densenet121实现图片大小为48*48分类代码

以下是使用Pytorch实现densenet121对大小为48x48的图像进行分类的代码示例：

首先，导入必要的库：

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

定义超参数：

num_epochs = 10
batch_size = 128
learning_rate = 0.001

定义数据预处理：

transform = transforms.Compose([
    transforms.Resize(48),
    transforms.CenterCrop(48),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

加载数据集：

train_dataset = datasets.ImageFolder(root='path/to/train/data', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = datasets.ImageFolder(root='path/to/test/data', transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

定义模型：

class DenseNet(nn.Module):
    def __init__(self):
        super(DenseNet, self).__init__()
        self.densenet121 = nn.Sequential(*list(torchvision.models.densenet121(pretrained=True).children())[:-1])
        self.fc = nn.Linear(1024, 10)

    def forward(self, x):
        features = self.densenet121(x)
        out = self.fc(features.view(features.size(0), -1))
        return out

定义损失函数和优化器：

model = DenseNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

训练模型：

for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.cuda()
        labels = labels.cuda()

        optimizer.zero_grad()
        outputs = model(images)

        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if (i+1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

    # Test the model
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in test_loader:
            images = images.cuda()
            labels = labels.cuda()
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        print('Test Accuracy of the model on the test images: {} %'.format(100 * correct / total))

这样就完成了使用densenet121对大小为48x48的图像进行分类的代码实现。