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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.core.bipartite_matcher.""" | |
| import unittest | |
| import numpy as np | |
| import tensorflow.compat.v1 as tf | |
| from object_detection.utils import test_case | |
| from object_detection.utils import tf_version | |
| if tf_version.is_tf1(): | |
| from object_detection.matchers import bipartite_matcher # pylint: disable=g-import-not-at-top | |
| class GreedyBipartiteMatcherTest(test_case.TestCase): | |
| def test_get_expected_matches_when_all_rows_are_valid(self): | |
| similarity_matrix = np.array([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]], | |
| dtype=np.float32) | |
| valid_rows = np.ones([2], dtype=np.bool) | |
| expected_match_results = [-1, 1, 0] | |
| def graph_fn(similarity_matrix, valid_rows): | |
| matcher = bipartite_matcher.GreedyBipartiteMatcher() | |
| match = matcher.match(similarity_matrix, valid_rows=valid_rows) | |
| return match._match_results | |
| match_results_out = self.execute(graph_fn, [similarity_matrix, valid_rows]) | |
| self.assertAllEqual(match_results_out, expected_match_results) | |
| def test_get_expected_matches_with_all_rows_be_default(self): | |
| similarity_matrix = np.array([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]], | |
| dtype=np.float32) | |
| expected_match_results = [-1, 1, 0] | |
| def graph_fn(similarity_matrix): | |
| matcher = bipartite_matcher.GreedyBipartiteMatcher() | |
| match = matcher.match(similarity_matrix) | |
| return match._match_results | |
| match_results_out = self.execute(graph_fn, [similarity_matrix]) | |
| self.assertAllEqual(match_results_out, expected_match_results) | |
| def test_get_no_matches_with_zero_valid_rows(self): | |
| similarity_matrix = np.array([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]], | |
| dtype=np.float32) | |
| valid_rows = np.zeros([2], dtype=np.bool) | |
| expected_match_results = [-1, -1, -1] | |
| def graph_fn(similarity_matrix, valid_rows): | |
| matcher = bipartite_matcher.GreedyBipartiteMatcher() | |
| match = matcher.match(similarity_matrix, valid_rows=valid_rows) | |
| return match._match_results | |
| match_results_out = self.execute(graph_fn, [similarity_matrix, valid_rows]) | |
| self.assertAllEqual(match_results_out, expected_match_results) | |
| def test_get_expected_matches_with_only_one_valid_row(self): | |
| similarity_matrix = np.array([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]], | |
| dtype=np.float32) | |
| valid_rows = np.array([True, False], dtype=np.bool) | |
| expected_match_results = [-1, -1, 0] | |
| def graph_fn(similarity_matrix, valid_rows): | |
| matcher = bipartite_matcher.GreedyBipartiteMatcher() | |
| match = matcher.match(similarity_matrix, valid_rows=valid_rows) | |
| return match._match_results | |
| match_results_out = self.execute(graph_fn, [similarity_matrix, valid_rows]) | |
| self.assertAllEqual(match_results_out, expected_match_results) | |
| def test_get_expected_matches_with_only_one_valid_row_at_bottom(self): | |
| similarity_matrix = np.array([[0.15, 0.2, 0.3], [0.50, 0.1, 0.8]], | |
| dtype=np.float32) | |
| valid_rows = np.array([False, True], dtype=np.bool) | |
| expected_match_results = [-1, -1, 0] | |
| def graph_fn(similarity_matrix, valid_rows): | |
| matcher = bipartite_matcher.GreedyBipartiteMatcher() | |
| match = matcher.match(similarity_matrix, valid_rows=valid_rows) | |
| return match._match_results | |
| match_results_out = self.execute(graph_fn, [similarity_matrix, valid_rows]) | |
| self.assertAllEqual(match_results_out, expected_match_results) | |
| if __name__ == '__main__': | |
| tf.test.main() | |