fix requires_grad passthrough

test/test_prototype_datapoints.py

@@ -3,6 +3,25 @@
 from torchvision.prototype import datapoints
 
 
+@pytest.mark.parametrize(
+    ("data", "input_requires_grad", "expected_requires_grad"),
+    [
+        ([0.0], None, False),
+        ([0.0], False, False),
+        ([0.0], True, True),
+        (torch.tensor([0.0], requires_grad=False), None, False),
+        (torch.tensor([0.0], requires_grad=False), False, False),
+        (torch.tensor([0.0], requires_grad=False), True, True),
+        (torch.tensor([0.0], requires_grad=True), None, True),
+        (torch.tensor([0.0], requires_grad=True), False, False),
+        (torch.tensor([0.0], requires_grad=True), True, True),
+    ],
+)
+def test_new_requires_grad(data, input_requires_grad, expected_requires_grad):
+    datapoint = datapoints.Label(data, requires_grad=input_requires_grad)
+    assert datapoint.requires_grad is expected_requires_grad
+
+
 def test_isinstance():
     assert isinstance(
         datapoints.Label([0, 1, 0], categories=["foo", "bar"]),

torchvision/prototype/datapoints/_bounding_box.py

@@ -34,7 +34,7 @@ def __new__(
         spatial_size: Tuple[int, int],
         dtype: Optional[torch.dtype] = None,
         device: Optional[Union[torch.device, str, int]] = None,
-        requires_grad: bool = False,
+        requires_grad: Optional[bool] = None,
     ) -> BoundingBox:
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
 

torchvision/prototype/datapoints/_datapoint.py

@@ -23,8 +23,10 @@ def _to_tensor(
         data: Any,
         dtype: Optional[torch.dtype] = None,
         device: Optional[Union[torch.device, str, int]] = None,
-        requires_grad: bool = False,
+        requires_grad: Optional[bool] = None,
     ) -> torch.Tensor:
+        if requires_grad is None:
+            requires_grad = data.requires_grad if isinstance(data, torch.Tensor) else False
         return torch.as_tensor(data, dtype=dtype, device=device).requires_grad_(requires_grad)
 
     # FIXME: this is just here for BC with the prototype datasets. Some datasets use the Datapoint directly to have a
@@ -36,7 +38,7 @@ def __new__(
         data: Any,
         dtype: Optional[torch.dtype] = None,
         device: Optional[Union[torch.device, str, int]] = None,
-        requires_grad: bool = False,
+        requires_grad: Optional[bool] = None,
     ) -> Datapoint:
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         return tensor.as_subclass(Datapoint)

torchvision/prototype/datapoints/_image.py

@@ -21,7 +21,7 @@ def __new__(
         *,
         dtype: Optional[torch.dtype] = None,
         device: Optional[Union[torch.device, str, int]] = None,
-        requires_grad: bool = False,
+        requires_grad: Optional[bool] = None,
     ) -> Image:
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         if tensor.ndim < 2:

torchvision/prototype/datapoints/_label.py

@@ -27,7 +27,7 @@ def __new__(
         categories: Optional[Sequence[str]] = None,
         dtype: Optional[torch.dtype] = None,
         device: Optional[Union[torch.device, str, int]] = None,
-        requires_grad: bool = False,
+        requires_grad: Optional[bool] = None,
     ) -> L:
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         return cls._wrap(tensor, categories=categories)

torchvision/prototype/datapoints/_mask.py

@@ -19,7 +19,7 @@ def __new__(
         *,
         dtype: Optional[torch.dtype] = None,
         device: Optional[Union[torch.device, str, int]] = None,
-        requires_grad: bool = False,
+        requires_grad: Optional[bool] = None,
     ) -> Mask:
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         return cls._wrap(tensor)

torchvision/prototype/datapoints/_video.py

@@ -20,7 +20,7 @@ def __new__(
         *,
         dtype: Optional[torch.dtype] = None,
         device: Optional[Union[torch.device, str, int]] = None,
-        requires_grad: bool = False,
+        requires_grad: Optional[bool] = None,
     ) -> Video:
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         if data.ndim < 4: