def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, use_explicit_padding=False, num_layers=6, is_training=True): """Constructs a SsdInceptionV2FeatureExtractor. 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. 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. num_layers: number of SSD layers. is_training: whether the network is in training mode. Returns: an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor. """ min_depth = 32 return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor( is_training, depth_multiplier, min_depth, pad_to_multiple, self.conv_hyperparams_fn, num_layers=num_layers, override_base_feature_extractor_hyperparams=True)
def _create_feature_extractor(self, depth_multiplier): """Constructs a SsdInceptionV2FeatureExtractor. Args: depth_multiplier: float depth multiplier for feature extractor Returns: an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor. """ min_depth = 32 conv_hyperparams = {} return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor( depth_multiplier, min_depth, conv_hyperparams)
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, is_training=True, batch_norm_trainable=True): """Constructs a SsdInceptionV2FeatureExtractor. 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. batch_norm_trainable: Whether to update batch norm parameters during training or not Returns: an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor. """ min_depth = 32 conv_hyperparams = {} return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable)
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, is_training=True): """Constructs a SsdInceptionV2FeatureExtractor. 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. Returns: an ssd_inception_v2_feature_extractor.SsdInceptionV2FeatureExtractor. """ min_depth = 32 return ssd_inception_v2_feature_extractor.SSDInceptionV2FeatureExtractor( is_training, depth_multiplier, min_depth, pad_to_multiple, self.conv_hyperparams_fn, override_base_feature_extractor_hyperparams=True)