基于pytorch的DenseNet输入图片大小为11*11的分类模型 ，一通道的图像分类代码

以下是基于PyTorch的DenseNet模型，用于11*11黑白图像分类的代码：

import torch
import torch.nn as nn
import torch.optim as optim

class DenseNet(nn.Module):
    def __init__(self):
        super(DenseNet, self).__init__()
        
        # Convolutional layer
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, bias=False)
        
        # Dense block 1
        self.dense1 = nn.Sequential(
            nn.BatchNorm2d(16),
            nn.ReLU(inplace=True),
            nn.Conv2d(16, 12, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(12),
            nn.ReLU(inplace=True),
            nn.Conv2d(12, 12, kernel_size=3, stride=1, padding=1, bias=False)
        )
        
        # Transition layer 1
        self.trans1 = nn.Sequential(
            nn.BatchNorm2d(24),
            nn.ReLU(inplace=True),
            nn.Conv2d(24, 6, kernel_size=1, stride=1, bias=False),
            nn.AvgPool2d(kernel_size=2, stride=2)
        )
        
        # Dense block 2
        self.dense2 = nn.Sequential(
            nn.BatchNorm2d(6),
            nn.ReLU(inplace=True),
            nn.Conv2d(6, 12, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(12),
            nn.ReLU(inplace=True),
            nn.Conv2d(12, 12, kernel_size=3, stride=1, padding=1, bias=False)
        )
        
        # Transition layer 2
        self.trans2 = nn.Sequential(
            nn.BatchNorm2d(18),
            nn.ReLU(inplace=True),
            nn.Conv2d(18, 6, kernel_size=1, stride=1, bias=False),
            nn.AvgPool2d(kernel_size=2, stride=2)
        )
        
        # Dense block 3
        self.dense3 = nn.Sequential(
            nn.BatchNorm2d(6),
            nn.ReLU(inplace=True),
            nn.Conv2d(6, 12, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(12),
            nn.ReLU(inplace=True),
            nn.Conv2d(12, 12, kernel_size=3, stride=1, padding=1, bias=False)
        )
        
        # Global average pooling
        self.avgpool = nn.AdaptiveAvgPool2d((1,1))
        
        # Fully connected layer
        self.fc = nn.Linear(12, 10)
        
    def forward(self, x):
        # Convolutional layer
        x = self.conv1(x)
        
        # Dense block 1
        x1 = self.dense1(x)
        x = torch.cat([x, x1], dim=1)
        
        # Transition layer 1
        x = self.trans1(x)
        
        # Dense block 2
        x2 = self.dense2(x)
        x = torch.cat([x, x2], dim=1)
        
        # Transition layer 2
        x = self.trans2(x)
        
        # Dense block 3
        x3 = self.dense3(x)
        x = torch.cat([x, x3], dim=1)
        
        # Global average pooling
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        
        # Fully connected layer
        x = self.fc(x)
        return x

# Define the model
model = DenseNet()

# Define the loss function
criterion = nn.CrossEntropyLoss()

# Define the optimizer
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0001)

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        
        # Zero the parameter gradients
        optimizer.zero_grad()
        
        # Forward + backward + optimize
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        # Print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

其中，`trainloader`是数据集的dataloader。在训练之前，需要将数据集转换为tensor并标准化。此外，还需要对模型进行调参以取得更好的效果。