Fixed error condition in RandomCrop (#6548)

Author: vfdev-5

test/test_transforms.py

@@ -1670,7 +1670,7 @@ def test_random_crop():
     assert result.size(1) == height + 1
     assert result.size(2) == width + 1
 
-    t = transforms.RandomCrop(48)
+    t = transforms.RandomCrop(33)
     img = torch.ones(3, 32, 32)
     with pytest.raises(ValueError, match=r"Required crop size .+ is larger than input image size .+"):
         t(img)

torchvision/prototype/transforms/_geometry.py

@@ -443,7 +443,7 @@ def _get_params(self, sample: Any) -> Dict[str, Any]:
             if height < output_height:
                 height += 2 * (output_height - height)
 
-        if height + 1 < output_height or width + 1 < output_width:
+        if height < output_height or width < output_width:
             raise ValueError(
                 f"Required crop size {(output_height, output_width)} is larger then input image size {(height, width)}"
             )

torchvision/transforms/transforms.py

@@ -628,7 +628,7 @@ def get_params(img: Tensor, output_size: Tuple[int, int]) -> Tuple[int, int, int
         _, h, w = F.get_dimensions(img)
         th, tw = output_size
 
-        if h + 1 < th or w + 1 < tw:
+        if h < th or w < tw:
             raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}")
 
         if w == tw and h == th: