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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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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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