iter_start_time = time.time() if total_steps % opt.print_freq == 0: t_data = iter_start_time - iter_data_time total_steps += opt.batch_size epoch_iter += opt.batch_size model.set_input(data) model.optimize_parameters() loss_mat.append(model.loss.cpu().data.numpy()) #CE_mat.append(model.CE_loss.cpu().data.numpy()) #prior_mat.append(model.prior_loss.cpu().data.numpy()) if total_steps % opt.print_freq == 0: loss = model.loss t = (time.time() - iter_start_time) / opt.batch_size writer.print_current_losses(epoch, epoch_iter, loss, t, t_data) writer.plot_loss(loss, epoch, epoch_iter, dataset_size) if i % opt.save_latest_freq == 0: print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) model.save_network('latest') iter_data_time = time.time() writer.save_losses(loss_mat, CE_mat, prior_mat, epoch) if epoch % opt.save_epoch_freq == 0: print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) model.save_network('latest') model.save_network(epoch)