mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-19 18:49:52 +00:00
Khalil Meftah
254 lines
9.1 KiB
Python
254 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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TOPRewardEncoderProcessorStep,
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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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# ---------------------------------------------------------------------------
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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) # (T, C, H, W) floats in [0, 1]
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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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"""``max_frames`` should keep the **last** K frames (most recent)."""
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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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"""If ``array.max() > 1`` the helper assumes the tensor is already in the
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uint8 range; values pass through unchanged (but are still clipped to 255)."""
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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 — input/output shapes + dataclass surface
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# ---------------------------------------------------------------------------
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def _make_transition(observation: dict, complementary: dict | None = None) -> dict:
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"""Build a tiny ``EnvTransition`` dict for the encoder step."""
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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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def test_encoder_step_writes_namespaced_frames_and_task():
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"""The encoder step's output is the contract the model reads from. It
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must populate exactly two namespaced keys: ``frames`` and ``task``."""
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step = TOPRewardEncoderProcessorStep(
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image_key="observation.images.top",
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task_key="task",
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max_frames=None,
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)
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frames_batch = torch.zeros(2, 4, 3, 8, 8) # (B=2, T=4, C, H, W)
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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", "place"]},
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)
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)
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obs_out = out[TransitionKey.OBSERVATION]
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frames_out = obs_out[f"{TOPREWARD_FEATURE_PREFIX}frames"]
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tasks_out = obs_out[f"{TOPREWARD_FEATURE_PREFIX}task"]
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assert len(frames_out) == 2
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assert all(arr.shape == (4, 8, 8, 3) and arr.dtype == np.uint8 for arr in frames_out)
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assert tasks_out == ["pick", "place"]
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def test_encoder_step_adds_singleton_time_dim_for_4d_input():
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"""A ``(B, C, H, W)`` observation is the single-frame case; the encoder
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must unsqueeze the time dim so the model still sees a video."""
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step = TOPRewardEncoderProcessorStep(image_key="observation.images.top", max_frames=None)
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frames_batch = torch.zeros(1, 3, 8, 8) # (B=1, C, H, W) — no time dim
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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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frames_out = out[TransitionKey.OBSERVATION][f"{TOPREWARD_FEATURE_PREFIX}frames"]
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assert len(frames_out) == 1
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assert frames_out[0].shape == (1, 8, 8, 3) # (T=1, H, W, C)
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def test_encoder_step_uses_default_task_when_complementary_is_missing():
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step = TOPRewardEncoderProcessorStep(
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image_key="observation.images.top",
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default_task="perform the task",
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)
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frames_batch = torch.zeros(1, 2, 3, 4, 4)
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out = step(_make_transition(observation={"observation.images.top": frames_batch}))
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tasks_out = out[TransitionKey.OBSERVATION][f"{TOPREWARD_FEATURE_PREFIX}task"]
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assert tasks_out == ["perform the task"]
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def test_encoder_step_rejects_missing_image_key():
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step = TOPRewardEncoderProcessorStep(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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def test_encoder_step_rejects_non_dict_observation():
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step = TOPRewardEncoderProcessorStep()
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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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def test_encoder_step_rejects_3d_or_6d_input():
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"""The encoder accepts ``(B,C,H,W)`` or ``(B,T,C,H,W)`` only."""
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step = TOPRewardEncoderProcessorStep(image_key="observation.images.top")
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with pytest.raises(ValueError, match=r"\(B,C,H,W\)"):
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step(
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_make_transition(
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observation={"observation.images.top": torch.zeros(8, 8, 3)},
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complementary={"task": "pick"},
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)
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)
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def test_encoder_step_get_config_roundtrips_user_fields():
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"""``get_config`` must serialise every user-tunable field — these are
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what the processor pipeline saves under ``preprocessor_config.json``."""
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step = TOPRewardEncoderProcessorStep(
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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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)
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assert step.get_config() == {
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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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}
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def test_encoder_step_transform_features_is_identity():
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"""The encoder writes plain Python objects (numpy arrays / strings)
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into ``observation`` at call time but does NOT advertise new typed
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features at pipeline-build time — the model reads them via the
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``TOPREWARD_FEATURE_PREFIX`` namespace, not via the typed feature map.
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"""
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step = TOPRewardEncoderProcessorStep()
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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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