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138 lines
5.7 KiB
138 lines
5.7 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for post_processing_builder."""
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import tensorflow as tf
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from google.protobuf import text_format
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from object_detection.builders import post_processing_builder
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from object_detection.protos import post_processing_pb2
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class PostProcessingBuilderTest(tf.test.TestCase):
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def test_build_non_max_suppressor_with_correct_parameters(self):
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post_processing_text_proto = """
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batch_non_max_suppression {
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score_threshold: 0.7
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iou_threshold: 0.6
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max_detections_per_class: 100
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max_total_detections: 300
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}
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"""
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post_processing_config = post_processing_pb2.PostProcessing()
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text_format.Merge(post_processing_text_proto, post_processing_config)
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non_max_suppressor, _ = post_processing_builder.build(
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post_processing_config)
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self.assertEqual(non_max_suppressor.keywords['max_size_per_class'], 100)
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self.assertEqual(non_max_suppressor.keywords['max_total_size'], 300)
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self.assertAlmostEqual(non_max_suppressor.keywords['score_thresh'], 0.7)
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self.assertAlmostEqual(non_max_suppressor.keywords['iou_thresh'], 0.6)
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def test_build_identity_score_converter(self):
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post_processing_text_proto = """
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score_converter: IDENTITY
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"""
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post_processing_config = post_processing_pb2.PostProcessing()
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text_format.Merge(post_processing_text_proto, post_processing_config)
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_, score_converter = post_processing_builder.build(
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post_processing_config)
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self.assertEqual(score_converter.__name__, 'identity_with_logit_scale')
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inputs = tf.constant([1, 1], tf.float32)
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outputs = score_converter(inputs)
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with self.test_session() as sess:
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converted_scores = sess.run(outputs)
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expected_converted_scores = sess.run(inputs)
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self.assertAllClose(converted_scores, expected_converted_scores)
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def test_build_identity_score_converter_with_logit_scale(self):
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post_processing_text_proto = """
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score_converter: IDENTITY
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logit_scale: 2.0
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"""
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post_processing_config = post_processing_pb2.PostProcessing()
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text_format.Merge(post_processing_text_proto, post_processing_config)
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_, score_converter = post_processing_builder.build(post_processing_config)
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self.assertEqual(score_converter.__name__, 'identity_with_logit_scale')
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inputs = tf.constant([1, 1], tf.float32)
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outputs = score_converter(inputs)
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with self.test_session() as sess:
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converted_scores = sess.run(outputs)
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expected_converted_scores = sess.run(tf.constant([.5, .5], tf.float32))
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self.assertAllClose(converted_scores, expected_converted_scores)
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def test_build_sigmoid_score_converter(self):
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post_processing_text_proto = """
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score_converter: SIGMOID
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"""
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post_processing_config = post_processing_pb2.PostProcessing()
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text_format.Merge(post_processing_text_proto, post_processing_config)
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_, score_converter = post_processing_builder.build(post_processing_config)
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self.assertEqual(score_converter.__name__, 'sigmoid_with_logit_scale')
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def test_build_softmax_score_converter(self):
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post_processing_text_proto = """
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score_converter: SOFTMAX
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"""
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post_processing_config = post_processing_pb2.PostProcessing()
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text_format.Merge(post_processing_text_proto, post_processing_config)
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_, score_converter = post_processing_builder.build(post_processing_config)
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self.assertEqual(score_converter.__name__, 'softmax_with_logit_scale')
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def test_build_softmax_score_converter_with_temperature(self):
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post_processing_text_proto = """
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score_converter: SOFTMAX
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logit_scale: 2.0
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"""
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post_processing_config = post_processing_pb2.PostProcessing()
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text_format.Merge(post_processing_text_proto, post_processing_config)
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_, score_converter = post_processing_builder.build(post_processing_config)
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self.assertEqual(score_converter.__name__, 'softmax_with_logit_scale')
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def test_build_calibrator_with_nonempty_config(self):
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"""Test that identity function used when no calibration_config specified."""
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# Calibration config maps all scores to 0.5.
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post_processing_text_proto = """
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score_converter: SOFTMAX
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calibration_config {
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function_approximation {
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x_y_pairs {
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x_y_pair {
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x: 0.0
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y: 0.5
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}
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x_y_pair {
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x: 1.0
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y: 0.5
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}}}}"""
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post_processing_config = post_processing_pb2.PostProcessing()
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text_format.Merge(post_processing_text_proto, post_processing_config)
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_, calibrated_score_conversion_fn = post_processing_builder.build(
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post_processing_config)
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self.assertEqual(calibrated_score_conversion_fn.__name__,
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'calibrate_with_function_approximation')
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input_scores = tf.constant([1, 1], tf.float32)
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outputs = calibrated_score_conversion_fn(input_scores)
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with self.test_session() as sess:
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calibrated_scores = sess.run(outputs)
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expected_calibrated_scores = sess.run(tf.constant([0.5, 0.5], tf.float32))
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self.assertAllClose(calibrated_scores, expected_calibrated_scores)
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if __name__ == '__main__':
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tf.test.main()
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