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| import unittest |
|
|
| 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 |
|
|
|
|
| if is_vision_available(): |
| from transformers import CLIPImageProcessor |
|
|
| if is_torchvision_available(): |
| from transformers import CLIPImageProcessorFast |
|
|
|
|
| class CLIPImageProcessingTester: |
| 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_center_crop=True, |
| crop_size=None, |
| do_normalize=True, |
| image_mean=[0.48145466, 0.4578275, 0.40821073], |
| image_std=[0.26862954, 0.26130258, 0.27577711], |
| do_convert_rgb=True, |
| ): |
| super().__init__() |
| 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} |
| 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_center_crop = do_center_crop |
| self.crop_size = crop_size |
| self.do_normalize = do_normalize |
| self.image_mean = image_mean |
| self.image_std = image_std |
| self.do_convert_rgb = do_convert_rgb |
|
|
| def prepare_image_processor_dict(self): |
| return { |
| "do_resize": self.do_resize, |
| "size": self.size, |
| "do_center_crop": self.do_center_crop, |
| "crop_size": self.crop_size, |
| "do_normalize": self.do_normalize, |
| "image_mean": self.image_mean, |
| "image_std": self.image_std, |
| "do_convert_rgb": self.do_convert_rgb, |
| } |
|
|
| def expected_output_image_shape(self, images): |
| return self.num_channels, self.crop_size["height"], self.crop_size["width"] |
|
|
| 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 |
| class CLIPImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): |
| image_processing_class = CLIPImageProcessor if is_vision_available() else None |
| fast_image_processing_class = CLIPImageProcessorFast if is_torchvision_available() else None |
|
|
| def setUp(self): |
| super().setUp() |
| self.image_processor_tester = CLIPImageProcessingTester(self) |
|
|
| @property |
| def image_processor_dict(self): |
| return self.image_processor_tester.prepare_image_processor_dict() |
|
|
| 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, "size")) |
| self.assertTrue(hasattr(image_processing, "do_center_crop")) |
| self.assertTrue(hasattr(image_processing, "center_crop")) |
| 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, "do_convert_rgb")) |
|
|
| 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.size, {"shortest_edge": 20}) |
| self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18}) |
|
|
| 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}) |
|
|
