GHM loss 在pytorch中的代码实现

GHM loss是一种用于解决目标类别分布不均衡问题的损失函数。在PyTorch中可以通过以下代码实现GHM loss：

import torch

class GHMLoss(torch.nn.Module):
    def __init__(self, bins=10, alpha=0.75):
        super(GHMLoss, self).__init__()
        self.bins = bins
        self.alpha = alpha
        self.edges = [x / bins for x in range(bins + 1)]
        self.edges[-1] += 1e-6

    def forward(self, input, target):
        N, C = input.size()
        grad_input = input.clone().detach()
        grad_input.zero_()
        target = target.view(-1, 1)
        edges = self.edges

        inds = (torch.arange(1, self.bins + 1).float() / self.bins).to(input.device)
        weights = torch.zeros((self.bins,)).to(input.device)
        weights[0] = inds[0]
        weights[1:] = inds[1:] - inds[:-1]

        inds = (target * self.bins).long().clamp(0, self.bins - 1)
        weights = weights[inds.view(-1)]

        Ns = torch.zeros((self.bins,)).to(input.device)
        for i in range(self.bins):
            Ns[i] = ((inds == i).sum()).float()

        Ns[Ns == 0] = float('inf')
        weights = (weights * Ns).sqrt()
        weights = (weights / weights.sum()) * self.bins

        inds = torch.bucketize(input.softmax(dim=1)[:, 0], edges)
        g = -(target - input.softmax(dim=1)[:, 0]).detach().abs()
        grad_input[:, 0] = g / (2 * g.abs().mean() + 1e-8)
        grad_input[:, 0] *= weights[inds.view(-1)].view(N, 1)

        return torch.nn.functional.binary_cross_entropy_with_logits(input, target, weight=None, reduction='mean', pos_weight=None, label_smoothing=None) + grad_input.sum() * self.alpha / N

其中，`bins`表示将概率分布分成的区间数量，`alpha`为平衡交叉熵损失和GHM损失的权重。在`forward`函数中，首先计算每个样本的概率分布落在哪个区间，并根据该区间的样本数量和梯度权重计算出每个样本的权重。然后，根据权重计算GHM损失，并计算交叉熵损失和GHM损失的加权和。最后，将GHM损失的梯度乘以`alpha`并加入到交叉熵损失的梯度中，返回总的损失值。