# Copyright 2023 The TensorFlow Authors. All Rights Reserved. # # 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 ops.""" import numpy as np import tensorflow as tf, tf_keras from official.vision.utils.object_detection import ops class OpsTest(tf.test.TestCase): def test_merge_boxes_with_multiple_labels(self): boxes = tf.constant( [ [0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75], [0.25, 0.25, 0.75, 0.75], ], dtype=tf.float32, ) class_indices = tf.constant([0, 4, 2], dtype=tf.int32) class_confidences = tf.constant([0.8, 0.2, 0.1], dtype=tf.float32) num_classes = 5 merged_boxes, merged_classes, merged_confidences, merged_box_indices = ( ops.merge_boxes_with_multiple_labels( boxes, class_indices, class_confidences, num_classes ) ) expected_merged_boxes = np.array( [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=np.float32 ) expected_merged_classes = np.array( [[1, 0, 1, 0, 0], [0, 0, 0, 0, 1]], dtype=np.int32 ) expected_merged_confidences = np.array( [[0.8, 0, 0.1, 0, 0], [0, 0, 0, 0, 0.2]], dtype=np.float32 ) expected_merged_box_indices = np.array([0, 1], dtype=np.int32) self.assertAllClose(merged_boxes.numpy(), expected_merged_boxes) self.assertAllClose(merged_classes.numpy(), expected_merged_classes) self.assertAllClose(merged_confidences.numpy(), expected_merged_confidences) self.assertAllClose(merged_box_indices.numpy(), expected_merged_box_indices) def test_merge_boxes_with_multiple_labels_corner_case(self): boxes = tf.constant( [ [0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 0, 1, 1], [0, 1, 1, 1], [0, 0, 1, 1], ], dtype=tf.float32, ) class_indices = tf.constant([0, 1, 2, 3, 2, 1, 0, 3], dtype=tf.int32) class_confidences = tf.constant( [0.1, 0.9, 0.2, 0.8, 0.3, 0.7, 0.4, 0.6], dtype=tf.float32 ) num_classes = 4 merged_boxes, merged_classes, merged_confidences, merged_box_indices = ( ops.merge_boxes_with_multiple_labels( boxes, class_indices, class_confidences, num_classes ) ) expected_merged_boxes = np.array( [[0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1]], dtype=np.float32, ) expected_merged_classes = np.array( [[1, 0, 0, 1], [1, 1, 0, 0], [0, 1, 1, 0], [0, 0, 1, 1]], dtype=np.int32 ) expected_merged_confidences = np.array( [ [0.1, 0, 0, 0.6], [0.4, 0.9, 0, 0], [0, 0.7, 0.2, 0], [0, 0, 0.3, 0.8], ], dtype=np.float32, ) expected_merged_box_indices = np.array([0, 1, 2, 3], dtype=np.int32) self.assertAllClose(merged_boxes.numpy(), expected_merged_boxes) self.assertAllClose(merged_classes.numpy(), expected_merged_classes) self.assertAllClose(merged_confidences.numpy(), expected_merged_confidences) self.assertAllClose(merged_box_indices.numpy(), expected_merged_box_indices) def test_merge_boxes_with_empty_inputs(self): boxes = tf.zeros([0, 4], dtype=tf.float32) class_indices = tf.constant([], dtype=tf.int32) class_confidences = tf.constant([], dtype=tf.float32) num_classes = 5 merged_boxes, merged_classes, merged_confidences, merged_box_indices = ( ops.merge_boxes_with_multiple_labels( boxes, class_indices, class_confidences, num_classes ) ) self.assertAllEqual(merged_boxes.shape, [0, 4]) self.assertAllEqual(merged_classes.shape, [0, 5]) self.assertAllEqual(merged_confidences.shape, [0, 5]) self.assertAllEqual(merged_box_indices.shape, [0]) if __name__ == '__main__': tf.test.main()