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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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12
tests/models/vision_encoder_decoder/test_modeling_vision_encoder_decoder.py
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@@ -1142,7 +1142,7 @@ class TrOCRModelIntegrationTest(unittest.TestCase):
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]
).to(torch_device)
self.assertTrue(torch.allclose(logits[0, 0, :10], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, 0, :10], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_printed(self):
@@ -1176,7 +1176,7 @@ class TrOCRModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, 0, :10], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, 0, :10], expected_slice, rtol=1e-4, atol=1e-4)
@require_vision
@@ -1272,7 +1272,7 @@ class DonutModelIntegrationTest(unittest.TestCase):
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([24.3873, -6.4491, 32.5394]).to(torch_device)
self.assertTrue(torch.allclose(logits[0, 0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# step 2: generation
task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
@@ -1336,7 +1336,7 @@ class DonutModelIntegrationTest(unittest.TestCase):
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-27.4344, -3.2686, -19.3524], device=torch_device)
self.assertTrue(torch.allclose(logits[0, 0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# step 2: generation
task_prompt = "<s_cord-v2>"
@@ -1398,7 +1398,7 @@ class DonutModelIntegrationTest(unittest.TestCase):
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-17.6490, -4.8381, -15.7577], device=torch_device)
self.assertTrue(torch.allclose(logits[0, 0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# step 2: generation
task_prompt = "<s_rvlcdip>"
@@ -1475,7 +1475,7 @@ class NougatModelIntegrationTest(unittest.TestCase):
[1.6253, -4.2179, 5.8532, -2.7911, -5.0609, -4.7397, -4.2890, -5.1073, -4.8908, -4.9729]
).to(torch_device)
self.assertTrue(torch.allclose(logits[0, 0, :10], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, 0, :10], expected_slice, rtol=1e-4, atol=1e-4)
def test_generation(self):
processor = self.default_processor