# set visualizer
visualizer = Visualizer(opt, dataLoader.dataset).reset()
steps = 0
for epoch in range(opt.epochStart, opt.nEpochStart + opt.nEpochDecay + 1):
for i, data in enumerate(dataLoader):
steps += 1
model.set_input(data)
model.optimize_parameters()
visualizer.print_process('Train', epoch, loss=model.current_losses())
if steps % opt.displayInterval == 0: # default: 5
visualizer.displayImage(model.current_images(), steps)
visualizer.displayLoss(model.current_losses(), steps)
if steps % opt.saveLatestInterval == 0: # default: 5000
print('saving the latest model (epoch %d, total_steps %d)\n' %
(epoch, steps))
model.save_networks('latest') # latest model will override
if epoch % opt.saveEpochInterval == 0: # default: 5
print('saving the model at the end of epoch %d, iters %d\n' %
(epoch, steps))
model.save_networks('latest')
model.save_networks(epoch)
visualizer.print('Train', epoch, miou=model.current_mious())
# important
