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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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4
tests/models/oneformer/test_modeling_oneformer.py
View File

@@ -576,7 +576,7 @@ class OneFormerModelIntegrationTest(unittest.TestCase):
)
expected_slice = [[[3.1848, 4.2141, 4.1993], [2.9000, 3.5721, 3.6603], [2.5358, 3.0883, 3.6168]]]
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(
@@ -586,7 +586,7 @@ class OneFormerModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor(
[[3.0668, -1.1833, -5.1103], [3.3440, -3.3620, -5.1101], [2.6017, -4.3613, -4.1444]]
).to(torch_device)
self.assertTrue(torch.allclose(class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(class_queries_logits[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
@require_torch_accelerator
@require_torch_fp16

12
tests/models/oneformer/test_processor_oneformer.py
View File

@@ -504,9 +504,9 @@ class OneFormerProcessingTest(unittest.TestCase):
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
expected_class_labels = torch.tensor([4, 17, 32, 42, 12, 3, 5, 0, 43, 96, 104, 31, 125, 138, 87, 149]) # noqa: E231 # fmt: skip
self.assertTrue(torch.allclose(inputs["class_labels"][0], expected_class_labels))
torch.testing.assert_close(inputs["class_labels"][0], expected_class_labels)
expected_class_labels = torch.tensor([19, 67, 82, 17, 12, 42, 3, 14, 5, 0, 115, 43, 8, 138, 125, 143]) # noqa: E231 # 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 task inputs
self.assertEqual(len(inputs["task_inputs"]), 2)
@@ -592,9 +592,9 @@ class OneFormerProcessingTest(unittest.TestCase):
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
expected_class_labels = torch.tensor([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, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 43, 43, 43, 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], expected_class_labels))
torch.testing.assert_close(inputs["class_labels"][0], expected_class_labels)
expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 12, 12, 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 task inputs
self.assertEqual(len(inputs["task_inputs"]), 2)
@@ -680,9 +680,9 @@ class OneFormerProcessingTest(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], expected_class_labels))
torch.testing.assert_close(inputs["class_labels"][0], 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 task inputs
self.assertEqual(len(inputs["task_inputs"]), 2)