给我一个pytorch代码在unet中加入hd95损失和dice损失
时间: 2024-05-05 18:20:19 浏览: 15
以下是一个示例的PyTorch代码,在U-Net中添加了HD95损失和DICE损失:
```
import torch
import torch.nn as nn
import torch.optim as optim
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss,self).__init__()
def forward(self,outputs,targets):
smooth = 1
intersection = (outputs * targets).sum()
dice = (2.*intersection + smooth)/(outputs.sum()+targets.sum()+smooth)
return 1-dice
class HD95Loss(nn.Module):
def __init__(self):
super(HD95Loss,self).__init__()
def forward(self,outputs,targets):
diff = torch.abs(targets-outputs)
percentile = 95
hd95 = torch.percentile(diff, percentile)
return hd95
class UNet(nn.Module):
def __init__(self):
super(UNet,self).__init__()
# define U-Net architecture here
self.dice_loss = DiceLoss()
self.hd95_loss = HD95Loss()
def forward(self,x,target):
# define forward pass here
output = # define output here
loss_dice = self.dice_loss(output,target)
loss_hd95 = self.hd95_loss(output,target)
loss = loss_dice + loss_hd95
return output, loss
net = UNet()
optimizer = optim.Adam(net.parameters())
# define your data here
for epoch in range(num_epochs):
for inputs, targets in data_loader:
optimizer.zero_grad()
outputs, loss = net(inputs, targets)
loss.backward()
optimizer.step()
```
其中,`DiceLoss`表示Dice损失,`HD95Loss`表示HD95损失,`UNet`表示U-Net网络,`forward`方法包含了U-Net的前向传递过程以及损失计算。在训练循环中,每次迭代都需要将输入和目标传递给网络,计算得到输出和损失,然后进行反向传递和参数优化。