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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot]
2025-03-24 13:16:38 +00:00
committed by Michel Aractingi
parent cdcf346061
commit 1c8daf11fd
95 changed files with 1592 additions and 491 deletions
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18
tests/datasets/test_online_buffer.py
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@@ -176,7 +176,9 @@ def test_delta_timestamps_within_tolerance():
buffer.tolerance_s = 0.04
item = buffer[2]
data, is_pad = item["index"], item[f"index{OnlineBuffer.IS_PAD_POSTFIX}"]
torch.testing.assert_close(data, torch.tensor([0, 2, 3]), msg="Data does not match expected values")
torch.testing.assert_close(
data, torch.tensor([0, 2, 3]), msg="Data does not match expected values"
)
assert not is_pad.any(), "Unexpected padding detected"
@@ -212,7 +214,9 @@ def test_delta_timestamps_outside_tolerance_outside_episode_range():
buffer.tolerance_s = 0.04
item = buffer[2]
data, is_pad = item["index"], item["index_is_pad"]
assert torch.equal(data, torch.tensor([0, 0, 2, 4, 4])), "Data does not match expected values"
assert torch.equal(data, torch.tensor([0, 0, 2, 4, 4])), (
"Data does not match expected values"
)
assert torch.equal(is_pad, torch.tensor([True, False, False, True, True])), (
"Padding does not match expected values"
)
@@ -275,7 +279,8 @@ def test_compute_sampler_weights_nontrivial_ratio(lerobot_dataset_factory, tmp_p
online_sampling_ratio=online_sampling_ratio,
)
torch.testing.assert_close(
weights, torch.tensor([0.05, 0.05, 0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
weights,
torch.tensor([0.05, 0.05, 0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]),
)
@@ -297,7 +302,8 @@ def test_compute_sampler_weights_nontrivial_ratio_and_drop_last_n(
online_drop_n_last_frames=1,
)
torch.testing.assert_close(
weights, torch.tensor([0.05, 0.05, 0.05, 0.05, 0.2, 0.0, 0.2, 0.0, 0.2, 0.0, 0.2, 0.0])
weights,
torch.tensor([0.05, 0.05, 0.05, 0.05, 0.2, 0.0, 0.2, 0.0, 0.2, 0.0, 0.2, 0.0]),
)
@@ -318,4 +324,6 @@ def test_compute_sampler_weights_drop_n_last_frames(lerobot_dataset_factory, tmp
online_sampling_ratio=0.5,
online_drop_n_last_frames=1,
)
torch.testing.assert_close(weights, torch.tensor([0.5, 0, 0.125, 0, 0.125, 0, 0.125, 0, 0.125, 0]))
torch.testing.assert_close(
weights, torch.tensor([0.5, 0, 0.125, 0, 0.125, 0, 0.125, 0, 0.125, 0])
)