mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-15 16:49:55 +00:00
Pepijn
a635a32290
Adds the steerable annotation pipeline (`lerobot-annotate`) that populates the `language_persistent` and `language_events` columns introduced in PR 1 directly into `data/chunk-*/file-*.parquet`. No flavor namespace, no sidecar tree. Modules produced: - Module 1 (plan_subtasks_memory): Pi0.7-style subtasks, plan (init + refresh on interjection), MEM-style memory at subtask boundaries. - Module 2 (interjections_and_speech): t=0 speech-only acknowledgement, mid-episode paired interjection + speech tool-call atom. - Module 3 (general_vqa): bbox/keypoint/count/attribute/spatial pairs at configurable cadence with one-retry JSON validation. Writer enforces: per-episode persistent identity, exact-frame event timestamps, column routing per `column_for_style`, dataset-level `tools` column with the `say` schema, drops legacy `subtask_index`. Validator runs against staged JSONL artifacts before the writer rewrites parquet. Adds `lerobot-annotate` console script, `annotations` extra (datatrove + optional vllm), `make annotation-e2e` opt-in smoke target, and `docs/source/annotation_pipeline.mdx`. Branched from PR 1 (`feat/language-columns`). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Shared fixtures for annotation-pipeline tests.
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Builds a minimal LeRobot-shaped dataset on disk so writer/validator tests
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can exercise real parquet reads and writes without needing a checked-in
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LFS dataset.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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import pyarrow as pa
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import pyarrow.parquet as pq
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import pytest
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def _make_episode_table(
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episode_index: int,
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num_frames: int,
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*,
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fps: int = 10,
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task_index: int = 0,
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) -> pa.Table:
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timestamps = [round(i / fps, 6) for i in range(num_frames)]
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frame_indices = list(range(num_frames))
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return pa.Table.from_pydict(
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{
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"episode_index": [episode_index] * num_frames,
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"frame_index": frame_indices,
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"timestamp": timestamps,
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"task_index": [task_index] * num_frames,
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"subtask_index": [0] * num_frames, # legacy column the writer must drop
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}
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)
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def _build_dataset(root: Path, episode_specs: list[tuple[int, int, str]], *, fps: int = 10) -> Path:
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"""Create a fixture dataset under ``root``.
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``episode_specs`` is a list of ``(episode_index, num_frames, task_text)``.
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Each episode goes into its own ``data/chunk-000/file-{ep:03d}.parquet``
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so the writer's per-shard rewrite path is exercised.
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"""
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data_dir = root / "data" / "chunk-000"
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data_dir.mkdir(parents=True, exist_ok=True)
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tasks = {}
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for episode_index, num_frames, task_text in episode_specs:
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task_index = len(tasks)
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if task_text not in tasks.values():
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tasks[task_index] = task_text
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else:
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task_index = next(k for k, v in tasks.items() if v == task_text)
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table = _make_episode_table(episode_index, num_frames, fps=fps, task_index=task_index)
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path = data_dir / f"file-{episode_index:03d}.parquet"
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pq.write_table(table, path)
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meta_dir = root / "meta"
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meta_dir.mkdir(parents=True, exist_ok=True)
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tasks_table = pa.Table.from_pydict(
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{
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"task_index": list(tasks.keys()),
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"task": list(tasks.values()),
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}
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)
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pq.write_table(tasks_table, meta_dir / "tasks.parquet")
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info = {
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"codebase_version": "v3.1",
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"fps": fps,
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"total_episodes": len(episode_specs),
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}
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(meta_dir / "info.json").write_text(json.dumps(info, indent=2))
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return root
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@pytest.fixture
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def fixture_dataset_root(tmp_path: Path) -> Path:
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"""A tiny dataset with two episodes, 12 frames each at 10 fps."""
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return _build_dataset(
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tmp_path / "ds",
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episode_specs=[
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(0, 12, "Could you tidy the kitchen please?"),
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(1, 12, "Please clean up the kitchen"),
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],
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fps=10,
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)
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@pytest.fixture
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def single_episode_root(tmp_path: Path) -> Path:
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return _build_dataset(
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tmp_path / "ds_one",
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episode_specs=[(0, 30, "Pour water from the bottle into the cup.")],
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fps=10,
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)
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