# 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()