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"""Tests for object_detection.utils.per_image_vrd_evaluation.""" |
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import numpy as np |
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import tensorflow as tf |
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from object_detection.utils import per_image_vrd_evaluation |
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class SingleClassPerImageVrdEvaluationTest(tf.test.TestCase): |
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def setUp(self): |
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matching_iou_threshold = 0.5 |
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self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation( |
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matching_iou_threshold) |
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box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))]) |
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self.detected_box_tuples = np.array( |
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[([0, 0, 1.1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 1, 2, 2]), |
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([1, 1, 2, 2], [0, 0, 1.1, 1])], |
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dtype=box_data_type) |
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self.detected_scores = np.array([0.8, 0.2, 0.1], dtype=float) |
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self.groundtruth_box_tuples = np.array( |
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[([0, 0, 1, 1], [1, 1, 2, 2])], dtype=box_data_type) |
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def test_tp_fp_eval(self): |
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tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_box_tuples, self.groundtruth_box_tuples) |
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expected_tp_fp_labels = np.array([True, False, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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def test_tp_fp_eval_empty_gt(self): |
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box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))]) |
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tp_fp_labels = self.eval._compute_tp_fp_for_single_class( |
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self.detected_box_tuples, np.array([], dtype=box_data_type)) |
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expected_tp_fp_labels = np.array([False, False, False], dtype=bool) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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class MultiClassPerImageVrdEvaluationTest(tf.test.TestCase): |
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def setUp(self): |
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matching_iou_threshold = 0.5 |
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self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation( |
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matching_iou_threshold) |
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box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))]) |
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label_data_type = np.dtype([('subject', 'i4'), ('object', 'i4'), |
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('relation', 'i4')]) |
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self.detected_box_tuples = np.array( |
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[([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1.1, 1], [1, 1, 2, 2]), |
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([1, 1, 2, 2], [0, 0, 1.1, 1]), ([0, 0, 1, 1], [3, 4, 5, 6])], |
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dtype=box_data_type) |
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self.detected_class_tuples = np.array( |
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[(1, 2, 3), (1, 2, 3), (1, 2, 3), (1, 4, 5)], dtype=label_data_type) |
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self.detected_scores = np.array([0.2, 0.8, 0.1, 0.5], dtype=float) |
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self.groundtruth_box_tuples = np.array( |
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[([0, 0, 1, 1], [1, 1, 2, 2]), ([1, 1, 2, 2], [0, 0, 1.1, 1]), |
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([0, 0, 1, 1], [3, 4, 5, 5.5])], |
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dtype=box_data_type) |
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self.groundtruth_class_tuples = np.array( |
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[(1, 2, 3), (1, 7, 3), (1, 4, 5)], dtype=label_data_type) |
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def test_tp_fp_eval(self): |
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scores, tp_fp_labels, mapping = self.eval.compute_detection_tp_fp( |
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self.detected_box_tuples, self.detected_scores, |
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self.detected_class_tuples, self.groundtruth_box_tuples, |
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self.groundtruth_class_tuples) |
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expected_scores = np.array([0.8, 0.5, 0.2, 0.1], dtype=float) |
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expected_tp_fp_labels = np.array([True, True, False, False], dtype=bool) |
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expected_mapping = np.array([1, 3, 0, 2]) |
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self.assertTrue(np.allclose(expected_scores, scores)) |
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self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels)) |
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self.assertTrue(np.allclose(expected_mapping, mapping)) |
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if __name__ == '__main__': |
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tf.test.main() |
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