BCL与focal loss相结合的pytorch代码

下面是使用BCL与Focal Loss相结合的PyTorch代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class BCLFocalLoss(nn.Module):
    def __init__(self, alpha=0.25, gamma=2, reduction='mean'):
        super(BCLFocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction

    def forward(self, input, target):
        pt = F.softmax(input, dim=1)
        pt = pt.clamp(1e-7, 1.0 - 1e-7)
        alpha = torch.ones_like(pt) * self.alpha
        alpha[:, 1] -= self.alpha
        alpha = alpha.gather(1, target.unsqueeze(1))
        loss = -alpha * (1.0 - pt) ** self.gamma * torch.log(pt)
        if self.reduction == 'mean':
            loss = loss.mean()
        elif self.reduction == 'sum':
            loss = loss.sum()
        return loss

使用方法：

criterion = BCLFocalLoss(alpha=0.25, gamma=2, reduction='mean')
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

在使用时，可以设置`alpha`和`gamma`参数来调整BCL和Focal Loss的权重，`reduction`参数用于控制损失函数的求和或平均。