Merge branch 'main' into port/center-crop

test/test_transforms_v2_refactored.py

@@ -312,11 +312,12 @@ def adapt_fill(value, *, dtype):
         return value
 
     max_value = get_max_value(dtype)
+    value_type = float if dtype.is_floating_point else int
 
     if isinstance(value, (int, float)):
-        return type(value)(value * max_value)
+        return value_type(value * max_value)
     elif isinstance(value, (list, tuple)):
-        return type(value)(type(v)(v * max_value) for v in value)
+        return type(value)(value_type(v * max_value) for v in value)
     else:
         raise ValueError(f"fill should be an int or float, or a list or tuple of the former, but got '{value}'.")
 
@@ -417,6 +418,10 @@ def affine_bounding_boxes(bounding_boxes):
     )
 
 
+# turns all warnings into errors for this module
+pytestmark = pytest.mark.filterwarnings("error")
+
+
 class TestResize:
     INPUT_SIZE = (17, 11)
     OUTPUT_SIZES = [17, [17], (17,), [12, 13], (12, 13)]
@@ -2577,15 +2582,19 @@ def test_functional_image_correctness(self, kwargs):
     def test_transform(self, param, value, make_input):
         input = make_input(self.INPUT_SIZE)
 
-        kwargs = {param: value}
         if param == "fill":
-            # 1. size is required
-            # 2. the fill parameter only has an affect if we need padding
-            kwargs["size"] = [s + 4 for s in self.INPUT_SIZE]
-
             if isinstance(input, tv_tensors.Mask) and isinstance(value, (tuple, list)):
                 pytest.skip("F.pad_mask doesn't support non-scalar fill.")
 
+            kwargs = dict(
+                # 1. size is required
+                # 2. the fill parameter only has an affect if we need padding
+                size=[s + 4 for s in self.INPUT_SIZE],
+                fill=adapt_fill(value, dtype=input.dtype if isinstance(input, torch.Tensor) else torch.uint8),
+            )
+        else:
+            kwargs = {param: value}
+
         check_transform(
             transforms.RandomCrop(**kwargs, pad_if_needed=True),
             input,
@@ -3478,6 +3487,8 @@ def test_transform_errors(self):
     def test_image_correctness(self, padding, padding_mode, fill, fn):
         image = make_image(dtype=torch.uint8, device="cpu")
 
+        fill = adapt_fill(fill, dtype=torch.uint8)
+
         actual = fn(image, padding=padding, padding_mode=padding_mode, fill=fill)
         expected = F.to_image(F.pad(F.to_pil_image(image), padding=padding, padding_mode=padding_mode, fill=fill))
 

torchvision/models/detection/transform.py

@@ -31,8 +31,10 @@ def _resize_image_and_masks(
 ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
     if torchvision._is_tracing():
         im_shape = _get_shape_onnx(image)
-    else:
+    elif torch.jit.is_scripting():
         im_shape = torch.tensor(image.shape[-2:])
+    else:
+        im_shape = image.shape[-2:]
 
     size: Optional[List[int]] = None
     scale_factor: Optional[float] = None

torchvision/transforms/v2/functional/_geometry.py

@@ -1235,7 +1235,11 @@ def _pad_with_vector_fill(
 
     output = _pad_with_scalar_fill(image, torch_padding, fill=0, padding_mode="constant")
     left, right, top, bottom = torch_padding
-    fill = torch.tensor(fill, dtype=image.dtype, device=image.device).reshape(-1, 1, 1)
+
+    # We are creating the tensor in the autodetected dtype first and convert to the right one after to avoid an implicit
+    # float -> int conversion. That happens for example for the valid input of a uint8 image with floating point fill
+    # value.
+    fill = torch.tensor(fill, device=image.device).to(dtype=image.dtype).reshape(-1, 1, 1)
 
     if top > 0:
         output[..., :top, :] = fill