resnet18代码复现pytorch

可以使用PyTorch自带的torchvision.models模块中的resnet18模型进行复现。

示例代码：

import torch
from torchvision import models

resnet18 = models.resnet18(pretrained=True)

# 打印模型结构
print(resnet18)

如果需要训练resnet18，可以使用如下代码:

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(resnet18.parameters(), lr=0.001, momentum=0.9)

# 训练过程
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = resnet18(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
    print('Epoch %d loss: %.3f' %(epoch + 1, running_loss / (i+1)))

print('Finished Training')

注意: 上面代码块中的trainloader是训练数据的读取方式。