10-卷积神经网络

导论

卷积主要用于进行空间变换；卷积神经网络的用途是为了自动提取特征。

image-20210305150600626

对图像进行卷积操作，本质上是对图像的某块的所有频道（即一个Batch）进行操作。操作会改变图像的$c,w,h$值。

在这里插入图片描述

通道

单通道

用Kernel阵遍历输入阵，对应进行数值乘法再求和，得到输出阵的一个元素。

图中的红色框称为“窗口”，而其特性就是滑动；进行卷积对应相乘运算并求得均值后，滑动窗便开始向右边滑动并根据步长的不同选择滑动幅度。

image-20210305152846105
image-20210305152953900

$\cdots$

image-20210305153012840
import torch

inputs = torch.Tensor([
    [3,4,6,5,7],
    [2,4,6,8,2],
    [1,6,7,8,4],
    [9,7,4,6,2],
    [3,7,5,4,1]
])

kernel = torch.Tensor([
    [1,2,3],
    [4,5,6],
    [7,8,9]
])

m,n = inputs.shape
mk,nk = kernel.shape
outputs = torch.zeros((m-mk+1,n-nk+1))
mo,no = outputs.shape

for i in range(mo):
    for j in range(no):
        outputs[i,j] = torch.mul(inputs[i:i+mk,j:j+nk],kernel).sum()

3通道

本质上是将单通道的卷积累加。需要注意的是，输入Kernel的通道数要和输入的通道数相等。该操作将3通道的输入转化成1通道的输出。

image-20210305153940018
import torch

inputs = torch.Tensor([
    [3,4,6,5,7],
    [2,4,6,8,2],
    [1,6,7,8,4],
    [9,7,4,6,2],
    [3,7,5,4,1]
])

kernel = torch.Tensor([
    [
        [1,2,3],
        [4,5,6],
        [7,8,9]
    ],
    [
        [9,8,7],
        [6,5,4],
        [3,2,1]
    ],
    [
        [1,4,7],
        [2,5,8],
        [3,6,9]
    ],
])

m,n = inputs.shape
zk,mk,nk = kernel.shape
outputs = torch.zeros((m-mk+1,n-nk+1))
mo,no = outputs.shape

for k in range(zk):
    for i in range(mo):
        for j in range(no):
            outputs[i,j] += torch.mul(inputs[i:i+mk,j:j+nk]
            							,kernel[k,:,:]).sum()
image-20210305154933113

n通道

image-20210305155851329

如果需要得到$m$通道的输出，则要准备$m$份的卷积阵。此时对应的权值是 $m_n_k_{width}*k_{height}$的四维量。

image-20210305155949583

卷积层

image-20210305162243312
import torch

in_channels,out_channels = 5,10
width, height = 100,100
kernel_size = 3
batch_size = 1

inputs = torch.randn(batch_size,in_channels,width,height)

conv_layer = torch.nn.Conv2d(in_channels,out_channels,
									kernel_size=kernel_size)

outputs = conv_layer(inputs)

print("inputs.shape = {};
	\noutputs.shape = {};
	\nconv_layer.weight.shape = {}".format(inputs.shape,
                                        outputs.shape,
                                        conv_layer.weight.shape))
image-20210305162900318

输出的维度：$n_{in}-kernel+1$

特征图

经过一系列卷积对应相乘，求均值运算后，把一张完整的feature map填满。

img

feature map是每一个feature从原始图像中提取出来的“特征”。其中的值，越接近为1表示对应位置和feature的匹配越完整，越是接近-1，表示对应位置和feature的反面匹配越完整，而值接近0的表示对应位置没有任何匹配或者说没有什么关联。

一个feature作用于图片产生一张feature map，对这张图来说，用的是3个feature，因此最终产生3个 feature map。

preview

padding

padding，即边缘填充，可以分为四类：零填充，常数填充，镜像填充，重复填充。

image-20210305164802953
import torch
import numpy as np

inputs = torch.Tensor([
    3,4,6,5,7,
    2,4,6,8,2,
    1,6,7,8,4,
    9,7,4,6,2,
    3,7,5,4,1])
inputs = inputs.view(1,1,5,5)

conv_layer = torch.nn.Conv2d(1,1,kernel_size=3,padding=1,bias=False)

kernel = torch.Tensor(list(range(1,10))).view(1,1,3,3)
conv_layer.weight.data = kernel.data

outputs = conv_layer(inputs)
print(outputs)

以低光照增强任务为例，最终对比效果如下图。零填充会产生边缘伪影，而镜像填充很好地缓解了这一效应。

img

步长

也就是窗口移动的步福，比如设置步长为2：

image-20210305170448674
image-20210305170455973
image-20210305170545745
import torch
import numpy as np

inputs = torch.Tensor([
    3,4,6,5,7,
    2,4,6,8,2,
    1,6,7,8,4,
    9,7,4,6,2,
    3,7,5,4,1])
inputs = inputs.view(1,1,5,5)

conv_layer = torch.nn.Conv2d(1,1,kernel_size=3,
					padding=0,stride=2,bias=False)

kernel = torch.Tensor(list(range(1,10))).view(1,1,3,3)
conv_layer.weight.data = kernel.data

outputs = conv_layer(inputs)
print(outputs)

池化层

卷积操作后，得到了一张张有着不同值的feature map，尽管数据量比原图少了很多，但还是过于庞大（比较深度学习动不动就几十万张训练图片），因此接下来的池化操作就可以发挥作用了，它最大的目标就是减少数据量。

池化分为两种：Max Pooling 最大池化、Average Pooling平均池化。顾名思义，最大池化就是取最大值，平均池化就是取平均值。

  • Max Pooling Layer

image-20210305170737309
import torch
import numpy as np

inputs = torch.Tensor([
    3,4,6,5,7,
    2,4,6,8,2,
    1,6,7,8,4,
    9,7,4,6,2,
    3,7,5,4,1])
inputs = inputs.view(1,1,5,5)

conv_layer = torch.nn.MaxPool2d(kernel_size=2,padding=0,stride=2)

outputs = conv_layer(inputs)
print(outputs)

在pytorch中MaxPool2d中的stride默认值为2。

  • Average Pooling Layer

import torch
import numpy as np

inputs = torch.Tensor([
    3,4,6,5,7,
    2,4,6,8,2,
    1,6,7,8,4,
    9,7,4,6,2,
    3,7,5,4,1])
inputs = inputs.view(1,1,5,5)

conv_layer = torch.nn.AvgPool2d(kernel_size=2)

outputs = conv_layer(inputs)
print(outputs)

一个简单的卷积神经网络

image-20210305171348397

将全连接神经网络改写成卷积神经网络：

image-20210306102612730
import torch
import torch.nn.functional as F # 使用激活函数

class Net(torch.nn.Module):
    
    def __init__(self,):
        super(Net,self).__init__()
        self.conv1 = torch.nn.Conv2d(1,10,kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10,20,kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)
        self.linear = torch.nn.Linear(320,10)
        
    def forward(self,x):
        # 将x从(n,1,28,28)转换到(n,784)
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size,-1)
        x = self.linear(x)
        return x
        
model = Net()

如何使用GPU

迁移模型到GPU

用来选择设备

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

将模型数据迁移到GPU上

model.to(device)

迁移张量到GPU

inputs,target = inputs.to(device),target.to(device)

通过使用.to(device)将数据迁移到GPU上

def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs,target = data
        inputs,target = inputs.to(device),target.to(device)
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs,target)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
                
        if batch_idx % 300 == 299:
            print('[{:d},{:5d}] loss:{:.3f}'.format(epoch+1,batch_idx+1,
            												running_loss/300))
            running_loss = 0.0
            
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs,target = data
            inputs,target = inputs.to(device),target.to(device)
            outputs = model(inputs)
            _,predicted = torch.max(outputs.data,dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
            
    print("Accuracy on test set:{:.2%}".format(correct/total))

作业1

image-20210311093631326
import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim


batch_size = 64

# 用于处理图像
transform = transforms.Compose([
    transforms.ToTensor(), 
    transforms.Normalize((0.1307,),(0.3081,))
])

# 导入训练数据
train_dataset = datasets.MNIST(root='./数据集/mnist/',train=True,download=True,
                              transform = transform)
# 打乱顺序
train_loader = DataLoader(train_dataset,
                         shuffle=True,
                         batch_size=batch_size)

# 导入测试数据
test_dataset = datasets.MNIST(root='./数据集/mnist/',train=False,download=True,
                              transform = transform)
# 打乱顺序
test_loader = DataLoader(test_dataset,
                        shuffle=True,
                        batch_size=batch_size)

class ImageNet(torch.nn.Module):
    def __init__(self,):
        super(ImageNet,self).__init__()
        #(batch,1,28,28) -> (batch,10,24,24) -> (batch,10,24,24) -> 
        #(batch,10,12,12) -> (batch,20,10,10)   -> (batch,20,10,10)  ->
        #(batch,20,5,5) -> (batch,30,4,4) -> (batch,30,4,4) -> (batch,30,2,2) -> (batch,120) 
        self.conv1 = torch.nn.Conv2d(1,10,kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10,20,kernel_size=3)
        self.conv3 = torch.nn.Conv2d(20,30,kernel_size=2)
        self.pooling = torch.nn.MaxPool2d(2)
        self.linear = torch.nn.Linear(120,10)
        
    def forward(self,x):
        batch_size = x.size(0)
        x = self.pooling(F.relu(self.conv1(x)))
        x = self.pooling(F.relu(self.conv2(x)))
        x = self.pooling(F.relu(self.conv3(x)))
        x = x.view(batch_size,-1)
        x = self.linear(x)
        return x
    
model = ImageNet()
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs,target = data
        inputs,target = inputs.to(device),target.to(device)
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs,target)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        
        if batch_idx % 300 == 299:
            print('[{:d},{:5d}] loss:{:.3f}'.format(epoch+1,batch_idx+1,
                                                    running_loss/300))
            running_loss = 0.0
            
            
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs,target = data
            inputs,target = inputs.to(device),target.to(device)
            outputs = model(inputs)
            _,predicted = torch.max(outputs.data,dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
            
    print("Accuracy on test set:{:.2%}".format(correct/total))

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

高级篇

回顾

image-20210311133027368

GoogleNet

image-20210311133644444

在这张图片会发现很多重复的，在构造时很复杂，为了减少代码冗余，可以定义为函数或者类。

image-20210311172748130

  • Inception Module：类似于套娃，重复使用的部分定义为一个模型。

Inception Module

image-20210311134359271

初衷是由于kernel_size不知道定义多少合适，于是把全部需要的kernel_size都定义了，类似于一个权重选取。其中Concatenate是将这些模块沿着通道拼接在一起。

什么是1X1 convolution

image-20210311161223028

$1\times 1$卷积能够跨越不同通道，进行信息融合。

image-20210311161456790

使用$1\times 1$的卷积能够降低运算量。

Inception module

image-20210311164058391
image-20210311164312991
import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

class InceptionModule(torch.nn.Module):
    def __init__(self,in_channels):
        super(InceptionModule,self).__init__()
        self.branch1x1 = torch.nn.Conv2d(in_channels,16,kernel_size=1)
        
        self.branch5x5_1 = torch.nn.Conv2d(in_channels,16,kernel_size=1)
        self.branch5x5_2 = torch.nn.Conv2d(16,24,kernel_size=5,padding=2)
        
        self.branch3x3_1 = torch.nn.Conv2d(in_channels,16,kernel_size=1)
        self.branch3x3_2 = torch.nn.Conv2d(16,24,kernel_size=3,padding=1)
        self.branch3x3_3 = torch.nn.Conv2d(24,24,kernel_size=3,padding=1)
        
        self.branch_pool = torch.nn.Conv2d(in_channels,24,kernel_size=1)
        
    def forward(self,x):
        branch1x1 = self.branch1x1(x)
        
        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)
        
        branch3x3 = self.branch3x3_1(x)
        branch3x3 = self.branch3x3_2(branch3x3)
        branch3x3 = self.branch3x3_3(branch3x3)
        
        
        branch_pool = F.avg_pool2d(x,kernel_size=3,stride=1,padding=1)
        branch_pool = self.branch_pool(branch_pool)
        
        outputs = [branch1x1,branch5x5,branch3x3,branch_pool]
        
        return torch.cat(outputs,dim=1)

使用Inception Module

class Net(torch.nn.Module):
    def __init__(self,):
        super(Net,self).__init__()
        self.conv1 = torch.nn.Conv2d(1,10,kernel_size=5)
        # 24x3+16 = 88
        self.conv2 = torch.nn.Conv2d(88,20,kernel_size=5)
        
        self.incep1 = InceptionModule(in_channels=10)
        self.incep2 = InceptionModule(in_channels=20)
        
        self.pool = torch.nn.MaxPool2d(2)
        self.linear = torch.nn.Linear(1408,10)
        
    def forward(self,x):
        in_size = x.size(0)
        x = F.relu(self.pool(self.conv1(x)))
        x = self.incep1(x)
        x = F.relu(self.pool(self.conv2(x)))
        x = self.incep2(x)
        x = x.view(in_size,-1)
        x = self.linear(x)
        
        return x

其中，self.linear = torch.nn.Linear(1408,10)的1408可以通过代码计算，先跑一遍，计算x的大小即可。

结果

model = Net()
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs,target = data
        inputs,target = inputs.to(device),target.to(device)
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs,target)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        
        if batch_idx % 300 == 299:
            print('[{:d},{:5d}] loss:{:.3f}'.format(epoch+1,batch_idx+1,
                                                    running_loss/300))
            running_loss = 0.0    
            
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs,target = data
            inputs,target = inputs.to(device),target.to(device)
            outputs = model(inputs)
            _,predicted = torch.max(outputs.data,dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
            
    print("Accuracy on test set:{:.2%}".format(correct/total))

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

深残余学习

image-20210311190108114

在神经网络中，由于深度学习可能会设置很多层，这样可能会导致梯度消失，为了避免这种情况，一般采用relu激活函数。

Residual Network

image-20210311191639524

简单的残差网络

image-20210311191758841

  • 定义残差网络

image-20210311192006799
class ResidualBlock(torch.nn.Module):
    def __init__(self,channels):
        super(ResidualBlock,self).__init__()
        self.channels = channels
        self.conv1 = torch.nn.Conv2d(channels,channels,kernel_size=3,padding=1)
        self.conv2 = torch.nn.Conv2d(channels,channels,kernel_size=3,padding=1)
        
    def forward(self,x):
        y = F.relu(self.conv1(x))
        y = self.conv2(y)
        return F.relu(x+y)

  • 定义网络

class Net(torch.nn.Module):
    def __init__(self,):
        super(Net,self).__init__()
        self.conv1 = torch.nn.Conv2d(1,16,kernel_size=5)
        self.conv2 = torch.nn.Conv2d(16,32,kernel_size=5)
        self.mp = torch.nn.MaxPool2d(2)
        
        self.rblock1 = ResidualBlock(16)
        self.rblock2 = ResidualBlock(32)
        
        self.linear = torch.nn.Linear(512,10)
        
    def forward(self,x):
        in_size = x.size(0)
        x = self.mp(F.relu(self.conv1(x)))
        x = self.rblock1(x)
        x = self.mp(F.relu(self.conv2(x)))
        x = self.rblock2(x)
        x = x.view(in_size,-1)
        x = self.linear(x)
        return x

  • 配置参数

model = Net()
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

  • 训练与测试

def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs,target = data
        inputs,target = inputs.to(device),target.to(device)
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs,target)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        
        if batch_idx % 300 == 299:
            print('[{:d},{:5d}] loss:{:.3f}'.format(epoch+1,batch_idx+1,
                                                    running_loss/300))
            running_loss = 0.0
            
            
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs,target = data
            inputs,target = inputs.to(device),target.to(device)
            outputs = model(inputs)
            _,predicted = torch.max(outputs.data,dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
            
    print("Accuracy on test set:{:.2%}".format(correct/total))

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

作业2

  • 复现论文1

论文：Identity Mappings in Deep Residual Networks.

  • 复现论文2

论文：Densely Connected Convolutional Networks[J].

学习目标

  1. 扩充理论知识，比如“圣经”

  2. 阅读pytorch文档，便于写代码

  3. 复现经典工作（写代码与读代码是一个循环，跑代码只是证明环境配置成功了）

  4. 扩充视野

扩展阅读

  • CNN：https://zhuanlan.zhihu.com/p/27908027

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