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| # coding=utf-8 | |
| # Copyright 2022 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 | |
| import numpy as np | |
| from transformers.testing_utils import require_torch, require_vision | |
| from transformers.utils import is_torch_available, is_vision_available | |
| from ...test_image_processing_common import ImageProcessingSavingTestMixin | |
| if is_torch_available(): | |
| import torch | |
| if is_vision_available(): | |
| from PIL import Image | |
| from transformers import Swin2SRImageProcessor | |
| from transformers.image_transforms import get_image_size | |
| class Swin2SRImageProcessingTester(unittest.TestCase): | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=7, | |
| num_channels=3, | |
| image_size=18, | |
| min_resolution=30, | |
| max_resolution=400, | |
| do_rescale=True, | |
| rescale_factor=1 / 255, | |
| do_pad=True, | |
| pad_size=8, | |
| ): | |
| 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_rescale = do_rescale | |
| self.rescale_factor = rescale_factor | |
| self.do_pad = do_pad | |
| self.pad_size = pad_size | |
| def prepare_image_processor_dict(self): | |
| return { | |
| "do_rescale": self.do_rescale, | |
| "rescale_factor": self.rescale_factor, | |
| "do_pad": self.do_pad, | |
| "pad_size": self.pad_size, | |
| } | |
| def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False): | |
| """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, | |
| or a list of PyTorch tensors if one specifies torchify=True. | |
| """ | |
| assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" | |
| if equal_resolution: | |
| image_inputs = [] | |
| for i in range(self.batch_size): | |
| image_inputs.append( | |
| np.random.randint( | |
| 255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8 | |
| ) | |
| ) | |
| else: | |
| image_inputs = [] | |
| for i in range(self.batch_size): | |
| width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2) | |
| image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8)) | |
| if not numpify and not torchify: | |
| # PIL expects the channel dimension as last dimension | |
| image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] | |
| if torchify: | |
| image_inputs = [torch.from_numpy(x) for x in image_inputs] | |
| return image_inputs | |
| class Swin2SRImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): | |
| image_processing_class = Swin2SRImageProcessor if is_vision_available() else None | |
| def setUp(self): | |
| self.image_processor_tester = Swin2SRImageProcessingTester(self) | |
| def image_processor_dict(self): | |
| 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")) | |
| def test_batch_feature(self): | |
| pass | |
| def calculate_expected_size(self, image): | |
| old_height, old_width = get_image_size(image) | |
| size = self.image_processor_tester.pad_size | |
| pad_height = (old_height // size + 1) * size - old_height | |
| pad_width = (old_width // size + 1) * size - old_width | |
| return old_height + pad_height, old_width + pad_width | |
| def test_call_pil(self): | |
| # Initialize image_processor | |
| image_processor = self.image_processing_class(**self.image_processor_dict) | |
| # create random PIL images | |
| image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, Image.Image) | |
| # Test not batched input | |
| encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values | |
| expected_height, expected_width = self.calculate_expected_size(np.array(image_inputs[0])) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| 1, | |
| self.image_processor_tester.num_channels, | |
| expected_height, | |
| expected_width, | |
| ), | |
| ) | |
| def test_call_numpy(self): | |
| # Initialize image_processor | |
| image_processor = self.image_processing_class(**self.image_processor_dict) | |
| # create random numpy tensors | |
| image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, numpify=True) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, np.ndarray) | |
| # Test not batched input | |
| encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values | |
| expected_height, expected_width = self.calculate_expected_size(image_inputs[0]) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| 1, | |
| self.image_processor_tester.num_channels, | |
| expected_height, | |
| expected_width, | |
| ), | |
| ) | |
| def test_call_pytorch(self): | |
| # Initialize image_processor | |
| image_processor = self.image_processing_class(**self.image_processor_dict) | |
| # create random PyTorch tensors | |
| image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, torchify=True) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, torch.Tensor) | |
| # Test not batched input | |
| encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values | |
| expected_height, expected_width = self.calculate_expected_size(image_inputs[0]) | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| 1, | |
| self.image_processor_tester.num_channels, | |
| expected_height, | |
| expected_width, | |
| ), | |
| ) | |