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