Adding RandomShortestSize transform

references/detection/transforms.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple, Dict, Optional
+from typing import List, Tuple, Dict, Optional, Union
 
 import torch
 import torchvision
@@ -401,3 +401,39 @@ def forward(self, img, target=None):
             img, target = self._pad(img, target, [0, 0, pad_right, pad_bottom])
 
         return img, target
+
+
+class RandomShortestSize(nn.Module):
+    def __init__(
+        self,
+        min_size: Union[List[int], Tuple[int], int],
+        max_size: int,
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+    ):
+        super().__init__()
+        self.min_size = [min_size] if isinstance(min_size, int) else list(min_size)
+        self.max_size = max_size
+        self.interpolation = interpolation
+
+    def forward(
+        self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
+    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
+        _, orig_height, orig_width = F.get_dimensions(image)
+
+        min_size = self.min_size[torch.randint(len(self.min_size), (1,)).item()]
+        r = min(min_size / min(orig_height, orig_width), self.max_size / max(orig_height, orig_width))
+
+        new_width = int(orig_width * r)
+        new_height = int(orig_height * r)
+
+        image = F.resize(image, [new_height, new_width], interpolation=self.interpolation)
+
+        if target is not None:
+            target["boxes"][:, 0::2] *= new_width / orig_width
+            target["boxes"][:, 1::2] *= new_height / orig_height
+            if "masks" in target:
+                target["masks"] = F.resize(
+                    target["masks"], [new_height, new_width], interpolation=InterpolationMode.NEAREST
+                )
+
+        return image, target