pytorch cifar10图像分类densenet

DenseNet是一种用于图像分类的深度学习模型，它在PyTorch CIFAR10图像分类项目中有相应的篇章和演示。你可以在B站上观看DenseNet的视频讲解，并在Colab上找到相应的演示代码。\[1\]此外，你还可以在GitHub上找到相关的资源和使用方法。\[3\]如果你对DenseNet的图像分类感兴趣，可以参考这些资源进行学习和实践。\[2\]
#### 引用[.reference_title]
- *1* *2* *3* [Pytorch CIFAR10 图像分类篇 汇总](https://blog.csdn.net/weixin_45508265/article/details/119285255)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down28v1,239^v3^insert_chatgpt"}} ] [.reference_item]
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相关问题

基于pytorch的DenseNet 11*11图像大小分类代码

以下是使用Pytorch实现基于DenseNet的11 * 11图像分类的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.datasets import CIFAR10
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR

# Define hyperparameters
num_epochs = 50
batch_size = 128
learning_rate = 0.01
num_classes = 10
num_layers_per_block = 4
growth_rate = 32
compression_factor = 0.5

# Define data transforms
transform = transforms.Compose([
    transforms.RandomCrop(11),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# Load CIFAR10 dataset
train_dataset = CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = CIFAR10(root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# Define DenseNet block
class DenseNetBlock(nn.Module):
    def __init__(self, in_channels, growth_rate, num_layers):
        super(DenseNetBlock, self).__init__()
        self.layers = nn.ModuleList()
        for i in range(num_layers):
            self.layers.append(nn.Sequential(
                nn.BatchNorm2d(in_channels + i * growth_rate),
                nn.ReLU(inplace=True),
                nn.Conv2d(in_channels + i * growth_rate, growth_rate, kernel_size=1, bias=False),
                nn.BatchNorm2d(growth_rate),
                nn.ReLU(inplace=True),
                nn.Conv2d(growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)
            ))
    
    def forward(self, x):
        for layer in self.layers:
            out = layer(x)
            x = torch.cat([x, out], 1)
        return x

# Define DenseNet model
class DenseNet(nn.Module):
    def __init__(self, num_classes, num_layers_per_block, growth_rate, compression_factor):
        super(DenseNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 2*growth_rate, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(2*growth_rate),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        num_channels = 2 * growth_rate
        self.num_blocks = len(num_layers_per_block)
        for i, num_layers in enumerate(num_layers_per_block):
            block = DenseNetBlock(num_channels, growth_rate, num_layers)
            self.features.add_module("denseblock%d" % (i + 1), block)
            num_channels += num_layers * growth_rate
            if i != self.num_blocks - 1:
                num_channels = int(num_channels * compression_factor)
                trans = nn.Sequential(
                    nn.Conv2d(num_channels, num_channels, kernel_size=1, bias=False),
                    nn.BatchNorm2d(num_channels),
                    nn.ReLU(inplace=True),
                    nn.AvgPool2d(kernel_size=2, stride=2)
                )
                self.features.add_module("transition%d" % (i + 1), trans)
        self.classifier = nn.Linear(num_channels, num_classes)
    
    def forward(self, x):
        x = self.features(x)
        x = nn.functional.avg_pool2d(x, kernel_size=(x.size(2), x.size(3)))
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

# Initialize model and optimizer
model = DenseNet(num_classes, num_layers_per_block, growth_rate, compression_factor)
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)
scheduler = MultiStepLR(optimizer, milestones=[int(num_epochs*0.5), int(num_epochs*0.75)], gamma=0.1)

# Train model
model.train()
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # Forward pass
        outputs = model(images)
        loss = nn.functional.cross_entropy(outputs, labels)
        
        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
    # Print training progress
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
    
    # Update learning rate
    scheduler.step()

# Test model
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    
    # Print test accuracy
    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

这个示例代码基于DenseNet的思想，使用了一个堆叠的DenseNet块来构建网络。每个块由多个DenseNet层组成，每个层都通过连接前面所有层的方式进行连接，以最大化信息流动。这个代码还包括数据增强、学习率调度器等常用的训练技巧，以帮助提高模型的性能。

DCGAN-DenseNet图像分类代码

这里是一个使用PyTorch实现的DCGAN和DenseNet的图像分类代码示例：

DCGAN代码示例：

import torch
import torch.nn as nn

# 定义生成器 Generator
class Generator(nn.Module):
    def __init__(self, nz, ngf, nc):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)

# 定义判别器 Discriminator
class Discriminator(nn.Module):
    def __init__(self, nc, ndf):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input).view(-1, 1).squeeze(1)
        
# 定义训练过程
def train(netG, netD, dataloader, criterion, optimizerG, optimizerD, device, nz, ngf, ndf, epochs):
    for epoch in range(epochs):
        for i, data in enumerate(dataloader, 0):
            # 训练判别器
            netD.zero_grad()
            real_imgs = data[0].to(device)
            b_size = real_imgs.size(0)
            label = torch.full((b_size,), 1, dtype=torch.float, device=device)
            output = netD(real_imgs).view(-1)
            errD_real = criterion(output, label)

            noise = torch.randn(b_size, nz, 1, 1, device=device)
            fake_imgs = netG(noise)
            label.fill_(0)
            output = netD(fake_imgs.detach()).view(-1)
            errD_fake = criterion(output, label)

            errD = errD_real + errD_fake
            errD.backward()
            optimizerD.step()

            # 训练生成器
            netG.zero_grad()
            label.fill_(1)
            output = netD(fake_imgs).view(-1)
            errG = criterion(output, label)
            errG.backward()
            optimizerG.step()

            # 输出训练状态
            print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f'
                  % (epoch, epochs, i, len(dataloader),
                     errD.item(), errG.item()))

# 定义超参
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
nz = 100
ngf = 64
ndf = 64
epochs = 5
lr = 0.0002
beta1 = 0.5
batch_size = 128
image_size = 64
nc = 3

# 加载数据集
dataset = torchvision.datasets.CIFAR10(root='./data', download=True,
                                       transform=torchvision.transforms.Compose([
                                           torchvision.transforms.Resize(image_size),
                                           torchvision.transforms.ToTensor(),
                                           torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                                       ]))
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                         shuffle=True, num_workers=2)

# 初始化网络
netG = Generator(nz, ngf, nc).to(device)
netD = Discriminator(nc, ndf).to(device)
criterion = nn.BCELoss()
optimizerG = torch.optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerD = torch.optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))

# 训练网络
train(netG, netD, dataloader, criterion, optimizerG, optimizerD, device, nz, ngf, ndf, epochs)

DenseNet代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义DenseNet模型
class DenseNet(nn.Module):
    def __init__(self):
        super(DenseNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Sequential(*self._make_dense_layers(64, 12)),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 128, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.AvgPool2d(kernel_size=8, stride=1),
        )
        self.classifier = nn.Linear(128, 10)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

    def _make_dense_layers(self, in_channels, num_blocks):
        layers = []
        for i in range(num_blocks):
            layers.append(Bottleneck(in_channels))
            in_channels += 32
        return layers

# 定义Bottleneck模块
class Bottleneck(nn.Module):
    def __init__(self, in_channels):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 32, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(32)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = nn.ReLU(inplace=True)(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = nn.ReLU(inplace=True)(out)
        out = torch.cat((x, out), 1)
        return out

# 训练模型
def train(net, trainloader, criterion, optimizer, device, epochs):
    for epoch in range(epochs):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data[0].to(device), data[1].to(device)
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            if i % 2000 == 1999:
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

# 定义超参
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
epochs = 10
lr = 0.1
momentum = 0.9
batch_size = 128
image_size = 32

# 加载数据集
transform_train = transforms.Compose([
        transforms.RandomCrop(image_size, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          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=batch_size,
                                         shuffle=False, num_workers=2)

# 初始化网络
net = DenseNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=momentum)

# 训练网络
train(net, trainloader, criterion, optimizer, device, epochs)

这里的代码示例可以用来训练和测试CIFAR-10数据集的分类任务。注意要根据数据集的要求调整模型的输入和输出大小。