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main.py
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main.py
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import tensorflow as tf
from data_loader.data_generator import DataGenerator
from utils.arg_parser import get_args
from utils.config import process_config
from utils.dirs import create_dirs
from utils.logger import Logger
def main():
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(-1)
sess = tf.Session()
if (args.model == 'dcgan'):
from models.dcgan_model import DCGANModel
from trainers.dcgan_trainer import DCGANTrain
create_dirs([config.summary_dir, config.checkpoint_dir, config.resized_data_dir])
data = DataGenerator(config.data_dir, config.resized_data_dir, config.t_size, config.batch_size)
model = DCGANModel(config)
logger = Logger(sess, config)
trainer = DCGANTrain(sess, model, data, config, logger)
model.load(sess)
trainer.train()
elif (args.model == 'cyclegan'):
from models.cyclegan_model import CycleGANModel
from trainers.cyclegan_trainer import CycleGANTrain
create_dirs([config.summary_dir, config.checkpoint_dir, config.resized_data_dir_a, config.resized_data_dir_b])
dataA = DataGenerator(config.data_dir_a, config.resized_data_dir_a, config.t_size, config.batch_size)
dataB = DataGenerator(config.data_dir_b, config.resized_data_dir_b, config.t_size, config.batch_size)
model = CycleGANModel(config)
logger = Logger(sess, config)
trainer = CycleGANTrain(sess, model, [dataA, dataB], config, logger)
model.load(sess)
trainer.train()
else:
print("model doesn't exist")
exit(-1)
if __name__ == '__main__':
main()