pytorch NegSTOILoss

PyTorch中没有内置的NegSTOILoss函数，但是你可以自己定义一个。NegSTOILoss是Negative Short-Term Online Instance Loss的缩写，它在目标检测任务中用于训练对象检测器。

下面是一个示例的NegSTOILoss实现：

import torch
import torch.nn as nn

class NegSTOILoss(nn.Module):
    def __init__(self, alpha=2.0, beta=0.25, eps=1e-6):
        super(NegSTOILoss, self).__init__()
        self.alpha = alpha
        self.beta = beta
        self.eps = eps

    def forward(self, classification_pred, classification_target, regression_pred, regression_target):
        classification_loss = self.focal_loss(classification_pred, classification_target)
        regression_loss = self.iou_loss(regression_pred, regression_target)
        total_loss = classification_loss + self.alpha * regression_loss
        return total_loss

    def focal_loss(self, inputs, targets):
        targets = targets.float()
        inputs = inputs.sigmoid()

        pos_mask = targets >= 1
        neg_mask = targets < 1

        pos_loss = -torch.log(inputs + self.eps) * torch.pow(1 - inputs, self.alpha) * pos_mask.float()
        neg_loss = -torch.log(1 - inputs + self.eps) * torch.pow(inputs, self.alpha) * neg_mask.float()

        num_pos = pos_mask.sum()
        num_neg = neg_mask.sum()

        pos_loss = pos_loss.sum() / (num_pos + self.eps)
        neg_loss = neg_loss.sum() / (num_neg + self.eps)

        focal_loss = self.beta * pos_loss + (1 - self.beta) * neg_loss
        return focal_loss

    def iou_loss(self, pred, target):
        pred_left = pred[:, 0]
        pred_top = pred[:, 1]
        pred_right = pred[:, 2]
        pred_bottom = pred[:, 3]

        target_left = target[:, 0]
        target_top = target[:, 1]
        target_right = target[:, 2]
        target_bottom = target[:, 3]

        target_area = (target_left + target_right) * (target_top + target_bottom)
        pred_area = (pred_left + pred_right) * (pred_top + pred_bottom)

        x_left = torch.max(pred_left, target_left)
        y_top = torch.max(pred_top, target_top)
        x_right = torch.min(pred_right, target_right)
        y_bottom = torch.min(pred_bottom, target_bottom)

        intersection_area = torch.clamp(x_right - x_left, min=0) * torch.clamp(y_bottom - y_top, min=0)
        union_area = target_area + pred_area - intersection_area
        iou_loss = -torch.log((intersection_area + self.eps) / (union_area + self.eps))
        return iou_loss.mean()

这是一个简单的实现，你可以根据自己的需求进行修改和调整。希望能帮到你！