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

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for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot]
2025-03-24 13:41:27 +00:00
committed by Michel Aractingi
parent 2abbd60a0d
commit 0ea27704f6
123 changed files with 1161 additions and 3425 deletions
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53
tests/datasets/test_compute_stats.py
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@@ -31,11 +31,7 @@ from lerobot.common.datasets.compute_stats import (
def mock_load_image_as_numpy(path, dtype, channel_first):
return (
np.ones((3, 32, 32), dtype=dtype)
if channel_first
else np.ones((32, 32, 3), dtype=dtype)
)
return np.ones((3, 32, 32), dtype=dtype) if channel_first else np.ones((32, 32, 3), dtype=dtype)
@pytest.fixture
@@ -81,20 +77,9 @@ def test_sample_images(mock_load):
def test_get_feature_stats_images():
data = np.random.rand(100, 3, 32, 32)
stats = get_feature_stats(data, axis=(0, 2, 3), keepdims=True)
assert (
"min" in stats
and "max" in stats
and "mean" in stats
and "std" in stats
and "count" in stats
)
assert "min" in stats and "max" in stats and "mean" in stats and "std" in stats and "count" in stats
np.testing.assert_equal(stats["count"], np.array([100]))
assert (
stats["min"].shape
== stats["max"].shape
== stats["mean"].shape
== stats["std"].shape
)
assert stats["min"].shape == stats["max"].shape == stats["mean"].shape == stats["std"].shape
def test_get_feature_stats_axis_0_keepdims(sample_array):
@@ -315,47 +300,31 @@ def test_aggregate_stats():
for ep_stats in all_stats:
for fkey, stats in ep_stats.items():
for k in stats:
stats[k] = np.array(
stats[k], dtype=np.int64 if k == "count" else np.float32
)
stats[k] = np.array(stats[k], dtype=np.int64 if k == "count" else np.float32)
if fkey == "observation.image" and k != "count":
stats[k] = stats[k].reshape(
3, 1, 1
) # for normalization on image channels
stats[k] = stats[k].reshape(3, 1, 1) # for normalization on image channels
else:
stats[k] = stats[k].reshape(1)
# cast to numpy
for fkey, stats in expected_agg_stats.items():
for k in stats:
stats[k] = np.array(
stats[k], dtype=np.int64 if k == "count" else np.float32
)
stats[k] = np.array(stats[k], dtype=np.int64 if k == "count" else np.float32)
if fkey == "observation.image" and k != "count":
stats[k] = stats[k].reshape(
3, 1, 1
) # for normalization on image channels
stats[k] = stats[k].reshape(3, 1, 1) # for normalization on image channels
else:
stats[k] = stats[k].reshape(1)
results = aggregate_stats(all_stats)
for fkey in expected_agg_stats:
np.testing.assert_allclose(
results[fkey]["min"], expected_agg_stats[fkey]["min"]
)
np.testing.assert_allclose(
results[fkey]["max"], expected_agg_stats[fkey]["max"]
)
np.testing.assert_allclose(
results[fkey]["mean"], expected_agg_stats[fkey]["mean"]
)
np.testing.assert_allclose(results[fkey]["min"], expected_agg_stats[fkey]["min"])
np.testing.assert_allclose(results[fkey]["max"], expected_agg_stats[fkey]["max"])
np.testing.assert_allclose(results[fkey]["mean"], expected_agg_stats[fkey]["mean"])
np.testing.assert_allclose(
results[fkey]["std"],
expected_agg_stats[fkey]["std"],
atol=1e-04,
rtol=1e-04,
)
np.testing.assert_allclose(
results[fkey]["count"], expected_agg_stats[fkey]["count"]
)
np.testing.assert_allclose(results[fkey]["count"], expected_agg_stats[fkey]["count"])