Update rescale method to reflect changes in huggingface#18677

Author: amyeroberts

src/transformers/image_transforms.py

@@ -69,7 +69,7 @@ def to_channel_dimension_format(image: np.ndarray, channel_dim: Union[ChannelDim
     raise ValueError("Unsupported channel dimension format: {}".format(channel_dim))
 
 
-def rescale(
+def rescale_image(
     image: np.ndarray, scale: Union[float, int] = 255, data_format: Optional[ChannelDimension] = None, dtype=np.float32
 ) -> np.ndarray:
     """
@@ -127,7 +127,7 @@ def to_pil_image(
     # PIL.Image can only store uint8 values, so we rescale the image to be between 0 and 255 if needed.
     do_rescale = isinstance(image.flat[0], float) if do_rescale is None else do_rescale
     if do_rescale:
-        image = rescale(image, 255)
+        image = rescale_image(image, 255)
     image = image.astype(np.uint8)
     return PIL.Image.fromarray(image)
 

src/transformers/models/glpn/image_processing_glpn.py

@@ -22,7 +22,7 @@
 from transformers.utils.generic import TensorType
 
 from ...image_processing_utils import BaseImageProcessor, BatchFeature
-from ...image_transforms import rescale, resize, to_channel_dimension_format
+from ...image_transforms import rescale_image, resize, to_channel_dimension_format
 from ...image_utils import ChannelDimension, get_image_size, is_batched, to_numpy_array, valid_images
 from ...utils import logging
 
@@ -93,7 +93,7 @@ def resize(
         image = resize(image, (new_h, new_w), resample=resample, data_format=data_format, **kwargs)
         return image
 
-    def rescale(
+    def rescale_image(
         self, image: np.ndarray, scale: Union[int, float], data_format: Optional[ChannelDimension] = None, **kwargs
     ) -> np.ndarray:
         """
@@ -110,7 +110,7 @@ def rescale(
                 - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                 - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
         """
-        return rescale(image=image, scale=scale, data_format=data_format, **kwargs)
+        return rescale_image(image=image, scale=scale, data_format=data_format, **kwargs)
 
     def preprocess(
         self,
@@ -172,7 +172,7 @@ def preprocess(
             images = [self.resize(image, size_divisor=size_divisor, resample=resample) for image in images]
 
         if do_rescale:
-            images = [self.rescale(image, scale=1 / 255) for image in images]
+            images = [self.rescale_image(image, scale=1 / 255) for image in images]
 
         images = [to_channel_dimension_format(image, data_format) for image in images]