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train_teacher.py
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train_teacher.py
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import tensorflow as tf
import tensorflow.contrib.slim as slim
import model
from data_reader import DataReader
import time
import argparse
import utils
def get_args():
parser = argparse.ArgumentParser(description='Face recognition')
parser.add_argument('--data_path', type=str, default='./dlcv_final_2_dataset/', help='path to dataset folder')
parser.add_argument('--log_path', type=str, default='./log/', help='path to logfile folder')
parser.add_argument('--weight_path', type=str, default='./log/', help='path to store/read weights')
parser.add_argument('--batch_size', type=int, default=100, help='Number of instance in each training batch')
parser.add_argument('--target_epoch', type=int, default=500, help='Target training epoch')
parser.add_argument('--load', action='store_true', help='Either to load pre-train weights or not')
parser.add_argument('--optim_type', type=str, default='adam', help='the type of optimizer')
parser.add_argument('--finetune_level', type=int, default=0,
help='0: without data augmentation(DA), 1: with DA, 2: with seaweed augmentation')
return parser.parse_args()
def main(args):
print(args)
LOG_STEP = 250
SAVE_STEP = 500
LOG_ALL_TRAIN_PARAMS = False
LEARNING_RATE = 1e-4 / args.finetune_level if args.finetune_level > 1 else 1e-3
with tf.variable_scope('Data_Generator'):
data_reader = DataReader(
data_path=args.data_path
)
train_x, train_y = data_reader.get_instance(batch_size=args.batch_size, mode='train',
augmentation_level=args.finetune_level)
valid_x, valid_y = data_reader.get_instance(batch_size=args.batch_size * 2, mode='valid')
class_num = len(data_reader.dict_class.keys())
network = model.TeacherNetwork()
logits, net_dict = network.build_network(train_x, class_num=len(data_reader.dict_class.keys()), reuse=False,
is_train=True)
v_logits, v_net_dict = network.build_network(valid_x, class_num=len(data_reader.dict_class.keys()), reuse=True,
is_train=True, dropout=1)
prelogits = net_dict['PreLogitsFlatten']
v_prelogits = v_net_dict['PreLogitsFlatten']
use_center, use_pln, use_triplet, use_him = [False for _ in range(4)]
pln_factor, center_factor = [0, 0]
if args.finetune_level == 2:
use_center, use_pln, use_triplet, use_him = [True for _ in range(4)]
with tf.variable_scope('Output'):
embed = slim.fully_connected(prelogits, 128, tf.identity, scope='Embedding')
v_embed = slim.fully_connected(v_prelogits, 128, tf.identity, reuse=True, scope='Embedding')
pln_factor = 1e-4
center_factor = 1e-4
else:
embed = None
v_embed = None
if args.finetune_level == 1:
use_center = True
use_pln = True
pln_factor = 1e-5
center_factor = 1e-5
loss_func = utils.LossFunctions(
prelogit_norm_factor=pln_factor,
center_loss_factor=center_factor,
)
loss, accu = loss_func.calculate_loss(logits, train_y, prelogits, class_num, use_center_loss=use_center,
embed=embed, use_triplet_loss=use_triplet, use_prelogits_norm=use_pln,
use_hard_instance_mining=use_him, scope_name='Training')
_, v_accu = loss_func.calculate_loss(v_logits, valid_y, v_prelogits, class_num, use_center_loss=use_center,
embed=v_embed, use_triplet_loss=use_triplet, use_prelogits_norm=use_pln,
use_hard_instance_mining=use_him, scope_name='Validation')
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(LEARNING_RATE, global_step, 10000, 0.9, staircase=True)
if args.optim_type == 'adam':
optim = tf.train.AdamOptimizer(learning_rate)
elif args.optim_type == 'adagrad':
optim = tf.train.AdagradOptimizer(learning_rate)
else:
optim = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optim.minimize(loss)
train_params = list(filter(lambda x: 'Adam' not in x.op.name and 'Inception' in x.op.name,
tf.contrib.slim.get_variables()))
saver = tf.train.Saver(var_list=train_params)
if LOG_ALL_TRAIN_PARAMS:
for i in train_params:
tf.summary.histogram(i.op.name, i)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
if args.load:
saver.restore(sess, args.weight_path + 'teacher.ckpt')
train_writer = tf.summary.FileWriter(args.log_path, sess.graph)
merged = tf.summary.merge_all(tf.GraphKeys.SUMMARIES)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
tf.Graph().finalize()
start_time = time.time()
step = 0
while step * args.batch_size / len(data_reader.train_label) < args.target_epoch:
_ = sess.run(train_op)
if step % LOG_STEP == 0:
time_cost = (time.time() - start_time) / LOG_STEP if step > 0 else 0
np_loss, np_accu, np_v_accu, s = sess.run([loss, accu, v_accu, merged])
train_writer.add_summary(s, step)
print('======================= Step {} ====================='.format(step))
print('[Log file saved] {:.2f} secs for one step'.format(time_cost))
print('Current loss: {:.2f}, train accu: {:.2f}%, valid accu: {:.2f}%'.format(np_loss, np_accu, np_v_accu))
start_time = time.time()
if step % SAVE_STEP == 0:
saver.save(sess, args.weight_path + 'teacher.ckpt', step)
print('[Weights saved] weights saved at {}'.format(args.weight_path + 'teacher'))
step += 1
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
main(get_args())