# Copyright 2024 HuggingFace Inc. # # 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. import unittest from transformers.image_utils import load_image from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torchvision_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs from ...test_processing_common import url_to_local_path if is_vision_available(): from PIL import Image from transformers import Siglip2ImageProcessor if is_torchvision_available(): from transformers import Siglip2ImageProcessorFast class Siglip2ImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], resample=None, patch_size=16, max_num_patches=256, ): size = size if size is not None else {"height": 18, "width": 18} resample = resample if resample is not None else Image.Resampling.BILINEAR self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.resample = resample self.patch_size = patch_size self.max_num_patches = max_num_patches def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "resample": self.resample, "patch_size": self.patch_size, "max_num_patches": self.max_num_patches, } def expected_output_image_shape(self, images): return self.max_num_patches, self.patch_size * self.patch_size * self.num_channels def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Siglip2 class Siglip2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = Siglip2ImageProcessor if is_vision_available() else None fast_image_processing_class = Siglip2ImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = Siglip2ImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() # Ignore copy def test_image_processor_properties(self): 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, "resample")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "patch_size")) self.assertTrue(hasattr(image_processing, "max_num_patches")) # Ignore copy def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.max_num_patches, 256) self.assertEqual(image_processor.patch_size, 16) image_processor = self.image_processing_class.from_dict( self.image_processor_dict, patch_size=32, max_num_patches=512 ) self.assertEqual(image_processor.patch_size, 32) self.assertEqual(image_processor.max_num_patches, 512) @unittest.skip(reason="not supported") # Ignore copy def test_call_numpy_4_channels(self): pass # Ignore copy def test_slow_fast_equivalence(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")) image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) encoding_slow = image_processor_slow(dummy_image, return_tensors="pt") encoding_fast = image_processor_fast(dummy_image, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) # increase mean tolerance to 1e-3 -> 2e-3 # Ignore copy def test_slow_fast_equivalence_batched(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop: self.skipTest( reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors" ) dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, 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) encoding_slow = image_processor_slow(dummy_images, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)