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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/maskformer/test_image_processing_maskformer.py
View File

@@ -293,8 +293,8 @@ class MaskFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor([30, 55])))
self.assertTrue(torch.allclose(inputs["class_labels"][1], torch.tensor([4, 4, 23, 55])))
torch.testing.assert_close(inputs["class_labels"][0], torch.tensor([30, 55]))
torch.testing.assert_close(inputs["class_labels"][1], torch.tensor([4, 4, 23, 55]))
# verify the mask labels
self.assertEqual(len(inputs["mask_labels"]), 2)
@@ -335,8 +335,8 @@ class MaskFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor([2, 4, 60])))
self.assertTrue(torch.allclose(inputs["class_labels"][1], torch.tensor([0, 3, 7, 8, 15, 28, 30, 143])))
torch.testing.assert_close(inputs["class_labels"][0], torch.tensor([2, 4, 60]))
torch.testing.assert_close(inputs["class_labels"][1], torch.tensor([0, 3, 7, 8, 15, 28, 30, 143]))
# verify the mask labels
self.assertEqual(len(inputs["mask_labels"]), 2)
@@ -395,9 +395,9 @@ class MaskFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # fmt: skip
self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor(expected_class_labels)))
torch.testing.assert_close(inputs["class_labels"][0], torch.tensor(expected_class_labels))
expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # fmt: skip
self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels))
torch.testing.assert_close(inputs["class_labels"][1], expected_class_labels)
# verify the mask labels
self.assertEqual(len(inputs["mask_labels"]), 2)

12
tests/models/maskformer/test_modeling_maskformer.py
View File

@@ -567,7 +567,7 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
[-1.5795398, -1.9269832, -2.093942],
]
expected_slice = torch.tensor(expected_slice).to(torch_device)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(masks_queries_logits[0, 0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
# class_queries_logits
class_queries_logits = outputs.class_queries_logits
self.assertEqual(
@@ -580,7 +580,9 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
[1.0766e-04, -7.7630e00, -5.1263e00],
]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(
outputs.class_queries_logits[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE
)
def test_inference_instance_segmentation_head_resnet_backbone(self):
model = (
@@ -607,7 +609,7 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
)
expected_slice = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
expected_slice = torch.tensor(expected_slice).to(torch_device)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(masks_queries_logits[0, 0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
# class_queries_logits
class_queries_logits = outputs.class_queries_logits
self.assertEqual(
@@ -616,7 +618,9 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor(
[[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(
outputs.class_queries_logits[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE
)
@require_torch_accelerator
@require_torch_fp16