port Pad to prototype transforms

test/test_prototype_transforms.py

@@ -71,6 +71,7 @@ class TestSmoke:
         transforms.CenterCrop([16, 16]),
         transforms.ConvertImageDtype(),
         transforms.RandomHorizontalFlip(),
+        transforms.Pad(5),
     )
     def test_common(self, transform, input):
         transform(input)

torchvision/prototype/transforms/__init__.py

@@ -15,6 +15,7 @@
     TenCrop,
     BatchMultiCrop,
     RandomHorizontalFlip,
+    Pad,
     RandomZoomOut,
 )
 from ._meta import ConvertBoundingBoxFormat, ConvertImageDtype, ConvertImageColorSpace

torchvision/prototype/transforms/_geometry.py

@@ -1,5 +1,6 @@
 import collections.abc
 import math
+import numbers
 import warnings
 from typing import Any, Dict, List, Union, Sequence, Tuple, cast
 
@@ -9,6 +10,7 @@
 from torchvision.prototype.transforms import Transform, InterpolationMode, functional as F
 from torchvision.transforms.functional import pil_to_tensor
 from torchvision.transforms.transforms import _setup_size, _interpolation_modes_from_int
+from typing_extensions import Literal
 
 from ._utils import query_image, get_image_dimensions, has_any, is_simple_tensor
 
@@ -272,42 +274,31 @@ def apply_recursively(obj: Any) -> Any:
         return apply_recursively(inputs if len(inputs) > 1 else inputs[0])
 
 
-class RandomZoomOut(Transform):
+class Pad(Transform):
     def __init__(
-        self, fill: Union[float, Sequence[float]] = 0.0, side_range: Tuple[float, float] = (1.0, 4.0), p: float = 0.5
+        self,
+        padding: Union[int, Sequence[int]],
+        fill: Union[float, Sequence[float]] = 0.0,
+        padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
     ) -> None:
         super().__init__()
+        if not isinstance(padding, (numbers.Number, tuple, list)):
+            raise TypeError("Got inappropriate padding arg")
 
-        if fill is None:
-            fill = 0.0
-        self.fill = fill
-
-        self.side_range = side_range
-        if side_range[0] < 1.0 or side_range[0] > side_range[1]:
-            raise ValueError(f"Invalid canvas side range provided {side_range}.")
-
-        self.p = p
-
-    def _get_params(self, sample: Any) -> Dict[str, Any]:
-        image = query_image(sample)
-        orig_c, orig_h, orig_w = get_image_dimensions(image)
-
-        r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0])
-        canvas_width = int(orig_w * r)
-        canvas_height = int(orig_h * r)
+        if not isinstance(fill, (numbers.Number, str, tuple, list)):
+            raise TypeError("Got inappropriate fill arg")
 
-        r = torch.rand(2)
-        left = int((canvas_width - orig_w) * r[0])
-        top = int((canvas_height - orig_h) * r[1])
-        right = canvas_width - (left + orig_w)
-        bottom = canvas_height - (top + orig_h)
-        padding = [left, top, right, bottom]
+        if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
+            raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
 
-        fill = self.fill
-        if not isinstance(fill, collections.abc.Sequence):
-            fill = [fill] * orig_c
+        if isinstance(padding, Sequence) and len(padding) not in [1, 2, 4]:
+            raise ValueError(
+                f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple"
+            )
 
-        return dict(padding=padding, fill=fill)
+        self.padding = padding
+        self.fill = fill
+        self.padding_mode = padding_mode
 
     def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
         if isinstance(input, features.Image) or is_simple_tensor(input):
@@ -349,6 +340,48 @@ def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
         else:
             return input
 
+
+class RandomZoomOut(Transform):
+    def __init__(
+        self, fill: Union[float, Sequence[float]] = 0.0, side_range: Tuple[float, float] = (1.0, 4.0), p: float = 0.5
+    ) -> None:
+        super().__init__()
+
+        if fill is None:
+            fill = 0.0
+        self.fill = fill
+
+        self.side_range = side_range
+        if side_range[0] < 1.0 or side_range[0] > side_range[1]:
+            raise ValueError(f"Invalid canvas side range provided {side_range}.")
+
+        self.p = p
+
+    def _get_params(self, sample: Any) -> Dict[str, Any]:
+        image = query_image(sample)
+        orig_c, orig_h, orig_w = get_image_dimensions(image)
+
+        r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0])
+        canvas_width = int(orig_w * r)
+        canvas_height = int(orig_h * r)
+
+        r = torch.rand(2)
+        left = int((canvas_width - orig_w) * r[0])
+        top = int((canvas_height - orig_h) * r[1])
+        right = canvas_width - (left + orig_w)
+        bottom = canvas_height - (top + orig_h)
+        padding = [left, top, right, bottom]
+
+        fill = self.fill
+        if not isinstance(fill, collections.abc.Sequence):
+            fill = [fill] * orig_c
+
+        return dict(padding=padding, fill=fill)
+
+    def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
+        transform = Pad(**params, padding_mode="constant")
+        return transform(input)
+
     def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
         if torch.rand(1) >= self.p: