Update YOLOS slow test values (#28187)

Update test values

tests/models/yolos/test_image_processing_yolos.py

@@ -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))

tests/models/yolos/test_modeling_yolos.py

@@ -352,11 +352,11 @@ class YolosModelIntegrationTest(unittest.TestCase):
         self.assertEqual(outputs.logits.shape, expected_shape)
 
         expected_slice_logits = torch.tensor(
-            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]],
+            [[-23.7219, -10.3165, -14.9083], [-41.5429, -15.2403, -24.1478], [-29.3909, -12.7173, -19.4650]],
             device=torch_device,
         )
         expected_slice_boxes = torch.tensor(
-            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]], device=torch_device
+            [[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))
@@ -365,9 +365,9 @@ class YolosModelIntegrationTest(unittest.TestCase):
         results = image_processor.post_process_object_detection(
             outputs, threshold=0.3, target_sizes=[image.size[::-1]]
         )[0]
-        expected_scores = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(torch_device)
+        expected_scores = torch.tensor([0.9991, 0.9801, 0.9978, 0.9875, 0.9848]).to(torch_device)
         expected_labels = [75, 75, 17, 63, 17]
-        expected_slice_boxes = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(torch_device)
+        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))