Adding FixedSizeCrop transform (#5607)

references/detection/transforms.py

@@ -326,3 +326,78 @@ class ScaleJitter(nn.Module):
                 )
 
         return image, target
+
+
+class FixedSizeCrop(nn.Module):
+    def __init__(self, size, fill=0, padding_mode="constant"):
+        super().__init__()
+        size = tuple(T._setup_size(size, error_msg="Please provide only two dimensions (h, w) for size."))
+        self.crop_height = size[0]
+        self.crop_width = size[1]
+        self.fill = fill  # TODO: Fill is currently respected only on PIL. Apply tensor patch.
+        self.padding_mode = padding_mode
+
+    def _pad(self, img, target, padding):
+        # Taken from the functional_tensor.py pad
+        if isinstance(padding, int):
+            pad_left = pad_right = pad_top = pad_bottom = padding
+        elif len(padding) == 1:
+            pad_left = pad_right = pad_top = pad_bottom = padding[0]
+        elif len(padding) == 2:
+            pad_left = pad_right = padding[0]
+            pad_top = pad_bottom = padding[1]
+        else:
+            pad_left = padding[0]
+            pad_top = padding[1]
+            pad_right = padding[2]
+            pad_bottom = padding[3]
+
+        padding = [pad_left, pad_top, pad_right, pad_bottom]
+        img = F.pad(img, padding, self.fill, self.padding_mode)
+        if target is not None:
+            target["boxes"][:, 0::2] += pad_left
+            target["boxes"][:, 1::2] += pad_top
+            if "masks" in target:
+                target["masks"] = F.pad(target["masks"], padding, 0, "constant")
+
+        return img, target
+
+    def _crop(self, img, target, top, left, height, width):
+        img = F.crop(img, top, left, height, width)
+        if target is not None:
+            boxes = target["boxes"]
+            boxes[:, 0::2] -= left
+            boxes[:, 1::2] -= top
+            boxes[:, 0::2].clamp_(min=0, max=width)
+            boxes[:, 1::2].clamp_(min=0, max=height)
+
+            is_valid = (boxes[:, 0] < boxes[:, 2]) & (boxes[:, 1] < boxes[:, 3])
+
+            target["boxes"] = boxes[is_valid]
+            target["labels"] = target["labels"][is_valid]
+            if "masks" in target:
+                target["masks"] = F.crop(target["masks"][is_valid], top, left, height, width)
+
+        return img, target
+
+    def forward(self, img, target=None):
+        _, height, width = F.get_dimensions(img)
+        new_height = min(height, self.crop_height)
+        new_width = min(width, self.crop_width)
+
+        if new_height != height or new_width != width:
+            offset_height = max(height - self.crop_height, 0)
+            offset_width = max(width - self.crop_width, 0)
+
+            r = torch.rand(1)
+            top = int(offset_height * r)
+            left = int(offset_width * r)
+
+            img, target = self._crop(img, target, top, left, new_height, new_width)
+
+        pad_bottom = max(self.crop_height - new_height, 0)
+        pad_right = max(self.crop_width - new_width, 0)
+        if pad_bottom != 0 or pad_right != 0:
+            img, target = self._pad(img, target, [0, 0, pad_right, pad_bottom])
+
+        return img, target