Repository: huggingface/transformers
Pull request: added Textnet fast image processor

# What does this PR do?

Related [#36978](https://github.com/huggingface/transformers/issues/36978)
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Adds a fast image processor to the textnet model.  


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
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- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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Changed files:
filename: docs/source/en/model_doc/textnet.md
patch: @@ -47,6 +47,11 @@ TextNet is the backbone for Fast, but can also be used as an efficient text/imag
 [[autodoc]] TextNetImageProcessor
     - preprocess
 
+## TextNetImageProcessorFast
+
+[[autodoc]] TextNetImageProcessorFast
+    - preprocess
+
 ## TextNetModel
 
 [[autodoc]] TextNetModel

filename: src/transformers/models/auto/image_processing_auto.py
patch: @@ -169,7 +169,8 @@
             ("swin", ("ViTImageProcessor", "ViTImageProcessorFast")),
             ("swin2sr", ("Swin2SRImageProcessor", "Swin2SRImageProcessorFast")),
             ("swinv2", ("ViTImageProcessor", "ViTImageProcessorFast")),
-            ("table-transformer", ("DetrImageProcessor", None)),
+            ("table-transformer", ("DetrImageProcessor", "DetrImageProcessorFast")),
+            ("textnet", ("TextNetImageProcessor", "TextNetImageProcessorFast")),
             ("timesformer", ("VideoMAEImageProcessor", None)),
             ("timm_wrapper", ("TimmWrapperImageProcessor", None)),
             ("tvlt", ("TvltImageProcessor", None)),

filename: src/transformers/models/textnet/__init__.py
patch: @@ -20,6 +20,7 @@
 if TYPE_CHECKING:
     from .configuration_textnet import *
     from .image_processing_textnet import *
+    from .image_processing_textnet_fast import *
     from .modeling_textnet import *
 else:
     import sys

filename: src/transformers/models/textnet/image_processing_textnet_fast.py
patch: @@ -0,0 +1,163 @@
+# coding=utf-8
+# Copyright 2025 the Fast authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for TextNet."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import BaseImageProcessorFast, DefaultFastImageProcessorKwargs
+from ...image_transforms import (
+    get_resize_output_image_size,
+    group_images_by_shape,
+    reorder_images,
+)
+from ...image_utils import (
+    IMAGENET_DEFAULT_MEAN,
+    IMAGENET_DEFAULT_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    SizeDict,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+    TensorType,
+    auto_docstring,
+    is_torch_available,
+    is_torchvision_available,
+    is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+    import torch
+
+if is_torchvision_available():
+    if is_torchvision_v2_available():
+        from torchvision.transforms.v2 import functional as F
+    else:
+        from torchvision.transforms import functional as F
+
+
+class TextNetFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+    """
+    size_divisor (`int`, *optional*, defaults to 32):
+        Ensures height and width are rounded to a multiple of this value after resizing.
+    """
+
+    size_divisor: Optional[int]
+
+
+@auto_docstring
+class TextNetImageProcessorFast(BaseImageProcessorFast):
+    resample = PILImageResampling.BILINEAR
+    image_mean = IMAGENET_DEFAULT_MEAN
+    image_std = IMAGENET_DEFAULT_STD
+    size = {"shortest_edge": 640}
+    default_to_square = False
+    crop_size = {"height": 224, "width": 224}
+    do_resize = True
+    do_center_crop = False
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    size_divisor = 32
+    valid_kwargs = TextNetFastImageProcessorKwargs
+
+    def __init__(self, **kwargs: Unpack[TextNetFastImageProcessorKwargs]) -> None:
+        super().__init__(**kwargs)
+
+    @auto_docstring
+    def preprocess(self, images: ImageInput, **kwargs: Unpack[TextNetFastImageProcessorKwargs]) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    def resize(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+        interpolation: "F.InterpolationMode" = None,
+        antialias: bool = True,
+        size_divisor: int = 32,
+        **kwargs,
+    ) -> "torch.Tensor":
+        if size.shortest_edge:
+            new_size = get_resize_output_image_size(
+                image,
+                size=size.shortest_edge,
+                default_to_square=False,
+                input_data_format=ChannelDimension.FIRST,
+            )
+        else:
+            raise ValueError(f"Size must contain 'shortest_edge' key. Got {size}.")
+        # ensure height and width are divisible by size_divisor
+        height, width = new_size
+        if height % size_divisor != 0:
+            height += size_divisor - (height % size_divisor)
+        if width % size_divisor != 0:
+            width += size_divisor - (width % size_divisor)
+
+        return super().resize(
+            image, SizeDict(height=height, width=width), interpolation=interpolation, antialias=antialias
+        )
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        size_divisor: int,
+        interpolation: Optional["F.InterpolationMode"],
+        do_center_crop: bool,
+        crop_size: SizeDict,
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: Optional[Union[float, list[float]]],
+        image_std: Optional[Union[float, list[float]]],
+        disable_grouping: Optional[bool],
+        return_tensors: Optional[Union[str, TensorType]],
+        **kwargs,
+    ) -> BatchFeature:
+        # Group images by size for batched resizing
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            if do_resize:
+                stacked_images = self.resize(
+                    image=stacked_images, size=size, interpolation=interpolation, size_divisor=size_divisor
+                )
+            resized_images_grouped[shape] = stacked_images
+        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+        # Group images by size for further processing
+        # Needed in case do_resize is False, or resize returns images with different sizes
+        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+        processed_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            if do_center_crop:
+                stacked_images = self.center_crop(stacked_images, crop_size)
+            # Fused rescale and normalize
+            stacked_images = self.rescale_and_normalize(
+                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            processed_images_grouped[shape] = stacked_images
+
+        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+        return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+
+__all__ = ["TextNetImageProcessorFast"]

filename: tests/models/textnet/test_image_processing_textnet.py
patch: @@ -16,14 +16,17 @@
 import unittest
 
 from transformers.testing_utils import require_torch, require_vision
-from transformers.utils import is_vision_available
+from transformers.utils import is_torchvision_available, is_vision_available
 
 from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
 
 
 if is_vision_available():
     from transformers import TextNetImageProcessor
 
+    if is_torchvision_available():
+        from transformers import TextNetImageProcessorFast
+
 
 class TextNetImageProcessingTester:
     def __init__(
@@ -94,6 +97,7 @@ def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=F
 @require_vision
 class TextNetImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
     image_processing_class = TextNetImageProcessor if is_vision_available() else None
+    fast_image_processing_class = TextNetImageProcessorFast if is_torchvision_available() else None
 
     def setUp(self):
         super().setUp()
@@ -104,22 +108,24 @@ def image_processor_dict(self):
         return self.image_processor_tester.prepare_image_processor_dict()
 
     def test_image_processor_properties(self):
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        self.assertTrue(hasattr(image_processing, "do_resize"))
-        self.assertTrue(hasattr(image_processing, "size"))
-        self.assertTrue(hasattr(image_processing, "size_divisor"))
-        self.assertTrue(hasattr(image_processing, "do_center_crop"))
-        self.assertTrue(hasattr(image_processing, "center_crop"))
-        self.assertTrue(hasattr(image_processing, "do_normalize"))
-        self.assertTrue(hasattr(image_processing, "image_mean"))
-        self.assertTrue(hasattr(image_processing, "image_std"))
-        self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "size_divisor"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "center_crop"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
 
     def test_image_processor_from_dict_with_kwargs(self):
-        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
-        self.assertEqual(image_processor.size, {"shortest_edge": 20})
-        self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
-
-        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
-        self.assertEqual(image_processor.size, {"shortest_edge": 42})
-        self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"shortest_edge": 20})
+            self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
+            self.assertEqual(image_processor.size, {"shortest_edge": 42})
+            self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})