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')