基于pytorch的Alexnet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AlexNet输入11*11的单通道图像分类代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.utils.data import Dataset

# 定义数据集类
class MyDataset(Dataset):
    def __init__(self, data, target, transform=None):
        self.data = data
        self.target = target
        self.transform = transform

    def __getitem__(self, index):
        x = self.data[index]
        y = self.target[index]
        if self.transform:
            x = self.transform(x)
        return x, y

    def __len__(self):
        return len(self.data)

# 定义AlexNet模型
class AlexNet(nn.Module):
    def __init__(self, num_classes=2):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x

# 定义训练函数
def train(model, device, train_loader, optimizer, criterion):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

# 定义测试函数
def test(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
        test_loss, correct, len(test_loader.dataset), accuracy))

# 定义主函数
def main():
    # 设置超参数
    batch_size = 32
    learning_rate = 0.001
    num_epochs = 10

    # 加载数据集
    transform = transforms.Compose([transforms.ToTensor()])
    train_data = MyDataset(data=train_images, target=train_labels, transform=transform)
    test_data = MyDataset(data=test_images, target=test_labels, transform=transform)
    train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)

    # 初始化模型和优化器
    model = AlexNet(num_classes=2)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    criterion = nn.CrossEntropyLoss()

    # 训练和测试模型
    for epoch in range(num_epochs):
        train(model, device, train_loader, optimizer, criterion)
        test(model, device, test_loader, criterion)

    # 保存模型
    torch.save(model.state_dict(), 'alexnet.pth')

if __name__ == '__main__':
    main()

其中，`train_images`和`test_images`是包含11*11的单通道图像数据的NumPy数组，`train_labels`和`test_labels`是对应的标签NumPy数组。请根据实际情况修改数据集加载部分的代码。