| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
|
|
| import unittest |
| from typing import Optional, Union |
|
|
| import requests |
|
|
| from transformers.testing_utils import require_torch, require_vision |
| from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available |
|
|
| from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs |
|
|
|
|
| if is_torch_available(): |
| import torch |
|
|
| if is_vision_available(): |
| from PIL import Image |
|
|
| from transformers import BridgeTowerImageProcessor |
|
|
| if is_torchvision_available(): |
| from transformers import BridgeTowerImageProcessorFast |
|
|
|
|
| class BridgeTowerImageProcessingTester: |
| def __init__( |
| self, |
| parent, |
| do_resize: bool = True, |
| size: dict[str, int] = None, |
| size_divisor: int = 32, |
| do_rescale: bool = True, |
| rescale_factor: Union[int, float] = 1 / 255, |
| do_normalize: bool = True, |
| do_center_crop: bool = True, |
| image_mean: Optional[Union[float, list[float]]] = [0.48145466, 0.4578275, 0.40821073], |
| image_std: Optional[Union[float, list[float]]] = [0.26862954, 0.26130258, 0.27577711], |
| do_pad: bool = True, |
| batch_size=7, |
| min_resolution=30, |
| max_resolution=400, |
| num_channels=3, |
| ): |
| self.parent = parent |
| self.do_resize = do_resize |
| self.size = size if size is not None else {"shortest_edge": 288} |
| self.size_divisor = size_divisor |
| self.do_rescale = do_rescale |
| self.rescale_factor = rescale_factor |
| self.do_normalize = do_normalize |
| self.do_center_crop = do_center_crop |
| self.image_mean = image_mean |
| self.image_std = image_std |
| self.do_pad = do_pad |
| self.batch_size = batch_size |
| self.num_channels = num_channels |
| self.min_resolution = min_resolution |
| self.max_resolution = max_resolution |
|
|
| def prepare_image_processor_dict(self): |
| return { |
| "image_mean": self.image_mean, |
| "image_std": self.image_std, |
| "do_normalize": self.do_normalize, |
| "do_resize": self.do_resize, |
| "size": self.size, |
| "size_divisor": self.size_divisor, |
| } |
|
|
| def get_expected_values(self, image_inputs, batched=False): |
| return self.size["shortest_edge"], self.size["shortest_edge"] |
|
|
| def expected_output_image_shape(self, images): |
| height, width = self.get_expected_values(images, batched=True) |
| return self.num_channels, height, 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 BridgeTowerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): |
| image_processing_class = BridgeTowerImageProcessor if is_vision_available() else None |
| fast_image_processing_class = BridgeTowerImageProcessorFast if is_torchvision_available() else None |
|
|
| def setUp(self): |
| super().setUp() |
| self.image_processor_tester = BridgeTowerImageProcessingTester(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, "image_mean")) |
| self.assertTrue(hasattr(image_processing, "image_std")) |
| self.assertTrue(hasattr(image_processing, "do_normalize")) |
| self.assertTrue(hasattr(image_processing, "do_resize")) |
| self.assertTrue(hasattr(image_processing, "size")) |
| self.assertTrue(hasattr(image_processing, "size_divisor")) |
|
|
| def _assertEquivalence(self, a, b): |
| self.assertTrue(torch.allclose(a, b, atol=1e-1)) |
| self.assertLessEqual(torch.mean(torch.abs(a - b)).item(), 1e-3) |
|
|
| @require_vision |
| @require_torch |
| 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 = Image.open( |
| requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw |
| ) |
| 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._assertEquivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) |
| self._assertEquivalence(encoding_slow.pixel_mask.float(), encoding_fast.pixel_mask.float()) |
|
|
| @require_vision |
| @require_torch |
| 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._assertEquivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) |
| self._assertEquivalence(encoding_slow.pixel_mask.float(), encoding_fast.pixel_mask.float()) |
|
|
