Manifold Mixup和PatchUp是对mixup数据增强算法的两种改进方法，作者都来自Yoshua Bengio团队。这两种方法都是mixup方法在中间隐层的推广，因此原文开源代码都需要对网络各层的内部代码进行修改，使用起来并不方便，不能做到即插即用。我用pytorch中的钩子方法(hook)对这两个方法进行重新实现，这样就可以实现即插即用，方便的应用到各种网络结构中，而且我实现的代码比原开源代码速度还能提高60%左右。
Manifold Mixup 论文：https://arxiv.org/abs/1806.05236
Manifold Mixup 官方开源：https://github.com/vikasverma1077/manifold_mixup
PatchUp 论文：https://arxiv.org/abs/2006.07794
PatchUp 官方开源：https://github.com/chandar-lab/PatchUp

一、Manifold Mixup简介及代码

  manifold mixup是对mixup的扩展，把输入数据（raw input data）混合扩展到对中间隐层输出混合。至于对中间隐层混合更有效的原因，作者的解释比较深奥。首先给出了现象级的解释，即这种混合带来了三个优势：平滑决策边界、拉大低置信空间（拉开各类别高置信空间的间距）、展平隐层输出的数值。至于这三点为什么有效，从作者说法看这应该是一种业界共识。然后作者又从数学上分析了第三点，即为什么manifold mixup可以实现展平中间隐层输出。
  由于需要修改网络中间层的输出张量，如果不修改网络内部，也可以使用钩子操作（hook）在外部进行。核心部分代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

def to_one_hot(inp, num_classes):
    y_onehot = torch.FloatTensor(inp.size(0), num_classes).to(inp.device)
    y_onehot.zero_()
    y_onehot.scatter_(1, inp.unsqueeze(1).data, 1)
    return y_onehot

bce_loss = nn.BCELoss()
softmax = nn.Softmax(dim=1)

class ManifoldMixupModel(nn.Module):
    def __init__(self, model, num_classes = 10, alpha = 1):
        super().__init__()
        self.model = model
        self.alpha = alpha
        self.lam = None
        self.num_classes = num_classes
        ##选择需要操作的层，在ResNet中各block的层名为layer1,layer2...所以可以写成如下。其他网络请自行修改
        self. module_list = []
        for n,m in self.model.named_modules():
            #if 'conv' in n:
            if n[:-1]=='layer':
                self.module_list.append(m)

    def forward(self, x, target=None):
        if target==None:
            out = self.model(x)
            return out
        else:
            if self.alpha <= 0:
                self.lam = 1
            else:
                self.lam = np.random.beta(self.alpha, self.alpha)
            k = np.random.randint(-1, len(self.module_list))
            self.indices = torch.randperm(target.size(0)).cuda()
            target_onehot = to_one_hot(target, self.num_classes)
            target_shuffled_onehot = target_onehot[self.indices]
            if k == -1:
                x = x * self.lam + x[self.indices] * (1 - self.lam)
                out = self.model(x)
            else:
                modifier_hook = self.module_list[k].register_forward_hook(self.hook_modify)
                out = self.model(x)
                modifier_hook.remove()
            target_reweighted = target_onehot* self.lam + target_shuffled_onehot * (1 - self.lam)
            
            loss = bce_loss(softmax(out), target_reweighted)
            return out, loss
        
    def hook_modify(self, module, input, output):
        output = self.lam * output + (1 - self.lam) * output[self.indices]
        return output

调用代码如下：

net = ResNet18()
net = ManifoldMixupModel(net,num_classes=10, alpha=args.alpha)
def train(epoch):
    net.train()
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        inputs, targets = inputs.cuda(), targets.cuda()
        outputs, loss = net(inputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

def test(epoch):
    net.eval()
    with torch.no_grad():
	    for batch_idx, (inputs, targets) in enumerate(testloader):
	        inputs, targets = inputs.cuda(), targets.cuda()
	        outputs = net(inputs)

二、PatchUp简介及代码

  PatchUp方法在manifold mixup基础上，又借鉴了cutMix在空间维度剪裁的思路，对中间隐层输出也进行剪裁，对两个不同样本的中间隐层剪裁块(patches)进行互换或插值，文中称互换法为硬patchUp，插值法为软patchUp。试验发现互换法在识别精度上更好，插值法在对抗攻击的鲁棒性上更好。这篇论文中没有对方法理论进行深度解释，仅仅给出了一个现象级对比，就是patchUp方法的隐层激活值比较高。
  使用hook实现的核心代码PatchUpModel类如下，注意在该代码中强制k=-1就可以变成CutMix：

class PatchUpModel(nn.Module):
    def __init__(self, model, num_classes = 10, block_size=7, gamma=.9, patchup_type='hard',keep_prob=.9):
        super().__init__()
        self.patchup_type = patchup_type
        self.block_size = block_size
        self.gamma = gamma
        self.gamma_adj = None
        self.kernel_size = (block_size, block_size)
        self.stride = (1, 1)
        self.padding = (block_size // 2, block_size // 2)
        self.computed_lam = None
        
        self.model = model
        self.num_classes = num_classes
        self. module_list = []
        for n,m in self.model.named_modules():
            if n[:-1]=='layer':
            #if 'conv' in n:
                self.module_list.append(m)

    def adjust_gamma(self, x):
        return self.gamma * x.shape[-1] ** 2 / \
               (self.block_size ** 2 * (x.shape[-1] - self.block_size + 1) ** 2)

    def forward(self, x, target=None):
        if target==None:
            out = self.model(x)
            return out
        else:

            self.lam = np.random.beta(2.0, 2.0)
            k = np.random.randint(-1, len(self.module_list))
            self.indices = torch.randperm(target.size(0)).cuda()
            self.target_onehot = to_one_hot(target, self.num_classes)
            self.target_shuffled_onehot = self.target_onehot[self.indices]
            
            if k == -1:  #CutMix
                W,H = x.size(2),x.size(3)
                cut_rat = np.sqrt(1. - self.lam)
                cut_w = np.int(W * cut_rat)
                cut_h = np.int(H * cut_rat)
                cx = np.random.randint(W)
                cy = np.random.randint(H)
        
                bbx1 = np.clip(cx - cut_w // 2, 0, W)
                bby1 = np.clip(cy - cut_h // 2, 0, H)
                bbx2 = np.clip(cx + cut_w // 2, 0, W)
                bby2 = np.clip(cy + cut_h // 2, 0, H)
                
                x[:, :, bbx1:bbx2, bby1:bby2] = x[self.indices, :, bbx1:bbx2, bby1:bby2]
                lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (W * H))
                out = self.model(x)
                loss = bce_loss(softmax(out), self.target_onehot) * lam +\
                    bce_loss(softmax(out), self.target_shuffled_onehot) * (1. - lam)
                
            else:
                modifier_hook = self.module_list[k].register_forward_hook(self.hook_modify)
                out = self.model(x)
                modifier_hook.remove()
                
                loss = 1.0 * bce_loss(softmax(out), self.target_a) * self.total_unchanged_portion + \
                     bce_loss(softmax(out), self.target_b) * (1. - self.total_unchanged_portion) + \
                    1.0 * bce_loss(softmax(out), self.target_reweighted)
            return out, loss
        
    def hook_modify(self, module, input, output):
        self.gamma_adj = self.adjust_gamma(output)
        p = torch.ones_like(output[0]) * self.gamma_adj
        m_i_j = torch.bernoulli(p)
        mask_shape = len(m_i_j.shape)
        m_i_j = m_i_j.expand(output.size(0), m_i_j.size(0), m_i_j.size(1), m_i_j.size(2))
        holes = F.max_pool2d(m_i_j, self.kernel_size, self.stride, self.padding)
        mask = 1 - holes
        unchanged = mask * output
        if mask_shape == 1:
            total_feats = output.size(1)
        else:
            total_feats = output.size(1) * (output.size(2) ** 2)
        total_changed_pixels = holes[0].sum()
        total_changed_portion = total_changed_pixels / total_feats
        self.total_unchanged_portion = (total_feats - total_changed_pixels) / total_feats
        if self.patchup_type == 'hard':
            self.target_reweighted = self.total_unchanged_portion * self.target_onehot +\
                total_changed_portion * self.target_shuffled_onehot
            patches = holes * output[self.indices]
            self.target_b = self.target_onehot[self.indices]
        elif self.patchup_type == 'soft':
            self.target_reweighted = self.total_unchanged_portion * self.target_onehot +\
                self.lam * total_changed_portion * self.target_onehot +\
                (1 - self.lam) * total_changed_portion * self.target_shuffled_onehot
            patches = holes * output
            patches = patches * self.lam + patches[self.indices] * (1 - self.lam)
            self.target_b = self.lam * self.target_onehot + (1 - self.lam) * self.target_shuffled_onehot
        else:
            raise ValueError("patchup_type must be \'hard\' or \'soft\'.")
        
        output = unchanged + patches
        self.target_a = self.target_onehot
        return output

  调用过程同上，其中模型包装语句如下：

net = ResNet18()
net = PatchUpModel(net,num_classes=10, block_size=7, gamma=.9, patchup_type='hard')

三、在CIFAR-10上试验结果

  试验主要目的是验证代码可运行。仅靠在一个简单数据集上一次试验非常不充分，不能公平对比效果，所以不作为各方法的性能对比。