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94 lines
3.9 KiB
94 lines
3.9 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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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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