dpn网络的pytorch实现方式

下面是关于“dpn网络的pytorch实现方式”的完整攻略：

DPN网络简介

DPN（Dual Path Network）网络是一种深度卷积神经网络。与传统的卷积神经网络不同，DPN网络引入了双向路径机制，以提高网络的性能和稳定性。其核心思想是将特征图分成两个路径，分别进行特征提取和特征融合。

DPN网络的pytorch实现方式

下面是DPN网络的pytorch实现方式：

导入需要的模块

首先需要导入PyTorch、torchvision以及numpy等模块：

import torch
import torch.nn as nn
import torchvision
import numpy as np

定义DPN网络

定义DPN网络的Python类，代码如下：

class BasicBlock(nn.Module):
    def __init__(self,in_planes,planes,stride=1):
        super(BasicBlock,self).__init__()
        self.conv1 = nn.Conv2d(in_planes,planes,kernel_size=3,padding=1,bias=False,stride=stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes,planes,kernel_size=3,padding=1,bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu=nn.ReLU(inplace=True)

    def forward(self,x):
        residual = x
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += residual
        out = self.relu(out)
        return out
class DPN(nn.Module):
    def __init__(self,block, num_classes=10):
        super(DPN,self).__init__()
        self.in_planes = 64
        self.conv1 = nn.Conv2d(3,64,kernel_size=3,padding=1,bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block,64,1)
        self.layer2 = self._make_layer(block,128,2)
        self.layer3 = self._make_layer(block,256,2)
        self.layer4 = self._make_layer(block,512,2)
        self.linear = nn.Linear(512, num_classes)

    def _make_layer(self,block,planes,num_blocks,stride=1):

        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes,planes,stride))
            self.in_planes = planes
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.bn1(self.conv1(x))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = nn.functional.avg_pool2d(out,4)
        out = out.view(out.size(0),-1)
        out = self.linear(out)
        return out

上述代码中，BasicBlock是DPN网络中的基本块，DPN是整个网络。其中，_make_layer()方法用于创建每一个层，forward()方法用于网络的前向传播。

定义训练函数

定义用于训练DPN网络的函数：

def train(net, device, train_loader, optimizer, criterion, epoch):
    net.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(train_loader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

    print('Train Epoch: {} | Loss: {:.4f} | Acc: {:.4f}'.format(
        epoch, train_loss/(batch_idx+1), 100.*correct/total))

定义测试函数

定义用于测试DPN网络的函数：

def test(net, device, test_loader):
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(test_loader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = net(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

        print('Test set: Average loss: {:.4f}, Accuracy: {:.4f}'.format(
            test_loss/(batch_idx+1), 100.*correct/total))

载入数据集

载入CIFAR10数据集

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

训练和测试模型

最后，我们就可以调用上述函数对DPN网络进行训练和测试了：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dpn = DPN(BasicBlock, num_classes=10)
dpn = dpn.to(device)
optimizer = torch.optim.SGD(dpn.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
criterion = nn.CrossEntropyLoss()

for epoch in range(200):
    train(dpn, device, trainloader, optimizer, criterion, epoch)
    test(dpn, device, testloader)

    if (epoch + 1) % 50 == 0:
        for param_group in optimizer.param_groups:
            param_group['lr'] /= 2

这个例子中，我们使用了CIFAR10数据集进行训练，训练过程中分别使用了SGD优化器和交叉熵损失函数。我们训练了200个epoch，其中每50个epoch将学习率缩小一半。

至此，关于“dpn网络的pytorch实现方式”的攻略就完成了。