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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 | |
| from transformers.file_utils import is_vision_available | |
| from transformers.testing_utils import require_torch, require_vision | |
| from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs | |
| if is_vision_available(): | |
| from transformers import DPTImageProcessor | |
| class DPTImageProcessingTester(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_normalize=True, | |
| image_mean=[0.5, 0.5, 0.5], | |
| image_std=[0.5, 0.5, 0.5], | |
| ): | |
| size = size if 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_normalize = do_normalize | |
| self.image_mean = image_mean | |
| self.image_std = image_std | |
| 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, | |
| } | |
| 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 DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): | |
| image_processing_class = DPTImageProcessor if is_vision_available() else None | |
| def setUp(self): | |
| self.image_processor_tester = DPTImageProcessingTester(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, "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")) | |
| 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": 18}) | |
| image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) | |
| self.assertEqual(image_processor.size, {"height": 42, "width": 42}) | |