| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| """Tests for post_processing_builder.""" |
|
|
| import tensorflow as tf |
| from google.protobuf import text_format |
| from object_detection.builders import post_processing_builder |
| from object_detection.protos import post_processing_pb2 |
|
|
|
|
| class PostProcessingBuilderTest(tf.test.TestCase): |
|
|
| def test_build_non_max_suppressor_with_correct_parameters(self): |
| post_processing_text_proto = """ |
| batch_non_max_suppression { |
| score_threshold: 0.7 |
| iou_threshold: 0.6 |
| max_detections_per_class: 100 |
| max_total_detections: 300 |
| } |
| """ |
| post_processing_config = post_processing_pb2.PostProcessing() |
| text_format.Merge(post_processing_text_proto, post_processing_config) |
| non_max_suppressor, _ = post_processing_builder.build( |
| post_processing_config) |
| self.assertEqual(non_max_suppressor.keywords['max_size_per_class'], 100) |
| self.assertEqual(non_max_suppressor.keywords['max_total_size'], 300) |
| self.assertAlmostEqual(non_max_suppressor.keywords['score_thresh'], 0.7) |
| self.assertAlmostEqual(non_max_suppressor.keywords['iou_thresh'], 0.6) |
|
|
| def test_build_identity_score_converter(self): |
| post_processing_text_proto = """ |
| score_converter: IDENTITY |
| """ |
| post_processing_config = post_processing_pb2.PostProcessing() |
| text_format.Merge(post_processing_text_proto, post_processing_config) |
| _, score_converter = post_processing_builder.build( |
| post_processing_config) |
| self.assertEqual(score_converter.__name__, 'identity_with_logit_scale') |
|
|
| inputs = tf.constant([1, 1], tf.float32) |
| outputs = score_converter(inputs) |
| with self.test_session() as sess: |
| converted_scores = sess.run(outputs) |
| expected_converted_scores = sess.run(inputs) |
| self.assertAllClose(converted_scores, expected_converted_scores) |
|
|
| def test_build_identity_score_converter_with_logit_scale(self): |
| post_processing_text_proto = """ |
| score_converter: IDENTITY |
| logit_scale: 2.0 |
| """ |
| post_processing_config = post_processing_pb2.PostProcessing() |
| text_format.Merge(post_processing_text_proto, post_processing_config) |
| _, score_converter = post_processing_builder.build(post_processing_config) |
| self.assertEqual(score_converter.__name__, 'identity_with_logit_scale') |
|
|
| inputs = tf.constant([1, 1], tf.float32) |
| outputs = score_converter(inputs) |
| with self.test_session() as sess: |
| converted_scores = sess.run(outputs) |
| expected_converted_scores = sess.run(tf.constant([.5, .5], tf.float32)) |
| self.assertAllClose(converted_scores, expected_converted_scores) |
|
|
| def test_build_sigmoid_score_converter(self): |
| post_processing_text_proto = """ |
| score_converter: SIGMOID |
| """ |
| post_processing_config = post_processing_pb2.PostProcessing() |
| text_format.Merge(post_processing_text_proto, post_processing_config) |
| _, score_converter = post_processing_builder.build(post_processing_config) |
| self.assertEqual(score_converter.__name__, 'sigmoid_with_logit_scale') |
|
|
| def test_build_softmax_score_converter(self): |
| post_processing_text_proto = """ |
| score_converter: SOFTMAX |
| """ |
| post_processing_config = post_processing_pb2.PostProcessing() |
| text_format.Merge(post_processing_text_proto, post_processing_config) |
| _, score_converter = post_processing_builder.build(post_processing_config) |
| self.assertEqual(score_converter.__name__, 'softmax_with_logit_scale') |
|
|
| def test_build_softmax_score_converter_with_temperature(self): |
| post_processing_text_proto = """ |
| score_converter: SOFTMAX |
| logit_scale: 2.0 |
| """ |
| post_processing_config = post_processing_pb2.PostProcessing() |
| text_format.Merge(post_processing_text_proto, post_processing_config) |
| _, score_converter = post_processing_builder.build(post_processing_config) |
| self.assertEqual(score_converter.__name__, 'softmax_with_logit_scale') |
|
|
| def test_build_calibrator_with_nonempty_config(self): |
| """Test that identity function used when no calibration_config specified.""" |
| |
| post_processing_text_proto = """ |
| score_converter: SOFTMAX |
| calibration_config { |
| function_approximation { |
| x_y_pairs { |
| x_y_pair { |
| x: 0.0 |
| y: 0.5 |
| } |
| x_y_pair { |
| x: 1.0 |
| y: 0.5 |
| }}}}""" |
| post_processing_config = post_processing_pb2.PostProcessing() |
| text_format.Merge(post_processing_text_proto, post_processing_config) |
| _, calibrated_score_conversion_fn = post_processing_builder.build( |
| post_processing_config) |
| self.assertEqual(calibrated_score_conversion_fn.__name__, |
| 'calibrate_with_function_approximation') |
|
|
| input_scores = tf.constant([1, 1], tf.float32) |
| outputs = calibrated_score_conversion_fn(input_scores) |
| with self.test_session() as sess: |
| calibrated_scores = sess.run(outputs) |
| expected_calibrated_scores = sess.run(tf.constant([0.5, 0.5], tf.float32)) |
| self.assertAllClose(calibrated_scores, expected_calibrated_scores) |
|
|
|
|
| if __name__ == '__main__': |
| tf.test.main() |
|
|
