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| # coding=utf-8 | |
| # Copyright 2021 The Deeplab2 Authors. | |
| # | |
| # 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. | |
| """Tests for input_preprocessing.""" | |
| import numpy as np | |
| import tensorflow as tf | |
| from deeplab2.data.preprocessing import input_preprocessing | |
| class InputPreprocessingTest(tf.test.TestCase): | |
| def setUp(self): | |
| super().setUp() | |
| self._image = tf.convert_to_tensor(np.random.randint(256, size=[33, 33, 3])) | |
| self._label = tf.convert_to_tensor(np.random.randint(19, size=[33, 33, 1])) | |
| def test_cropping(self): | |
| crop_height = np.random.randint(33) | |
| crop_width = np.random.randint(33) | |
| original_image, processed_image, processed_label, prev_image, prev_label = ( | |
| input_preprocessing.preprocess_image_and_label( | |
| image=self._image, | |
| label=self._label, | |
| prev_image=tf.identity(self._image), | |
| prev_label=tf.identity(self._label), | |
| crop_height=crop_height, | |
| crop_width=crop_width, | |
| ignore_label=255)) | |
| self.assertListEqual(original_image.shape.as_list(), | |
| [33, 33, 3]) | |
| self.assertListEqual(processed_image.shape.as_list(), | |
| [crop_height, crop_width, 3]) | |
| self.assertListEqual(processed_label.shape.as_list(), | |
| [crop_height, crop_width, 1]) | |
| np.testing.assert_equal(processed_image.numpy(), prev_image.numpy()) | |
| np.testing.assert_equal(processed_label.numpy(), prev_label.numpy()) | |
| def test_resizing(self): | |
| height, width = 65, 65 | |
| original_image, processed_image, processed_label, prev_image, prev_label = ( | |
| input_preprocessing.preprocess_image_and_label( | |
| image=self._image, | |
| label=self._label, | |
| prev_image=tf.identity(self._image), | |
| prev_label=tf.identity(self._label), | |
| crop_height=height, | |
| crop_width=width, | |
| min_resize_value=65, | |
| max_resize_value=65, | |
| resize_factor=32, | |
| ignore_label=255)) | |
| self.assertListEqual(original_image.shape.as_list(), | |
| [height, width, 3]) | |
| self.assertListEqual(processed_image.shape.as_list(), | |
| [height, width, 3]) | |
| self.assertListEqual(processed_label.shape.as_list(), | |
| [height, width, 1]) | |
| np.testing.assert_equal(processed_image.numpy(), prev_image.numpy()) | |
| np.testing.assert_equal(processed_label.numpy(), prev_label.numpy()) | |
| def test_scaling(self): | |
| height, width = 65, 65 | |
| original_image, processed_image, processed_label, prev_image, prev_label = ( | |
| input_preprocessing.preprocess_image_and_label( | |
| image=self._image, | |
| label=self._label, | |
| prev_image=tf.identity(self._image), | |
| prev_label=tf.identity(self._label), | |
| crop_height=height, | |
| crop_width=width, | |
| min_scale_factor=0.5, | |
| max_scale_factor=2.0, | |
| ignore_label=255)) | |
| self.assertListEqual(original_image.shape.as_list(), | |
| [33, 33, 3]) | |
| self.assertListEqual(processed_image.shape.as_list(), | |
| [height, width, 3]) | |
| self.assertListEqual(processed_label.shape.as_list(), | |
| [height, width, 1]) | |
| np.testing.assert_equal(processed_image.numpy(), prev_image.numpy()) | |
| np.testing.assert_equal(processed_label.numpy(), prev_label.numpy()) | |
| def test_return_padded_image_and_label(self): | |
| image = np.dstack([[[5, 6], [9, 0]], [[4, 3], [3, 5]], [[7, 8], [1, 2]]]) | |
| image = tf.convert_to_tensor(image, dtype=tf.float32) | |
| label = np.array([[[1], [2]], [[3], [4]]]) | |
| expected_image = np.dstack([[[127.5, 127.5, 127.5, 127.5, 127.5], | |
| [127.5, 127.5, 127.5, 127.5, 127.5], | |
| [127.5, 5, 6, 127.5, 127.5], | |
| [127.5, 9, 0, 127.5, 127.5], | |
| [127.5, 127.5, 127.5, 127.5, 127.5]], | |
| [[127.5, 127.5, 127.5, 127.5, 127.5], | |
| [127.5, 127.5, 127.5, 127.5, 127.5], | |
| [127.5, 4, 3, 127.5, 127.5], | |
| [127.5, 3, 5, 127.5, 127.5], | |
| [127.5, 127.5, 127.5, 127.5, 127.5]], | |
| [[127.5, 127.5, 127.5, 127.5, 127.5], | |
| [127.5, 127.5, 127.5, 127.5, 127.5], | |
| [127.5, 7, 8, 127.5, 127.5], | |
| [127.5, 1, 2, 127.5, 127.5], | |
| [127.5, 127.5, 127.5, 127.5, 127.5]]]) | |
| expected_label = np.array([[[255], [255], [255], [255], [255]], | |
| [[255], [255], [255], [255], [255]], | |
| [[255], [1], [2], [255], [255]], | |
| [[255], [3], [4], [255], [255]], | |
| [[255], [255], [255], [255], [255]]]) | |
| padded_image, padded_label = input_preprocessing._pad_image_and_label( | |
| image, label, 2, 1, 5, 5, 255) | |
| np.testing.assert_allclose(padded_image.numpy(), expected_image) | |
| np.testing.assert_allclose(padded_label.numpy(), expected_label) | |
| def test_return_original_image_when_target_size_is_equal_to_image_size(self): | |
| height, width, _ = tf.shape(self._image) | |
| padded_image, _ = input_preprocessing._pad_image_and_label( | |
| self._image, None, 0, 0, height, width) | |
| np.testing.assert_allclose(padded_image.numpy(), self._image) | |
| def test_die_on_target_size_greater_than_image_size(self): | |
| height, width, _ = tf.shape(self._image) | |
| with self.assertRaises(tf.errors.InvalidArgumentError): | |
| _ = input_preprocessing._pad_image_and_label(self._image, None, 0, 0, | |
| height, width - 1) | |
| with self.assertRaises(tf.errors.InvalidArgumentError): | |
| _ = input_preprocessing._pad_image_and_label(self._image, None, 0, 0, | |
| height - 1, width) | |
| def test_die_if_target_size_not_possible_with_given_offset(self): | |
| height, width, _ = tf.shape(self._image) | |
| with self.assertRaises(tf.errors.InvalidArgumentError): | |
| _ = input_preprocessing._pad_image_and_label(self._image, None, 3, 3, | |
| height + 2, width + 2) | |
| def test_set_min_resize_value_only_during_training(self): | |
| crop_height = np.random.randint(33) | |
| crop_width = np.random.randint(33) | |
| _, processed_image, _, _, _ = ( | |
| input_preprocessing.preprocess_image_and_label( | |
| image=self._image, | |
| label=self._label, | |
| crop_height=crop_height, | |
| crop_width=crop_width, | |
| min_resize_value=[10], | |
| max_resize_value=None, | |
| ignore_label=255)) | |
| self.assertListEqual(processed_image.shape.as_list(), | |
| [crop_height, crop_width, 3]) | |
| if __name__ == '__main__': | |
| tf.test.main() | |