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Update YOLOS slow test values (#28187)

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Update test values
This commit is contained in:
amyeroberts
2023-12-21 18:17:07 +00:00
committed by GitHub
parent cd1350ce9b
commit 3657748b4d
2 changed files with 11 additions and 11 deletions
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14
tests/models/yolos/test_image_processing_yolos.py
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@@ -209,14 +209,14 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
encoding = image_processing(images=image, annotations=target, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
expected_shape = torch.Size([1, 3, 800, 1056])
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))
# verify area
expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
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))
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
@@ -236,7 +236,7 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
# verify size
expected_size = torch.tensor([800, 1066])
expected_size = torch.tensor([800, 1056])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
@slow
@@ -255,14 +255,14 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
expected_shape = torch.Size([1, 3, 800, 1056])
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))
# verify area
expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
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))
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
@@ -279,11 +279,11 @@ class YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMix
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
# verify masks
expected_masks_sum = 822873
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))
# verify size
expected_size = torch.tensor([800, 1066])
expected_size = torch.tensor([800, 1056])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))