Spaces:
Runtime error
Runtime error
| # 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 is_flaky, require_torch, require_vision | |
| from transformers.utils import is_torch_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 DonutImageProcessor | |
| class DonutImageProcessingTester(unittest.TestCase): | |
| 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_thumbnail=True, | |
| do_align_axis=False, | |
| do_pad=True, | |
| do_normalize=True, | |
| image_mean=[0.5, 0.5, 0.5], | |
| image_std=[0.5, 0.5, 0.5], | |
| ): | |
| 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 if size is not None else {"height": 18, "width": 20} | |
| self.do_thumbnail = do_thumbnail | |
| self.do_align_axis = do_align_axis | |
| self.do_pad = do_pad | |
| self.do_normalize = do_normalize | |
| self.image_mean = image_mean | |
| self.image_std = image_std | |
| def prepare_image_processor_dict(self): | |
| return { | |
| "do_resize": self.do_resize, | |
| "size": self.size, | |
| "do_thumbnail": self.do_thumbnail, | |
| "do_align_long_axis": self.do_align_axis, | |
| "do_pad": self.do_pad, | |
| "do_normalize": self.do_normalize, | |
| "image_mean": self.image_mean, | |
| "image_std": self.image_std, | |
| } | |
| def expected_output_image_shape(self, images): | |
| return self.num_channels, self.size["height"], self.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, | |
| ) | |
| class DonutImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): | |
| image_processing_class = DonutImageProcessor if is_vision_available() else None | |
| def setUp(self): | |
| self.image_processor_tester = DonutImageProcessingTester(self) | |
| def image_processor_dict(self): | |
| return self.image_processor_tester.prepare_image_processor_dict() | |
| def test_image_processor_properties(self): | |
| image_processing = self.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_thumbnail")) | |
| self.assertTrue(hasattr(image_processing, "do_align_long_axis")) | |
| self.assertTrue(hasattr(image_processing, "do_pad")) | |
| self.assertTrue(hasattr(image_processing, "do_normalize")) | |
| self.assertTrue(hasattr(image_processing, "image_mean")) | |
| self.assertTrue(hasattr(image_processing, "image_std")) | |
| def test_image_processor_from_dict_with_kwargs(self): | |
| image_processor = self.image_processing_class.from_dict(self.image_processor_dict) | |
| self.assertEqual(image_processor.size, {"height": 18, "width": 20}) | |
| image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) | |
| self.assertEqual(image_processor.size, {"height": 42, "width": 42}) | |
| # Previous config had dimensions in (width, height) order | |
| image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=(42, 84)) | |
| self.assertEqual(image_processor.size, {"height": 84, "width": 42}) | |
| def test_call_pil(self): | |
| # Initialize image_processing | |
| image_processing = self.image_processing_class(**self.image_processor_dict) | |
| # create random PIL images | |
| image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, Image.Image) | |
| # Test not batched input | |
| encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| 1, | |
| self.image_processor_tester.num_channels, | |
| self.image_processor_tester.size["height"], | |
| self.image_processor_tester.size["width"], | |
| ), | |
| ) | |
| # Test batched | |
| encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| self.image_processor_tester.batch_size, | |
| self.image_processor_tester.num_channels, | |
| self.image_processor_tester.size["height"], | |
| self.image_processor_tester.size["width"], | |
| ), | |
| ) | |
| def test_call_numpy(self): | |
| # Initialize image_processing | |
| image_processing = self.image_processing_class(**self.image_processor_dict) | |
| # create random numpy tensors | |
| image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, np.ndarray) | |
| # Test not batched input | |
| encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| 1, | |
| self.image_processor_tester.num_channels, | |
| self.image_processor_tester.size["height"], | |
| self.image_processor_tester.size["width"], | |
| ), | |
| ) | |
| # Test batched | |
| encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| self.image_processor_tester.batch_size, | |
| self.image_processor_tester.num_channels, | |
| self.image_processor_tester.size["height"], | |
| self.image_processor_tester.size["width"], | |
| ), | |
| ) | |
| def test_call_pytorch(self): | |
| # Initialize image_processing | |
| image_processing = self.image_processing_class(**self.image_processor_dict) | |
| # create random PyTorch tensors | |
| image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) | |
| for image in image_inputs: | |
| self.assertIsInstance(image, torch.Tensor) | |
| # Test not batched input | |
| encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| 1, | |
| self.image_processor_tester.num_channels, | |
| self.image_processor_tester.size["height"], | |
| self.image_processor_tester.size["width"], | |
| ), | |
| ) | |
| # Test batched | |
| encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values | |
| self.assertEqual( | |
| encoded_images.shape, | |
| ( | |
| self.image_processor_tester.batch_size, | |
| self.image_processor_tester.num_channels, | |
| self.image_processor_tester.size["height"], | |
| self.image_processor_tester.size["width"], | |
| ), | |
| ) | |