python GHM loss代码

以下是一个简单的 PyTorch GHM Loss 的实现代码：

import torch

class GHM_Loss(torch.nn.Module):
    def __init__(self, bins=10, alpha=0.75, momentum=0):
        super(GHM_Loss, self).__init__()
        self.bins = bins
        self.alpha = alpha
        self.momentum = momentum
        self.edges = [float(x) / bins for x in range(bins+1)]
        self.edges[-1] += 1e-6
        if momentum > 0:
            self.acc_sum = [0.0] * bins

    def forward(self, pred, target):
        edges = self.edges
        mmt = self.momentum
        weights = torch.zeros_like(pred)

        g = torch.abs(pred - target)
        tot = target.size(0) * target.size(1) * target.size(2) * target.size(3)
        n = 0  # n valid bins
        for i in range(self.bins):
            inds = (g >= edges[i]) & (g < edges[i+1])
            num_in_bin = inds.sum().item()
            if num_in_bin > 0:
                if mmt > 0:
                    self.acc_sum[i] = mmt * self.acc_sum[i] + (1 - mmt) * num_in_bin
                    weights[inds] = tot / self.acc_sum[i]
                else:
                    weights[inds] = tot / num_in_bin
                n += 1
        if n > 0:
            weights = weights / n

        loss = torch.nn.functional.binary_cross_entropy_with_logits(pred, target, weights, reduction='sum') / tot
        return loss

其中，`bins` 表示 GHM Loss 中的 bin 数量，`alpha` 表示 GHM Loss 中的 $\alpha$ 参数，`momentum` 表示动量参数。在 `forward` 函数中，首先根据预测值和真实值计算出 $g=|pred-target|$，然后根据 $g$ 的分布情况计算出权重 `weights`。最后，使用 `binary_cross_entropy_with_logits` 函数计算 GHM Loss。