Add MobileViT fast image processor

docs/source/en/model_doc/mobilevit.md

@@ -93,6 +93,11 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] MobileViTImageProcessor
     - preprocess
+
+## MobileViTImageProcessorFast
+
+[[autodoc]] MobileViTImageProcessorFast
+    - preprocess
     - post_process_semantic_segmentation
 
 <frameworkcontent>

src/transformers/models/auto/image_processing_auto.py

@@ -119,8 +119,8 @@
             ("mllama", ("MllamaImageProcessor",)),
             ("mobilenet_v1", ("MobileNetV1ImageProcessor", "MobileNetV1ImageProcessorFast")),
             ("mobilenet_v2", ("MobileNetV2ImageProcessor", "MobileNetV2ImageProcessorFast")),
-            ("mobilevit", ("MobileViTImageProcessor",)),
-            ("mobilevitv2", ("MobileViTImageProcessor",)),
+            ("mobilevit", ("MobileViTImageProcessor", "MobileViTImageProcessorFast")),
+            ("mobilevitv2", ("MobileViTImageProcessor", "MobileViTImageProcessorFast")),
             ("nat", ("ViTImageProcessor", "ViTImageProcessorFast")),
             ("nougat", ("NougatImageProcessor",)),
             ("oneformer", ("OneFormerImageProcessor",)),

src/transformers/models/mobilevit/__init__.py

@@ -21,6 +21,7 @@
     from .configuration_mobilevit import *
     from .feature_extraction_mobilevit import *
     from .image_processing_mobilevit import *
+    from .image_processing_mobilevit_fast import *
     from .modeling_mobilevit import *
     from .modeling_tf_mobilevit import *
 else:

src/transformers/models/mobilevit/image_processing_mobilevit_fast.py

@@ -0,0 +1,170 @@
+# 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 MobileViT."""
+
+from typing import List, Optional, Tuple, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+    BaseImageProcessorFast,
+    DefaultFastImageProcessorKwargs,
+    group_images_by_shape,
+    reorder_images,
+)
+from ...image_utils import PILImageResampling, SizeDict
+from ...processing_utils import Unpack
+from ...utils import TensorType, auto_docstring, is_torch_available, is_torch_tensor
+
+
+if is_torch_available():
+    import torch
+
+
+class MobileViTFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+    """
+    Keyword arguments for MobileViTImageProcessorFast that extend the default ones
+    to include channel flipping support.
+
+    Args:
+        do_flip_channel_order (`bool`, *optional*, defaults to `True`):
+            Whether to flip the color channels from RGB to BGR. This matches the behavior of the
+            slow MobileViT image processor.
+    """
+
+    do_flip_channel_order: Optional[bool]
+
+
+@auto_docstring
+class MobileViTImageProcessorFast(BaseImageProcessorFast):
+    # Default values verified against the slow MobileViTImageProcessor
+    resample = PILImageResampling.BILINEAR
+    size = {"shortest_edge": 224}
+    crop_size = {"height": 256, "width": 256}
+    default_to_square = False
+    do_resize = True
+    do_center_crop = True
+    do_rescale = True
+    rescale_factor = 1 / 255
+    do_flip_channel_order = True
+    # MobileViT slow processor does NOT have normalization, so set to None
+    do_normalize = None
+    valid_kwargs = MobileViTFastImageProcessorKwargs
+
+    def __init__(self, **kwargs: Unpack[MobileViTFastImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    @auto_docstring
+    def preprocess(self, images, **kwargs: Unpack[MobileViTFastImageProcessorKwargs]) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    def _preprocess(
+        self,
+        images: List["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        interpolation: Optional["torch.nn.functional.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]]],
+        do_flip_channel_order: 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)
+        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)
+            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,
+            )
+            # Handle channel flipping (RGB to BGR conversion)
+            if do_flip_channel_order:
+                # Flip the channel dimension (channels are at dimension 1 for batched tensors)
+                stacked_images = stacked_images.flip(1)
+            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)
+
+    # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileViT
+    def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple]] = None):
+        """
+        Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
+
+        Args:
+            outputs ([`MobileViTForSemanticSegmentation`]):
+                Raw outputs of the model.
+            target_sizes (`List[Tuple]` of length `batch_size`, *optional*):
+                List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
+                predictions will not be resized.
+
+        Returns:
+            semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic
+            segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
+            specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
+        """
+        # TODO: add support for other frameworks
+        logits = outputs.logits
+
+        # Resize logits and compute semantic segmentation maps
+        if target_sizes is not None:
+            if len(logits) != len(target_sizes):
+                raise ValueError(
+                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+                )
+
+            if is_torch_tensor(target_sizes):
+                target_sizes = target_sizes.numpy()
+
+            semantic_segmentation = []
+
+            for idx in range(len(logits)):
+                resized_logits = torch.nn.functional.interpolate(
+                    logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+                )
+                semantic_map = resized_logits[0].argmax(dim=0)
+                semantic_segmentation.append(semantic_map)
+        else:
+            semantic_segmentation = logits.argmax(dim=1)
+            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+        return semantic_segmentation
+
+
+__all__ = ["MobileViTImageProcessorFast"]