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a8eb6e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | #!/usr/bin/env python
# Copyright 2024 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.
import pytest
import torch
from datasets import Dataset
from huggingface_hub import DatasetCard
from lerobot.datasets.feature_utils import combine_feature_dicts
from lerobot.datasets.io_utils import hf_transform_to_torch
from lerobot.datasets.utils import create_lerobot_dataset_card
from lerobot.utils.constants import ACTION, OBS_IMAGES
def calculate_episode_data_index(hf_dataset: Dataset) -> dict[str, torch.Tensor]:
"""Calculate episode data index for testing. Returns {"from": Tensor, "to": Tensor}."""
episode_data_index: dict[str, list[int]] = {"from": [], "to": []}
current_episode = None
if len(hf_dataset) == 0:
return {"from": torch.tensor([]), "to": torch.tensor([])}
for idx, episode_idx in enumerate(hf_dataset["episode_index"]):
if episode_idx != current_episode:
episode_data_index["from"].append(idx)
if current_episode is not None:
episode_data_index["to"].append(idx)
current_episode = episode_idx
episode_data_index["to"].append(idx + 1)
return {k: torch.tensor(v) for k, v in episode_data_index.items()}
def test_default_parameters():
card = create_lerobot_dataset_card()
assert isinstance(card, DatasetCard)
assert card.data.tags == ["LeRobot"]
assert card.data.task_categories == ["robotics"]
assert card.data.configs == [
{
"config_name": "default",
"data_files": "data/*/*.parquet",
}
]
def test_with_tags():
tags = ["tag1", "tag2"]
card = create_lerobot_dataset_card(tags=tags)
assert card.data.tags == ["LeRobot", "tag1", "tag2"]
def test_calculate_episode_data_index():
dataset = Dataset.from_dict(
{
"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
"index": [0, 1, 2, 3, 4, 5],
"episode_index": [0, 0, 1, 2, 2, 2],
},
)
dataset.set_transform(hf_transform_to_torch)
episode_data_index = calculate_episode_data_index(dataset)
assert torch.equal(episode_data_index["from"], torch.tensor([0, 2, 3]))
assert torch.equal(episode_data_index["to"], torch.tensor([2, 3, 6]))
def test_merge_simple_vectors():
g1 = {
ACTION: {
"dtype": "float32",
"shape": (2,),
"names": ["ee.x", "ee.y"],
}
}
g2 = {
ACTION: {
"dtype": "float32",
"shape": (2,),
"names": ["ee.y", "ee.z"],
}
}
out = combine_feature_dicts(g1, g2)
assert ACTION in out
assert out[ACTION]["dtype"] == "float32"
# Names merged with preserved order and de-dupuplication
assert out[ACTION]["names"] == ["ee.x", "ee.y", "ee.z"]
# Shape correctly recomputed from names length
assert out[ACTION]["shape"] == (3,)
def test_merge_multiple_groups_order_and_dedup():
g1 = {ACTION: {"dtype": "float32", "shape": (2,), "names": ["a", "b"]}}
g2 = {ACTION: {"dtype": "float32", "shape": (2,), "names": ["b", "c"]}}
g3 = {ACTION: {"dtype": "float32", "shape": (3,), "names": ["a", "c", "d"]}}
out = combine_feature_dicts(g1, g2, g3)
assert out[ACTION]["names"] == ["a", "b", "c", "d"]
assert out[ACTION]["shape"] == (4,)
def test_non_vector_last_wins_for_images():
# Non-vector (images) with same name should be overwritten by the last image specified
g1 = {
f"{OBS_IMAGES}.front": {
"dtype": "image",
"shape": (3, 480, 640),
"names": ["channels", "height", "width"],
}
}
g2 = {
f"{OBS_IMAGES}.front": {
"dtype": "image",
"shape": (3, 720, 1280),
"names": ["channels", "height", "width"],
}
}
out = combine_feature_dicts(g1, g2)
assert out[f"{OBS_IMAGES}.front"]["shape"] == (3, 720, 1280)
assert out[f"{OBS_IMAGES}.front"]["dtype"] == "image"
def test_dtype_mismatch_raises():
g1 = {ACTION: {"dtype": "float32", "shape": (1,), "names": ["a"]}}
g2 = {ACTION: {"dtype": "float64", "shape": (1,), "names": ["b"]}}
with pytest.raises(ValueError, match="dtype mismatch for 'action'"):
_ = combine_feature_dicts(g1, g2)
def test_non_dict_passthrough_last_wins():
g1 = {"misc": 123}
g2 = {"misc": 456}
out = combine_feature_dicts(g1, g2)
# For non-dict entries the last one wins
assert out["misc"] == 456
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