# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved. # # 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 ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs from ...test_processing_common import url_to_local_path class BridgeTowerImageProcessingTester: def __init__( self, parent, do_resize: bool = True, size: dict[str, int] | None = None, size_divisor: int = 32, do_rescale: bool = True, rescale_factor: int | float = 1 / 255, do_normalize: bool = True, do_center_crop: bool = True, image_mean: float | list[float] | None = [0.48145466, 0.4578275, 0.40821073], image_std: float | list[float] | None = [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): 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_processing_classes.values(): 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")) @require_vision @require_torch def test_backends_equivalence(self): if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")) encodings = {} for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) encodings[backend_name] = image_processor(dummy_image, return_tensors="pt") backend_names = list(encodings.keys()) reference_backend = backend_names[0] reference_pixel_values = encodings[reference_backend].pixel_values reference_pixel_mask = encodings[reference_backend].pixel_mask.float() for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_pixel_values, encodings[backend_name].pixel_values) self._assert_tensors_equivalence(reference_pixel_mask, encodings[backend_name].pixel_mask.float()) @require_vision @require_torch def test_slow_fast_equivalence_batched(self): if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") 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) encodings = {} for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) encodings[backend_name] = image_processor(dummy_images, return_tensors="pt") backend_names = list(encodings.keys()) reference_backend = backend_names[0] reference_pixel_values = encodings[reference_backend].pixel_values reference_pixel_mask = encodings[reference_backend].pixel_mask.float() for backend_name in backend_names[1:]: self._assert_tensors_equivalence(reference_pixel_values, encodings[backend_name].pixel_values) self._assert_tensors_equivalence(reference_pixel_mask, encodings[backend_name].pixel_mask.float())