def embedding_fn(images, reuse=False):
with slim.arg_scope(arg_scope):
return convolutional_alexnet(
images,
reuse=reuse,
split=alexnet_config['split'],
depthwise_list=alexnet_config['depthwise_list'])
def embedding_fn(images, reuse=False):
with slim.arg_scope(arg_scope):
return convolutional_alexnet(images, reuse=reuse)
def embedding_fn(images, reuse=False):
with slim.arg_scope(arg_scope):
return convolutional_alexnet(images, reuse=reuse)
input_image = tf.placeholder(tf.float32,
shape=[args.scale, size_x, size_x, 3],
name='input_image')
template_image = tf.expand_dims(template_image, 0)
embed_config = model_config['embed_config']
# build cnn for feature extraction from either template image or input image
feature_extactor = model_config['embed_config']['feature_extractor']
if feature_extactor == "alexnet":
alexnet_config = model_config['alexnet']
arg_scope = convolutional_alexnet_arg_scope(
embed_config,
trainable=embed_config['train_embedding'],
is_training=False)
with slim.arg_scope(arg_scope):
embed_x, end_points = convolutional_alexnet(
input_image, reuse=False, split=alexnet_config['split'])
embed_z, end_points_z = convolutional_alexnet(
template_image, reuse=True, split=alexnet_config['split'])
elif feature_extactor == "mobilenet_v1":
mobilenent_config = model_config['mobilenet_v1']
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(is_training=False)):
with tf.variable_scope('MobilenetV1', reuse=False) as scope:
embed_x, end_points = mobilenet_v1.mobilenet_v1_base(
input_image,
final_endpoint=mobilenent_config['final_endpoint'],
conv_defs=mobilenet.CONV_DEFS,
depth_multiplier=mobilenent_config['depth_multiplier'],
scope=scope)
with tf.variable_scope('MobilenetV1', reuse=True) as scope:
