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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. | |
| """Test for panoptic_deeplab.py.""" | |
| import numpy as np | |
| import tensorflow as tf | |
| from deeplab2.model.post_processor import panoptic_deeplab | |
| class PostProcessingTest(tf.test.TestCase): | |
| def test_py_func_merge_semantic_and_instance_maps_can_run(self): | |
| batch = 1 | |
| height = 5 | |
| width = 5 | |
| semantic_prediction = tf.random.uniform((batch, height, width), | |
| minval=0, | |
| maxval=20, | |
| dtype=tf.int32) | |
| instance_maps = tf.random.uniform((batch, height, width), | |
| minval=0, | |
| maxval=3, | |
| dtype=tf.int32) | |
| thing_class_ids = tf.convert_to_tensor([1, 2, 3]) | |
| label_divisor = 256 | |
| stuff_area_limit = 3 | |
| void_label = 255 | |
| panoptic_prediction = panoptic_deeplab._merge_semantic_and_instance_maps( | |
| semantic_prediction, instance_maps, thing_class_ids, label_divisor, | |
| stuff_area_limit, void_label) | |
| self.assertListEqual(semantic_prediction.get_shape().as_list(), | |
| panoptic_prediction.get_shape().as_list()) | |
| def test_merge_semantic_and_instance_maps_with_a_simple_example(self): | |
| semantic_prediction = tf.convert_to_tensor( | |
| [[[0, 0, 0, 0], | |
| [0, 1, 1, 0], | |
| [0, 2, 2, 0], | |
| [2, 2, 3, 3]]], dtype=tf.int32) | |
| instance_maps = tf.convert_to_tensor( | |
| [[[0, 0, 0, 0], | |
| [0, 0, 0, 0], | |
| [0, 1, 1, 0], | |
| [2, 2, 3, 3]]], dtype=tf.int32) | |
| thing_class_ids = tf.convert_to_tensor([2, 3]) | |
| label_divisor = 256 | |
| stuff_area_limit = 3 | |
| void_label = 255 | |
| # The expected_panoptic_prediction is computed as follows. | |
| # For `thing` segmentation, instance 1, 2, and 3 are kept, but instance 3 | |
| # will have a new instance ID 1, since it is the first instance in its | |
| # own semantic label. | |
| # For `stuff` segmentation, class-0 region is kept, while class-1 region | |
| # is re-labeled as `void_label * label_divisor` since its area is smaller | |
| # than stuff_area_limit. | |
| expected_panoptic_prediction = tf.convert_to_tensor( | |
| [[[0, 0, 0, 0], | |
| [0, void_label * label_divisor, void_label * label_divisor, 0], | |
| [0, 2 * label_divisor + 1, 2 * label_divisor + 1, 0], | |
| [2 * label_divisor + 2, 2 * label_divisor + 2, 3 * label_divisor + 1, | |
| 3 * label_divisor + 1]]], dtype=tf.int32) | |
| panoptic_prediction = panoptic_deeplab._merge_semantic_and_instance_maps( | |
| semantic_prediction, instance_maps, thing_class_ids, label_divisor, | |
| stuff_area_limit, void_label) | |
| np.testing.assert_equal(expected_panoptic_prediction.numpy(), | |
| panoptic_prediction.numpy()) | |
| def test_gets_panoptic_predictions_with_score(self): | |
| batch = 1 | |
| height = 5 | |
| width = 5 | |
| classes = 3 | |
| semantic_logits = tf.random.uniform((batch, 1, 1, classes)) | |
| semantic_logits = tf.tile(semantic_logits, (1, height, width, 1)) | |
| center_heatmap = tf.convert_to_tensor([ | |
| [1.0, 0.0, 0.0, 0.0, 0.0], | |
| [0.8, 0.0, 0.0, 0.0, 0.0], | |
| [0.0, 0.0, 0.0, 0.0, 0.0], | |
| [0.0, 0.0, 0.0, 0.1, 0.7], | |
| [0.0, 0.0, 0.0, 0.0, 0.2], | |
| ], | |
| dtype=tf.float32) | |
| center_heatmap = tf.expand_dims(center_heatmap, 0) | |
| center_heatmap = tf.expand_dims(center_heatmap, 3) | |
| center_offsets = tf.zeros((batch, height, width, 2)) | |
| center_threshold = 0.0 | |
| thing_class_ids = tf.range(classes) # No "stuff" classes. | |
| label_divisor = 256 | |
| stuff_area_limit = 16 | |
| void_label = classes | |
| nms_kernel_size = 3 | |
| keep_k_centers = 2 | |
| merge_semantic_and_instance_with_tf_op = True | |
| result = panoptic_deeplab._get_panoptic_predictions( | |
| semantic_logits, center_heatmap, center_offsets, center_threshold, | |
| thing_class_ids, label_divisor, stuff_area_limit, void_label, | |
| nms_kernel_size, keep_k_centers, merge_semantic_and_instance_with_tf_op) | |
| instance_maps = result[2].numpy() | |
| instance_scores = result[4].numpy() | |
| self.assertSequenceEqual(instance_maps.shape, (batch, height, width)) | |
| expected_instances = [[ | |
| [1, 1, 1, 1, 2], | |
| [1, 1, 1, 2, 2], | |
| [1, 1, 2, 2, 2], | |
| [1, 2, 2, 2, 2], | |
| [1, 2, 2, 2, 2], | |
| ]] | |
| np.testing.assert_array_equal(instance_maps, expected_instances) | |
| self.assertSequenceEqual(instance_scores.shape, (batch, height, width)) | |
| expected_instance_scores = [[ | |
| [1.0, 1.0, 1.0, 1.0, 0.7], | |
| [1.0, 1.0, 1.0, 0.7, 0.7], | |
| [1.0, 1.0, 0.7, 0.7, 0.7], | |
| [1.0, 0.7, 0.7, 0.7, 0.7], | |
| [1.0, 0.7, 0.7, 0.7, 0.7], | |
| ]] | |
| np.testing.assert_array_almost_equal(instance_scores, | |
| expected_instance_scores) | |
| if __name__ == '__main__': | |
| tf.test.main() | |