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Add segmentation_maps support to MobileNetV2ImageProcessor (#37312)

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* Add `segmentation_maps` support to mobilenet_v2 image processor and `reduce_labels` to mobilevit

* Changed mobilenetv2 tests to support fastimageprocessor

* added `segmentation_maps` support to fast image processor

* reverted to upstream/main

* Add optional

* Use autodocstring

* Changed docs

* Docs fix

* Changed fp to match beit fp

* Change typing imports

* Fixed repo inconsistency

* Added fast-slow equivalence tests

* Removed unnecessary call

* Add `reduce_labels` to Mobilevit fast processor

---------

Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
This commit is contained in:
Mikhail Moskovchenko
2025-07-07 22:34:59 +05:00
committed by GitHub
parent b96f213fcf
commit 3993ee1e98
7 changed files with 723 additions and 52 deletions
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26
tests/models/mobilevit/test_image_processing_mobilevit.py
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@@ -50,6 +50,7 @@ class MobileViTImageProcessingTester:
do_center_crop=True,
crop_size=None,
do_flip_channel_order=True,
do_reduce_labels=False,
):
size = size if size is not None else {"shortest_edge": 20}
crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
@@ -64,6 +65,7 @@ class MobileViTImageProcessingTester:
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_flip_channel_order = do_flip_channel_order
self.do_reduce_labels = do_reduce_labels
def prepare_image_processor_dict(self):
return {
@@ -72,6 +74,7 @@ class MobileViTImageProcessingTester:
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
"do_reduce_labels": self.do_reduce_labels,
}
def expected_output_image_shape(self, images):
@@ -122,16 +125,21 @@ class MobileViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(hasattr(image_processing, "do_center_crop"))
self.assertTrue(hasattr(image_processing, "center_crop"))
self.assertTrue(hasattr(image_processing, "do_flip_channel_order"))
self.assertTrue(hasattr(image_processing, "do_reduce_labels"))
def test_image_processor_from_dict_with_kwargs(self):
for image_processing_class in self.image_processor_list:
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})
self.assertEqual(image_processor.do_reduce_labels, False)
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
image_processor = self.image_processing_class.from_dict(
self.image_processor_dict, size=42, crop_size=84, do_reduce_labels=True
)
self.assertEqual(image_processor.size, {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
self.assertEqual(image_processor.do_reduce_labels, True)
def test_call_segmentation_maps(self):
for image_processing_class in self.image_processor_list:
@@ -240,6 +248,22 @@ class MobileViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
def test_reduce_labels(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
image, map = prepare_semantic_single_inputs()
encoding = image_processing(image, map, return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 150)
image_processing.do_reduce_labels = True
encoding = image_processing(image, map, return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
@require_vision
@require_torch
def test_slow_fast_equivalence(self):