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use torch.testing.assertclose instead to get more details about error in cis (#35659)

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* use torch.testing.assertclose instead to get more details about error in cis

* fix

* style

* test_all

* revert for I bert

* fixes and updates

* more image processing fixes

* more image processors

* fix mamba and co

* style

* less strick

* ok I won't be strict

* skip and be done

* up
This commit is contained in:
Arthur
2025-01-24 16:55:28 +01:00
committed by GitHub
parent 72d1a4cd53
commit b912f5ee43
255 changed files with 1048 additions and 969 deletions
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2
tests/models/pop2piano/test_feature_extraction_pop2piano.py
View File

@@ -148,7 +148,7 @@ class Pop2PianoFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittes
EXPECTED_INPUT_FEATURES = torch.tensor(
[[-7.1493, -6.8701, -4.3214], [-5.9473, -5.7548, -3.8438], [-6.1324, -5.9018, -4.3778]]
)
self.assertTrue(torch.allclose(input_features[0, :3, :3], EXPECTED_INPUT_FEATURES, atol=1e-4))
torch.testing.assert_close(input_features[0, :3, :3], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)
def test_attention_mask(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())

2
tests/models/pop2piano/test_modeling_pop2piano.py
View File

@@ -691,7 +691,7 @@ class Pop2PianoModelIntegrationTests(unittest.TestCase):
[[1.0475305318832397, 0.29052114486694336, -0.47778210043907166], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
)
self.assertTrue(torch.allclose(outputs[0, :3, :3], EXPECTED_OUTPUTS, atol=1e-4))
torch.testing.assert_close(outputs[0, :3, :3], EXPECTED_OUTPUTS, rtol=1e-4, atol=1e-4)
@slow
@require_essentia

8
tests/models/pop2piano/test_tokenization_pop2piano.py
View File

@@ -87,8 +87,8 @@ class Pop2PianoTokenizerTest(unittest.TestCase):
)
expected_output_attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0]])
self.assertTrue(torch.allclose(output["token_ids"], expected_output_token_ids, atol=1e-4))
self.assertTrue(torch.allclose(output["attention_mask"], expected_output_attention_mask, atol=1e-4))
torch.testing.assert_close(output["token_ids"], expected_output_token_ids, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(output["attention_mask"], expected_output_attention_mask, rtol=1e-4, atol=1e-4)
def test_batch_decode(self):
# test batch decode with model, feature-extractor outputs(beatsteps, extrapolated_beatstep)
@@ -174,7 +174,7 @@ class Pop2PianoTokenizerTest(unittest.TestCase):
)
predicted_start_timings = torch.tensor(predicted_start_timings)
self.assertTrue(torch.allclose(expected_start_timings, predicted_start_timings, atol=1e-4))
torch.testing.assert_close(expected_start_timings, predicted_start_timings, rtol=1e-4, atol=1e-4)
# Checking note end timings
expected_end_timings = torch.tensor(
@@ -187,7 +187,7 @@ class Pop2PianoTokenizerTest(unittest.TestCase):
)
predicted_end_timings = torch.tensor(predicted_end_timings)
self.assertTrue(torch.allclose(expected_end_timings, predicted_end_timings, atol=1e-4))
torch.testing.assert_close(expected_end_timings, predicted_end_timings, rtol=1e-4, atol=1e-4)
def test_get_vocab(self):
vocab_dict = self.tokenizer.get_vocab()