Pytorch(5): LeNet，ResNet，RNN，LSTM代码

1、 LeNet5与ResNet18实战

第一部分：LeNet5代码：

import torch
from torch import nn
from torch.nn import  functional as F


class Lenet5(nn.Module):

    def __init__(self):
        super(Lenet5, self).__init__()

        self.conv_unit=nn.Sequential(
            nn.Conv2d(3,16,kernel_size=5,stride=1,padding=0),
            nn.MaxPool2d(kernel_size=2,stride=2,padding=0),
            nn.Conv2d(16,32,kernel_size=5,stride=1,padding=0),
            nn.MaxPool2d(kernel_size=2,stride=2,padding=0),
        )

        self.fc_unit=nn.Sequential(
            nn.Linear(32*5*5,32),
            nn.ReLU(),
            nn.Linear(32,10)
        )

    def forward(self,x):
        batchsz=x.size(0)

        out=self.conv_unit(x)
        out=out.view(batchsz,-1)
        out=self.fc_unit(out)
        return out

def main():

    net = Lenet5()

    tmp = torch.randn(2, 3, 32, 32)
    out = net(tmp)
    print('lenet out:', out.shape)




if __name__ == '__main__':
    main()

代码步骤

• 1、建立一个卷积sequential：
  第一层将三个channel变为16个，核函数size为5，步长为1，不padding，使用maxpooling将feature 的h和w缩小一倍
  第二层将16个channel变为32个，设定和pooling与前一个一致
• 2、将dim铺平[b,-1]
• 3、经过两个线性全连接层将dim size变为10[b,x]=>[b,10]
• 4、建立forward函数

第二部分 ResNet18 代码：

import torch
from torch import nn
from torch.nn import functional as F


class ResBlk(nn.Module):

    def __init__(self,ch_in,ch_out,stride=1):
        super(ResBlk, self).__init__()

        self.conv1=nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=stride,padding=1)
        self.bn1=nn.BatchNorm2d(ch_out)
        self.conv2=nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1)
        self.bn2=nn.BatchNorm2d(ch_out)

        self.extra=nn.Sequential()

        if ch_in!=ch_out:
            self.extra=nn.Sequential(nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=stride),
                                     nn.BatchNorm2d(ch_out))

    def forward(self,x):
        out=F.relu(self.bn1(self.conv1(x)))
        out=self.bn2(self.conv2(out))
        extra=self.extra(x)
        out=out+extra
        out=F.relu(out)
        return out

class ResNet18(nn.Module):

    def __init__(self):
        super(ResNet18, self).__init__()
        self.conv_first=nn.Sequential(nn.Conv2d(3,64,kernel_size=3,stride=3,padding=0),
                                      nn.BatchNorm2d(64))
        self.BLK1= ResBlk(64,128,stride=2)
        self.BLK2 = ResBlk(128, 256, stride=2)
        self.BLK3 = ResBlk(256, 512, stride=2)
        self.BLK4 = ResBlk(512, 512, stride=2)
        self.outlayer=nn.Linear(512*1*1,10)

    def forward(self,x):

        out=F.relu(self.conv_first(x))
        out=self.BLK1(out)
        out=self.BLK2(out)
        out=self.BLK3(out)
        out=self.BLK4(out)

        out=F.adaptive_avg_pool2d(out,[1,1])
        out=out.view(x.size(0),-1)
        out=self.outlayer(out)

        return out

def main():

    blk = ResBlk(64, 128, stride=4)
    tmp = torch.randn(2, 64, 32, 32)
    out = blk(tmp)
    print('block:', out.shape)

    x = torch.randn(2, 3, 32, 32)
    model = ResNet18()
    out = model(x)
    print('resnet:', out.shape)




if __name__ == '__main__':
    main()

代码流程

先建立一个resblock

• 1、每个block包含两个卷积层，每层还要做batch norm
• 2、建立一个short cut
• 3、如果输入的channel和输出的channel不一致，还要对short cut使用一个卷积，将shortcut和两个卷积层输出的channel数量是一致的，才可以相加
• 4、建立forward函数

建立ResNet18，包含四个basic block

• 1、先用一个卷积层，将3个channel变为64个，核函数size为3，步长为3，即将原来的feature缩小三分之一，加入batch norm
• 2、连续给与四个basic block
• 3、经过basic block后，输出channel从64变为512，stride使feature缩小至八分之一
• 4、经过一个outlayer，使最终输出的dim size 为10
• 5、建立forrward函数
• 6、要注意的是经过四个basic block后，使用F.adaptive_avg_pood2d使feature size都变为1x1

第三部分 main函数：

import torch
from torch.utils.data import DataLoader
from torchvision import datasets,transforms
from .lenet5 import Lenet5
from torch import nn
from torch import optim




def main():
    '''加载数据集'''
    batchsz=128
    cifar_train=datasets.CIFAR10("cifar",True,transform=transforms.Compose([
                                transforms.Resize((32,32)),
                                transforms.ToTensor(),
                                transforms.Normalize(mean=[0.485,0.456,0.406],
                                                    std=[0.229,0.225,0.225])
    ]),download=True)
    cifar_train=DataLoader(cifar_train,batch_size=batchsz,shuffle=True)

    cifar_test = datasets.CIFAR10("cifar", False, transform=transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.225, 0.225])
    ]), download=True)
    cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)

    x,label=next(iter(cifar_train))
    print("x:",x.shape,"label:",label.shape)

    device=torch.device("cuda")
    model=Lenet5().to(device)

    criterion=nn.CrossEntropyLoss.to(device)
    optimizer=optim.Adam(model.parameters(),lr=1e-3)

    for epoch in range(1000):
        model.train()
        for batch_idx, (x,label) in enumerate(cifar_train):
            x, label = x.to(device), label.to(device)
            logits=model(x)
            loss=criterion(logits,label)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        print(epoch,"loss:",loss.item())

        model.eval()
        with torch.no_grad:
            total_correct=0
            total_num=0
            for x,label in cifar_test:
                logits=model(x)
                pred=logits.argmax(dim=1)
                total_correct+=torch.eq(pred,label).float().sum().item()
                total_num+=x.size(0)

            acc=total_correct/total_num
            print(epoch,"acc:",acc)


if __name__ == '__main__':
    main()

代码流程：

• 1、数据集加载
• 2、使用device=torch.device(“cuda”)进行gpu加速
• 3、建立模型model=ResNet18()
• 4、建立损失函数标准 criterion=nn.CrossEntropyLoss(), 该方法包含了softmax方法，后续直接带入logits即可
• 5、建立一个优化器，使用adam方法，赋予model的变量和学习率
• 6、设定迭代次数
• 7、model.train()下：
  对于每个batch迭代，将x和label to(device)
  带入model
  计算损失
  optimizer.zero_grad()将优化器清洗
  loss.backward()做后向算法
  optimizer.step() 更新梯度
  打印每个step后的loss
• 8、model.eval()下：
  对于每个batch迭代
  将x放入model
  使用pred找到每次预测出的最大的index
  使用torch.eq(pred,label)做对比返回浮点型，求和，返回numpy型
  total_num为每次做的样本数，即加上每个batch
  计算总acc，打印

2、RNN

只用线性层的缺点：

• long sentence，too much parameter
• no context information

RNN:

• weight sharing
• memory:consider 前一个字对于当前时刻的影响

一个naive RNN，weight只有两个参数 w i h , w h h