实例级对比学习的pytorch代码案例

实例级对比学习（Instance-level Contrastive Learning）是一种自监督学习方法，可以用于无监督学习和半监督学习。下面是一个基于PyTorch的实例级对比学习代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# 定义模型
class Encoder(nn.Module):
    def __init__(self, z_dim=128):
        super(Encoder, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
        self.bn4 = nn.BatchNorm2d(256)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(256, z_dim)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        x = F.relu(self.bn4(self.conv4(x)))
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        z = self.fc(x)  # 输出z
        return z

# 定义对比学习模型
class ContrastiveModel(nn.Module):
    def __init__(self, encoder):
        super(ContrastiveModel, self).__init__()
        self.encoder = encoder

    def forward(self, x1, x2):
        z1 = self.encoder(x1)
        z2 = self.encoder(x2)
        return z1, z2

# 构建数据集
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

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

# 训练模型
model = ContrastiveModel(Encoder())
num_epochs = 10
for epoch in range(num_epochs):
    for i, (x1, x2) in enumerate(train_loader):
        z1, z2 = model(x1, x2)
        loss = criterion(z1, z2)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

上述代码中，定义了一个Encoder模型用于提取图像特征，然后构建了一个ContrastiveModel模型用于对比学习。在训练过程中，我们定义了一个交叉熵损失函数，并使用Adam优化器进行优化。在每个epoch中，我们遍历数据集并迭代模型。最后输出训练过程中的损失值。