total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.forward()
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)
model.optimize_parameters()
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
iter_start_time = time.time()
if epoch % 100 == 0:
print('saving the model (epoch %d, total_steps %d)' % (epoch, total_steps))
model.save(epoch)
model.switch_mode('eval')
