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Fix nightly (#775)

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This commit is contained in:
Simon Alibert
2025-02-26 16:36:03 +01:00
committed by GitHub
parent da265ca920
commit 659ec4434d
51 changed files with 145 additions and 172 deletions
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10
tests/test_online_buffer.py
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@@ -166,7 +166,7 @@ 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}"]
assert torch.allclose(data, torch.tensor([0, 2, 3])), "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"
@@ -236,7 +236,7 @@ def test_compute_sampler_weights_trivial(
elif online_sampling_ratio == 1:
expected_weights = torch.cat([torch.zeros(offline_dataset_size), torch.ones(online_dataset_size)])
expected_weights /= expected_weights.sum()
assert torch.allclose(weights, expected_weights)
torch.testing.assert_close(weights, expected_weights)
def test_compute_sampler_weights_nontrivial_ratio(lerobot_dataset_factory, tmp_path):
@@ -248,7 +248,7 @@ def test_compute_sampler_weights_nontrivial_ratio(lerobot_dataset_factory, tmp_p
weights = compute_sampler_weights(
offline_dataset, online_dataset=online_dataset, online_sampling_ratio=online_sampling_ratio
)
assert torch.allclose(
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])
)
@@ -261,7 +261,7 @@ def test_compute_sampler_weights_nontrivial_ratio_and_drop_last_n(lerobot_datase
weights = compute_sampler_weights(
offline_dataset, online_dataset=online_dataset, online_sampling_ratio=0.8, online_drop_n_last_frames=1
)
assert torch.allclose(
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])
)
@@ -279,4 +279,4 @@ def test_compute_sampler_weights_drop_n_last_frames(lerobot_dataset_factory, tmp
online_sampling_ratio=0.5,
online_drop_n_last_frames=1,
)
assert torch.allclose(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]))