三维unet用nn.CrossEntropyLoss做3分类，每个输入数据batch size大小为4

如果你要在使用三维 Unet 模型进行三分类时，每个输入数据的 batch size 大小为 4，你需要对损失函数的计算稍作修改。具体来说，你需要对每个输入数据的损失值进行平均，然后再将这些平均值相加得到最终的损失值。这样做的目的是防止不同 batch size 下的损失值大小不一致导致训练不稳定。

下面是一个示例代码：

import torch
import torch.nn as nn

class Unet3D(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Unet3D, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        
        # Encoder
        self.conv1 = nn.Conv3d(in_channels, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv3d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv3d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv3d(256, 512, kernel_size=3, padding=1)
        
        # Decoder
        self.upconv1 = nn.ConvTranspose3d(512, 256, kernel_size=2, stride=2)
        self.conv5 = nn.Conv3d(512, 256, kernel_size=3, padding=1)
        self.upconv2 = nn.ConvTranspose3d(256, 128, kernel_size=2, stride=2)
        self.conv6 = nn.Conv3d(256, 128, kernel_size=3, padding=1)
        self.upconv3 = nn.ConvTranspose3d(128, 64, kernel_size=2, stride=2)
        self.conv7 = nn.Conv3d(128, 64, kernel_size=3, padding=1)
        
        # Output
        self.conv8 = nn.Conv3d(64, out_channels, kernel_size=1)
        
        # Activation functions
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        # Encoder
        x1 = self.relu(self.conv1(x))
        x2 = self.relu(self.conv2(x1))
        x3 = self.relu(self.conv3(x2))
        x4 = self.relu(self.conv4(x3))
        
        # Decoder
        x = self.upconv1(x4)
        x = torch.cat([x, x3], dim=1)
        x = self.relu(self.conv5(x))
        x = self.upconv2(x)
        x = torch.cat([x, x2], dim=1)
        x = self.relu(self.conv6(x))
        x = self.upconv3(x)
        x = torch.cat([x, x1], dim=1)
        x = self.relu(self.conv7(x))
        
        # Output
        x = self.conv8(x)
        x = self.softmax(x)
        
        return x

# 定义模型
model = Unet3D(in_channels=1, out_channels=3)

# 定义损失函数
loss_fn = nn.CrossEntropyLoss()

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

# 训练模型
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = loss_fn(outputs, labels)
        loss /= inputs.shape[0] # 对每个输入数据的损失值进行平均
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 10 == 9:    # 每 10 个 batch 输出一次损失值
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

在这个例子中，我们使用了一个名为 `train_loader` 的数据加载器来加载训练数据。在训练过程中，我们对每个输入数据的损失值进行平均，然后将所有平均值相加得到最终的损失值。这样做可以确保不同 batch size 下的损失值大小一致，从而保证训练的稳定性。