数据增强----mixup

在数据量较少的时候可以采用数据mixup数据增强，具体实现代码如下，将两张图叠加

alpha=1.0   
loss_fun = nn.CrossEntropyLoss()
loss_fun = loss_fun.to(device) 

for x,data in enumerate(tqdm(train_dataloader)):#加个tqdm可以显示进度条
        imgs,targets = data
        imgs = imgs.to(device)
        targets = targets.to(device)
        lam = np.random.beta(alpha, alpha)
        index = torch.randperm(imgs.size(0))
        images_a, images_b = imgs, imgs[index]
        labels_a, labels_b = targets, targets[index]
        mixed_images = lam * images_a + (1 - lam) * images_b
        outputs = model(mixed_images)
        _, preds = torch.max(outputs, 1)
        loss = lam * loss_fun(outputs, labels_a) + (1 - lam) * loss_fun(outputs, labels_b)

#计算正确率
acc = (lam * preds.eq(labels_a.data).cpu().sum().float()
                    + (1 - lam) * preds.eq(labels_b.data).cpu().sum().float())/100