Add Fast Image Processor for MobileNetV1 (#37111)

* fast image processor template for MobileNetV1 via transformers-cli

* Add fast image processors and unify tests for slow/fast image processor classes

* added loop over image_processor_list for all tests and removed boilerplate comments.

---------

Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>

docs/source/en/model_doc/mobilenet_v1.md

@@ -77,6 +77,11 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] MobileNetV1ImageProcessor
     - preprocess
 
+## MobileNetV1ImageProcessorFast
+
+[[autodoc]] MobileNetV1ImageProcessorFast
+    - preprocess
+
 ## MobileNetV1Model
 
 [[autodoc]] MobileNetV1Model

src/transformers/models/auto/image_processing_auto.py

@@ -117,7 +117,7 @@ else:
             ("mistral3", ("PixtralImageProcessor", "PixtralImageProcessorFast")),
             ("mlcd", ("CLIPImageProcessor", "CLIPImageProcessorFast")),
             ("mllama", ("MllamaImageProcessor",)),
-            ("mobilenet_v1", ("MobileNetV1ImageProcessor",)),
+            ("mobilenet_v1", ("MobileNetV1ImageProcessor", "MobileNetV1ImageProcessorFast")),
             ("mobilenet_v2", ("MobileNetV2ImageProcessor", "MobileNetV2ImageProcessorFast")),
             ("mobilevit", ("MobileViTImageProcessor",)),
             ("mobilevitv2", ("MobileViTImageProcessor",)),

src/transformers/models/mobilenet_v1/__init__.py

@@ -21,6 +21,7 @@ if TYPE_CHECKING:
     from .configuration_mobilenet_v1 import *
     from .feature_extraction_mobilenet_v1 import *
     from .image_processing_mobilenet_v1 import *
+    from .image_processing_mobilenet_v1_fast import *
     from .modeling_mobilenet_v1 import *
 else:
     import sys

src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1_fast.py

@@ -0,0 +1,47 @@
+# 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 MobileNetV1."""
+
+from ...image_processing_utils_fast import (
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
+    BaseImageProcessorFast,
+    DefaultFastImageProcessorKwargs,
+    Unpack,
+)
+from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling
+from ...utils import add_start_docstrings
+
+
+@add_start_docstrings(
+    "Constructs a fast MobileNetV1 image processor.",
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
+)
+class MobileNetV1ImageProcessorFast(BaseImageProcessorFast):
+    resample = PILImageResampling.BILINEAR
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"shortest_edge": 256}
+    default_to_square = False
+    crop_size = {"height": 224, "width": 224}
+    do_resize = True
+    do_center_crop = True
+    do_rescale = True
+    do_normalize = True
+
+    def __init__(self, **kwargs: Unpack[DefaultFastImageProcessorKwargs]) -> None:
+        super().__init__(**kwargs)
+
+
+__all__ = ["MobileNetV1ImageProcessorFast"]

tests/models/mobilenet_v1/test_image_processing_mobilenet_v1.py

@@ -16,7 +16,7 @@
 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
 
@@ -24,6 +24,9 @@ from ...test_image_processing_common import ImageProcessingTestMixin, prepare_im
 if is_vision_available():
     from transformers import MobileNetV1ImageProcessor
 
+    if is_torchvision_available():
+        from transformers import MobileNetV1ImageProcessorFast
+
 
 class MobileNetV1ImageProcessingTester:
     def __init__(
@@ -79,6 +82,7 @@ class MobileNetV1ImageProcessingTester:
 @require_vision
 class MobileNetV1ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
     image_processing_class = MobileNetV1ImageProcessor if is_vision_available() else None
+    fast_image_processing_class = MobileNetV1ImageProcessorFast if is_torchvision_available() else None
 
     def setUp(self):
         super().setUp()
@@ -89,17 +93,19 @@ class MobileNetV1ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase
         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, "do_center_crop"))
-        self.assertTrue(hasattr(image_processing, "center_crop"))
+        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, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "center_crop"))
 
     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})
+        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 = 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 = 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})