def main(argv=None):
# Configurations
config = Config(gpu='1',
root_dir='./data/test/',
root_dir_val=None,
mode='testing')
config.BATCH_SIZE = 1
# Get images and labels.
dataset_test = Dataset(config, 'test')
# Train
_M, _s, _b, _C, _T, _imname = _step(config, dataset_test, False)
# Add ops to save and restore all the variables.
saver = tf.train.Saver(max_to_keep=50,)
with tf.Session(config=config.GPU_CONFIG) as sess:
# Restore the model
ckpt = tf.train.get_checkpoint_state(config.LOG_DIR)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
last_epoch = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
print('**********************************************************')
print('Restore from Epoch '+str(last_epoch))
print('**********************************************************')
else:
init = tf.initializers.global_variables()
last_epoch = 0
sess.run(init)
print('**********************************************************')
print('Train from scratch.')
print('**********************************************************')
step_per_epoch = int(len(dataset_test.name_list) / config.BATCH_SIZE)
with open(config.LOG_DIR + '/test/score.txt', 'w') as f:
for step in range(step_per_epoch):
M, s, b, C, T, imname = sess.run([_M, _s, _b, _C, _T, _imname])
# save the score
for i in range(config.BATCH_SIZE):
_name = imname[i].decode('UTF-8')
_line = _name + ',' + str("{0:.3f}".format(M[i])) + ','\
+ str("{0:.3f}".format(s[i])) + ','\
+ str("{0:.3f}".format(b[i])) + ','\
+ str("{0:.3f}".format(C[i])) + ','\
+ str("{0:.3f}".format(T[i]))
f.write(_line + '\n')
print(str(step+1)+'/'+str(step_per_epoch)+':'+_line, end='\r')
print("\n")
"/home/umit/xDataset/deepFake-dat/Train_Live_Much_3",
"/home/umit/xDataset/deepFake-dat/Train_Fake_Much_4",
"/home/umit/xDataset/deepFake-dat/Train_Live_Much_4",
"/home/umit/xDataset/deepFake-dat/Train_Fake_Much_5",
"/home/umit/xDataset/deepFake-dat/Train_Live_Much_5",
"/home/umit/xDataset/deepFake-dat/Train_Fake_Much_6"
"/home/umit/xDataset/deepFake-dat/Train_Live_Much_6",
"/home/umit/xDataset/deepFake-dat/Train_Fake_Much_7"]
config.LOG_DIR = './log/model'
config.MODE = 'training'
config.STEPS_PER_EPOCH = 2000
config.MAX_EPOCH = 1000
config.LEARNING_RATE = 0.00001 #0.00005 #0.0001 #0.0005 #0.001
config.BATCH_SIZE = 20
# Validation
config.DATA_DIR_VAL = ["/home/umit/xDataset/deepFake-dat/Train_Fake_Much_1",
"/home/umit/xDataset/deepFake-dat/Train_Live_Few_1"]
config.STEPS_PER_EPOCH_VAL = 500
config.display()
# Get images and labels.
dataset_train = Dataset(config,'train')
#dataset_validation = Dataset(config,'validation')
# Build a Graph
model = Model(config)
# # Train the model
