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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
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
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()
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