mirror of
https://github.com/huggingface/lerobot.git
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Steven Palma
102 lines
4.5 KiB
Python
102 lines
4.5 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 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 the Foxglove backend's pure helpers.
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These cover topic naming, series labelling and feature-name parsing. They import
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``foxglove_visualization`` directly and need NO ``foxglove`` extra: the SDK is imported lazily inside
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the functions that talk to the server, so the helpers below run in the base test tier.
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"""
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import numpy as np
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from lerobot.utils import foxglove_visualization as fv
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from lerobot.utils.constants import ACTION, OBS_STATE
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def test_foxglove_safe_name_collapses_dots():
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assert fv._foxglove_safe_name("observation.images.front") == "observation_images_front"
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assert fv._foxglove_safe_name("plain") == "plain"
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def test_foxglove_topic_image_strips_prefix_without_doubling_images():
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# Fully-qualified camera key -> single clean segment (no doubled "images").
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assert fv._foxglove_topic("observation.images.front", is_image=True) == "/observation/images/front"
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# A nested camera name keeps its structure via safe-name collapsing.
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assert (
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fv._foxglove_topic("observation.images.wrist.left", is_image=True) == "/observation/images/wrist_left"
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)
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# Bare camera name (as real robots emit).
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assert fv._foxglove_topic("front", is_image=True) == "/observation/images/front"
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def test_foxglove_topic_scalar_sources():
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assert fv._foxglove_topic(OBS_STATE) == "/observation/state"
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assert fv._foxglove_topic("observation.environment_state") == "/observation/state"
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assert fv._foxglove_topic(ACTION) == "/action/state"
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assert fv._foxglove_topic("action.delta") == "/action/state"
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def test_labeled_scalars_uses_labels_then_index_fallback():
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assert fv._labeled_scalars("state", np.array([1.0, 2.0, 3.0])) == {
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"state_0": 1.0,
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"state_1": 2.0,
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"state_2": 3.0,
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}
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assert fv._labeled_scalars("state", [1.0, 2.0], ["pan", "lift"]) == {"pan": 1.0, "lift": 2.0}
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# Wrong-length labels fall back to index naming (never silently mislabels).
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assert fv._labeled_scalars("q", [1.0, 2.0], ["only_one"]) == {"q_0": 1.0, "q_1": 2.0}
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def test_frame_to_scalars_matches_live_labeling_and_handles_scalar():
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frame = {OBS_STATE: np.array([1.0, 2.0])}
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# No metadata -> {short_name}_{i}, identical to the live-stream fallback.
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assert fv._frame_to_scalars(frame, OBS_STATE) == fv._labeled_scalars("state", np.array([1.0, 2.0]))
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assert fv._frame_to_scalars(frame, OBS_STATE) == {"state_0": 1.0, "state_1": 2.0}
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# Metadata labels are honored.
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assert fv._frame_to_scalars(frame, OBS_STATE, ["pan", "lift"]) == {"pan": 1.0, "lift": 2.0}
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# A 0-d scalar becomes a single entry named by the short feature name.
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assert fv._frame_to_scalars({ACTION: np.array(5.0)}, ACTION) == {"action": 5.0}
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# A missing feature yields an empty mapping.
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assert fv._frame_to_scalars({}, OBS_STATE) == {}
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def test_feature_dim_names_formats():
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# Flat list of names.
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assert fv._feature_dim_names({"shape": [2], "names": ["x", "y"]}) == ["x", "y"]
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# Category mapping (dict of lists).
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assert fv._feature_dim_names({"shape": [2], "names": {"motors": ["m0", "m1"]}}) == ["m0", "m1"]
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# name -> index mapping (returned sorted by index).
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assert fv._feature_dim_names({"shape": [2], "names": {"delta_x": 0, "delta_y": 1}}) == [
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"delta_x",
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"delta_y",
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]
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# Bool values must NOT be treated as an index map (bool is a subclass of int).
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assert fv._feature_dim_names({"shape": [2], "names": {"a": True, "b": False}}) is None
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# Mismatched length -> None (won't silently mislabel).
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assert fv._feature_dim_names({"shape": [3], "names": ["x", "y"]}) is None
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# Missing / absent names -> None.
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assert fv._feature_dim_names(None) is None
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assert fv._feature_dim_names({"shape": [2]}) is None
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def test_is_scalar():
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assert fv._is_scalar(1.0)
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assert fv._is_scalar(np.float32(2.0))
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assert fv._is_scalar(np.array(3.0)) # 0-d array
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assert not fv._is_scalar(np.array([1.0, 2.0]))
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assert not fv._is_scalar("x")
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