Minor update to expose padding mode and make resize flexible

torch_em/transform/generic.py

@@ -108,10 +108,11 @@ def __call__(self, inputs):
 
 
 class ResizeLongestSideInputs:
-    def __init__(self, target_shape, is_label=False, is_rgb=False):
+    def __init__(self, target_shape, is_label=False, is_rgb=False, padding_mode="constant"):
         self.target_shape = target_shape
         self.is_label = is_label
         self.is_rgb = is_rgb
+        self.padding_mode = padding_mode
 
         h, w = self.target_shape[-2], self.target_shape[-1]
         if h != w:  # We currently support resize feature for square-shaped target shape only.
@@ -135,7 +136,7 @@ def _get_preprocess_shape(self, oldh, oldw):
         newh = int(newh + 0.5)
         return (newh, neww)
 
-    def convert_transformed_inputs_to_original_shape(self, resized_inputs):
+    def convert_transformed_inputs_to_original_shape(self, resized_inputs, resize_kwargs=None):
         if not hasattr(self, "pre_pad_shape"):
             raise RuntimeError(
                 "'convert_transformed_inputs_to_original_shape' is only valid after the '__call__' method has run."
@@ -144,8 +145,15 @@ def convert_transformed_inputs_to_original_shape(self, resized_inputs):
         # First step is to remove the padded region
         inputs = resized_inputs[tuple(self.pre_pad_shape)]
         # Next, we resize the inputs to original shape
+
+        if resize_kwargs is None:  # This allows the user to change resize parameters, eg. for labels, if desired.
+            resize_kwargs = self.kwargs
+        else:
+            if not isinstance(resize_kwargs, dict):
+                raise RuntimeError("If the 'resize_kwargs' are provided, it must be a dictionary.")
+
         inputs = resize(
-            image=inputs, output_shape=self.original_shape, preserve_range=True, **self.kwargs
+            image=inputs, output_shape=self.original_shape, preserve_range=True, **resize_kwargs
         )
         return inputs
 
@@ -181,13 +189,14 @@ def __call__(self, inputs):
         # NOTE: We store this in case we would like to unpad the inputs.
         self.pre_pad_shape = [slice(pw[0], -pw[1] if pw[1] > 0 else None) for pw in pad_width]
 
-        inputs = np.pad(array=inputs, pad_width=pad_width, mode="constant")
+        inputs = np.pad(array=inputs, pad_width=pad_width, mode=self.padding_mode)
         return inputs
 
 
 class PadIfNecessary:
-    def __init__(self, shape):
+    def __init__(self, shape, padding_mode="reflect"):
         self.shape = tuple(shape)
+        self.padding_mode = padding_mode
 
     def _pad_if_necessary(self, data):
         if data.ndim == len(self.shape):
@@ -204,7 +213,7 @@ def _pad_if_necessary(self, data):
         pad_width = [sh - dsh for dsh, sh in zip(data_shape, pad_shape)]
         assert all(pw >= 0 for pw in pad_width)
         pad_width = [(0, pw) for pw in pad_width]
-        return np.pad(data, pad_width, mode="reflect")
+        return np.pad(data, pad_width, mode=self.padding_mode)
 
     def __call__(self, *inputs):
         outputs = tuple(self._pad_if_necessary(input_) for input_ in inputs)