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"""Tests for models.faster_rcnn_inception_resnet_v2_feature_extractor.""" |
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import tensorflow as tf |
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from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res |
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class FasterRcnnInceptionResnetV2FeatureExtractorTest(tf.test.TestCase): |
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def _build_feature_extractor(self, first_stage_features_stride): |
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return frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor( |
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is_training=False, |
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first_stage_features_stride=first_stage_features_stride, |
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batch_norm_trainable=False, |
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reuse_weights=None, |
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weight_decay=0.0) |
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def test_extract_proposal_features_returns_expected_size(self): |
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feature_extractor = self._build_feature_extractor( |
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first_stage_features_stride=16) |
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preprocessed_inputs = tf.random_uniform( |
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[1, 299, 299, 3], maxval=255, dtype=tf.float32) |
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rpn_feature_map, _ = feature_extractor.extract_proposal_features( |
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preprocessed_inputs, scope='TestScope') |
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features_shape = tf.shape(rpn_feature_map) |
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init_op = tf.global_variables_initializer() |
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with self.test_session() as sess: |
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sess.run(init_op) |
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features_shape_out = sess.run(features_shape) |
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self.assertAllEqual(features_shape_out, [1, 19, 19, 1088]) |
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def test_extract_proposal_features_stride_eight(self): |
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feature_extractor = self._build_feature_extractor( |
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first_stage_features_stride=8) |
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preprocessed_inputs = tf.random_uniform( |
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[1, 224, 224, 3], maxval=255, dtype=tf.float32) |
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rpn_feature_map, _ = feature_extractor.extract_proposal_features( |
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preprocessed_inputs, scope='TestScope') |
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features_shape = tf.shape(rpn_feature_map) |
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init_op = tf.global_variables_initializer() |
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with self.test_session() as sess: |
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sess.run(init_op) |
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features_shape_out = sess.run(features_shape) |
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self.assertAllEqual(features_shape_out, [1, 28, 28, 1088]) |
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def test_extract_proposal_features_half_size_input(self): |
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feature_extractor = self._build_feature_extractor( |
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first_stage_features_stride=16) |
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preprocessed_inputs = tf.random_uniform( |
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[1, 112, 112, 3], maxval=255, dtype=tf.float32) |
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rpn_feature_map, _ = feature_extractor.extract_proposal_features( |
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preprocessed_inputs, scope='TestScope') |
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features_shape = tf.shape(rpn_feature_map) |
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init_op = tf.global_variables_initializer() |
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with self.test_session() as sess: |
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sess.run(init_op) |
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features_shape_out = sess.run(features_shape) |
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self.assertAllEqual(features_shape_out, [1, 7, 7, 1088]) |
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def test_extract_proposal_features_dies_on_invalid_stride(self): |
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with self.assertRaises(ValueError): |
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self._build_feature_extractor(first_stage_features_stride=99) |
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def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self): |
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feature_extractor = self._build_feature_extractor( |
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first_stage_features_stride=16) |
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preprocessed_inputs = tf.random_uniform( |
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[224, 224, 3], maxval=255, dtype=tf.float32) |
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with self.assertRaises(ValueError): |
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feature_extractor.extract_proposal_features( |
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preprocessed_inputs, scope='TestScope') |
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def test_extract_box_classifier_features_returns_expected_size(self): |
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feature_extractor = self._build_feature_extractor( |
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first_stage_features_stride=16) |
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proposal_feature_maps = tf.random_uniform( |
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[2, 17, 17, 1088], maxval=255, dtype=tf.float32) |
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proposal_classifier_features = ( |
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feature_extractor.extract_box_classifier_features( |
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proposal_feature_maps, scope='TestScope')) |
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features_shape = tf.shape(proposal_classifier_features) |
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init_op = tf.global_variables_initializer() |
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with self.test_session() as sess: |
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sess.run(init_op) |
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features_shape_out = sess.run(features_shape) |
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self.assertAllEqual(features_shape_out, [2, 8, 8, 1536]) |
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if __name__ == '__main__': |
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tf.test.main() |
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