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Add Swin2SR ImageProcessorFast (#37169)

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* Add fast image processor support for Swin2SR

* Add Swin2SR tests of fast image processing

* Update docs and remove unnecessary test func

* Fix docstring formatting

* Skip fast vs slow processing test

---------

Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
This commit is contained in:
Eon Kim
2025-05-08 01:20:16 +09:00
committed by GitHub
parent 17742bd9c8
commit 5c47d08b0d
5 changed files with 171 additions and 7 deletions
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32
tests/models/swin2sr/test_image_processing_swin2sr.py
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@@ -18,7 +18,7 @@ import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
@@ -30,6 +30,9 @@ if is_vision_available():
from PIL import Image
from transformers import Swin2SRImageProcessor
if is_torchvision_available():
from transformers import Swin2SRImageProcessorFast
from transformers.image_transforms import get_image_size
@@ -97,6 +100,7 @@ class Swin2SRImageProcessingTester:
@require_vision
class Swin2SRImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = Swin2SRImageProcessor if is_vision_available() else None
fast_image_processing_class = Swin2SRImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
@@ -107,11 +111,12 @@ class Swin2SRImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_rescale"))
self.assertTrue(hasattr(image_processor, "rescale_factor"))
self.assertTrue(hasattr(image_processor, "do_pad"))
self.assertTrue(hasattr(image_processor, "pad_size"))
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_rescale"))
self.assertTrue(hasattr(image_processing, "rescale_factor"))
self.assertTrue(hasattr(image_processing, "do_pad"))
self.assertTrue(hasattr(image_processing, "pad_size"))
def calculate_expected_size(self, image):
old_height, old_width = get_image_size(image)
@@ -181,3 +186,18 @@ class Swin2SRImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
@unittest.skip(reason="No speed gain on CPU due to minimal processing.")
def test_fast_is_faster_than_slow(self):
pass
def test_slow_fast_equivalence_batched(self):
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
encoded_slow = image_processor_slow(image_inputs, return_tensors="pt").pixel_values
encoded_fast = image_processor_fast(image_inputs, return_tensors="pt").pixel_values
self.assertTrue(torch.allclose(encoded_slow, encoded_fast, atol=1e-1))