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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/hiera/test_modeling_hiera.py
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@@ -545,7 +545,7 @@ class HieraModelIntegrationTest(unittest.TestCase):
]
).to(torch_device)
self.assertTrue(torch.allclose(inputs.pixel_values[0, :3, :3, :3], expected_pixel_values, atol=1e-4))
torch.testing.assert_close(inputs.pixel_values[0, :3, :3, :3], expected_pixel_values, rtol=1e-4, atol=1e-4)
# forward pass
with torch.no_grad():
@@ -557,7 +557,7 @@ class HieraModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor([[0.8028, 0.2409, -0.2254, -0.3712, -0.2848]]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :5], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.logits[0, :5], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_interpolate_pos_encoding(self):
model = HieraModel.from_pretrained("facebook/hiera-tiny-224-hf").to(torch_device)
@@ -581,7 +581,7 @@ class HieraModelIntegrationTest(unittest.TestCase):
[[1.7853, 0.0690, 0.3177], [2.6853, -0.2334, 0.0889], [1.5445, -0.1515, -0.0300]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_for_pretraining(self):
@@ -619,7 +619,7 @@ class HieraModelIntegrationTest(unittest.TestCase):
]
)
self.assertTrue(torch.allclose(outputs.logits[0, :5, :5], expected_slice.to(torch_device), atol=1e-4))
torch.testing.assert_close(outputs.logits[0, :5, :5], expected_slice.to(torch_device), rtol=1e-4, atol=1e-4)
@require_torch