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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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44
tests/models/rt_detr/test_image_processing_rt_detr.py
View File

@@ -171,31 +171,31 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.5490, 0.5647, 0.5725])
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([2827.9883, 5403.4761, 235036.7344, 402070.2188, 71068.8281, 79601.2812])
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([640, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
@slow
def test_image_processor_outputs(self):
@@ -211,7 +211,7 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
# verify pixel values: output values
expected_slice = torch.tensor([0.5490196347236633, 0.5647059082984924, 0.572549045085907])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-5))
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-5, atol=1e-5)
def test_multiple_images_processor_outputs(self):
images_urls = [
@@ -255,7 +255,7 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
[0.19607844948768616, 0.21176472306251526, 0.3607843220233917],
]
)
self.assertTrue(torch.allclose(encoding["pixel_values"][:, 1, 0, :3], expected_slices, atol=1e-5))
torch.testing.assert_close(encoding["pixel_values"][:, 1, 0, :3], expected_slices, rtol=1e-5, atol=1e-5)
@slow
def test_batched_coco_detection_annotations(self):
@@ -321,8 +321,8 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
[0.7715, 0.4115, 0.4570, 0.7161],
]
)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)
# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
# format and not in the range [0, 1]
@@ -369,8 +369,8 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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)
@slow
@require_torch_gpu
@@ -400,7 +400,7 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
)
)
# verify area
self.assertTrue(torch.allclose(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu")))
torch.testing.assert_close(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu"))
# verify boxes
self.assertEqual(encoding_cpu["labels"][0]["boxes"].shape, encoding_gpu["labels"][0]["boxes"].shape)
self.assertTrue(
@@ -409,12 +409,12 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
)
)
# verify image_id
self.assertTrue(
torch.allclose(encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu"))
torch.testing.assert_close(
encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu")
)
# verify is_crowd
self.assertTrue(
torch.allclose(encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu"))
torch.testing.assert_close(
encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu")
)
# verify class_labels
self.assertTrue(
@@ -423,8 +423,8 @@ class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
)
)
# verify orig_size
self.assertTrue(
torch.allclose(encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu"))
torch.testing.assert_close(
encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu")
)
# verify size
self.assertTrue(torch.allclose(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu")))
torch.testing.assert_close(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu"))

8
tests/models/rt_detr/test_modeling_rt_detr.py
View File

@@ -745,11 +745,11 @@ class RTDetrModelIntegrationTest(unittest.TestCase):
]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-4, atol=1e-4)
expected_shape_boxes = torch.Size((1, 300, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-4, atol=1e-4)
# verify postprocessing
results = image_processor.post_process_object_detection(
@@ -769,6 +769,6 @@ class RTDetrModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(results["scores"][:4], expected_scores, atol=1e-4))
torch.testing.assert_close(results["scores"][:4], expected_scores, rtol=1e-4, atol=1e-4)
self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
self.assertTrue(torch.allclose(results["boxes"][:4], expected_slice_boxes, atol=1e-4))
torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes, rtol=1e-4, atol=1e-4)