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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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4
tests/models/dac/test_feature_extraction_dac.py
View File

@@ -165,9 +165,9 @@ class DacFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.Test
feature_extractor = DacFeatureExtractor()
input_values = feature_extractor(input_audio, return_tensors="pt")["input_values"]
self.assertEqual(input_values.shape, (1, 1, 93696))
self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))
torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-4, atol=1e-4)
audio_input_end = torch.tensor(input_audio[0][-30:], dtype=torch.float32)
self.assertTrue(torch.allclose(input_values[0, 0, -46:-16], audio_input_end, atol=1e-4))
torch.testing.assert_close(input_values[0, 0, -46:-16], audio_input_end, rtol=1e-4, atol=1e-4)
# Ignore copy
@unittest.skip("The DAC model doesn't support stereo logic")

24
tests/models/dac/test_modeling_dac.py
View File

@@ -438,14 +438,14 @@ class DacIntegrationTest(unittest.TestCase):
encoder_outputs_mean = torch.tensor([v.float().mean().cpu().item() for v in encoder_outputs.to_tuple()])
# make sure audio encoded codes are correct
self.assertTrue(torch.allclose(encoder_outputs_mean, expected_encoder_sums, atol=1e-3))
torch.testing.assert_close(encoder_outputs_mean, expected_encoder_sums, rtol=1e-3, atol=1e-3)
_, quantized_representation, _, _ = encoder_outputs.to_tuple()
input_values_dec = model.decode(quantized_representation)[0]
input_values_enc_dec = model(inputs["input_values"])[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)
arr = inputs["input_values"][0].cpu().numpy()
arr_enc_dec = input_values_enc_dec[0].cpu().numpy()
@@ -515,10 +515,10 @@ class DacIntegrationTest(unittest.TestCase):
input_values_from_codes = model.decode(audio_codes=encoder_outputs.audio_codes)[0]
# make sure decode from audio codes and quantized values give more or less the same results
self.assertTrue(torch.allclose(input_values_from_codes, input_values_dec, atol=1e-5))
torch.testing.assert_close(input_values_from_codes, input_values_dec, rtol=1e-5, atol=1e-5)
# 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)
arr = inputs["input_values"][0].cpu().numpy()
arr_enc_dec = input_values_enc_dec[0].cpu().numpy()
@@ -565,14 +565,14 @@ class DacIntegrationTest(unittest.TestCase):
encoder_outputs_mean = torch.tensor([v.float().mean().cpu().item() for v in encoder_outputs.to_tuple()])
# make sure audio encoded codes are correct
self.assertTrue(torch.allclose(encoder_outputs_mean, expected_encoder_sums, atol=1e-3))
torch.testing.assert_close(encoder_outputs_mean, expected_encoder_sums, rtol=1e-3, atol=1e-3)
_, quantized_representation, _, _ = encoder_outputs.to_tuple()
input_values_dec = model.decode(quantized_representation)[0]
input_values_enc_dec = model(inputs["input_values"])[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)
arr = inputs["input_values"][0].cpu().numpy()
arr_enc_dec = input_values_enc_dec[0].cpu().numpy()
@@ -622,14 +622,14 @@ class DacIntegrationTest(unittest.TestCase):
encoder_outputs_mean = torch.tensor([v.float().mean().item() for v in encoder_outputs.to_tuple()])
# make sure audio encoded codes are correct
self.assertTrue(torch.allclose(encoder_outputs_mean, expected_encoder_sums, atol=1e-3))
torch.testing.assert_close(encoder_outputs_mean, expected_encoder_sums, rtol=1e-3, atol=1e-3)
_, quantized_representation, _, _ = encoder_outputs.to_tuple()
input_values_dec = model.decode(quantized_representation)[0]
input_values_enc_dec = model(inputs["input_values"])[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)
arr = inputs["input_values"].cpu().numpy()
arr_enc_dec = input_values_enc_dec.cpu().numpy()
@@ -679,14 +679,14 @@ class DacIntegrationTest(unittest.TestCase):
encoder_outputs_mean = torch.tensor([v.float().mean().cpu().item() for v in encoder_outputs.to_tuple()])
# make sure audio encoded codes are correct
self.assertTrue(torch.allclose(encoder_outputs_mean, expected_encoder_sums, atol=1e-3))
torch.testing.assert_close(encoder_outputs_mean, expected_encoder_sums, rtol=1e-3, atol=1e-3)
_, quantized_representation, _, _ = encoder_outputs.to_tuple()
input_values_dec = model.decode(quantized_representation)[0]
input_values_enc_dec = model(inputs["input_values"])[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)
arr = inputs["input_values"].cpu().numpy()
arr_enc_dec = input_values_enc_dec.cpu().numpy()
@@ -736,14 +736,14 @@ class DacIntegrationTest(unittest.TestCase):
encoder_outputs_mean = torch.tensor([v.float().mean().cpu().item() for v in encoder_outputs.to_tuple()])
# make sure audio encoded codes are correct
self.assertTrue(torch.allclose(encoder_outputs_mean, expected_encoder_sums, atol=1e-3))
torch.testing.assert_close(encoder_outputs_mean, expected_encoder_sums, rtol=1e-3, atol=1e-3)
_, quantized_representation, _, _ = encoder_outputs.to_tuple()
input_values_dec = model.decode(quantized_representation)[0]
input_values_enc_dec = model(inputs["input_values"])[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)
arr = inputs["input_values"].cpu().numpy()
arr_enc_dec = input_values_enc_dec.cpu().numpy()