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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 | #!/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 logging
import pytest
import torch
from datasets import Dataset
from lerobot.datasets.io_utils import (
hf_transform_to_torch,
)
from lerobot.datasets.sampler import EpisodeAwareSampler
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_drop_n_first_frames():
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)
sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], drop_n_first_frames=1)
assert sampler.indices == [1, 4, 5]
assert len(sampler) == 3
assert list(sampler) == [1, 4, 5]
def test_drop_n_last_frames():
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)
sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], drop_n_last_frames=1)
assert sampler.indices == [0, 3, 4]
assert len(sampler) == 3
assert list(sampler) == [0, 3, 4]
def test_episode_indices_to_use():
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)
sampler = EpisodeAwareSampler(
episode_data_index["from"], episode_data_index["to"], episode_indices_to_use=[0, 2]
)
assert sampler.indices == [0, 1, 3, 4, 5]
assert len(sampler) == 5
assert list(sampler) == [0, 1, 3, 4, 5]
def test_shuffle():
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)
sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], shuffle=False)
assert sampler.indices == [0, 1, 2, 3, 4, 5]
assert len(sampler) == 6
assert list(sampler) == [0, 1, 2, 3, 4, 5]
sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], shuffle=True)
assert sampler.indices == [0, 1, 2, 3, 4, 5]
assert len(sampler) == 6
assert set(sampler) == {0, 1, 2, 3, 4, 5}
def test_negative_drop_first_frames_raises():
with pytest.raises(ValueError, match="drop_n_first_frames must be >= 0"):
EpisodeAwareSampler([0], [10], drop_n_first_frames=-1)
def test_negative_drop_last_frames_raises():
with pytest.raises(ValueError, match="drop_n_last_frames must be >= 0"):
EpisodeAwareSampler([0], [10], drop_n_last_frames=-1)
def test_all_episodes_dropped_raises():
# All episodes have 1 frame, drop_n_first_frames=1 removes all
with pytest.raises(ValueError, match="No valid frames remain"):
EpisodeAwareSampler([0, 1, 2], [1, 2, 3], drop_n_first_frames=1)
def test_partial_episode_drop_warns(caplog):
# Episode 0: 1 frame (dropped), Episode 1: 5 frames (kept)
with caplog.at_level(logging.WARNING, logger="lerobot.datasets.sampler"):
sampler = EpisodeAwareSampler([0, 1], [1, 6], drop_n_first_frames=1)
# Episode 0 is skipped (1 frame, drop 1), Episode 1 keeps frames 2-5
assert sampler.indices == [2, 3, 4, 5]
assert "Episode 0" in caplog.text
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