# 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.matchers.argmax_matcher.""" import numpy as np import tensorflow as tf from object_detection.matchers import argmax_matcher class ArgMaxMatcherTest(tf.test.TestCase): def test_return_correct_matches_with_default_thresholds(self): similarity = np.array([[1., 1, 1, 3, 1], [2, -1, 2, 0, 4], [3, 0, -1, 0, 0]]) matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None) expected_matched_rows = np.array([2, 0, 1, 0, 1]) sim = tf.constant(similarity) match = matcher.match(sim) matched_cols = match.matched_column_indices() matched_rows = match.matched_row_indices() unmatched_cols = match.unmatched_column_indices() with self.test_session() as sess: res_matched_cols = sess.run(matched_cols) res_matched_rows = sess.run(matched_rows) res_unmatched_cols = sess.run(unmatched_cols) self.assertAllEqual(res_matched_rows, expected_matched_rows) self.assertAllEqual(res_matched_cols, np.arange(similarity.shape[1])) self.assertEmpty(res_unmatched_cols) def test_return_correct_matches_with_empty_rows(self): matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None) sim = 0.2*tf.ones([0, 5]) match = matcher.match(sim) unmatched_cols = match.unmatched_column_indices() with self.test_session() as sess: res_unmatched_cols = sess.run(unmatched_cols) self.assertAllEqual(res_unmatched_cols, np.arange(5)) def test_return_correct_matches_with_matched_threshold(self): similarity = np.array([[1, 1, 1, 3, 1], [2, -1, 2, 0, 4], [3, 0, -1, 0, 0]], dtype=np.int32) matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3) expected_matched_cols = np.array([0, 3, 4]) expected_matched_rows = np.array([2, 0, 1]) expected_unmatched_cols = np.array([1, 2]) sim = tf.constant(similarity) match = matcher.match(sim) matched_cols = match.matched_column_indices() matched_rows = match.matched_row_indices() unmatched_cols = match.unmatched_column_indices() init_op = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init_op) res_matched_cols = sess.run(matched_cols) res_matched_rows = sess.run(matched_rows) res_unmatched_cols = sess.run(unmatched_cols) self.assertAllEqual(res_matched_rows, expected_matched_rows) self.assertAllEqual(res_matched_cols, expected_matched_cols) self.assertAllEqual(res_unmatched_cols, expected_unmatched_cols) def test_return_correct_matches_with_matched_and_unmatched_threshold(self): similarity = np.array([[1, 1, 1, 3, 1], [2, -1, 2, 0, 4], [3, 0, -1, 0, 0]], dtype=np.int32) matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3, unmatched_threshold=2) expected_matched_cols = np.array([0, 3, 4]) expected_matched_rows = np.array([2, 0, 1]) expected_unmatched_cols = np.array([1]) # col 2 has too high maximum val sim = tf.constant(similarity) match = matcher.match(sim) matched_cols = match.matched_column_indices() matched_rows = match.matched_row_indices() unmatched_cols = match.unmatched_column_indices() with self.test_session() as sess: res_matched_cols = sess.run(matched_cols) res_matched_rows = sess.run(matched_rows) res_unmatched_cols = sess.run(unmatched_cols) self.assertAllEqual(res_matched_rows, expected_matched_rows) self.assertAllEqual(res_matched_cols, expected_matched_cols) self.assertAllEqual(res_unmatched_cols, expected_unmatched_cols) def test_return_correct_matches_negatives_lower_than_unmatched_false(self): similarity = np.array([[1, 1, 1, 3, 1], [2, -1, 2, 0, 4], [3, 0, -1, 0, 0]], dtype=np.int32) matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3, unmatched_threshold=2, negatives_lower_than_unmatched=False) expected_matched_cols = np.array([0, 3, 4]) expected_matched_rows = np.array([2, 0, 1]) expected_unmatched_cols = np.array([2]) # col 1 has too low maximum val sim = tf.constant(similarity) match = matcher.match(sim) matched_cols = match.matched_column_indices() matched_rows = match.matched_row_indices() unmatched_cols = match.unmatched_column_indices() with self.test_session() as sess: res_matched_cols = sess.run(matched_cols) res_matched_rows = sess.run(matched_rows) res_unmatched_cols = sess.run(unmatched_cols) self.assertAllEqual(res_matched_rows, expected_matched_rows) self.assertAllEqual(res_matched_cols, expected_matched_cols) self.assertAllEqual(res_unmatched_cols, expected_unmatched_cols) def test_return_correct_matches_unmatched_row_not_using_force_match(self): similarity = np.array([[1, 1, 1, 3, 1], [-1, 0, -2, -2, -1], [3, 0, -1, 2, 0]], dtype=np.int32) matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3, unmatched_threshold=2) expected_matched_cols = np.array([0, 3]) expected_matched_rows = np.array([2, 0]) expected_unmatched_cols = np.array([1, 2, 4]) sim = tf.constant(similarity) match = matcher.match(sim) matched_cols = match.matched_column_indices() matched_rows = match.matched_row_indices() unmatched_cols = match.unmatched_column_indices() with self.test_session() as sess: res_matched_cols = sess.run(matched_cols) res_matched_rows = sess.run(matched_rows) res_unmatched_cols = sess.run(unmatched_cols) self.assertAllEqual(res_matched_rows, expected_matched_rows) self.assertAllEqual(res_matched_cols, expected_matched_cols) self.assertAllEqual(res_unmatched_cols, expected_unmatched_cols) def test_return_correct_matches_unmatched_row_while_using_force_match(self): similarity = np.array([[1, 1, 1, 3, 1], [-1, 0, -2, -2, -1], [3, 0, -1, 2, 0]], dtype=np.int32) matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3, unmatched_threshold=2, force_match_for_each_row=True) expected_matched_cols = np.array([0, 1, 3]) expected_matched_rows = np.array([2, 1, 0]) expected_unmatched_cols = np.array([2, 4]) # col 2 has too high max val sim = tf.constant(similarity) match = matcher.match(sim) matched_cols = match.matched_column_indices() matched_rows = match.matched_row_indices() unmatched_cols = match.unmatched_column_indices() with self.test_session() as sess: res_matched_cols = sess.run(matched_cols) res_matched_rows = sess.run(matched_rows) res_unmatched_cols = sess.run(unmatched_cols) self.assertAllEqual(res_matched_rows, expected_matched_rows) self.assertAllEqual(res_matched_cols, expected_matched_cols) self.assertAllEqual(res_unmatched_cols, expected_unmatched_cols) def test_valid_arguments_corner_case(self): argmax_matcher.ArgMaxMatcher(matched_threshold=1, unmatched_threshold=1) def test_invalid_arguments_corner_case_negatives_lower_than_thres_false(self): with self.assertRaises(ValueError): argmax_matcher.ArgMaxMatcher(matched_threshold=1, unmatched_threshold=1, negatives_lower_than_unmatched=False) def test_invalid_arguments_no_matched_threshold(self): with self.assertRaises(ValueError): argmax_matcher.ArgMaxMatcher(matched_threshold=None, unmatched_threshold=4) def test_invalid_arguments_unmatched_thres_larger_than_matched_thres(self): with self.assertRaises(ValueError): argmax_matcher.ArgMaxMatcher(matched_threshold=1, unmatched_threshold=2) def test_set_values_using_indicator(self): input_a = np.array([3, 4, 5, 1, 4, 3, 2]) expected_b = np.array([3, 0, 0, 1, 0, 3, 2]) # Set a>3 to 0 expected_c = np.array( [3., 4., 5., -1., 4., 3., -1.]) # Set a<3 to -1. Float32 idxb_ = input_a > 3 idxc_ = input_a < 3 matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None) a = tf.constant(input_a) idxb = tf.constant(idxb_) idxc = tf.constant(idxc_) b = matcher._set_values_using_indicator(a, idxb, 0) c = matcher._set_values_using_indicator(tf.cast(a, tf.float32), idxc, -1) with self.test_session() as sess: res_b = sess.run(b) res_c = sess.run(c) self.assertAllEqual(res_b, expected_b) self.assertAllEqual(res_c, expected_c) if __name__ == '__main__': tf.test.main()