Change Torchvision functional alias to TVF

src/transformers/image_processing_utils_fast.py

@@ -60,7 +60,7 @@
     import torch
 
 if is_torchvision_available():
-    from torchvision.transforms.v2 import functional as F
+    import torchvision.transforms.v2.functional as TVF
 
     from .image_utils import pil_torch_interpolation_mapping
 
@@ -82,7 +82,7 @@ def validate_fast_preprocess_arguments(
     crop_size: SizeDict | None = None,
     do_resize: bool | None = None,
     size: SizeDict | None = None,
-    interpolation: Optional["F.InterpolationMode"] = None,
+    interpolation: Optional["TVF.InterpolationMode"] = None,
     return_tensors: str | TensorType | None = None,
     data_format: ChannelDimension = ChannelDimension.FIRST,
 ):
@@ -398,7 +398,7 @@ def pad(
                 )
             if image_size != pad_size:
                 padding = (0, 0, padding_width, padding_height)
-                stacked_images = F.pad(stacked_images, padding, fill=fill_value, padding_mode=padding_mode)
+                stacked_images = TVF.pad(stacked_images, padding, fill=fill_value, padding_mode=padding_mode)
             processed_images_grouped[shape] = stacked_images
 
             if return_mask:
@@ -418,7 +418,7 @@ def resize(
         self,
         image: "torch.Tensor",
         size: SizeDict,
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
         antialias: bool = True,
         **kwargs,
     ) -> "torch.Tensor":
@@ -438,7 +438,7 @@ def resize(
         Returns:
             `torch.Tensor`: The resized image.
         """
-        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
+        interpolation = interpolation if interpolation is not None else TVF.InterpolationMode.BILINEAR
         if size.shortest_edge and size.longest_edge:
             # Resize the image so that the shortest edge or the longest edge is of the given size
             # while maintaining the aspect ratio of the original image.
@@ -468,31 +468,31 @@ def resize(
         # TODO: remove this once the bug is fixed (detected with torch==2.7.0+git1fee196, torchvision==0.22.0+9eb57cd)
         if is_torchdynamo_compiling() and is_rocm_platform():
             return self.compile_friendly_resize(image, new_size, interpolation, antialias)
-        return F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
+        return TVF.resize(image, new_size, interpolation=interpolation, antialias=antialias)
 
     @staticmethod
     def compile_friendly_resize(
         image: "torch.Tensor",
         new_size: tuple[int, int],
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
         antialias: bool = True,
     ) -> "torch.Tensor":
         """
-        A wrapper around `F.resize` so that it is compatible with torch.compile when the image is a uint8 tensor.
+        A wrapper around `TVF.resize` so that it is compatible with torch.compile when the image is a uint8 tensor.
         """
         if image.dtype == torch.uint8:
             # 256 is used on purpose instead of 255 to avoid numerical differences
             # see https://github.com/huggingface/transformers/pull/38540#discussion_r2127165652
             image = image.float() / 256
-            image = F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
+            image = TVF.resize(image, new_size, interpolation=interpolation, antialias=antialias)
             image = image * 256
             # torch.where is used on purpose instead of torch.clamp to avoid bug in torch.compile
             # see https://github.com/huggingface/transformers/pull/38540#discussion_r2126888471
             image = torch.where(image > 255, 255, image)
             image = torch.where(image < 0, 0, image)
             image = image.round().to(torch.uint8)
         else:
-            image = F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
+            image = TVF.resize(image, new_size, interpolation=interpolation, antialias=antialias)
         return image
 
     def rescale(
@@ -536,7 +536,7 @@ def normalize(
         Returns:
             `torch.Tensor`: The normalized image.
         """
-        return F.normalize(image, mean, std)
+        return TVF.normalize(image, mean, std)
 
     @lru_cache(maxsize=10)
     def _fuse_mean_std_and_rescale_factor(
@@ -615,14 +615,14 @@ def center_crop(
                 (crop_width - image_width + 1) // 2 if crop_width > image_width else 0,
                 (crop_height - image_height + 1) // 2 if crop_height > image_height else 0,
             ]
-            image = F.pad(image, padding_ltrb, fill=0)  # PIL uses fill value 0
+            image = TVF.pad(image, padding_ltrb, fill=0)  # PIL uses fill value 0
             image_height, image_width = image.shape[-2:]
             if crop_width == image_width and crop_height == image_height:
                 return image
 
         crop_top = int((image_height - crop_height) / 2.0)
         crop_left = int((image_width - crop_width) / 2.0)
-        return F.crop(image, crop_top, crop_left, crop_height, crop_width)
+        return TVF.crop(image, crop_top, crop_left, crop_height, crop_width)
 
     def convert_to_rgb(
         self,
@@ -687,9 +687,9 @@ def _process_image(
             image = self.convert_to_rgb(image)
 
         if image_type == ImageType.PIL:
-            image = F.pil_to_tensor(image)
+            image = TVF.pil_to_tensor(image)
         elif image_type == ImageType.NUMPY:
-            # not using F.to_tensor as it doesn't handle (C, H, W) numpy arrays
+            # not using TVF.to_tensor as it doesn't handle (C, H, W) numpy arrays
             image = torch.from_numpy(image).contiguous()
 
         # If the image is 2D, we need to unsqueeze it to add a channel dimension for processing
@@ -813,7 +813,7 @@ def _validate_preprocess_kwargs(
         size: SizeDict | None = None,
         do_center_crop: bool | None = None,
         crop_size: SizeDict | None = None,
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
         return_tensors: str | TensorType | None = None,
         data_format: ChannelDimension | None = None,
         **kwargs,
@@ -892,7 +892,7 @@ def _preprocess(
         images: list["torch.Tensor"],
         do_resize: bool,
         size: SizeDict,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: Optional["TVF.InterpolationMode"],
         do_center_crop: bool,
         crop_size: SizeDict,
         do_rescale: bool,

src/transformers/models/beit/image_processing_beit_fast.py

@@ -16,7 +16,7 @@
 from typing import Optional, Union
 
 import torch
-from torchvision.transforms.v2 import functional as F
+import torchvision.transforms.v2.functional as TVF
 
 from ...image_processing_utils import BatchFeature
 from ...image_processing_utils_fast import (
@@ -124,7 +124,7 @@ def _preprocess(
         do_reduce_labels: bool,
         do_resize: bool,
         size: SizeDict,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: Optional["TVF.InterpolationMode"],
         do_center_crop: bool,
         crop_size: SizeDict,
         do_rescale: bool,

src/transformers/models/bridgetower/image_processing_bridgetower_fast.py

@@ -17,7 +17,7 @@
 from typing import Optional
 
 import torch
-from torchvision.transforms.v2 import functional as F
+import torchvision.transforms.v2.functional as TVF
 
 from ...image_processing_utils_fast import (
     BaseImageProcessorFast,
@@ -113,7 +113,7 @@ def resize(
         image: "torch.Tensor",
         size: SizeDict,
         size_divisor: int = 32,
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
         antialias: bool = True,
         **kwargs,
     ) -> "torch.Tensor":
@@ -137,7 +137,7 @@ def resize(
         Returns:
             `torch.Tensor`: The resized image.
         """
-        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
+        interpolation = interpolation if interpolation is not None else TVF.InterpolationMode.BILINEAR
         if not size.shortest_edge:
             raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
         shorter = size.shortest_edge
@@ -172,7 +172,7 @@ def center_crop(
                 Size of the output image in the form `{"height": h, "width": w}`.
         """
         output_size = size.shortest_edge
-        return F.center_crop(
+        return TVF.center_crop(
             image,
             output_size=(output_size, output_size),
             **kwargs,
@@ -193,7 +193,7 @@ def _pad_image(
         pad_bottom = output_height - input_height
         pad_right = output_width - input_width
         padding = (0, 0, pad_right, pad_bottom)
-        padded_image = F.pad(
+        padded_image = TVF.pad(
             image,
             padding,
             fill=constant_values,
@@ -206,7 +206,7 @@ def _preprocess(
         do_resize: bool,
         size: SizeDict,
         size_divisor: int | None,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: Optional["TVF.InterpolationMode"],
         do_pad: bool,
         do_center_crop: bool,
         crop_size: SizeDict,

src/transformers/models/chameleon/image_processing_chameleon_fast.py

@@ -18,7 +18,7 @@
 import numpy as np
 import PIL
 import torch
-from torchvision.transforms.v2 import functional as F
+import torchvision.transforms.v2.functional as TVF
 
 from ...image_processing_utils_fast import BaseImageProcessorFast
 from ...image_utils import ImageInput, PILImageResampling, SizeDict
@@ -74,7 +74,7 @@ def resize(
         self,
         image: "torch.Tensor",
         size: SizeDict,
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
         **kwargs,
     ) -> "torch.Tensor":
         """
@@ -91,14 +91,14 @@ def resize(
         Returns:
             `torch.Tensor`: The resized image.
         """
-        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
-        if interpolation == F.InterpolationMode.LANCZOS:
+        interpolation = interpolation if interpolation is not None else TVF.InterpolationMode.BILINEAR
+        if interpolation == TVF.InterpolationMode.LANCZOS:
             logger.warning_once(
                 "You have used fast image processor with LANCZOS resample which not yet supported for torch.Tensor. "
                 "BICUBIC resample will be used as an alternative. Please fall back to slow image processor if you "
                 "want full consistency with the original model."
             )
-            interpolation = F.InterpolationMode.BICUBIC
+            interpolation = TVF.InterpolationMode.BICUBIC
 
         return super().resize(
             image=image,

src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py

@@ -23,7 +23,7 @@
 
 import numpy as np
 import torch
-from torchvision.transforms.v2 import functional as F
+import torchvision.transforms.v2.functional as TVF
 
 from ...image_processing_utils import BatchFeature
 from ...image_processing_utils_fast import BaseImageProcessorFast, group_images_by_shape, reorder_images
@@ -136,7 +136,7 @@ def crop_image_to_patches(
         max_patches: int,
         use_thumbnail: bool = True,
         patch_size: tuple | int | dict | None = None,
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
     ):
         """
         Crop the images to patches and return a list of cropped images.
@@ -207,7 +207,7 @@ def _preprocess(
         crop_to_patches: bool,
         min_patches: int,
         max_patches: int,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: Optional["TVF.InterpolationMode"],
         do_center_crop: bool,
         crop_size: SizeDict,
         do_rescale: bool,

src/transformers/models/conditional_detr/image_processing_conditional_detr_fast.py

@@ -8,9 +8,9 @@
 from typing import Any, Optional
 
 import torch
+import torchvision.transforms.v2.functional as TVF
 from torch import nn
 from torchvision.io import read_image
-from torchvision.transforms.v2 import functional as F
 
 from ...image_processing_utils import BatchFeature, get_size_dict
 from ...image_processing_utils_fast import (
@@ -313,7 +313,7 @@ def resize(
         self,
         image: torch.Tensor,
         size: SizeDict,
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
         **kwargs,
     ) -> torch.Tensor:
         """
@@ -336,7 +336,7 @@ def resize(
             interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
                 Resampling filter to use if resizing the image.
         """
-        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
+        interpolation = interpolation if interpolation is not None else TVF.InterpolationMode.BILINEAR
         if size.shortest_edge and size.longest_edge:
             # Resize the image so that the shortest edge or the longest edge is of the given size
             # while maintaining the aspect ratio of the original image.
@@ -355,7 +355,7 @@ def resize(
                 f" {size.keys()}."
             )
 
-        image = F.resize(
+        image = TVF.resize(
             image,
             size=new_size,
             interpolation=interpolation,
@@ -369,7 +369,7 @@ def resize_annotation(
         orig_size: tuple[int, int],
         target_size: tuple[int, int],
         threshold: float = 0.5,
-        interpolation: Optional["F.InterpolationMode"] = None,
+        interpolation: Optional["TVF.InterpolationMode"] = None,
     ):
         """
         Resizes an annotation to a target size.
@@ -383,10 +383,10 @@ def resize_annotation(
                 The target size of the image, as returned by the preprocessing `resize` step.
             threshold (`float`, *optional*, defaults to 0.5):
                 The threshold used to binarize the segmentation masks.
-            resample (`InterpolationMode`, defaults to `F.InterpolationMode.NEAREST_EXACT`):
+            resample (`InterpolationMode`, defaults to `TVF.InterpolationMode.NEAREST_EXACT`):
                 The resampling filter to use when resizing the masks.
         """
-        interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
+        interpolation = interpolation if interpolation is not None else TVF.InterpolationMode.NEAREST_EXACT
         ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]
 
         new_annotation = {}
@@ -405,7 +405,7 @@ def resize_annotation(
                 new_annotation["area"] = scaled_area
             elif key == "masks":
                 masks = value[:, None]
-                masks = [F.resize(mask, target_size, interpolation=interpolation) for mask in masks]
+                masks = [TVF.resize(mask, target_size, interpolation=interpolation) for mask in masks]
                 masks = torch.stack(masks).to(torch.float32)
                 masks = masks[:, 0] > threshold
                 new_annotation["masks"] = masks
@@ -449,7 +449,7 @@ def _update_annotation_for_padded_image(
         for key, value in annotation.items():
             if key == "masks":
                 masks = value
-                masks = F.pad(
+                masks = TVF.pad(
                     masks,
                     padding,
                     fill=0,
@@ -484,7 +484,7 @@ def pad(
             )
         if original_size != padded_size:
             padding = [0, 0, padding_right, padding_bottom]
-            image = F.pad(image, padding, fill=fill)
+            image = TVF.pad(image, padding, fill=fill)
             if annotation is not None:
                 annotation = self._update_annotation_for_padded_image(
                     annotation, original_size, padded_size, padding, update_bboxes
@@ -504,7 +504,7 @@ def _preprocess(
         return_segmentation_masks: bool,
         do_resize: bool,
         size: SizeDict,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: Optional["TVF.InterpolationMode"],
         do_rescale: bool,
         rescale_factor: float,
         do_normalize: bool,

src/transformers/models/convnext/image_processing_convnext_fast.py

@@ -16,7 +16,7 @@
 from typing import Optional
 
 import torch
-from torchvision.transforms.v2 import functional as F
+import torchvision.transforms.v2.functional as TVF
 
 from ...image_processing_utils import BatchFeature
 from ...image_processing_utils_fast import BaseImageProcessorFast, group_images_by_shape, reorder_images
@@ -121,7 +121,7 @@ def _preprocess(
         do_resize: bool,
         size: dict[str, int],
         crop_pct: float,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: Optional["TVF.InterpolationMode"],
         do_center_crop: bool,
         crop_size: int,
         do_rescale: bool,