pytorch resnet-18 训练自己的数据集源码

下面是使用PyTorch的ResNet-18模型训练自己的数据集的源代码示例：

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

# 定义数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载自定义数据集
train_dataset = torchvision.datasets.ImageFolder(root='path_to_train_data', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=2)

# 加载预训练的ResNet-18模型
model = models.resnet18(pretrained=True)

# 冻结模型的参数
for param in model.parameters():
    param.requires_grad = False

# 替换最后一层全连接层
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, num_classes)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)

# 将模型移动到GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

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

        running_loss += loss.item()
        if i % 200 == 199:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

print('训练完成！')

注意替换代码中的`path_to_train_data`为你自己的训练数据集的路径。此外，还可以调整超参数并将模型训练在GPU上，以加快训练速度。