in dataset.features.items() if ft["dtype"] == "video"]
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video_keys = [
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key for key, ft in dataset.features.items() if ft["dtype"] == "video"
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]
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videos_info = [
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{
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"url": f"https://huggingface.co/datasets/{repo_id}/resolve/main/"
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@@ -197,20 +214,29 @@ def run_server(
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]
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response = requests.get(
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f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/episodes.jsonl", timeout=5
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f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/episodes.jsonl",
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timeout=5,
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)
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response.raise_for_status()
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# Split into lines and parse each line as JSON
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tasks_jsonl = [json.loads(line) for line in response.text.splitlines() if line.strip()]
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tasks_jsonl = [
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json.loads(line) for line in response.text.splitlines() if line.strip()
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]
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filtered_tasks_jsonl = [row for row in tasks_jsonl if row["episode_index"] == episode_id]
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filtered_tasks_jsonl = [
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row for row in tasks_jsonl if row["episode_index"] == episode_id
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]
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tasks = filtered_tasks_jsonl[0]["tasks"]
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videos_info[0]["language_instruction"] = tasks
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if episodes is None:
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episodes = list(
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range(dataset.num_episodes if isinstance(dataset, LeRobotDataset) else dataset.total_episodes)
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range(
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dataset.num_episodes
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if isinstance(dataset, LeRobotDataset)
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else dataset.total_episodes
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)
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)
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return render_template(
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@@ -237,7 +263,11 @@ def get_episode_data(dataset: LeRobotDataset | IterableNamespace, episode_index)
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This file will be loaded by Dygraph javascript to plot data in real time."""
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columns = []
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selected_columns = [col for col, ft in dataset.features.items() if ft["dtype"] in ["float32", "int32"]]
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selected_columns = [
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col
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for col, ft in dataset.features.items()
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if ft["dtype"] in ["float32", "int32"]
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]
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selected_columns.remove("timestamp")
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ignored_columns = []
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@@ -258,7 +288,10 @@ def get_episode_data(dataset: LeRobotDataset | IterableNamespace, episode_index)
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else dataset.features[column_name].shape[0]
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)
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if "names" in dataset.features[column_name] and dataset.features[column_name]["names"]:
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if (
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"names" in dataset.features[column_name]
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and dataset.features[column_name]["names"]
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):
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column_names = dataset.features[column_name]["names"]
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while not isinstance(column_names, list):
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column_names = list(column_names.values())[0]
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@@ -281,8 +314,12 @@ def get_episode_data(dataset: LeRobotDataset | IterableNamespace, episode_index)
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else:
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repo_id = dataset.repo_id
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url = f"https://huggingface.co/datasets/{repo_id}/resolve/main/" + dataset.data_path.format(
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episode_chunk=int(episode_index) // dataset.chunks_size, episode_index=episode_index
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url = (
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f"https://huggingface.co/datasets/{repo_id}/resolve/main/"
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+ dataset.data_path.format(
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episode_chunk=int(episode_index) // dataset.chunks_size,
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episode_index=episode_index,
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)
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)
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df = pd.read_parquet(url)
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data = df[selected_columns] # Select specific columns
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@@ -315,7 +352,9 @@ def get_episode_video_paths(dataset: LeRobotDataset, ep_index: int) -> list[str]
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]
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def get_episode_language_instruction(dataset: LeRobotDataset, ep_index: int) -> list[str]:
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def get_episode_language_instruction(
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dataset: LeRobotDataset, ep_index: int
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) -> list[str]:
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# check if the dataset has language instructions
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if "language_instruction" not in dataset.features:
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return None
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@@ -326,12 +365,15 @@ def get_episode_language_instruction(dataset: LeRobotDataset, ep_index: int) ->
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language_instruction = dataset.hf_dataset[first_frame_idx]["language_instruction"]
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# TODO (michel-aractingi) hack to get the sentence, some strings in openx are badly stored
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# with the tf.tensor appearing in the string
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return language_instruction.removeprefix("tf.Tensor(b'").removesuffix("', shape=(), dtype=string)")
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return language_instruction.removeprefix("tf.Tensor(b'").removesuffix(
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"', shape=(), dtype=string)"
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)
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def get_dataset_info(repo_id: str) -> IterableNamespace:
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response = requests.get(
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f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/info.json", timeout=5
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f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/info.json",
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timeout=5,
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)
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response.raise_for_status() # Raises an HTTPError for bad responses
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dataset_info = response.json()
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@@ -361,7 +403,9 @@ def visualize_dataset_html(
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if force_override:
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shutil.rmtree(output_dir)
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else:
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logging.info(f"Output directory already exists. Loading from it: '{output_dir}'")
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logging.info(
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f"Output directory already exists. Loading from it: '{output_dir}'"
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)
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output_dir.mkdir(parents=True, exist_ok=True)
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