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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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52
tests/models/yolos/test_image_processing_yolos.py
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@@ -185,8 +185,8 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
encoded_images_with_method = image_processing_1.pad(image_inputs, return_tensors="pt")
encoded_images = image_processing_2(image_inputs, return_tensors="pt")
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
torch.testing.assert_close(
encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], rtol=1e-4, atol=1e-4
)
@parameterized.expand(
@@ -234,31 +234,31 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify area
expected_area = torch.tensor([5832.7256, 11144.6689, 484763.2500, 829269.8125, 146579.4531, 164177.6250])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
# verify class_labels
expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1056])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
@slow
def test_call_pytorch_with_coco_panoptic_annotations(self):
@@ -280,34 +280,34 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify area
expected_area = torch.tensor([146591.5000, 163974.2500, 480092.2500, 11187.0000, 5824.5000, 7562.5000])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
# verify class_labels
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify masks
expected_masks_sum = 815161
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1056])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
# Output size is slight different from DETR as yolos takes mod of 16
@slow
@@ -373,8 +373,8 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
[0.5845, 0.4115, 0.3462, 0.7161],
]
)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3, atol=1e-3)
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1056]))
@@ -425,8 +425,8 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1, atol=1)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1, atol=1)
# Output size is slight different from DETR as yolos takes mod of 16
def test_batched_coco_panoptic_annotations(self):
@@ -495,8 +495,8 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
[0.3026, 0.2994, 0.6051, 0.5987],
]
)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3, atol=1e-3)
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1056]))
@@ -548,8 +548,8 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->Yolos
def test_max_width_max_height_resizing_and_pad_strategy(self):

8
tests/models/yolos/test_modeling_yolos.py
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@@ -361,8 +361,8 @@ class YolosModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.2536, 0.5449, 0.4643], [0.2037, 0.7735, 0.3672], [0.7692, 0.4056, 0.4549]], device=torch_device
)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
# verify postprocessing
results = image_processor.post_process_object_detection(
@@ -373,6 +373,6 @@ class YolosModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor([331.8438, 80.5440, 369.9546, 188.0579]).to(torch_device)
self.assertEqual(len(results["scores"]), 5)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-4, atol=1e-4)
self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes)