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| # Copyright 2017 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 object_detection.metrics.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| import tensorflow.compat.v1 as tf | |
| from object_detection.utils import metrics | |
| class MetricsTest(tf.test.TestCase): | |
| def test_compute_cor_loc(self): | |
| num_gt_imgs_per_class = np.array([100, 1, 5, 1, 1], dtype=int) | |
| num_images_correctly_detected_per_class = np.array( | |
| [10, 0, 1, 0, 0], dtype=int) | |
| corloc = metrics.compute_cor_loc(num_gt_imgs_per_class, | |
| num_images_correctly_detected_per_class) | |
| expected_corloc = np.array([0.1, 0, 0.2, 0, 0], dtype=float) | |
| self.assertTrue(np.allclose(corloc, expected_corloc)) | |
| def test_compute_cor_loc_nans(self): | |
| num_gt_imgs_per_class = np.array([100, 0, 0, 1, 1], dtype=int) | |
| num_images_correctly_detected_per_class = np.array( | |
| [10, 0, 1, 0, 0], dtype=int) | |
| corloc = metrics.compute_cor_loc(num_gt_imgs_per_class, | |
| num_images_correctly_detected_per_class) | |
| expected_corloc = np.array([0.1, np.nan, np.nan, 0, 0], dtype=float) | |
| self.assertAllClose(corloc, expected_corloc) | |
| def test_compute_precision_recall(self): | |
| num_gt = 10 | |
| scores = np.array([0.4, 0.3, 0.6, 0.2, 0.7, 0.1], dtype=float) | |
| labels = np.array([0, 1, 1, 0, 0, 1], dtype=bool) | |
| labels_float_type = np.array([0, 1, 1, 0, 0, 1], dtype=float) | |
| accumulated_tp_count = np.array([0, 1, 1, 2, 2, 3], dtype=float) | |
| expected_precision = accumulated_tp_count / np.array([1, 2, 3, 4, 5, 6]) | |
| expected_recall = accumulated_tp_count / num_gt | |
| precision, recall = metrics.compute_precision_recall(scores, labels, num_gt) | |
| precision_float_type, recall_float_type = metrics.compute_precision_recall( | |
| scores, labels_float_type, num_gt) | |
| self.assertAllClose(precision, expected_precision) | |
| self.assertAllClose(recall, expected_recall) | |
| self.assertAllClose(precision_float_type, expected_precision) | |
| self.assertAllClose(recall_float_type, expected_recall) | |
| def test_compute_precision_recall_float(self): | |
| num_gt = 10 | |
| scores = np.array([0.4, 0.3, 0.6, 0.2, 0.7, 0.1], dtype=float) | |
| labels_float = np.array([0, 1, 1, 0.5, 0, 1], dtype=float) | |
| expected_precision = np.array( | |
| [0., 0.5, 0.33333333, 0.5, 0.55555556, 0.63636364], dtype=float) | |
| expected_recall = np.array([0., 0.1, 0.1, 0.2, 0.25, 0.35], dtype=float) | |
| precision, recall = metrics.compute_precision_recall( | |
| scores, labels_float, num_gt) | |
| self.assertAllClose(precision, expected_precision) | |
| self.assertAllClose(recall, expected_recall) | |
| def test_compute_average_precision(self): | |
| precision = np.array([0.8, 0.76, 0.9, 0.65, 0.7, 0.5, 0.55, 0], dtype=float) | |
| recall = np.array([0.3, 0.3, 0.4, 0.4, 0.45, 0.45, 0.5, 0.5], dtype=float) | |
| processed_precision = np.array( | |
| [0.9, 0.9, 0.9, 0.7, 0.7, 0.55, 0.55, 0], dtype=float) | |
| recall_interval = np.array([0.3, 0, 0.1, 0, 0.05, 0, 0.05, 0], dtype=float) | |
| expected_mean_ap = np.sum(recall_interval * processed_precision) | |
| mean_ap = metrics.compute_average_precision(precision, recall) | |
| self.assertAlmostEqual(expected_mean_ap, mean_ap) | |
| def test_compute_precision_recall_and_ap_no_groundtruth(self): | |
| num_gt = 0 | |
| scores = np.array([0.4, 0.3, 0.6, 0.2, 0.7, 0.1], dtype=float) | |
| labels = np.array([0, 0, 0, 0, 0, 0], dtype=bool) | |
| expected_precision = None | |
| expected_recall = None | |
| precision, recall = metrics.compute_precision_recall(scores, labels, num_gt) | |
| self.assertEquals(precision, expected_precision) | |
| self.assertEquals(recall, expected_recall) | |
| ap = metrics.compute_average_precision(precision, recall) | |
| self.assertTrue(np.isnan(ap)) | |
| def test_compute_recall_at_k(self): | |
| num_gt = 4 | |
| tp_fp = [ | |
| np.array([1, 0, 0], dtype=float), | |
| np.array([0, 1], dtype=float), | |
| np.array([0, 0, 0, 0, 0], dtype=float) | |
| ] | |
| tp_fp_bool = [ | |
| np.array([True, False, False], dtype=bool), | |
| np.array([False, True], dtype=float), | |
| np.array([False, False, False, False, False], dtype=float) | |
| ] | |
| recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1) | |
| recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3) | |
| recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5) | |
| recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3) | |
| self.assertAlmostEqual(recall_1, 0.25) | |
| self.assertAlmostEqual(recall_3, 0.5) | |
| self.assertAlmostEqual(recall_3_bool, 0.5) | |
| self.assertAlmostEqual(recall_5, 0.5) | |
| def test_compute_median_rank_at_k(self): | |
| tp_fp = [ | |
| np.array([1, 0, 0], dtype=float), | |
| np.array([0, 0.1], dtype=float), | |
| np.array([0, 0, 0, 0, 0], dtype=float) | |
| ] | |
| tp_fp_bool = [ | |
| np.array([True, False, False], dtype=bool), | |
| np.array([False, True], dtype=float), | |
| np.array([False, False, False, False, False], dtype=float) | |
| ] | |
| median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1) | |
| median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3) | |
| median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5) | |
| median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3) | |
| self.assertEquals(median_ranks_1, 0) | |
| self.assertEquals(median_ranks_3, 0.5) | |
| self.assertEquals(median_ranks_3_bool, 0.5) | |
| self.assertEquals(median_ranks_5, 0.5) | |
| if __name__ == '__main__': | |
| tf.test.main() | |