# Copyright 2018 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 ssd_mobilenet_v1_fpn_feature_extractor.""" import numpy as np import tensorflow as tf from object_detection.models import ssd_feature_extractor_test from object_detection.models import ssd_mobilenet_v1_fpn_feature_extractor slim = tf.contrib.slim class SsdMobilenetV1FpnFeatureExtractorTest( ssd_feature_extractor_test.SsdFeatureExtractorTestBase): def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, is_training=True, use_explicit_padding=False): """Constructs a new feature extractor. Args: depth_multiplier: float depth multiplier for feature extractor pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. is_training: whether the network is in training mode. use_explicit_padding: Use 'VALID' padding for convolutions, but prepad inputs so that the output dimensions are the same as if 'SAME' padding were used. Returns: an ssd_meta_arch.SSDFeatureExtractor object. """ min_depth = 32 return (ssd_mobilenet_v1_fpn_feature_extractor. SSDMobileNetV1FpnFeatureExtractor( is_training, depth_multiplier, min_depth, pad_to_multiple, self.conv_hyperparams_fn, use_explicit_padding=use_explicit_padding)) def test_extract_features_returns_correct_shapes_256(self): image_height = 256 image_width = 256 depth_multiplier = 1.0 pad_to_multiple = 1 expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256), (2, 8, 8, 256), (2, 4, 4, 256), (2, 2, 2, 256)] self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=False) self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=True) def test_extract_features_returns_correct_shapes_384(self): image_height = 320 image_width = 320 depth_multiplier = 1.0 pad_to_multiple = 1 expected_feature_map_shape = [(2, 40, 40, 256), (2, 20, 20, 256), (2, 10, 10, 256), (2, 5, 5, 256), (2, 3, 3, 256)] self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=False) self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=True) def test_extract_features_with_dynamic_image_shape(self): image_height = 256 image_width = 256 depth_multiplier = 1.0 pad_to_multiple = 1 expected_feature_map_shape = [(2, 32, 32, 256), (2, 16, 16, 256), (2, 8, 8, 256), (2, 4, 4, 256), (2, 2, 2, 256)] self.check_extract_features_returns_correct_shapes_with_dynamic_inputs( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=False) self.check_extract_features_returns_correct_shapes_with_dynamic_inputs( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=True) def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self): image_height = 299 image_width = 299 depth_multiplier = 1.0 pad_to_multiple = 32 expected_feature_map_shape = [(2, 40, 40, 256), (2, 20, 20, 256), (2, 10, 10, 256), (2, 5, 5, 256), (2, 3, 3, 256)] self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=False) self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=True) def test_extract_features_returns_correct_shapes_enforcing_min_depth(self): image_height = 256 image_width = 256 depth_multiplier = 0.5**12 pad_to_multiple = 1 expected_feature_map_shape = [(2, 32, 32, 32), (2, 16, 16, 32), (2, 8, 8, 32), (2, 4, 4, 32), (2, 2, 2, 32)] self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=False) self.check_extract_features_returns_correct_shape( 2, image_height, image_width, depth_multiplier, pad_to_multiple, expected_feature_map_shape, use_explicit_padding=True) def test_extract_features_raises_error_with_invalid_image_size(self): image_height = 32 image_width = 32 depth_multiplier = 1.0 pad_to_multiple = 1 self.check_extract_features_raises_error_with_invalid_image_size( image_height, image_width, depth_multiplier, pad_to_multiple) def test_preprocess_returns_correct_value_range(self): image_height = 256 image_width = 256 depth_multiplier = 1 pad_to_multiple = 1 test_image = np.random.rand(2, image_height, image_width, 3) feature_extractor = self._create_feature_extractor(depth_multiplier, pad_to_multiple) preprocessed_image = feature_extractor.preprocess(test_image) self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0))) def test_variables_only_created_in_scope(self): depth_multiplier = 1 pad_to_multiple = 1 scope_name = 'MobilenetV1' self.check_feature_extractor_variables_under_scope( depth_multiplier, pad_to_multiple, scope_name) def test_fused_batchnorm(self): image_height = 256 image_width = 256 depth_multiplier = 1 pad_to_multiple = 1 image_placeholder = tf.placeholder(tf.float32, [1, image_height, image_width, 3]) feature_extractor = self._create_feature_extractor(depth_multiplier, pad_to_multiple) preprocessed_image = feature_extractor.preprocess(image_placeholder) _ = feature_extractor.extract_features(preprocessed_image) self.assertTrue( any(op.type == 'FusedBatchNorm' for op in tf.get_default_graph().get_operations())) if __name__ == '__main__': tf.test.main()