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109 lines
4.0 KiB
Python
109 lines
4.0 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 torch
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from datasets import Dataset
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from lerobot.datasets.sampler import EpisodeAwareSampler
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from lerobot.datasets.utils import (
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hf_transform_to_torch,
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)
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def calculate_episode_data_index(hf_dataset: Dataset) -> dict[str, torch.Tensor]:
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"""Calculate episode data index for testing. Returns {"from": Tensor, "to": Tensor}."""
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episode_data_index: dict[str, list[int]] = {"from": [], "to": []}
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current_episode = None
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if len(hf_dataset) == 0:
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return {"from": torch.tensor([]), "to": torch.tensor([])}
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for idx, episode_idx in enumerate(hf_dataset["episode_index"]):
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if episode_idx != current_episode:
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episode_data_index["from"].append(idx)
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if current_episode is not None:
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episode_data_index["to"].append(idx)
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current_episode = episode_idx
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episode_data_index["to"].append(idx + 1)
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return {k: torch.tensor(v) for k, v in episode_data_index.items()}
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def test_drop_n_first_frames():
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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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sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], drop_n_first_frames=1)
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assert sampler.indices == [1, 4, 5]
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assert len(sampler) == 3
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assert list(sampler) == [1, 4, 5]
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def test_drop_n_last_frames():
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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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sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], drop_n_last_frames=1)
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assert sampler.indices == [0, 3, 4]
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assert len(sampler) == 3
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assert list(sampler) == [0, 3, 4]
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def test_episode_indices_to_use():
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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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sampler = EpisodeAwareSampler(
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episode_data_index["from"], episode_data_index["to"], episode_indices_to_use=[0, 2]
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)
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assert sampler.indices == [0, 1, 3, 4, 5]
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assert len(sampler) == 5
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assert list(sampler) == [0, 1, 3, 4, 5]
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def test_shuffle():
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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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sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], shuffle=False)
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assert sampler.indices == [0, 1, 2, 3, 4, 5]
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assert len(sampler) == 6
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assert list(sampler) == [0, 1, 2, 3, 4, 5]
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sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], shuffle=True)
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assert sampler.indices == [0, 1, 2, 3, 4, 5]
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assert len(sampler) == 6
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assert set(sampler) == {0, 1, 2, 3, 4, 5}
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