def restore_weights_and_initialize()

in train.py [0:0]

• 19 lines of code
• 12 McCabe index (conditional complexity)

def restore_weights_and_initialize(opt):
	var_list = tf.trainable_variables()
	g_list = tf.global_variables()

	# add batch normalization params into trainable variables 
	bn_moving_vars = [g for g in g_list if 'moving_mean' in g.name]
	bn_moving_vars += [g for g in g_list if 'moving_variance' in g.name]
	var_list +=bn_moving_vars

	# create saver to save and restore weights
	resnet_vars = [v for v in var_list if 'resnet_v1_50' in v.name]
	facenet_vars = [v for v in var_list if 'InceptionResnetV1' in v.name]
	saver_resnet = tf.train.Saver(var_list = resnet_vars)
	saver_facenet = tf.train.Saver(var_list = facenet_vars)

	saver = tf.train.Saver(var_list = resnet_vars + [v for v in var_list if 'fc-' in v.name],max_to_keep = 50)

	# create session
	sess = tf.InteractiveSession(config = opt.config)

	# create summary op
	train_writer = tf.summary.FileWriter(opt.train_summary_path, sess.graph)
	val_writer = tf.summary.FileWriter(opt.val_summary_path, sess.graph)

	# initialization
	tf.global_variables_initializer().run()
	tf.local_variables_initializer().run()

	saver_resnet.restore(sess,opt.R_net_weights)
	saver_facenet.restore(sess,opt.Perceptual_net_weights)

	return saver, train_writer,val_writer, sess