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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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10
tests/models/unispeech_sat/test_modeling_unispeech_sat.py
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@@ -845,7 +845,9 @@ class UniSpeechSatModelIntegrationTest(unittest.TestCase):
)
# fmt: on
self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3))
torch.testing.assert_close(
outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, rtol=1e-3, atol=1e-3
)
def test_inference_encoder_large(self):
model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large")
@@ -871,7 +873,9 @@ class UniSpeechSatModelIntegrationTest(unittest.TestCase):
)
# fmt: on
self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3))
torch.testing.assert_close(
outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, rtol=1e-3, atol=1e-3
)
def test_inference_diarization(self):
model = UniSpeechSatForAudioFrameClassification.from_pretrained("microsoft/unispeech-sat-base-plus-sd").to(
@@ -900,7 +904,7 @@ class UniSpeechSatModelIntegrationTest(unittest.TestCase):
)
self.assertEqual(labels[0, :, 0].sum(), 270)
self.assertEqual(labels[0, :, 1].sum(), 647)
self.assertTrue(torch.allclose(outputs.logits[:, :4], expected_logits, atol=1e-2))
torch.testing.assert_close(outputs.logits[:, :4], expected_logits, rtol=1e-2, atol=1e-2)
def test_inference_speaker_verification(self):
model = UniSpeechSatForXVector.from_pretrained("microsoft/unispeech-sat-base-plus-sv").to(torch_device)