mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-16 15:57:03 +00:00
Pepijn
cec8ee0be6
Steerable annotation pipeline (lerobot-annotate) that populates the language_persistent and language_events columns introduced in PR 1 (#3467) directly into data/chunk-*/file-*.parquet. This is PR 2 of the three-PR plan: PR 1 (Add extensive language support #3467): schema + DSL + rendering, base of this PR PR 2 (this PR): annotation pipeline writing into PR 1's columns PR 3: model with language prediction and runtime A VLM (Qwen-VL family, served on vLLM) watches each episode's video and emits grounded language annotations: subtasks, plans, memory, task rephrasings, interjections + speech, and per-camera VQA. The pipeline is built for production annotation at scale — single-camera grounding, embedded-frame inputs, a describe-then-segment grounding flow, and a deterministic full-episode coverage guarantee — informed by Scale's dense-captioning findings (representation > sampling, rules > reasoning, model capacity is the biggest lever, two-pass systems compound errors)
134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
#!/usr/bin/env python
|
|
|
|
# Copyright 2026 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.
|
|
"""Validator behavior tests."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
|
|
# ``lerobot.annotations`` imports pull in ``lerobot.datasets`` (-> the HF
|
|
# ``datasets`` library), which only ships under the ``dataset`` extra. Skip
|
|
# this module in tiers without it instead of erroring at import.
|
|
pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
|
|
pytest.importorskip("pandas", reason="pandas is required (install lerobot[dataset])")
|
|
|
|
from lerobot.annotations.steerable_pipeline.reader import iter_episodes # noqa: E402
|
|
from lerobot.annotations.steerable_pipeline.staging import EpisodeStaging # noqa: E402
|
|
from lerobot.annotations.steerable_pipeline.validator import StagingValidator # noqa: E402
|
|
from lerobot.annotations.steerable_pipeline.writer import speech_atom # noqa: E402
|
|
|
|
|
|
def _validate(root: Path, staging_dir: Path):
|
|
records = list(iter_episodes(root))
|
|
return StagingValidator().validate(records, staging_dir)
|
|
|
|
|
|
def test_validator_catches_misaligned_timestamps(fixture_dataset_root: Path, tmp_path: Path) -> None:
|
|
staging_dir = tmp_path / "stage"
|
|
EpisodeStaging(staging_dir, 0).write(
|
|
"vqa",
|
|
[
|
|
{
|
|
"role": "assistant",
|
|
"content": json.dumps({"label": "cup", "count": 2}, sort_keys=True),
|
|
"style": "vqa",
|
|
"timestamp": 9.999, # not on any 10 fps frame
|
|
"tool_calls": None,
|
|
}
|
|
],
|
|
)
|
|
report = _validate(fixture_dataset_root, staging_dir)
|
|
assert not report.ok
|
|
assert any("does not match any source frame timestamp" in e for e in report.errors)
|
|
|
|
|
|
def test_validator_catches_orphan_speech(fixture_dataset_root: Path, tmp_path: Path) -> None:
|
|
staging_dir = tmp_path / "stage"
|
|
EpisodeStaging(staging_dir, 0).write(
|
|
"interjections",
|
|
[
|
|
speech_atom(0.0, "Got it."),
|
|
# interjection at 0.3s with NO paired speech
|
|
{
|
|
"role": "user",
|
|
"content": "skip it",
|
|
"style": "interjection",
|
|
"timestamp": 0.3,
|
|
"tool_calls": None,
|
|
},
|
|
],
|
|
)
|
|
report = _validate(fixture_dataset_root, staging_dir)
|
|
assert not report.ok
|
|
assert any("paired speech" in e for e in report.errors)
|
|
|
|
|
|
def test_validator_catches_inconsistent_plan_memory(fixture_dataset_root: Path, tmp_path: Path) -> None:
|
|
staging_dir = tmp_path / "stage"
|
|
EpisodeStaging(staging_dir, 0).write(
|
|
"plan",
|
|
[
|
|
{
|
|
"role": "assistant",
|
|
"content": "1. do x",
|
|
"style": "plan",
|
|
"timestamp": 0.0,
|
|
"tool_calls": None,
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "do x",
|
|
"style": "subtask",
|
|
"timestamp": 0.0,
|
|
"tool_calls": None,
|
|
},
|
|
],
|
|
)
|
|
EpisodeStaging(staging_dir, 0).write(
|
|
"interjections",
|
|
[
|
|
speech_atom(0.0, "Got it."),
|
|
speech_atom(0.4, "Replanning."),
|
|
{
|
|
"role": "user",
|
|
"content": "replan",
|
|
"style": "interjection",
|
|
"timestamp": 0.4,
|
|
"tool_calls": None,
|
|
},
|
|
],
|
|
)
|
|
report = _validate(fixture_dataset_root, staging_dir)
|
|
# missing co-timestamped plan refresh at 0.4s → error
|
|
assert not report.ok
|
|
assert any("co-timestamped plan update" in e for e in report.errors)
|
|
|
|
|
|
def test_validator_catches_wrong_column(fixture_dataset_root: Path, tmp_path: Path) -> None:
|
|
staging_dir = tmp_path / "stage"
|
|
EpisodeStaging(staging_dir, 0).write(
|
|
"plan",
|
|
[
|
|
{"role": "user", "content": "where?", "style": "vqa", "timestamp": 0.0, "tool_calls": None},
|
|
],
|
|
)
|
|
report = _validate(fixture_dataset_root, staging_dir)
|
|
assert not report.ok
|
|
assert any("plan emitted style 'vqa'" in e or "must be persistent" in e for e in report.errors)
|