d2782cf66b
* chore: replace hard-coded 'action' values with constants throughout all the source code * chore(tests): replace hard-coded action values with constants throughout all the test code
134 lines
4.1 KiB
Python
134 lines
4.1 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 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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import pytest
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import torch
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from datasets import Dataset
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from huggingface_hub import DatasetCard
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from lerobot.datasets.push_dataset_to_hub.utils import calculate_episode_data_index
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from lerobot.datasets.utils import combine_feature_dicts, create_lerobot_dataset_card, hf_transform_to_torch
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from lerobot.utils.constants import ACTION, OBS_IMAGES
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def test_default_parameters():
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card = create_lerobot_dataset_card()
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assert isinstance(card, DatasetCard)
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assert card.data.tags == ["LeRobot"]
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assert card.data.task_categories == ["robotics"]
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assert card.data.configs == [
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{
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"config_name": "default",
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"data_files": "data/*/*.parquet",
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}
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]
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def test_with_tags():
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tags = ["tag1", "tag2"]
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card = create_lerobot_dataset_card(tags=tags)
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assert card.data.tags == ["LeRobot", "tag1", "tag2"]
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def test_calculate_episode_data_index():
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dataset = Dataset.from_dict(
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{
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"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
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"index": [0, 1, 2, 3, 4, 5],
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"episode_index": [0, 0, 1, 2, 2, 2],
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},
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)
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dataset.set_transform(hf_transform_to_torch)
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episode_data_index = calculate_episode_data_index(dataset)
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assert torch.equal(episode_data_index["from"], torch.tensor([0, 2, 3]))
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assert torch.equal(episode_data_index["to"], torch.tensor([2, 3, 6]))
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def test_merge_simple_vectors():
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g1 = {
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ACTION: {
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"dtype": "float32",
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"shape": (2,),
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"names": ["ee.x", "ee.y"],
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}
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}
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g2 = {
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ACTION: {
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"dtype": "float32",
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"shape": (2,),
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"names": ["ee.y", "ee.z"],
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}
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}
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out = combine_feature_dicts(g1, g2)
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assert ACTION in out
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assert out[ACTION]["dtype"] == "float32"
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# Names merged with preserved order and de-dupuplication
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assert out[ACTION]["names"] == ["ee.x", "ee.y", "ee.z"]
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# Shape correctly recomputed from names length
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assert out[ACTION]["shape"] == (3,)
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def test_merge_multiple_groups_order_and_dedup():
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g1 = {ACTION: {"dtype": "float32", "shape": (2,), "names": ["a", "b"]}}
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g2 = {ACTION: {"dtype": "float32", "shape": (2,), "names": ["b", "c"]}}
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g3 = {ACTION: {"dtype": "float32", "shape": (3,), "names": ["a", "c", "d"]}}
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out = combine_feature_dicts(g1, g2, g3)
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assert out[ACTION]["names"] == ["a", "b", "c", "d"]
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assert out[ACTION]["shape"] == (4,)
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def test_non_vector_last_wins_for_images():
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# Non-vector (images) with same name should be overwritten by the last image specified
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g1 = {
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f"{OBS_IMAGES}.front": {
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"dtype": "image",
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"shape": (3, 480, 640),
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"names": ["channels", "height", "width"],
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}
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}
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g2 = {
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f"{OBS_IMAGES}.front": {
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"dtype": "image",
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"shape": (3, 720, 1280),
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"names": ["channels", "height", "width"],
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}
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}
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out = combine_feature_dicts(g1, g2)
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assert out[f"{OBS_IMAGES}.front"]["shape"] == (3, 720, 1280)
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assert out[f"{OBS_IMAGES}.front"]["dtype"] == "image"
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def test_dtype_mismatch_raises():
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g1 = {ACTION: {"dtype": "float32", "shape": (1,), "names": ["a"]}}
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g2 = {ACTION: {"dtype": "float64", "shape": (1,), "names": ["b"]}}
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with pytest.raises(ValueError, match="dtype mismatch for 'action'"):
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_ = combine_feature_dicts(g1, g2)
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def test_non_dict_passthrough_last_wins():
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g1 = {"misc": 123}
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g2 = {"misc": 456}
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out = combine_feature_dicts(g1, g2)
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# For non-dict entries the last one wins
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assert out["misc"] == 456
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