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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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6
tests/models/encodec/test_feature_extraction_encodec.py
View File

@@ -159,7 +159,7 @@ class EnCodecFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.
feature_extractor = EncodecFeatureExtractor()
input_values = feature_extractor(input_audio, return_tensors="pt").input_values
self.assertEqual(input_values.shape, (1, 1, 93680))
self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-6))
torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-6, atol=1e-6)
def test_integration_stereo(self):
# fmt: off
@@ -178,8 +178,8 @@ class EnCodecFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.
feature_extractor = EncodecFeatureExtractor(feature_size=2)
input_values = feature_extractor(input_audio, return_tensors="pt").input_values
self.assertEqual(input_values.shape, (1, 2, 93680))
self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-6))
self.assertTrue(torch.allclose(input_values[0, 1, :30], EXPECTED_INPUT_VALUES * 0.5, atol=1e-6))
torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-6, atol=1e-6)
torch.testing.assert_close(input_values[0, 1, :30], EXPECTED_INPUT_VALUES * 0.5, rtol=1e-6, atol=1e-6)
def test_truncation_and_padding(self):
input_audio = self._load_datasamples(2)

12
tests/models/encodec/test_modeling_encodec.py
View File

@@ -324,7 +324,7 @@ class EncodecModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
inputs = self._prepare_for_class(inputs_dict, model_class)
inputs["input_values"] = inputs["input_values"].repeat(1, 1, 10)
hidden_states_no_chunk = model(**inputs)[0]
hidden_states_no_chunk = model(**inputs)[1]
torch.manual_seed(0)
config.chunk_length_s = 1
@@ -335,8 +335,8 @@ class EncodecModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
model.to(torch_device)
model.eval()
hidden_states_with_chunk = model(**inputs)[0]
self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
hidden_states_with_chunk = model(**inputs)[1]
torch.testing.assert_close(hidden_states_no_chunk, hidden_states_with_chunk, rtol=1e-1, atol=1e-2)
@unittest.skip(
reason="The EncodecModel is not transformers based, thus it does not have the usual `hidden_states` logic"
@@ -507,7 +507,7 @@ class EncodecIntegrationTest(unittest.TestCase):
)[-1]
# make sure forward and decode gives same result
self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, atol=1e-3))
torch.testing.assert_close(input_values_dec, input_values_enc_dec, rtol=1e-3, atol=1e-3)
# make sure shape matches
self.assertTrue(inputs["input_values"].shape == input_values_enc_dec.shape)
@@ -563,7 +563,7 @@ class EncodecIntegrationTest(unittest.TestCase):
)[-1]
# make sure forward and decode gives same result
self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, atol=1e-3))
torch.testing.assert_close(input_values_dec, input_values_enc_dec, rtol=1e-3, atol=1e-3)
# make sure shape matches
self.assertTrue(inputs["input_values"].shape == input_values_enc_dec.shape)
@@ -622,7 +622,7 @@ class EncodecIntegrationTest(unittest.TestCase):
input_values_enc_dec = model(input_values, bandwidth=float(bandwidth))[-1]
# make sure forward and decode gives same result
self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, atol=1e-3))
torch.testing.assert_close(input_values_dec, input_values_enc_dec, rtol=1e-3, atol=1e-3)
# make sure shape matches
self.assertTrue(input_values.shape == input_values_enc_dec.shape)