[fbsync] [proto] Argument fill can accept dict of base types (#6586)

Summary: Co-authored-by: Vasilis Vryniotis <[email protected]>

Reviewed By: jdsgomes

Differential Revision: D39543288

fbshipit-source-id: 5763f1d98152303251ff3d3dd60eac0ebaeada2a

Author: YosuaMichael

test/test_prototype_transforms.py

@@ -378,6 +378,28 @@ def test__transform(self, padding, fill, padding_mode, mocker):
 
         fn.assert_called_once_with(inpt, padding=padding, fill=fill, padding_mode=padding_mode)
 
+    @pytest.mark.parametrize("fill", [12, {features.Image: 12, features.Mask: 34}])
+    def test__transform_image_mask(self, fill, mocker):
+        transform = transforms.Pad(1, fill=fill, padding_mode="constant")
+
+        fn = mocker.patch("torchvision.prototype.transforms.functional.pad")
+        image = features.Image(torch.rand(3, 32, 32))
+        mask = features.Mask(torch.randint(0, 5, size=(32, 32)))
+        inpt = [image, mask]
+        _ = transform(inpt)
+
+        if isinstance(fill, int):
+            calls = [
+                mocker.call(image, padding=1, fill=fill, padding_mode="constant"),
+                mocker.call(mask, padding=1, fill=0, padding_mode="constant"),
+            ]
+        else:
+            calls = [
+                mocker.call(image, padding=1, fill=fill[type(image)], padding_mode="constant"),
+                mocker.call(mask, padding=1, fill=fill[type(mask)], padding_mode="constant"),
+            ]
+        fn.assert_has_calls(calls)
+
 
 class TestRandomZoomOut:
     def test_assertions(self):
@@ -400,7 +422,6 @@ def test__get_params(self, fill, side_range, mocker):
 
         params = transform._get_params(image)
 
-        assert params["fill"] == fill
         assert len(params["padding"]) == 4
         assert 0 <= params["padding"][0] <= (side_range[1] - 1) * w
         assert 0 <= params["padding"][1] <= (side_range[1] - 1) * h
@@ -426,7 +447,34 @@ def test__transform(self, fill, side_range, mocker):
         torch.rand(1)  # random apply changes random state
         params = transform._get_params(inpt)
 
-        fn.assert_called_once_with(inpt, **params)
+        fn.assert_called_once_with(inpt, **params, fill=fill)
+
+    @pytest.mark.parametrize("fill", [12, {features.Image: 12, features.Mask: 34}])
+    def test__transform_image_mask(self, fill, mocker):
+        transform = transforms.RandomZoomOut(fill=fill, p=1.0)
+
+        fn = mocker.patch("torchvision.prototype.transforms.functional.pad")
+        image = features.Image(torch.rand(3, 32, 32))
+        mask = features.Mask(torch.randint(0, 5, size=(32, 32)))
+        inpt = [image, mask]
+
+        torch.manual_seed(12)
+        _ = transform(inpt)
+        torch.manual_seed(12)
+        torch.rand(1)  # random apply changes random state
+        params = transform._get_params(inpt)
+
+        if isinstance(fill, int):
+            calls = [
+                mocker.call(image, **params, fill=fill),
+                mocker.call(mask, **params, fill=0),
+            ]
+        else:
+            calls = [
+                mocker.call(image, **params, fill=fill[type(image)]),
+                mocker.call(mask, **params, fill=fill[type(mask)]),
+            ]
+        fn.assert_has_calls(calls)
 
 
 class TestRandomRotation:

torchvision/prototype/features/_mask.py

@@ -58,7 +58,14 @@ def pad(
         if not isinstance(padding, int):
             padding = list(padding)
 
-        output = self._F.pad_mask(self, padding, padding_mode=padding_mode)
+        if isinstance(fill, (int, float)) or fill is None:
+            if fill is None:
+                fill = 0
+            output = self._F.pad_mask(self, padding, padding_mode=padding_mode, fill=fill)
+        else:
+            # Let's raise an error for vector fill on masks
+            raise ValueError("Non-scalar fill value is not supported")
+
         return Mask.new_like(self, output)
 
     def rotate(

torchvision/prototype/transforms/_geometry.py

@@ -1,7 +1,8 @@
 import math
 import numbers
 import warnings
-from typing import Any, cast, Dict, List, Optional, Sequence, Tuple, Union
+from collections import defaultdict
+from typing import Any, cast, Dict, List, Optional, Sequence, Tuple, Type, Union
 
 import PIL.Image
 import torch
@@ -16,6 +17,7 @@
 
 
 DType = Union[torch.Tensor, PIL.Image.Image, features._Feature]
+FillType = Union[int, float, Sequence[int], Sequence[float]]
 
 
 class RandomHorizontalFlip(_RandomApplyTransform):
@@ -196,9 +198,21 @@ def forward(self, *inputs: Any) -> Any:
         return super().forward(*inputs)
 
 
-def _check_fill_arg(fill: Union[int, float, Sequence[int], Sequence[float]]) -> None:
-    if not isinstance(fill, (numbers.Number, tuple, list)):
-        raise TypeError("Got inappropriate fill arg")
+def _check_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> None:
+    if isinstance(fill, dict):
+        for key, value in fill.items():
+            # Check key for type
+            _check_fill_arg(value)
+    else:
+        if not isinstance(fill, (numbers.Number, tuple, list)):
+            raise TypeError("Got inappropriate fill arg")
+
+
+def _setup_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> Dict[Type, FillType]:
+    if isinstance(fill, dict):
+        return fill
+    else:
+        return defaultdict(lambda: fill, {features.Mask: 0})  # type: ignore[arg-type, return-value]
 
 
 def _check_padding_arg(padding: Union[int, Sequence[int]]) -> None:
@@ -220,7 +234,7 @@ class Pad(Transform):
     def __init__(
         self,
         padding: Union[int, Sequence[int]],
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
     ) -> None:
         super().__init__()
@@ -230,24 +244,25 @@ def __init__(
         _check_padding_mode_arg(padding_mode)
 
         self.padding = padding
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
         self.padding_mode = padding_mode
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        return F.pad(inpt, padding=self.padding, fill=self.fill, padding_mode=self.padding_mode)
+        fill = self.fill[type(inpt)]
+        return F.pad(inpt, padding=self.padding, fill=fill, padding_mode=self.padding_mode)
 
 
 class RandomZoomOut(_RandomApplyTransform):
     def __init__(
         self,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
         side_range: Sequence[float] = (1.0, 4.0),
         p: float = 0.5,
     ) -> None:
         super().__init__(p=p)
 
         _check_fill_arg(fill)
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
 
         _check_sequence_input(side_range, "side_range", req_sizes=(2,))
 
@@ -256,7 +271,7 @@ def __init__(
             raise ValueError(f"Invalid canvas side range provided {side_range}.")
 
     def _get_params(self, sample: Any) -> Dict[str, Any]:
-        orig_c, orig_h, orig_w = query_chw(sample)
+        _, orig_h, orig_w = query_chw(sample)
 
         r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0])
         canvas_width = int(orig_w * r)
@@ -269,10 +284,11 @@ def _get_params(self, sample: Any) -> Dict[str, Any]:
         bottom = canvas_height - (top + orig_h)
         padding = [left, top, right, bottom]
 
-        return dict(padding=padding, fill=self.fill)
+        return dict(padding=padding)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        return F.pad(inpt, **params)
+        fill = self.fill[type(inpt)]
+        return F.pad(inpt, **params, fill=fill)
 
 
 class RandomRotation(Transform):

torchvision/prototype/transforms/functional/_geometry.py

@@ -635,14 +635,19 @@ def _pad_with_vector_fill(
     return output
 
 
-def pad_mask(mask: torch.Tensor, padding: Union[int, List[int]], padding_mode: str = "constant") -> torch.Tensor:
+def pad_mask(
+    mask: torch.Tensor,
+    padding: Union[int, List[int]],
+    padding_mode: str = "constant",
+    fill: Optional[Union[int, float]] = 0,
+) -> torch.Tensor:
     if mask.ndim < 3:
         mask = mask.unsqueeze(0)
         needs_squeeze = True
     else:
         needs_squeeze = False
 
-    output = pad_image_tensor(img=mask, padding=padding, fill=0, padding_mode=padding_mode)
+    output = pad_image_tensor(img=mask, padding=padding, fill=fill, padding_mode=padding_mode)
 
     if needs_squeeze:
         output = output.squeeze(0)