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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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8
tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py
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@@ -283,13 +283,13 @@ class SeamlessM4TFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
# Test not batched input
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="pt").input_features
encoded_sequences_2 = feature_extractor(pt_speech_inputs[0], return_tensors="pt").input_features
self.assertTrue(torch.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
torch.testing.assert_close(encoded_sequences_1, encoded_sequences_2, rtol=1e-3, atol=1e-3)
# Test batched
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="pt").input_features
encoded_sequences_2 = feature_extractor(pt_speech_inputs, return_tensors="pt").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(torch.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
torch.testing.assert_close(enc_seq_1, enc_seq_2, rtol=1e-3, atol=1e-3)
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
@@ -297,7 +297,7 @@ class SeamlessM4TFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="pt").input_features
encoded_sequences_2 = feature_extractor(pt_speech_inputs, return_tensors="pt").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(torch.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
torch.testing.assert_close(enc_seq_1, enc_seq_2, rtol=1e-3, atol=1e-3)
@require_torch
# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad
@@ -339,7 +339,7 @@ class SeamlessM4TFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
feature_extractor(input_speech, return_tensors="pt").input_features[0, 5, :30]
self.assertEqual(input_features.shape, (1, 279, 160))
self.assertTrue(torch.allclose(input_features[0, 5, :30], EXPECTED_INPUT_FEATURES, atol=1e-4))
torch.testing.assert_close(input_features[0, 5, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)
def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())