🚨 Fix EfficientNet image processor default interpolation to BICUBIC (#42956)

Fix EfficientNet image processor default interpolation to BICUBIC

The original EfficientNet implementation uses BICUBIC interpolation for image
preprocessing, but both EfficientNetImageProcessor and EfficientNetImageProcessorFast
defaulted to NEAREST. This change aligns the default with the original implementation.

Reference: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/preprocessing.py

Fixes part of #28180

Author: lukepayyapilli

src/transformers/models/efficientnet/image_processing_efficientnet.py

@@ -66,7 +66,7 @@ class EfficientNetImageProcessor(BaseImageProcessor):
             `do_resize` in `preprocess`.
         size (`dict[str, int]` *optional*, defaults to `{"height": 346, "width": 346}`):
             Size of the image after `resize`. Can be overridden by `size` in `preprocess`.
-        resample (`PILImageResampling` filter, *optional*, defaults to 0):
+        resample (`PILImageResampling` filter, *optional*, defaults to `Resampling.BICUBIC`):
             Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
         do_center_crop (`bool`, *optional*, defaults to `False`):
             Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
@@ -102,7 +102,7 @@ def __init__(
         self,
         do_resize: bool = True,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = PIL.Image.NEAREST,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
         do_center_crop: bool = False,
         crop_size: Optional[dict[str, int]] = None,
         rescale_factor: Union[int, float] = 1 / 255,
@@ -133,12 +133,11 @@ def __init__(
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
         self.include_top = include_top
 
-    # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.NEAREST
     def resize(
         self,
         image: np.ndarray,
         size: dict[str, int],
-        resample: PILImageResampling = PILImageResampling.NEAREST,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
         data_format: Optional[Union[str, ChannelDimension]] = None,
         input_data_format: Optional[Union[str, ChannelDimension]] = None,
         **kwargs,
@@ -151,8 +150,8 @@ def resize(
                 Image to resize.
             size (`dict[str, int]`):
                 Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.NEAREST`):
-                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.NEAREST`.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
             data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format for the output image. If unset, the channel dimension format of the input
                 image is used. Can be one of:

src/transformers/models/efficientnet/image_processing_efficientnet_fast.py

@@ -33,7 +33,7 @@
 
 @auto_docstring
 class EfficientNetImageProcessorFast(BaseImageProcessorFast):
-    resample = PILImageResampling.NEAREST
+    resample = PILImageResampling.BICUBIC
     image_mean = IMAGENET_STANDARD_MEAN
     image_std = IMAGENET_STANDARD_STD
     size = {"height": 346, "width": 346}