利用python实现Ohem损失函数

非常感谢您的提问。关于Ohem损失函数的实现，我们可以用以下的Python代码来实现：

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

class OhemLoss(nn.Module):
    def __init__(self, threshold=0.7, min_kept=100000):
        super(OhemLoss, self).__init__()
        self.threshold = threshold
        self.min_kept = min_kept

    def forward(self, cls_pred, cls_target):
        batch_size = cls_pred.size(0)
        num_classes = cls_pred.size(2)
        cls_pred = cls_pred.permute(0, 3, 1, 2).contiguous().view(-1, num_classes)
        cls_target = cls_target.permute(0, 2, 3, 1).contiguous().view(-1)

        pos = cls_target < num_classes - 1
        num_pos = pos.sum().float()

        # OHEM
        cls_target_ohem = cls_target.clone()
        valid_flag = cls_target >= 0
        cls_target_ohem[valid_flag] = cls_pred.size(1)
        loss = F.cross_entropy(cls_pred, cls_target_ohem.long(), reduction='none')
        loss[valid_flag] = 0
        loss, _ = loss.sort(descending=True)
        if self.min_kept > 0:
            num_pos = max(self.min_kept, num_pos)
        num_pos = num_pos.item()

        threshold = min(loss[int(num_pos) - 1], self.threshold)
        loss = loss[loss > threshold]
        return loss.mean()

其中，OhemLoss类是继承自nn.Module的，重载了forward函数。在forward函数中，根据输入的预测值和目标值，计算出损失函数的值。在计算过程中，利用了OHEM(Online Hard Example Mining)算法来挖掘难样本，即选择loss值大于某个阈值的样本计算损失函数。这样可以有效降低问题的难度，从而提高模型的训练效果。