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matcher_test.py
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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.matcher."""
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.core import matcher
from object_detection.utils import test_case
class MatchTest(test_case.TestCase):
def test_get_correct_matched_columnIndices(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
matched_column_indices = match.matched_column_indices()
return matched_column_indices
expected_column_indices = [0, 1, 3, 5]
matched_column_indices = self.execute(graph_fn, [])
self.assertAllEqual(matched_column_indices, expected_column_indices)
def test_get_correct_counts(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 1, -2])
match = matcher.Match(match_results)
num_matched_columns = match.num_matched_columns()
num_unmatched_columns = match.num_unmatched_columns()
num_ignored_columns = match.num_ignored_columns()
num_matched_rows = match.num_matched_rows()
return [num_matched_columns, num_unmatched_columns, num_ignored_columns,
num_matched_rows]
(num_matched_columns_out, num_unmatched_columns_out,
num_ignored_columns_out,
num_matched_rows_out) = self.execute_cpu(graph_fn, [])
exp_num_matched_columns = 4
exp_num_unmatched_columns = 2
exp_num_ignored_columns = 1
exp_num_matched_rows = 3
self.assertAllEqual(num_matched_columns_out, exp_num_matched_columns)
self.assertAllEqual(num_unmatched_columns_out, exp_num_unmatched_columns)
self.assertAllEqual(num_ignored_columns_out, exp_num_ignored_columns)
self.assertAllEqual(num_matched_rows_out, exp_num_matched_rows)
def testGetCorrectUnmatchedColumnIndices(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
unmatched_column_indices = match.unmatched_column_indices()
return unmatched_column_indices
unmatched_column_indices = self.execute(graph_fn, [])
expected_column_indices = [2, 4]
self.assertAllEqual(unmatched_column_indices, expected_column_indices)
def testGetCorrectMatchedRowIndices(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
matched_row_indices = match.matched_row_indices()
return matched_row_indices
matched_row_indices = self.execute(graph_fn, [])
expected_row_indices = [3, 1, 0, 5]
self.assertAllEqual(matched_row_indices, expected_row_indices)
def test_get_correct_ignored_column_indices(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
ignored_column_indices = match.ignored_column_indices()
return ignored_column_indices
ignored_column_indices = self.execute(graph_fn, [])
expected_column_indices = [6]
self.assertAllEqual(ignored_column_indices, expected_column_indices)
def test_get_correct_matched_column_indicator(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
matched_column_indicator = match.matched_column_indicator()
return matched_column_indicator
expected_column_indicator = [True, True, False, True, False, True, False]
matched_column_indicator = self.execute(graph_fn, [])
self.assertAllEqual(matched_column_indicator, expected_column_indicator)
def test_get_correct_unmatched_column_indicator(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
unmatched_column_indicator = match.unmatched_column_indicator()
return unmatched_column_indicator
expected_column_indicator = [False, False, True, False, True, False, False]
unmatched_column_indicator = self.execute(graph_fn, [])
self.assertAllEqual(unmatched_column_indicator, expected_column_indicator)
def test_get_correct_ignored_column_indicator(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
ignored_column_indicator = match.ignored_column_indicator()
return ignored_column_indicator
expected_column_indicator = [False, False, False, False, False, False, True]
ignored_column_indicator = self.execute(graph_fn, [])
self.assertAllEqual(ignored_column_indicator, expected_column_indicator)
def test_get_correct_unmatched_ignored_column_indices(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
unmatched_ignored_column_indices = (match.
unmatched_or_ignored_column_indices())
return unmatched_ignored_column_indices
expected_column_indices = [2, 4, 6]
unmatched_ignored_column_indices = self.execute(graph_fn, [])
self.assertAllEqual(unmatched_ignored_column_indices,
expected_column_indices)
def test_all_columns_accounted_for(self):
# Note: deliberately setting to small number so not always
# all possibilities appear (matched, unmatched, ignored)
def graph_fn():
match_results = tf.random_uniform(
[num_matches], minval=-2, maxval=5, dtype=tf.int32)
match = matcher.Match(match_results)
matched_column_indices = match.matched_column_indices()
unmatched_column_indices = match.unmatched_column_indices()
ignored_column_indices = match.ignored_column_indices()
return (matched_column_indices, unmatched_column_indices,
ignored_column_indices)
num_matches = 10
matched, unmatched, ignored = self.execute(graph_fn, [])
all_indices = np.hstack((matched, unmatched, ignored))
all_indices_sorted = np.sort(all_indices)
self.assertAllEqual(all_indices_sorted,
np.arange(num_matches, dtype=np.int32))
def test_scalar_gather_based_on_match(self):
def graph_fn():
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
input_tensor = tf.constant([0, 1, 2, 3, 4, 5, 6, 7], dtype=tf.float32)
match = matcher.Match(match_results)
gathered_tensor = match.gather_based_on_match(input_tensor,
unmatched_value=100.,
ignored_value=200.)
return gathered_tensor
expected_gathered_tensor = [3, 1, 100, 0, 100, 5, 200]
gathered_tensor_out = self.execute(graph_fn, [])
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
def test_multidimensional_gather_based_on_match(self):
def graph_fn():
match_results = tf.constant([1, -1, -2])
input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
dtype=tf.float32)
match = matcher.Match(match_results)
gathered_tensor = match.gather_based_on_match(input_tensor,
unmatched_value=tf.zeros(4),
ignored_value=tf.zeros(4))
return gathered_tensor
expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0], [0, 0, 0, 0]]
gathered_tensor_out = self.execute(graph_fn, [])
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
def test_multidimensional_gather_based_on_match_with_matmul_gather_op(self):
def graph_fn():
match_results = tf.constant([1, -1, -2])
input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
dtype=tf.float32)
match = matcher.Match(match_results, use_matmul_gather=True)
gathered_tensor = match.gather_based_on_match(input_tensor,
unmatched_value=tf.zeros(4),
ignored_value=tf.zeros(4))
return gathered_tensor
expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0], [0, 0, 0, 0]]
gathered_tensor_out = self.execute(graph_fn, [])
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
if __name__ == '__main__':
tf.test.main()