Update RandomResizedCrop for masks

src/otx/core/data/transform_libs/torchvision.py

@@ -630,8 +630,7 @@ class RandomResizedCrop(tvt_v2.Transform, NumpytoTVTensorMixin):
         interpolation (str): Interpolation method, accepted values are
             'nearest', 'bilinear', 'bicubic', 'area', 'lanczos'. Defaults to
             'bilinear'.
-        backend (str): The image resize backend type, accepted values are
-            'cv2' and 'pillow'. Defaults to 'cv2'.
+        transform_mask (bool): Whether to transform masks. Defaults to False.
         is_numpy_to_tvtensor (bool): Whether convert outputs to tensor. Defaults to False.
     """
 
@@ -642,7 +641,7 @@ def __init__(
         aspect_ratio_range: tuple[float, float] = (3.0 / 4.0, 4.0 / 3.0),
         max_attempts: int = 10,
         interpolation: str = "bilinear",
-        backend: str = "cv2",
+        transform_mask: bool = False,
         is_numpy_to_tvtensor: bool = False,
     ) -> None:
         super().__init__()
@@ -675,7 +674,7 @@ def __init__(
         self.aspect_ratio_range = aspect_ratio_range
         self.max_attempts = max_attempts
         self.interpolation = interpolation
-        self.backend = backend
+        self.transform_mask = transform_mask
         self.is_numpy_to_tvtensor = is_numpy_to_tvtensor
 
     @cache_randomness
@@ -817,15 +816,7 @@ def _crop_img(
         return patches
 
     def forward(self, *_inputs: T_OTXDataEntity) -> T_OTXDataEntity | None:
-        """Transform function to randomly resized crop images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Randomly resized cropped results, 'img_shape'
-                key in result dict is updated according to crop size.
-        """
+        """Transform function to randomly resized crop images and masks."""
         inputs = _inputs[0]
         if (img := getattr(inputs, "image", None)) is not None:
             img = to_np_image(img)
@@ -840,26 +831,28 @@ def forward(self, *_inputs: T_OTXDataEntity) -> T_OTXDataEntity | None:
             )
             img = self._crop_img(img, bboxes=bboxes)
             inputs.img_info = _crop_image_info(inputs.img_info, *img.shape[:2])
-
             img = cv2.resize(
                 img,
                 tuple(self.scale[::-1]),
                 dst=None,
                 interpolation=CV2_INTERP_CODES[self.interpolation],
             )
-            if (masks := getattr(inputs, "gt_seg_map", None)) is not None:
-                masks = masks.numpy()
+            inputs.image = img
+            inputs.img_info = _resize_image_info(inputs.img_info, img.shape[:2])
+
+            if self.transform_mask and (masks := getattr(inputs, "masks", None)) is not None:
+                masks = to_np_image(masks)
                 masks = self._crop_img(masks, bboxes=bboxes)
                 masks = cv2.resize(
                     masks,
                     tuple(self.scale[::-1]),
                     dst=None,
                     interpolation=CV2_INTERP_CODES["nearest"],
                 )
-                inputs.gt_seg_map = torch.from_numpy(masks)  # type: ignore[attr-defined]
+                if masks.ndim == 2:
+                    masks = masks[None]
+                inputs.masks = tv_tensors.Mask(masks)  # type: ignore[attr-defined]
 
-            inputs.image = img
-            inputs.img_info = _resize_image_info(inputs.img_info, img.shape[:2])
         return self.convert(inputs)
 
     def __repr__(self):
@@ -875,7 +868,8 @@ def __repr__(self):
         repr_str += f"{tuple(round(r, 4) for r in self.aspect_ratio_range)}"
         repr_str += f", max_attempts={self.max_attempts}"
         repr_str += f", interpolation={self.interpolation}"
-        repr_str += f", backend={self.backend})"
+        repr_str += f", transform_mask={self.transform_mask}"
+        repr_str += f", is_numpy_to_tvtensor={self.is_numpy_to_tvtensor})"
         return repr_str
 
 
@@ -2082,6 +2076,7 @@ class Pad(tvt_v2.Transform, NumpytoTVTensorMixin):
               on the edge. For example, padding [1, 2, 3, 4] with 2 elements on
               both sides in symmetric mode will result in
               [2, 1, 1, 2, 3, 4, 4, 3]
+        transform_mask (bool): Whether to transform masks. Defaults to False.
         is_numpy_to_tvtensor (bool): Whether convert outputs to tensor. Defaults to False.
     """