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import time
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
start = time.time()
opt = TrainOptions().parse()
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
model = create_model(opt)
model.setup(opt)
visualizer = Visualizer(opt)
total_steps = 0
model.save_networks2(opt.which_epoch)
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
if total_steps % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
visualizer.reset()
total_steps += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data)
model.optimize_parameters()
if total_steps % opt.display_freq == 0:
save_result = total_steps % opt.update_html_freq == 0
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
#print('display',total_steps)
if total_steps % opt.print_freq == 0:#print freq 100
losses = model.get_current_losses()
t = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_losses(epoch, epoch_iter, losses, t, t_data)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, opt, losses)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
#model.save_networks('latest')
model.save_networks2('latest')
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
#model.save_networks('latest')
#model.save_networks(epoch)
model.save_networks2('latest')
model.save_networks2(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
model.update_learning_rate()
print('Total Time Taken: %d sec' % (time.time() - start))
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