图像多类别分割 类别权重调整 代码

以下是一个基于PyTorch框架实现的图像多类别分割的类别权重调整代码示例：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义损失函数
class WeightedCrossEntropyLoss(nn.Module):
    def __init__(self, weight=None):
        super(WeightedCrossEntropyLoss, self).__init__()
        self.weight = weight

    def forward(self, input, target):
        logp = nn.functional.log_softmax(input, dim=1)
        loss = -self.weight[target] * logp.gather(1, target.unsqueeze(1))
        return loss.mean()

# 定义模型
class SegmentationModel(nn.Module):
    def __init__(self, input_channels, num_classes):
        super(SegmentationModel, self).__init__()
        self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, num_classes, kernel_size=1)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = self.conv3(x)
        return x

# 设置类别权重
weight = torch.tensor([0.1, 0.3, 0.6])

# 创建模型
model = SegmentationModel(input_channels=3, num_classes=3)

# 定义损失函数
criterion = WeightedCrossEntropyLoss(weight=weight)

# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(num_epochs):
    for data, target in dataloader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

# 在测试集上评估模型
with torch.no_grad():
    for data, target in test_dataloader:
        output = model(data)
        loss = criterion(output, target)
        predicted = torch.argmax(output, dim=1)
        accuracy = (predicted == target).sum().item() / (data.shape[0] * data.shape[2] * data.shape[3])
        print("Test loss: %.4f, accuracy: %.4f" % (loss, accuracy))

在上述代码中，我们定义了一个`WeightedCrossEntropyLoss`损失函数，其中`weight`参数用于设置类别权重。在定义模型时，我们使用了一个简单的卷积神经网络，用于实现图像的多类别分割任务。在训练过程中，我们使用了优化器`Adam`和定义的损失函数进行模型优化。最后，在测试集上使用预测结果计算了模型的损失和准确率。