in options.py [0:0]
def __init__(self,model_name=None,is_train=True):
#--------------------------------------------------------------------------------------
self.is_train = is_train
self.model_dir = 'result'
if model_name is None:
self.model_name = 'model_test'
else:
self.model_name = model_name
self.data_path = ['./processed_data']
self.val_data_path = ['./processed_data']
self.model_save_path = os.path.join(self.model_dir,self.model_name)
if self.is_train:
if not os.path.exists(self.model_save_path):
os.makedirs(self.model_save_path)
self.summary_dir = os.path.join(self.model_save_path,'summary')
self.train_summary_path = os.path.join(self.summary_dir, 'train')
self.val_summary_path = os.path.join(self.summary_dir, 'val')
#---------------------------------------------------------------------------------------
# visible gpu settings
self.config = tf.ConfigProto()
self.config.gpu_options.visible_device_list = '0'
self.use_pb = True
#---------------------------------------------------------------------------------------
# training parameters
self.w_photo = 1.92
self.w_lm = 1.6e-3
self.w_id = 0.2
self.w_reg = 3.0e-4
self.w_ref = 5.0
self.w_gamma = 10.0
self.w_ex = 0.8
self.w_tex = 1.7e-2
self.batch_size = 16
self.boundaries = [100000]
lr = [1e-4,2e-5]
self.global_step = tf.Variable(0,name='global_step',trainable = False)
self.lr = tf.train.piecewise_constant(self.global_step,self.boundaries,lr)
self.augment = True
self.train_maxiter = 200000
self.train_summary_iter = 50
self.image_summary_iter = 200
self.val_summary_iter = 1000
self.save_iter = 10000
#---------------------------------------------------------------------------------------
# initial weights for resnet and facenet
self.R_net_weights = os.path.join('./weights/resnet','resnet_v1_50.ckpt')
self.Perceptual_net_weights = './weights/id_net/model-20170512-110547.ckpt-250000'
self.pretrain_weights = os.path.join('train/model_test','iter_100000.ckpt')