mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-28 15:09:51 +00:00
Khalil Meftah
265 lines
9.1 KiB
Python
265 lines
9.1 KiB
Python
# 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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"""Tests for TOPReward's pre-processing helpers and encoder step."""
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from __future__ import annotations
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import numpy as np
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import pytest
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import torch
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from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
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from lerobot.rewards.topreward.processor_topreward import (
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TOPREWARD_FEATURE_PREFIX,
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_expand_tasks,
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_video_to_numpy,
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)
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from lerobot.types import TransitionKey
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from tests.utils import skip_if_package_missing
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# ---------------------------------------------------------------------------
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# _video_to_numpy — pure (T, C, H, W) -> (T, H, W, C) uint8 conversion
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# ---------------------------------------------------------------------------
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def test_video_to_numpy_chw_float_is_converted_to_thwc_uint8():
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video = torch.rand(4, 3, 8, 8)
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array = _video_to_numpy(video, max_frames=None)
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assert array.shape == (4, 8, 8, 3)
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assert array.dtype == np.uint8
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assert array.min() >= 0 and array.max() <= 255
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def test_video_to_numpy_already_thwc_uint8_passes_through():
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video = torch.randint(0, 256, (3, 8, 8, 3), dtype=torch.uint8)
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array = _video_to_numpy(video, max_frames=None)
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assert array.shape == (3, 8, 8, 3)
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assert array.dtype == np.uint8
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def test_video_to_numpy_max_frames_tail_crops_recent_frames():
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video = torch.zeros(10, 3, 4, 4)
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for t in range(10):
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video[t] = t / 9.0
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array = _video_to_numpy(video, max_frames=3)
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assert array.shape == (3, 4, 4, 3)
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assert int(array[0, 0, 0, 0]) == int(round(7 / 9 * 255))
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assert int(array[-1, 0, 0, 0]) == 255
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def test_video_to_numpy_rejects_3d_input():
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with pytest.raises(ValueError, match="Expected channel dim"):
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_video_to_numpy(torch.zeros(4, 8, 8), max_frames=None)
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def test_video_to_numpy_floats_above_one_pass_through_without_rescaling():
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video = torch.full((1, 3, 2, 2), 5.0)
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array = _video_to_numpy(video, max_frames=None)
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assert array.shape == (1, 2, 2, 3)
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assert int(array.max()) == 5
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def test_video_to_numpy_clips_very_large_floats_to_uint8_max():
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video = torch.full((1, 3, 2, 2), 300.0)
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array = _video_to_numpy(video, max_frames=None)
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assert int(array.max()) == 255
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# ---------------------------------------------------------------------------
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# _expand_tasks — string / list / tuple broadcasting to batch size
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# ---------------------------------------------------------------------------
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def test_expand_tasks_string_is_broadcast_to_batch_size():
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assert _expand_tasks("pick up", batch_size=3, default=None) == ["pick up", "pick up", "pick up"]
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def test_expand_tasks_list_of_matching_size_passes_through():
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assert _expand_tasks(["a", "b", "c"], batch_size=3, default=None) == ["a", "b", "c"]
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def test_expand_tasks_tuple_is_normalised_to_list():
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assert _expand_tasks(("a", "b"), batch_size=2, default=None) == ["a", "b"]
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def test_expand_tasks_single_element_list_is_broadcast():
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assert _expand_tasks(["only one"], batch_size=3, default=None) == ["only one"] * 3
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def test_expand_tasks_size_mismatch_raises():
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with pytest.raises(ValueError, match="Expected 3 tasks"):
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_expand_tasks(["a", "b"], batch_size=3, default=None)
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def test_expand_tasks_missing_uses_default():
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assert _expand_tasks(None, batch_size=2, default="fallback") == ["fallback", "fallback"]
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def test_expand_tasks_missing_without_default_raises():
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with pytest.raises(KeyError, match="task description"):
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_expand_tasks(None, batch_size=1, default=None)
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def test_expand_tasks_wrong_type_raises():
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with pytest.raises(TypeError, match="must be a string or list"):
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_expand_tasks(42, batch_size=1, default=None)
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# ---------------------------------------------------------------------------
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# Encoder step — stubbed AutoProcessor + process_vision_info
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# ---------------------------------------------------------------------------
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def _skip_if_topreward_extras_missing(func):
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func = skip_if_package_missing("qwen-vl-utils", import_name="qwen_vl_utils")(func)
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func = skip_if_package_missing("transformers")(func)
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return func
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class _FakeTokenizer:
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eos_token = "<|endoftext|>"
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pad_token = "<|endoftext|>"
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def __call__(self, *args, **kwargs):
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return {"input_ids": torch.zeros(1, 10, dtype=torch.long)}
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class _FakeAutoProcessor:
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def __init__(self) -> None:
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self.tokenizer = _FakeTokenizer()
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@classmethod
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def from_pretrained(cls, *args, **kwargs): # noqa: ARG003
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return cls()
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def apply_chat_template(self, messages, **kwargs): # noqa: ARG002
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return "fake_prompt_text"
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def __call__(self, text=None, images=None, videos=None, **kwargs): # noqa: ARG002
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seq_len = 10
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return {
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"input_ids": torch.randint(0, 100, (1, seq_len)),
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"attention_mask": torch.ones(1, seq_len, dtype=torch.long),
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}
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def _build_step(monkeypatch, **overrides):
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import importlib
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import sys
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import types
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from lerobot.rewards.topreward import processor_topreward
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from lerobot.utils import import_utils
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monkeypatch.setattr(processor_topreward, "AutoProcessor", _FakeAutoProcessor)
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# Stub qwen_vl_utils as a real module object (not MagicMock) so
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# ``require_package`` / ``find_spec`` don't choke on a missing ``__spec__``.
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fake_qwen_vl = types.ModuleType("qwen_vl_utils")
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fake_qwen_vl.process_vision_info = lambda messages: (None, None) # type: ignore[attr-defined]
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fake_qwen_vl.__spec__ = importlib.machinery.ModuleSpec("qwen_vl_utils", None)
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monkeypatch.setitem(sys.modules, "qwen_vl_utils", fake_qwen_vl)
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# Clear the require_package cache so the stub is picked up.
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import_utils._require_package_cache.pop("qwen_vl_utils", None)
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return processor_topreward.TOPRewardEncoderProcessorStep(**overrides)
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def _make_transition(observation: dict, complementary: dict | None = None) -> dict:
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transition: dict = {TransitionKey.OBSERVATION: observation}
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if complementary is not None:
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transition[TransitionKey.COMPLEMENTARY_DATA] = complementary
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return transition
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@_skip_if_topreward_extras_missing
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def test_encoder_step_emits_input_ids_and_prompt_length(monkeypatch):
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"""The processor must emit Qwen-VL tensors including ``input_ids`` and
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``prompt_length`` under the ``observation.topreward.*`` namespace."""
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step = _build_step(monkeypatch)
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frames_batch = torch.zeros(1, 4, 3, 8, 8)
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out = step(
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_make_transition(
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observation={"observation.images.top": frames_batch},
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complementary={"task": "pick"},
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)
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)
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obs_out = out[TransitionKey.OBSERVATION]
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assert f"{TOPREWARD_FEATURE_PREFIX}input_ids" in obs_out
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assert f"{TOPREWARD_FEATURE_PREFIX}attention_mask" in obs_out
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assert f"{TOPREWARD_FEATURE_PREFIX}prompt_length" in obs_out
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prompt_length = obs_out[f"{TOPREWARD_FEATURE_PREFIX}prompt_length"]
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assert prompt_length.dtype == torch.long
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assert prompt_length.shape == (1,)
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@_skip_if_topreward_extras_missing
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def test_encoder_step_get_config_roundtrips_user_fields(monkeypatch):
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step = _build_step(
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monkeypatch,
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vlm_name="Qwen/Qwen3-VL-8B-Instruct",
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image_key="observation.images.cam_top",
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task_key="task",
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default_task="do the thing",
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max_frames=8,
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fps=4.0,
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add_chat_template=True,
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max_length=2048,
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)
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cfg = step.get_config()
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assert cfg["vlm_name"] == "Qwen/Qwen3-VL-8B-Instruct"
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assert cfg["image_key"] == "observation.images.cam_top"
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assert cfg["default_task"] == "do the thing"
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assert cfg["max_frames"] == 8
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assert cfg["fps"] == 4.0
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assert cfg["add_chat_template"] is True
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assert cfg["max_length"] == 2048
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@_skip_if_topreward_extras_missing
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def test_encoder_step_transform_features_is_identity(monkeypatch):
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step = _build_step(monkeypatch)
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features = {
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PipelineFeatureType.OBSERVATION: {
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"observation.images.top": PolicyFeature(shape=(3, 224, 224), type=FeatureType.VISUAL),
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}
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}
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assert step.transform_features(features) == features
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@_skip_if_topreward_extras_missing
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def test_encoder_step_rejects_missing_image_key(monkeypatch):
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step = _build_step(monkeypatch, image_key="observation.images.top")
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with pytest.raises(KeyError, match="image key"):
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step(_make_transition(observation={}, complementary={"task": "pick"}))
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@_skip_if_topreward_extras_missing
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def test_encoder_step_rejects_non_dict_observation(monkeypatch):
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step = _build_step(monkeypatch)
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with pytest.raises(ValueError, match="observation dict"):
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step({TransitionKey.OBSERVATION: torch.zeros(1, 3, 8, 8)})
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