Repository: huggingface/transformers
Pull request: Add Fast Image Processor for PoolFormer

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Related #36978


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Changed files:
filename: docs/source/en/model_doc/poolformer.md
patch: @@ -73,6 +73,11 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] PoolFormerImageProcessor
     - preprocess
 
+## PoolFormerImageProcessorFast
+
+[[autodoc]] PoolFormerImageProcessorFast
+    - preprocess
+
 ## PoolFormerModel
 
 [[autodoc]] PoolFormerModel

filename: src/transformers/models/auto/image_processing_auto.py
patch: @@ -131,7 +131,7 @@
             ("phi4_multimodal", "Phi4MultimodalImageProcessorFast"),
             ("pix2struct", ("Pix2StructImageProcessor",)),
             ("pixtral", ("PixtralImageProcessor", "PixtralImageProcessorFast")),
-            ("poolformer", ("PoolFormerImageProcessor",)),
+            ("poolformer", ("PoolFormerImageProcessor", "PoolFormerImageProcessorFast")),
             ("prompt_depth_anything", ("PromptDepthAnythingImageProcessor",)),
             ("pvt", ("PvtImageProcessor", "PvtImageProcessorFast")),
             ("pvt_v2", ("PvtImageProcessor", "PvtImageProcessorFast")),

filename: src/transformers/models/poolformer/__init__.py
patch: @@ -21,6 +21,7 @@
     from .configuration_poolformer import *
     from .feature_extraction_poolformer import *
     from .image_processing_poolformer import *
+    from .image_processing_poolformer_fast import *
     from .modeling_poolformer import *
 else:
     import sys

filename: src/transformers/models/poolformer/image_processing_poolformer_fast.py
patch: @@ -0,0 +1,270 @@
+# coding=utf-8
+# Copyright 2025 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 PoolFormer."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils_fast import (
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
+    BaseImageProcessorFast,
+    BatchFeature,
+    DefaultFastImageProcessorKwargs,
+)
+from ...image_transforms import (
+    ChannelDimension,
+    get_resize_output_image_size,
+    get_size_with_aspect_ratio,
+    group_images_by_shape,
+    reorder_images,
+)
+from ...image_utils import (
+    IMAGENET_DEFAULT_MEAN,
+    IMAGENET_DEFAULT_STD,
+    ImageInput,
+    PILImageResampling,
+    SizeDict,
+    get_image_size_for_max_height_width,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+    TensorType,
+    add_start_docstrings,
+    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 PoolFormerFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+    crop_pct: Optional[float]
+
+
+@add_start_docstrings(
+    "Constructs a fast PoolFormer image processor.",
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
+    """
+        crop_pct (`float`, *optional*, defaults to `self.crop_pct`):
+            Percentage of the image to crop. Only has an effect if `do_resize` is set to `True`.
+    """,
+)
+class PoolFormerImageProcessorFast(BaseImageProcessorFast):
+    resample = PILImageResampling.BICUBIC
+    image_mean = IMAGENET_DEFAULT_MEAN
+    image_std = IMAGENET_DEFAULT_STD
+    size = {"shortest_edge": 224}
+    default_to_square = False
+    crop_size = {"height": 224, "width": 224}
+    crop_pct = 0.9
+    do_resize = True
+    do_center_crop = True
+    do_rescale = True
+    do_normalize = True
+    valid_kwargs = PoolFormerFastImageProcessorKwargs
+
+    def __init__(self, **kwargs: Unpack[PoolFormerFastImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    @add_start_docstrings(
+        BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
+        """
+            crop_pct (`float`, *optional*, defaults to `self.crop_pct`):
+                Percentage of the image to crop. Only has an effect if `do_resize` is set to `True`.
+        """,
+    )
+    def preprocess(self, images: ImageInput, **kwargs: Unpack[PoolFormerFastImageProcessorKwargs]) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    def resize(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+        crop_pct: Optional[float] = None,
+        interpolation: "F.InterpolationMode" = None,
+        antialias: bool = True,
+        **kwargs,
+    ) -> "torch.Tensor":
+        """
+        Resize an image.
+
+        If crop_pct is unset:
+            - size is `{"height": h, "width": w}`: the image is resized to `(h, w)`.
+            - size is `{"shortest_edge": s}`: the shortest edge of the image is resized to s whilst maintaining the
+              aspect ratio.
+
+        if crop_pct is set:
+            - size is `{"height": h, "width": w}`: the image is resized to `(int(floor(h/crop_pct)),
+              int(floor(w/crop_pct)))`
+            - size is `{"height": c, "width": c}`: the shortest edge of the image is resized to `int(floor(c/crop_pct)`
+              whilst maintaining the aspect ratio.
+            - size is `{"shortest_edge": c}`: the shortest edge of the image is resized to `int(floor(c/crop_pct)`
+              whilst maintaining the aspect ratio.
+
+        Args:
+            image (`torch.Tensor`):
+                Image to resize.
+            size (`SizeDict`):
+                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+            crop_pct (`float`, *optional*):
+                Percentage of the image that will be cropped from the center. If set, the image is resized
+            resample (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
+                `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`.
+
+        Returns:
+            `torch.Tensor`: The resized image.
+        """
+        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
+        if crop_pct is not None:
+            if size.shortest_edge:
+                scale_size = int(size.shortest_edge / crop_pct)
+            elif size.height and size.width:
+                if size.height == size.width:
+                    scale_size = int(size.height / crop_pct)
+                else:
+                    scale_size = (int(size.height / crop_pct), int(size.width / crop_pct))
+            else:
+                raise ValueError("Invalid size for resize: {}".format(size))
+
+            new_size = get_resize_output_image_size(
+                image,
+                size=scale_size,
+                default_to_square=False,
+                input_data_format=ChannelDimension.FIRST,
+            )
+        else:
+            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.
+                new_size = get_size_with_aspect_ratio(
+                    image.size()[-2:],
+                    size.shortest_edge,
+                    size.longest_edge,
+                )
+            elif size.shortest_edge:
+                new_size = get_resize_output_image_size(
+                    image,
+                    size=size.shortest_edge,
+                    default_to_square=False,
+                    input_data_format=ChannelDimension.FIRST,
+                )
+            elif size.max_height and size.max_width:
+                new_size = get_image_size_for_max_height_width(image.size()[-2:], size.max_height, size.max_width)
+            elif size.height and size.width:
+                new_size = (size.height, size.width)
+            else:
+                raise ValueError(
+                    "Size must contain 'height' and 'width' keys, or 'max_height' and 'max_width', or 'shortest_edge' key. Got"
+                    f" {size}."
+                )
+        return F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
+
+    def center_crop(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+        **kwargs,
+    ) -> "torch.Tensor":
+        """
+        Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along
+        any edge, the image is padded with 0's and then center cropped.
+
+        Args:
+            image (`"torch.Tensor"`):
+                Image to center crop.
+            size (`Dict[str, int]`):
+                Size of the output image.
+
+        Returns:
+            `torch.Tensor`: The center cropped image.
+        """
+        if size.height is None or size.width is None:
+            raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
+        image_height, image_width = image.shape[-2:]
+        crop_height, crop_width = size.height, size.width
+
+        if crop_width > image_width or crop_height > image_height:
+            padding_ltrb = [
+                (crop_width - image_width) // 2 if crop_width > image_width else 0,
+                (crop_height - image_height) // 2 if crop_height > image_height else 0,
+                (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_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)
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        crop_pct: float,
+        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]]],
+        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)
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            if do_resize:
+                stacked_images = self.resize(
+                    image=stacked_images, size=size, crop_pct=crop_pct, interpolation=interpolation
+                )
+            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)
+        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__ = ["PoolFormerImageProcessorFast"]

filename: tests/models/poolformer/test_image_processing_poolformer.py
patch: @@ -15,14 +15,17 @@
 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 PoolFormerImageProcessor
 
+    if is_torchvision_available():
+        from transformers import PoolFormerImageProcessorFast
+
 
 class PoolFormerImageProcessingTester:
     def __init__(
@@ -85,6 +88,7 @@ def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=F
 @require_vision
 class PoolFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
     image_processing_class = PoolFormerImageProcessor if is_vision_available() else None
+    fast_image_processing_class = PoolFormerImageProcessorFast if is_torchvision_available() else None
 
     def setUp(self):
         super().setUp()
@@ -95,19 +99,29 @@ 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_and_center_crop"))
-        self.assertTrue(hasattr(image_processing, "size"))
-        self.assertTrue(hasattr(image_processing, "crop_pct"))
-        self.assertTrue(hasattr(image_processing, "do_normalize"))
-        self.assertTrue(hasattr(image_processing, "image_mean"))
-        self.assertTrue(hasattr(image_processing, "image_std"))
+        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_and_center_crop"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "crop_pct"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
 
     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": 30})
-        self.assertEqual(image_processor.crop_size, {"height": 30, "width": 30})
+        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": 30})
+            self.assertEqual(image_processor.crop_size, {"height": 30, "width": 30})
+
+            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})
 
-        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})
+
+@require_torch
+@require_vision
+class PoolFormerImageProcessingNoCropPctTest(PoolFormerImageProcessingTest):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = PoolFormerImageProcessingTester(self, crop_pct=None)