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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/videomae/test_modeling_videomae.py
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@@ -385,7 +385,7 @@ class VideoMAEModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor([0.3669, -0.0688, -0.2421]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_for_pretraining(self):
@@ -409,11 +409,11 @@ class VideoMAEModelIntegrationTest(unittest.TestCase):
[[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]], device=torch_device
)
self.assertEqual(outputs.logits.shape, expected_shape)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify the loss (`config.norm_pix_loss` = `True`)
expected_loss = torch.tensor([0.5142], device=torch_device)
self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-4))
torch.testing.assert_close(outputs.loss, expected_loss, rtol=1e-4, atol=1e-4)
# verify the loss (`config.norm_pix_loss` = `False`)
model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short", norm_pix_loss=False).to(
@@ -424,4 +424,4 @@ class VideoMAEModelIntegrationTest(unittest.TestCase):
outputs = model(**inputs)
expected_loss = torch.tensor(torch.tensor([0.6469]), device=torch_device)
self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-4))
torch.testing.assert_close(outputs.loss, expected_loss, rtol=1e-4, atol=1e-4)