e6b394f7340cbeee16bf4424cdf0367f80b60619,code/deep/finetune_AlexNet_ResNet/office31.py,,,#,112

Before Change

    torch.manual_seed(10)
    root_dir = "data/OFFICE31/"
    src, tar = "amazon", "webcam"
    model_name = "alexnet"
    data_src, data_tar = data_loader.load_training(root_dir, src, BATCH_SIZE_SRC), \
                         data_loader.load_testing(root_dir, tar, BATCH_SIZE_TAR)
    print("Source:{}, target:{}".format(src, tar))

    model = load_model(model_name).to(DEVICE)
    lrs = LEARNING_RATE
    for e in tqdm(range(1, N_EPOCH + 1)):
        tqdm.write("learning rate: " + str(lrs))
        optimizer = get_optimizer(model_name,learning_rate=lrs)
        train(e, model, optimizer, data_src)
        test(e, model, data_tar)
        lrs = lr_decay(1e-4, N_EPOCH, e)

After Change

    // Load model
    model_name = str(args.model)
    model = load_model(model_name).to(DEVICE)
    print("Source:{}, target:{}, model: {}".format(domain["src"], domain["tar"], model_name))
    optimizer = get_optimizer(model_name)
    model_best, best_acc, acc_hist = finetune(model, dataloaders, optimizer)
    print("{}Best acc: {}".format("*" * 10, best_acc))

Instances