Upload processor

image_processing_spice_cnn.py

@@ -143,65 +143,7 @@ class SpiceCNNImageProcessor(BaseImageProcessor):
             `np.ndarray`: The padded image.
 
         """
-        if input_data_format is None:
-            input_data_format = infer_channel_dimension_format(image)
-
-        def _expand_for_data_format(values):
-            """
-            Convert values to be in the format expected by np.pad based on the data format.
-            """
-            if isinstance(values, (int, float)):
-                values = ((values, values), (values, values))
-            elif isinstance(values, tuple) and len(values) == 1:
-                values = ((values[0], values[0]), (values[0], values[0]))
-            elif (
-                isinstance(values, tuple)
-                and len(values) == 2
-                and isinstance(values[0], int)
-            ):
-                values = (values, values)
-            elif (
-                isinstance(values, tuple)
-                and len(values) == 2
-                and isinstance(values[0], tuple)
-            ):
-                values = values
-            else:
-                raise ValueError(f"Unsupported format: {values}")
-
-            # add 0 for channel dimension
-            values = (
-                ((0, 0), *values)
-                if input_data_format == ChannelDimension.FIRST
-                else (*values, (0, 0))
-            )
-
-            # Add additional padding if there's a batch dimension
-            values = (0, *values) if image.ndim == 4 else values
-            return values
-
-        padding = _expand_for_data_format(padding)
-
-        if mode == PaddingMode.CONSTANT:
-            constant_values = _expand_for_data_format(constant_values)
-            image = np.pad(
-                image, padding, mode="constant", constant_values=constant_values
-            )
-        elif mode == PaddingMode.REFLECT:
-            image = np.pad(image, padding, mode="reflect")
-        elif mode == PaddingMode.REPLICATE:
-            image = np.pad(image, padding, mode="edge")
-        elif mode == PaddingMode.SYMMETRIC:
-            image = np.pad(image, padding, mode="symmetric")
-        else:
-            raise ValueError(f"Invalid padding mode: {mode}")
-
-        image = (
-            to_channel_dimension_format(image, data_format)
-            if data_format is not None
-            else image
-        )
-        return image
+        return pad(image, padding=padding)
 
     def resize(
         self,