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
1059 lines
42 KiB
Python
1059 lines
42 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 logging
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import re
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from itertools import chain
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from pathlib import Path
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import numpy as np
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import pytest
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import torch
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from huggingface_hub import HfApi
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from PIL import Image
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from safetensors.torch import load_file
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import lerobot
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from lerobot.configs.default import DatasetConfig
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from lerobot.configs.train import TrainPipelineConfig
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from lerobot.datasets.factory import make_dataset
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from lerobot.datasets.image_writer import image_array_to_pil_image
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from lerobot.datasets.lerobot_dataset import (
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LeRobotDataset,
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MultiLeRobotDataset,
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)
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from lerobot.datasets.utils import (
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DEFAULT_CHUNK_SIZE,
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DEFAULT_DATA_FILE_SIZE_IN_MB,
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DEFAULT_VIDEO_FILE_SIZE_IN_MB,
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create_branch,
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get_hf_features_from_features,
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hf_transform_to_torch,
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hw_to_dataset_features,
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)
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from lerobot.envs.factory import make_env_config
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from lerobot.policies.factory import make_policy_config
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from lerobot.robots import make_robot_from_config
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from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
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from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_REPO_ID
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from tests.mocks.mock_robot import MockRobotConfig
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from tests.utils import require_x86_64_kernel
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@pytest.fixture
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def image_dataset(tmp_path, empty_lerobot_dataset_factory):
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features = {
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"image": {
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"dtype": "image",
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"shape": DUMMY_CHW,
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"names": [
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"channels",
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"height",
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"width",
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],
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}
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}
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return empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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def test_same_attributes_defined(tmp_path, lerobot_dataset_factory):
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"""
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Instantiate a LeRobotDataset both ways with '__init__()' and 'create()' and verify that instantiated
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objects have the same sets of attributes defined.
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"""
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# Instantiate both ways
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robot = make_robot_from_config(MockRobotConfig())
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action_features = hw_to_dataset_features(robot.action_features, ACTION, True)
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obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR, True)
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dataset_features = {**action_features, **obs_features}
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root_create = tmp_path / "create"
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dataset_create = LeRobotDataset.create(
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repo_id=DUMMY_REPO_ID, fps=30, features=dataset_features, root=root_create
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)
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root_init = tmp_path / "init"
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dataset_init = lerobot_dataset_factory(root=root_init, total_episodes=1, total_frames=1)
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init_attr = set(vars(dataset_init).keys())
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create_attr = set(vars(dataset_create).keys())
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assert init_attr == create_attr
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def test_dataset_initialization(tmp_path, lerobot_dataset_factory):
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kwargs = {
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"repo_id": DUMMY_REPO_ID,
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"total_episodes": 10,
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"total_frames": 400,
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"episodes": [2, 5, 6],
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}
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dataset = lerobot_dataset_factory(root=tmp_path / "test", **kwargs)
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assert dataset.repo_id == kwargs["repo_id"]
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assert dataset.meta.total_episodes == kwargs["total_episodes"]
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assert dataset.meta.total_frames == kwargs["total_frames"]
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assert dataset.episodes == kwargs["episodes"]
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assert dataset.num_episodes == len(kwargs["episodes"])
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assert dataset.num_frames == len(dataset)
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# TODO(rcadene, aliberts): do not run LeRobotDataset.create, instead refactor LeRobotDatasetMetadata.create
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# and test the small resulting function that validates the features
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def test_dataset_feature_with_forward_slash_raises_error():
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# make sure dir does not exist
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from lerobot.utils.constants import HF_LEROBOT_HOME
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dataset_dir = HF_LEROBOT_HOME / "lerobot/test/with/slash"
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# make sure does not exist
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if dataset_dir.exists():
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dataset_dir.rmdir()
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with pytest.raises(ValueError):
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LeRobotDataset.create(
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repo_id="lerobot/test/with/slash",
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fps=30,
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features={"a/b": {"dtype": "float32", "shape": 2, "names": None}},
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)
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def test_add_frame_missing_task(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError, match="Feature mismatch in `frame` dictionary:\nMissing features: {'task'}\n"
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):
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dataset.add_frame({"state": torch.randn(1)})
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def test_add_frame_missing_feature(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError, match="Feature mismatch in `frame` dictionary:\nMissing features: {'state'}\n"
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):
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dataset.add_frame({"task": "Dummy task"})
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def test_add_frame_extra_feature(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError, match="Feature mismatch in `frame` dictionary:\nExtra features: {'extra'}\n"
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):
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dataset.add_frame({"state": torch.randn(1), "task": "Dummy task", "extra": "dummy_extra"})
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def test_add_frame_wrong_type(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError, match="The feature 'state' of dtype 'float16' is not of the expected dtype 'float32'.\n"
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):
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dataset.add_frame({"state": torch.randn(1, dtype=torch.float16), "task": "Dummy task"})
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def test_add_frame_wrong_shape(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (2,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError,
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match=re.escape("The feature 'state' of shape '(1,)' does not have the expected shape '(2,)'.\n"),
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):
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dataset.add_frame({"state": torch.randn(1), "task": "Dummy task"})
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def test_add_frame_wrong_shape_python_float(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError,
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match=re.escape(
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"The feature 'state' is not a 'np.ndarray'. Expected type is 'float32', but type '<class 'float'>' provided instead.\n"
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),
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):
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dataset.add_frame({"state": 1.0, "task": "Dummy task"})
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def test_add_frame_wrong_shape_torch_ndim_0(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError,
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match=re.escape("The feature 'state' of shape '()' does not have the expected shape '(1,)'.\n"),
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):
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dataset.add_frame({"state": torch.tensor(1.0), "task": "Dummy task"})
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def test_add_frame_wrong_shape_numpy_ndim_0(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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with pytest.raises(
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ValueError,
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match=re.escape(
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"The feature 'state' is not a 'np.ndarray'. Expected type is 'float32', but type '<class 'numpy.float32'>' provided instead.\n"
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),
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):
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dataset.add_frame({"state": np.float32(1.0), "task": "Dummy task"})
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def test_add_frame(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"state": torch.randn(1), "task": "Dummy task"})
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dataset.save_episode()
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assert len(dataset) == 1
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assert dataset[0]["task"] == "Dummy task"
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assert dataset[0]["task_index"] == 0
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assert dataset[0]["state"].ndim == 0
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def test_add_frame_state_1d(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (2,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"state": torch.randn(2), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["state"].shape == torch.Size([2])
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def test_add_frame_state_2d(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (2, 4), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"state": torch.randn(2, 4), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["state"].shape == torch.Size([2, 4])
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def test_add_frame_state_3d(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (2, 4, 3), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"state": torch.randn(2, 4, 3), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["state"].shape == torch.Size([2, 4, 3])
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def test_add_frame_state_4d(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (2, 4, 3, 5), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"state": torch.randn(2, 4, 3, 5), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["state"].shape == torch.Size([2, 4, 3, 5])
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def test_add_frame_state_5d(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (2, 4, 3, 5, 1), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"state": torch.randn(2, 4, 3, 5, 1), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["state"].shape == torch.Size([2, 4, 3, 5, 1])
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def test_add_frame_state_numpy(tmp_path, empty_lerobot_dataset_factory):
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features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"state": np.array([1], dtype=np.float32), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["state"].ndim == 0
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def test_add_frame_string(tmp_path, empty_lerobot_dataset_factory):
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features = {"caption": {"dtype": "string", "shape": (1,), "names": None}}
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dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
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dataset.add_frame({"caption": "Dummy caption", "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["caption"] == "Dummy caption"
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def test_add_frame_image_wrong_shape(image_dataset):
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dataset = image_dataset
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with pytest.raises(
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ValueError,
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match=re.escape(
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"The feature 'image' of shape '(3, 128, 96)' does not have the expected shape '(3, 96, 128)' or '(96, 128, 3)'.\n"
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),
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):
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c, h, w = DUMMY_CHW
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dataset.add_frame({"image": torch.randn(c, w, h), "task": "Dummy task"})
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def test_add_frame_image_wrong_range(image_dataset):
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"""This test will display the following error message from a thread:
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```
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Error writing image ...test_add_frame_image_wrong_ran0/test/images/image/episode_000000/frame_000000.png:
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The image data type is float, which requires values in the range [0.0, 1.0]. However, the provided range is [0.009678772038470007, 254.9776492089887].
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Please adjust the range or provide a uint8 image with values in the range [0, 255]
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```
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Hence the image won't be saved on disk and save_episode will raise `FileNotFoundError`.
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"""
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dataset = image_dataset
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dataset.add_frame({"image": np.random.rand(*DUMMY_CHW) * 255, "task": "Dummy task"})
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with pytest.raises(FileNotFoundError):
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dataset.save_episode()
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def test_add_frame_image(image_dataset):
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dataset = image_dataset
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dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
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def test_add_frame_image_h_w_c(image_dataset):
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dataset = image_dataset
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dataset.add_frame({"image": np.random.rand(*DUMMY_HWC), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
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def test_add_frame_image_uint8(image_dataset):
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dataset = image_dataset
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image = np.random.randint(0, 256, DUMMY_HWC, dtype=np.uint8)
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dataset.add_frame({"image": image, "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
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def test_add_frame_image_pil(image_dataset):
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dataset = image_dataset
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image = np.random.randint(0, 256, DUMMY_HWC, dtype=np.uint8)
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dataset.add_frame({"image": Image.fromarray(image), "task": "Dummy task"})
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dataset.save_episode()
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assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
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def test_image_array_to_pil_image_wrong_range_float_0_255():
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image = np.random.rand(*DUMMY_HWC) * 255
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with pytest.raises(ValueError):
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image_array_to_pil_image(image)
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# TODO(aliberts):
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# - [ ] test various attributes & state from init and create
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# - [ ] test init with episodes and check num_frames
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# - [ ] test add_episode
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# - [ ] test push_to_hub
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# - [ ] test smaller methods
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# TODO(rcadene):
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# - [ ] fix code so that old test_factory + backward pass
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# - [ ] write new unit tests to test save_episode + getitem
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# - [ ] save_episode : case where new dataset, concatenate same file, write new file (meta/episodes, data, videos)
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# - [ ]
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# - [ ] remove old tests
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@pytest.mark.parametrize(
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"env_name, repo_id, policy_name",
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# Single dataset
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lerobot.env_dataset_policy_triplets,
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# Multi-dataset
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# TODO after fix multidataset
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# + [("aloha", ["lerobot/aloha_sim_insertion_human", "lerobot/aloha_sim_transfer_cube_human"], "act")],
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)
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def test_factory(env_name, repo_id, policy_name):
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"""
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Tests that:
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- we can create a dataset with the factory.
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- for a commonly used set of data keys, the data dimensions are correct.
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"""
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cfg = TrainPipelineConfig(
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# TODO(rcadene, aliberts): remove dataset download
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dataset=DatasetConfig(repo_id=repo_id, episodes=[0]),
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env=make_env_config(env_name),
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policy=make_policy_config(policy_name),
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)
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dataset = make_dataset(cfg)
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delta_timestamps = dataset.delta_timestamps
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camera_keys = dataset.meta.camera_keys
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item = dataset[0]
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keys_ndim_required = [
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(ACTION, 1, True),
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("episode_index", 0, True),
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("frame_index", 0, True),
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("timestamp", 0, True),
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# TODO(rcadene): should we rename it agent_pos?
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(OBS_STATE, 1, True),
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("next.reward", 0, False),
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("next.done", 0, False),
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]
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# test number of dimensions
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for key, ndim, required in keys_ndim_required:
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if key not in item:
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if required:
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assert key in item, f"{key}"
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else:
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logging.warning(f'Missing key in dataset: "{key}" not in {dataset}.')
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continue
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if delta_timestamps is not None and key in delta_timestamps:
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assert item[key].ndim == ndim + 1, f"{key}"
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assert item[key].shape[0] == len(delta_timestamps[key]), f"{key}"
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else:
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assert item[key].ndim == ndim, f"{key}"
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if key in camera_keys:
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assert item[key].dtype == torch.float32, f"{key}"
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# TODO(rcadene): we assume for now that image normalization takes place in the model
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assert item[key].max() <= 1.0, f"{key}"
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assert item[key].min() >= 0.0, f"{key}"
|
|
|
|
if delta_timestamps is not None and key in delta_timestamps:
|
|
# test t,c,h,w
|
|
assert item[key].shape[1] == 3, f"{key}"
|
|
else:
|
|
# test c,h,w
|
|
assert item[key].shape[0] == 3, f"{key}"
|
|
|
|
if delta_timestamps is not None:
|
|
# test missing keys in delta_timestamps
|
|
for key in delta_timestamps:
|
|
assert key in item, f"{key}"
|
|
|
|
|
|
# TODO(alexander-soare): If you're hunting for savings on testing time, this takes about 5 seconds.
|
|
@pytest.mark.skip("TODO after fix multidataset")
|
|
def test_multidataset_frames():
|
|
"""Check that all dataset frames are incorporated."""
|
|
# Note: use the image variants of the dataset to make the test approx 3x faster.
|
|
# Note: We really do need three repo_ids here as at some point this caught an issue with the chaining
|
|
# logic that wouldn't be caught with two repo IDs.
|
|
repo_ids = [
|
|
"lerobot/aloha_sim_insertion_human_image",
|
|
"lerobot/aloha_sim_transfer_cube_human_image",
|
|
"lerobot/aloha_sim_insertion_scripted_image",
|
|
]
|
|
sub_datasets = [LeRobotDataset(repo_id) for repo_id in repo_ids]
|
|
dataset = MultiLeRobotDataset(repo_ids)
|
|
assert len(dataset) == sum(len(d) for d in sub_datasets)
|
|
assert dataset.num_frames == sum(d.num_frames for d in sub_datasets)
|
|
assert dataset.num_episodes == sum(d.num_episodes for d in sub_datasets)
|
|
|
|
# Run through all items of the LeRobotDatasets in parallel with the items of the MultiLerobotDataset and
|
|
# check they match.
|
|
expected_dataset_indices = []
|
|
for i, sub_dataset in enumerate(sub_datasets):
|
|
expected_dataset_indices.extend([i] * len(sub_dataset))
|
|
|
|
for expected_dataset_index, sub_dataset_item, dataset_item in zip(
|
|
expected_dataset_indices, chain(*sub_datasets), dataset, strict=True
|
|
):
|
|
dataset_index = dataset_item.pop("dataset_index")
|
|
assert dataset_index == expected_dataset_index
|
|
assert sub_dataset_item.keys() == dataset_item.keys()
|
|
for k in sub_dataset_item:
|
|
assert torch.equal(sub_dataset_item[k], dataset_item[k])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"repo_id",
|
|
[
|
|
"lerobot/pusht",
|
|
"lerobot/aloha_sim_insertion_human",
|
|
"lerobot/xarm_lift_medium",
|
|
# (michel-aractingi) commenting the two datasets from openx as test is failing
|
|
# "lerobot/nyu_franka_play_dataset",
|
|
# "lerobot/cmu_stretch",
|
|
],
|
|
)
|
|
@require_x86_64_kernel
|
|
def test_backward_compatibility(repo_id):
|
|
"""The artifacts for this test have been generated by `tests/artifacts/datasets/save_dataset_to_safetensors.py`."""
|
|
|
|
# TODO(rcadene, aliberts): remove dataset download
|
|
dataset = LeRobotDataset(repo_id, episodes=[0])
|
|
|
|
test_dir = Path("tests/artifacts/datasets") / repo_id
|
|
|
|
def load_and_compare(i):
|
|
new_frame = dataset[i] # noqa: B023
|
|
old_frame = load_file(test_dir / f"frame_{i}.safetensors") # noqa: B023
|
|
|
|
# ignore language instructions (if exists) in language conditioned datasets
|
|
# TODO (michel-aractingi): transform language obs to language embeddings via tokenizer
|
|
new_frame.pop("language_instruction", None)
|
|
old_frame.pop("language_instruction", None)
|
|
new_frame.pop("task", None)
|
|
old_frame.pop("task", None)
|
|
|
|
# Remove task_index to allow for backward compatibility
|
|
# TODO(rcadene): remove when new features have been generated
|
|
if "task_index" not in old_frame:
|
|
del new_frame["task_index"]
|
|
|
|
new_keys = set(new_frame.keys())
|
|
old_keys = set(old_frame.keys())
|
|
assert new_keys == old_keys, f"{new_keys=} and {old_keys=} are not the same"
|
|
|
|
for key in new_frame:
|
|
assert torch.isclose(new_frame[key], old_frame[key]).all(), (
|
|
f"{key=} for index={i} does not contain the same value"
|
|
)
|
|
|
|
# test2 first frames of first episode
|
|
i = dataset.meta.episodes[0]["dataset_from_index"]
|
|
load_and_compare(i)
|
|
load_and_compare(i + 1)
|
|
|
|
# test 2 frames at the middle of first episode
|
|
i = int(
|
|
(dataset.meta.episodes[0]["dataset_to_index"] - dataset.meta.episodes[0]["dataset_from_index"]) / 2
|
|
)
|
|
load_and_compare(i)
|
|
load_and_compare(i + 1)
|
|
|
|
# test 2 last frames of first episode
|
|
i = dataset.meta.episodes[0]["dataset_to_index"]
|
|
load_and_compare(i - 2)
|
|
load_and_compare(i - 1)
|
|
|
|
|
|
@pytest.mark.skip("Requires internet access")
|
|
def test_create_branch():
|
|
api = HfApi()
|
|
|
|
repo_id = "cadene/test_create_branch"
|
|
repo_type = "dataset"
|
|
branch = "test"
|
|
ref = f"refs/heads/{branch}"
|
|
|
|
# Prepare a repo with a test branch
|
|
api.delete_repo(repo_id, repo_type=repo_type, missing_ok=True)
|
|
api.create_repo(repo_id, repo_type=repo_type)
|
|
create_branch(repo_id, repo_type=repo_type, branch=branch)
|
|
|
|
# Make sure the test branch exists
|
|
branches = api.list_repo_refs(repo_id, repo_type=repo_type).branches
|
|
refs = [branch.ref for branch in branches]
|
|
assert ref in refs
|
|
|
|
# Overwrite it
|
|
create_branch(repo_id, repo_type=repo_type, branch=branch)
|
|
|
|
# Clean
|
|
api.delete_repo(repo_id, repo_type=repo_type)
|
|
|
|
|
|
def test_check_cached_episodes_sufficient(tmp_path, lerobot_dataset_factory):
|
|
"""Test the _check_cached_episodes_sufficient method of LeRobotDataset."""
|
|
# Create a dataset with 5 episodes (0-4)
|
|
dataset = lerobot_dataset_factory(
|
|
root=tmp_path / "test",
|
|
total_episodes=5,
|
|
total_frames=200,
|
|
use_videos=False,
|
|
)
|
|
|
|
# Test hf_dataset is None
|
|
dataset.hf_dataset = None
|
|
assert dataset._check_cached_episodes_sufficient() is False
|
|
|
|
# Test hf_dataset is empty
|
|
import datasets
|
|
|
|
empty_features = get_hf_features_from_features(dataset.features)
|
|
dataset.hf_dataset = datasets.Dataset.from_dict(
|
|
{key: [] for key in empty_features}, features=empty_features
|
|
)
|
|
dataset.hf_dataset.set_transform(hf_transform_to_torch)
|
|
assert dataset._check_cached_episodes_sufficient() is False
|
|
|
|
# Restore the original dataset for remaining tests
|
|
dataset.hf_dataset = dataset.load_hf_dataset()
|
|
|
|
# Test all episodes requested (self.episodes = None) and all are available
|
|
dataset.episodes = None
|
|
assert dataset._check_cached_episodes_sufficient() is True
|
|
|
|
# Test specific episodes requested that are all available
|
|
dataset.episodes = [0, 2, 4]
|
|
assert dataset._check_cached_episodes_sufficient() is True
|
|
|
|
# Test request episodes that don't exist in the cached dataset
|
|
# Create a dataset with only episodes 0, 1, 2
|
|
limited_dataset = lerobot_dataset_factory(
|
|
root=tmp_path / "limited",
|
|
total_episodes=3,
|
|
total_frames=120,
|
|
use_videos=False,
|
|
)
|
|
|
|
# Request episodes that include non-existent ones
|
|
limited_dataset.episodes = [0, 1, 2, 3, 4]
|
|
assert limited_dataset._check_cached_episodes_sufficient() is False
|
|
|
|
# Test create a dataset with sparse episodes (e.g., only episodes 0, 2, 4)
|
|
# First create the full dataset structure
|
|
sparse_dataset = lerobot_dataset_factory(
|
|
root=tmp_path / "sparse",
|
|
total_episodes=5,
|
|
total_frames=200,
|
|
use_videos=False,
|
|
)
|
|
|
|
# Manually filter hf_dataset to only include episodes 0, 2, 4
|
|
episode_indices = sparse_dataset.hf_dataset["episode_index"]
|
|
mask = torch.zeros(len(episode_indices), dtype=torch.bool)
|
|
for ep in [0, 2, 4]:
|
|
mask |= torch.tensor(episode_indices) == ep
|
|
|
|
# Create a filtered dataset
|
|
filtered_data = {}
|
|
# Find image keys by checking features
|
|
image_keys = [key for key, ft in sparse_dataset.features.items() if ft.get("dtype") == "image"]
|
|
|
|
for key in sparse_dataset.hf_dataset.column_names:
|
|
values = sparse_dataset.hf_dataset[key]
|
|
# Filter values based on mask
|
|
filtered_values = [val for i, val in enumerate(values) if mask[i]]
|
|
|
|
# Convert float32 image tensors back to uint8 numpy arrays for HuggingFace dataset
|
|
if key in image_keys and len(filtered_values) > 0:
|
|
# Convert torch tensors (float32, [0, 1], CHW) back to numpy arrays (uint8, [0, 255], HWC)
|
|
filtered_values = [
|
|
(val.permute(1, 2, 0).numpy() * 255).astype(np.uint8) for val in filtered_values
|
|
]
|
|
|
|
filtered_data[key] = filtered_values
|
|
|
|
sparse_dataset.hf_dataset = datasets.Dataset.from_dict(
|
|
filtered_data, features=get_hf_features_from_features(sparse_dataset.features)
|
|
)
|
|
sparse_dataset.hf_dataset.set_transform(hf_transform_to_torch)
|
|
|
|
# Test requesting all episodes when only some are cached
|
|
sparse_dataset.episodes = None
|
|
assert sparse_dataset._check_cached_episodes_sufficient() is False
|
|
|
|
# Test requesting only the available episodes
|
|
sparse_dataset.episodes = [0, 2, 4]
|
|
assert sparse_dataset._check_cached_episodes_sufficient() is True
|
|
|
|
# Test requesting a mix of available and unavailable episodes
|
|
sparse_dataset.episodes = [0, 1, 2]
|
|
assert sparse_dataset._check_cached_episodes_sufficient() is False
|
|
|
|
|
|
def test_update_chunk_settings(tmp_path, empty_lerobot_dataset_factory):
|
|
"""Test the update_chunk_settings functionality for both LeRobotDataset and LeRobotDatasetMetadata."""
|
|
features = {
|
|
OBS_STATE: {
|
|
"dtype": "float32",
|
|
"shape": (6,),
|
|
"names": ["shoulder_pan", "shoulder_lift", "elbow", "wrist_1", "wrist_2", "wrist_3"],
|
|
},
|
|
ACTION: {
|
|
"dtype": "float32",
|
|
"shape": (6,),
|
|
"names": ["shoulder_pan", "shoulder_lift", "elbow", "wrist_1", "wrist_2", "wrist_3"],
|
|
},
|
|
}
|
|
|
|
# Create dataset with default chunk settings
|
|
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
|
|
|
|
# Test initial default values
|
|
initial_settings = dataset.meta.get_chunk_settings()
|
|
assert initial_settings["chunks_size"] == DEFAULT_CHUNK_SIZE
|
|
assert initial_settings["data_files_size_in_mb"] == DEFAULT_DATA_FILE_SIZE_IN_MB
|
|
assert initial_settings["video_files_size_in_mb"] == DEFAULT_VIDEO_FILE_SIZE_IN_MB
|
|
|
|
# Test updating all settings at once
|
|
new_chunks_size = 2000
|
|
new_data_size = 200
|
|
new_video_size = 1000
|
|
|
|
dataset.meta.update_chunk_settings(
|
|
chunks_size=new_chunks_size,
|
|
data_files_size_in_mb=new_data_size,
|
|
video_files_size_in_mb=new_video_size,
|
|
)
|
|
|
|
# Verify settings were updated
|
|
updated_settings = dataset.meta.get_chunk_settings()
|
|
assert updated_settings["chunks_size"] == new_chunks_size
|
|
assert updated_settings["data_files_size_in_mb"] == new_data_size
|
|
assert updated_settings["video_files_size_in_mb"] == new_video_size
|
|
|
|
# Test updating individual settings
|
|
dataset.meta.update_chunk_settings(chunks_size=1500)
|
|
settings_after_partial = dataset.meta.get_chunk_settings()
|
|
assert settings_after_partial["chunks_size"] == 1500
|
|
assert settings_after_partial["data_files_size_in_mb"] == new_data_size
|
|
assert settings_after_partial["video_files_size_in_mb"] == new_video_size
|
|
|
|
# Test updating only data file size
|
|
dataset.meta.update_chunk_settings(data_files_size_in_mb=150)
|
|
settings_after_data = dataset.meta.get_chunk_settings()
|
|
assert settings_after_data["chunks_size"] == 1500
|
|
assert settings_after_data["data_files_size_in_mb"] == 150
|
|
assert settings_after_data["video_files_size_in_mb"] == new_video_size
|
|
|
|
# Test updating only video file size
|
|
dataset.meta.update_chunk_settings(video_files_size_in_mb=800)
|
|
settings_after_video = dataset.meta.get_chunk_settings()
|
|
assert settings_after_video["chunks_size"] == 1500
|
|
assert settings_after_video["data_files_size_in_mb"] == 150
|
|
assert settings_after_video["video_files_size_in_mb"] == 800
|
|
|
|
# Test that settings persist in the info file
|
|
info_path = dataset.root / "meta" / "info.json"
|
|
assert info_path.exists()
|
|
|
|
# Verify the underlying metadata properties
|
|
assert dataset.meta.chunks_size == 1500
|
|
assert dataset.meta.data_files_size_in_mb == 150
|
|
assert dataset.meta.video_files_size_in_mb == 800
|
|
|
|
# Test error handling for invalid values
|
|
with pytest.raises(ValueError, match="chunks_size must be positive"):
|
|
dataset.meta.update_chunk_settings(chunks_size=0)
|
|
|
|
with pytest.raises(ValueError, match="chunks_size must be positive"):
|
|
dataset.meta.update_chunk_settings(chunks_size=-100)
|
|
|
|
with pytest.raises(ValueError, match="data_files_size_in_mb must be positive"):
|
|
dataset.meta.update_chunk_settings(data_files_size_in_mb=0)
|
|
|
|
with pytest.raises(ValueError, match="data_files_size_in_mb must be positive"):
|
|
dataset.meta.update_chunk_settings(data_files_size_in_mb=-50)
|
|
|
|
with pytest.raises(ValueError, match="video_files_size_in_mb must be positive"):
|
|
dataset.meta.update_chunk_settings(video_files_size_in_mb=0)
|
|
|
|
with pytest.raises(ValueError, match="video_files_size_in_mb must be positive"):
|
|
dataset.meta.update_chunk_settings(video_files_size_in_mb=-200)
|
|
|
|
# Test calling with None values (should not change anything)
|
|
settings_before_none = dataset.meta.get_chunk_settings()
|
|
dataset.meta.update_chunk_settings(
|
|
chunks_size=None, data_files_size_in_mb=None, video_files_size_in_mb=None
|
|
)
|
|
settings_after_none = dataset.meta.get_chunk_settings()
|
|
assert settings_before_none == settings_after_none
|
|
|
|
# Test metadata direct access
|
|
meta_settings = dataset.meta.get_chunk_settings()
|
|
assert meta_settings == dataset.meta.get_chunk_settings()
|
|
|
|
# Test updating via metadata directly
|
|
dataset.meta.update_chunk_settings(chunks_size=3000)
|
|
assert dataset.meta.get_chunk_settings()["chunks_size"] == 3000
|
|
|
|
|
|
def test_update_chunk_settings_video_dataset(tmp_path):
|
|
"""Test update_chunk_settings with a video dataset to ensure video-specific logic works."""
|
|
features = {
|
|
f"{OBS_IMAGES}.cam": {
|
|
"dtype": "video",
|
|
"shape": (480, 640, 3),
|
|
"names": ["height", "width", "channels"],
|
|
},
|
|
ACTION: {"dtype": "float32", "shape": (6,), "names": ["j1", "j2", "j3", "j4", "j5", "j6"]},
|
|
}
|
|
|
|
# Create video dataset
|
|
dataset = LeRobotDataset.create(
|
|
repo_id=DUMMY_REPO_ID, fps=30, features=features, root=tmp_path / "video_test", use_videos=True
|
|
)
|
|
|
|
# Test that video-specific settings work
|
|
original_video_size = dataset.meta.get_chunk_settings()["video_files_size_in_mb"]
|
|
new_video_size = original_video_size * 2
|
|
|
|
dataset.meta.update_chunk_settings(video_files_size_in_mb=new_video_size)
|
|
assert dataset.meta.get_chunk_settings()["video_files_size_in_mb"] == new_video_size
|
|
assert dataset.meta.video_files_size_in_mb == new_video_size
|
|
|
|
|
|
def test_episode_index_distribution(tmp_path, empty_lerobot_dataset_factory):
|
|
"""Test that all frames have correct episode indices across multiple episodes."""
|
|
features = {"state": {"dtype": "float32", "shape": (2,), "names": None}}
|
|
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False)
|
|
|
|
# Create 3 episodes with different lengths
|
|
num_episodes = 3
|
|
frames_per_episode = [10, 15, 8]
|
|
|
|
for episode_idx in range(num_episodes):
|
|
for _ in range(frames_per_episode[episode_idx]):
|
|
dataset.add_frame({"state": torch.randn(2), "task": f"task_{episode_idx}"})
|
|
dataset.save_episode()
|
|
|
|
# Load the dataset and check episode indices
|
|
loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root)
|
|
|
|
# Check specific frames across episode boundaries
|
|
cumulative = 0
|
|
for ep_idx, ep_length in enumerate(frames_per_episode):
|
|
# Check start, middle, and end of each episode
|
|
start_frame = cumulative
|
|
middle_frame = cumulative + ep_length // 2
|
|
end_frame = cumulative + ep_length - 1
|
|
|
|
for frame_idx in [start_frame, middle_frame, end_frame]:
|
|
frame_data = loaded_dataset[frame_idx]
|
|
actual_ep_idx = frame_data["episode_index"].item()
|
|
assert actual_ep_idx == ep_idx, (
|
|
f"Frame {frame_idx} has episode_index {actual_ep_idx}, should be {ep_idx}"
|
|
)
|
|
|
|
cumulative += ep_length
|
|
|
|
# Check episode index distribution
|
|
all_episode_indices = [loaded_dataset[i]["episode_index"].item() for i in range(len(loaded_dataset))]
|
|
from collections import Counter
|
|
|
|
distribution = Counter(all_episode_indices)
|
|
expected_dist = {i: frames_per_episode[i] for i in range(num_episodes)}
|
|
|
|
assert dict(distribution) == expected_dist, (
|
|
f"Episode distribution {dict(distribution)} != expected {expected_dist}"
|
|
)
|
|
|
|
|
|
def test_multi_episode_metadata_consistency(tmp_path, empty_lerobot_dataset_factory):
|
|
"""Test episode metadata consistency across multiple episodes."""
|
|
features = {
|
|
"state": {"dtype": "float32", "shape": (3,), "names": ["x", "y", "z"]},
|
|
ACTION: {"dtype": "float32", "shape": (2,), "names": ["v", "w"]},
|
|
}
|
|
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False)
|
|
|
|
num_episodes = 4
|
|
frames_per_episode = [20, 35, 10, 25]
|
|
tasks = ["pick", "place", "pick", "place"]
|
|
|
|
for episode_idx in range(num_episodes):
|
|
for _ in range(frames_per_episode[episode_idx]):
|
|
dataset.add_frame({"state": torch.randn(3), ACTION: torch.randn(2), "task": tasks[episode_idx]})
|
|
dataset.save_episode()
|
|
|
|
# Load and validate episode metadata
|
|
loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root)
|
|
|
|
assert loaded_dataset.meta.total_episodes == num_episodes
|
|
assert loaded_dataset.meta.total_frames == sum(frames_per_episode)
|
|
|
|
cumulative_frames = 0
|
|
for episode_idx in range(num_episodes):
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|
episode_metadata = loaded_dataset.meta.episodes[episode_idx]
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|
|
|
# Check basic episode properties
|
|
assert episode_metadata["episode_index"] == episode_idx
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|
assert episode_metadata["length"] == frames_per_episode[episode_idx]
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|
assert episode_metadata["tasks"] == [tasks[episode_idx]]
|
|
|
|
# Check dataset indices
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|
expected_from = cumulative_frames
|
|
expected_to = cumulative_frames + frames_per_episode[episode_idx]
|
|
|
|
assert episode_metadata["dataset_from_index"] == expected_from
|
|
assert episode_metadata["dataset_to_index"] == expected_to
|
|
|
|
cumulative_frames += frames_per_episode[episode_idx]
|
|
|
|
|
|
def test_data_consistency_across_episodes(tmp_path, empty_lerobot_dataset_factory):
|
|
"""Test that episodes have no gaps or overlaps in their data indices."""
|
|
features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
|
|
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False)
|
|
|
|
num_episodes = 5
|
|
frames_per_episode = [12, 8, 20, 15, 5]
|
|
|
|
for episode_idx in range(num_episodes):
|
|
for _ in range(frames_per_episode[episode_idx]):
|
|
dataset.add_frame({"state": torch.randn(1), "task": "consistency_test"})
|
|
dataset.save_episode()
|
|
|
|
loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root)
|
|
|
|
# Check data consistency - no gaps or overlaps
|
|
cumulative_check = 0
|
|
for episode_idx in range(num_episodes):
|
|
episode_metadata = loaded_dataset.meta.episodes[episode_idx]
|
|
from_idx = episode_metadata["dataset_from_index"]
|
|
to_idx = episode_metadata["dataset_to_index"]
|
|
|
|
# Check that episode starts exactly where previous ended
|
|
assert from_idx == cumulative_check, (
|
|
f"Episode {episode_idx} starts at {from_idx}, expected {cumulative_check}"
|
|
)
|
|
|
|
# Check that episode length matches expected
|
|
actual_length = to_idx - from_idx
|
|
expected_length = frames_per_episode[episode_idx]
|
|
assert actual_length == expected_length, (
|
|
f"Episode {episode_idx} length {actual_length} != expected {expected_length}"
|
|
)
|
|
|
|
cumulative_check = to_idx
|
|
|
|
# Final check: last episode should end at total frames
|
|
expected_total_frames = sum(frames_per_episode)
|
|
assert cumulative_check == expected_total_frames, (
|
|
f"Final frame count {cumulative_check} != expected {expected_total_frames}"
|
|
)
|
|
|
|
|
|
def test_statistics_metadata_validation(tmp_path, empty_lerobot_dataset_factory):
|
|
"""Test that statistics are properly computed and stored for all features."""
|
|
features = {
|
|
"state": {"dtype": "float32", "shape": (2,), "names": ["pos", "vel"]},
|
|
ACTION: {"dtype": "float32", "shape": (1,), "names": ["force"]},
|
|
}
|
|
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False)
|
|
|
|
# Create controlled data to verify statistics
|
|
num_episodes = 2
|
|
frames_per_episode = [10, 10]
|
|
|
|
# Use deterministic data for predictable statistics
|
|
torch.manual_seed(42)
|
|
for episode_idx in range(num_episodes):
|
|
for frame_idx in range(frames_per_episode[episode_idx]):
|
|
state_data = torch.tensor([frame_idx * 0.1, frame_idx * 0.2], dtype=torch.float32)
|
|
action_data = torch.tensor([frame_idx * 0.05], dtype=torch.float32)
|
|
dataset.add_frame({"state": state_data, ACTION: action_data, "task": "stats_test"})
|
|
dataset.save_episode()
|
|
|
|
loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root)
|
|
|
|
# Check that statistics exist for all features
|
|
assert loaded_dataset.meta.stats is not None, "No statistics found"
|
|
|
|
for feature_name in features.keys():
|
|
assert feature_name in loaded_dataset.meta.stats, f"No statistics for feature '{feature_name}'"
|
|
|
|
feature_stats = loaded_dataset.meta.stats[feature_name]
|
|
expected_stats = ["min", "max", "mean", "std", "count"]
|
|
|
|
for stat_key in expected_stats:
|
|
assert stat_key in feature_stats, f"Missing '{stat_key}' statistic for '{feature_name}'"
|
|
|
|
stat_value = feature_stats[stat_key]
|
|
# Basic sanity checks
|
|
if stat_key == "count":
|
|
assert stat_value == sum(frames_per_episode), f"Wrong count for '{feature_name}'"
|
|
elif stat_key in ["min", "max", "mean", "std"]:
|
|
# Check that statistics are reasonable (not NaN, proper shapes)
|
|
if hasattr(stat_value, "shape"):
|
|
expected_shape = features[feature_name]["shape"]
|
|
assert stat_value.shape == expected_shape or len(stat_value) == expected_shape[0], (
|
|
f"Wrong shape for {stat_key} of '{feature_name}'"
|
|
)
|
|
# Check no NaN values
|
|
if hasattr(stat_value, "__iter__"):
|
|
assert not any(np.isnan(v) for v in stat_value), f"NaN in {stat_key} for '{feature_name}'"
|
|
else:
|
|
assert not np.isnan(stat_value), f"NaN in {stat_key} for '{feature_name}'"
|
|
|
|
|
|
def test_episode_boundary_integrity(tmp_path, empty_lerobot_dataset_factory):
|
|
"""Test frame indices and episode transitions at episode boundaries."""
|
|
features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
|
|
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False)
|
|
|
|
num_episodes = 3
|
|
frames_per_episode = [7, 12, 5]
|
|
|
|
for episode_idx in range(num_episodes):
|
|
for frame_idx in range(frames_per_episode[episode_idx]):
|
|
dataset.add_frame({"state": torch.tensor([float(frame_idx)]), "task": f"episode_{episode_idx}"})
|
|
dataset.save_episode()
|
|
|
|
loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root)
|
|
|
|
# Test episode boundaries
|
|
cumulative = 0
|
|
for ep_idx, ep_length in enumerate(frames_per_episode):
|
|
if ep_idx > 0:
|
|
# Check last frame of previous episode
|
|
prev_frame = loaded_dataset[cumulative - 1]
|
|
assert prev_frame["episode_index"].item() == ep_idx - 1
|
|
|
|
# Check first frame of current episode
|
|
if cumulative < len(loaded_dataset):
|
|
curr_frame = loaded_dataset[cumulative]
|
|
assert curr_frame["episode_index"].item() == ep_idx
|
|
|
|
# Check frame_index within episode
|
|
for i in range(ep_length):
|
|
if cumulative + i < len(loaded_dataset):
|
|
frame = loaded_dataset[cumulative + i]
|
|
assert frame["frame_index"].item() == i, f"Frame {cumulative + i} has wrong frame_index"
|
|
assert frame["episode_index"].item() == ep_idx, (
|
|
f"Frame {cumulative + i} has wrong episode_index"
|
|
)
|
|
|
|
cumulative += ep_length
|
|
|
|
|
|
def test_task_indexing_and_validation(tmp_path, empty_lerobot_dataset_factory):
|
|
"""Test that tasks are properly indexed and retrievable."""
|
|
features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
|
|
dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features, use_videos=False)
|
|
|
|
# Use multiple tasks, including repeated ones
|
|
tasks = ["pick", "place", "pick", "navigate", "place"]
|
|
unique_tasks = list(set(tasks)) # ["pick", "place", "navigate"]
|
|
frames_per_episode = [5, 8, 3, 10, 6]
|
|
|
|
for episode_idx, task in enumerate(tasks):
|
|
for _ in range(frames_per_episode[episode_idx]):
|
|
dataset.add_frame({"state": torch.randn(1), "task": task})
|
|
dataset.save_episode()
|
|
|
|
loaded_dataset = LeRobotDataset(dataset.repo_id, root=dataset.root)
|
|
|
|
# Check that all unique tasks are in the tasks metadata
|
|
stored_tasks = set(loaded_dataset.meta.tasks.index)
|
|
assert stored_tasks == set(unique_tasks), f"Stored tasks {stored_tasks} != expected {set(unique_tasks)}"
|
|
|
|
# Check that task indices are consistent
|
|
cumulative = 0
|
|
for episode_idx, expected_task in enumerate(tasks):
|
|
episode_metadata = loaded_dataset.meta.episodes[episode_idx]
|
|
assert episode_metadata["tasks"] == [expected_task]
|
|
|
|
# Check frames in this episode have correct task
|
|
for i in range(frames_per_episode[episode_idx]):
|
|
frame = loaded_dataset[cumulative + i]
|
|
assert frame["task"] == expected_task, f"Frame {cumulative + i} has wrong task"
|
|
|
|
# Check task_index consistency
|
|
expected_task_index = loaded_dataset.meta.get_task_index(expected_task)
|
|
assert frame["task_index"].item() == expected_task_index
|
|
|
|
cumulative += frames_per_episode[episode_idx]
|
|
|
|
# Check total number of tasks
|
|
assert loaded_dataset.meta.total_tasks == len(unique_tasks)
|