ef train(args, model, train_loader, test_loader, boardio, textio, checkpoint): #learnable_params = filter(lambda p: p.requires_grad, model.parameters()) #optimizer = torch.optim.Adam(learnable_params) optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 100, 150], gamma=0.1) if checkpoint is not None: min_loss = checkpoint['min_loss'] optimizer.load_state_dict(checkpoint['optimizer']) best_test_loss = np.inf best_test_mse_ab = np.inf best_test_rmse_ab = np.inf best_test_mae_ab = np.inf best_test_r_mse_ab = np.inf best_test_r_rmse_ab = np.inf best_test_r_mae_ab = np.inf best_test_t_mse_ab = np.inf best_test_t_rmse_ab = np.inf best_test_t_mae_ab = np.inf for epoch in range(args.epochs): train_loss, train_mse_ab, train_mae_ab, train_rotations_ab, train_translations_ab, train_rotations_ab_pred, \ train_translations_ab_pred, train_eulers_ab, = train_one_epoch(args.device, model, train_loader, optimizer) test_loss, test_mse_ab, test_mae_ab, test_rotations_ab, test_translations_ab, test_rotations_ab_pred, \ test_translations_ab_pred, test_eulers_ab = test_one_epoch(args.device, model, test_loader)设置动态学习率

这段代码是用来设置动态学习率的。它使用了PyTorch中的lr_scheduler模块，具体来说，使用了MultiStepLR策略。这个策略会在训练过程中根据指定的milestones（里程碑）来调整学习率，每次乘以gamma（衰减因子）。在这个代码中，milestones被设置为[50, 100, 150]，gamma被设置为0.1，表示当训练到第50、100、150个epoch时，学习率会分别乘以0.1。这样做可以让模型在训练初期使用较大的学习率，快速收敛，而在训练后期使用较小的学习率，细致调整，避免过拟合。