YOLOX改进之损失函数修改（上）

文章内容：如何在YOLOX官网代码中修改–置信度预测损失

环境：pytorch1.8

损失函数修改内容：

（1）置信度预测损失更换：二元交叉熵损失替换为FocalLoss或者VariFocalLoss

（2）定位损失更换：IOU损失替换为GIOU、CIOU、EIOU以及a-IOU系列

提示：使用之前可以先了解YOLOX及上述损失函数原理

参考链接：

YOLOX官网链接：https://github.com/Megvii-BaseDetection/YOLOX

YOLOX原理解析（Bubbliiiing大佬版）：https://blog.csdn.net/weixin_44791964/article/details/120476949

FocalLoss损失解析：https://cyqhn.blog.csdn.net/article/details/87343004

VariFocalLoss损失解析：https://blog.csdn.net/weixin_42096202/article/details/108567189

GIOU、CIOU、EIOU等：https://blog.csdn.net/neil3611244/article/details/113794197

a-IOU：https://blog.csdn.net/wjytbest/article/details/121513560

使用方法：直接替换即可

代码修改过程：

1、置信度预测损失更换之FocalLoss（不需要创建新的py文件）

使用：直接在YOLOX-main/yolox/models/yolo_head.py的YOLOXHead类中创建focal_loss方法

（1）首先找到置信度预测损失计算位置loss_obj，并进行替换（位置在386-405行左右）

# loss_iou:定位损失；loss_obj：置信度预测损失；loss_cls：预测损失
        loss_iou = (
            self.iou_loss(bbox_preds.view(-1, 4)[fg_masks], reg_targets)
        ).sum() / num_fg
        #loss_obj = (  
        #    self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets)
        #).sum() / num_fg
        loss_obj = (
            self.focal_loss(obj_preds.sigmoid().view(-1, 1), obj_targets)
        ).sum() / num_fg
        loss_cls = (
            self.bcewithlog_loss(
                cls_preds.view(-1, self.num_classes)[fg_masks], cls_targets
            )
        ).sum() / num_fg

（2）创建focal_loss方法，放到def get_l1_target（…）之前即可，代码如下：

def focal_loss(self, pred, gt):
        pos_inds = gt.eq(1).float()
        neg_inds = gt.eq(0).float()
        pos_loss = torch.log(pred+1e-5) * torch.pow(1 - pred, 2) * pos_inds * 0.75
        neg_loss = torch.log(1 - pred+1e-5) * torch.pow(pred, 2) * neg_inds * 0.25
        loss = -(pos_loss + neg_loss)
        return loss

2、置信度预测损失更换之VariFocalLoss（代码较多，所以额外创建新的py文件）

步骤一：YOLOX-main/yolox/models文件夹下创建varifocalloss.py文件，内容如下：

import torch.nn as nn
import torch.nn.functional as F
 
def reduce_loss(loss, reduction):
    """Reduce loss as specified.
    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".
    Return:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    # none: 0, elementwise_mean:1, sum: 2
    if reduction_enum == 0:
        return loss
    elif reduction_enum == 1:
        return loss.mean()
    elif reduction_enum == 2:
        return loss.sum()
        
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
    """Apply element-wise weight and reduce loss.
    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights.
        reduction (str): Same as built-in losses of PyTorch.
        avg_factor (float): Avarage factor when computing the mean of losses.
    Returns:
        Tensor: Processed loss values.
    """
    # if weight is specified, apply element-wise weight
    if weight is not None:
        loss = loss * weight

    # if avg_factor is not specified, just reduce the loss
    if avg_factor is None:
        loss = reduce_loss(loss, reduction)
    else:
        # if reduction is mean, then average the loss by avg_factor
        if reduction == 'mean':
            loss = loss.sum() / avg_factor
        # if reduction is 'none', then do nothing, otherwise raise an error
        elif reduction != 'none':
            raise ValueError('avg_factor can not be used with reduction="sum"')
    return loss

def varifocal_loss(pred,
                   target,
                   weight=None,
                   alpha=0.75,
                   gamma=2.0,
                   iou_weighted=True,
                   reduction='mean',
                   avg_factor=None):
    """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
    Args:
        pred (torch.Tensor): The prediction with shape (N, C), C is the
            number of classes
        target (torch.Tensor): The learning target of the iou-aware
            classification score with shape (N, C), C is the number of classes.
        weight (torch.Tensor, optional): The weight of loss for each
            prediction. Defaults to None.
        alpha (float, optional): A balance factor for the negative part of
            Varifocal Loss, which is different from the alpha of Focal Loss.
            Defaults to 0.75.
        gamma (float, optional): The gamma for calculating the modulating
            factor. Defaults to 2.0.
        iou_weighted (bool, optional): Whether to weight the loss of the
            positive example with the iou target. Defaults to True.
        reduction (str, optional): The method used to reduce the loss into
            a scalar. Defaults to 'mean'. Options are "none", "mean" and
            "sum".
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
    """
    # pred and target should be of the same size
    assert pred.size() == target.size()
    pred_sigmoid = pred.sigmoid()
    target = target.type_as(pred)
    if iou_weighted:
        focal_weight = target * (target > 0.0).float() + \
            alpha * (pred_sigmoid - target).abs().pow(gamma) * \
            (target <= 0.0).float()
    else:
        focal_weight = (target > 0.0).float() + \
            alpha * (pred_sigmoid - target).abs().pow(gamma) * \
            (target <= 0.0).float()
    loss = F.binary_cross_entropy_with_logits(
        pred, target, reduction='none') * focal_weight
    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
    return loss
 
 
class VarifocalLoss(nn.Module):
 
    def __init__(self,
                 use_sigmoid=True,
                 alpha=0.75,
                 gamma=2.0,
                 iou_weighted=True,
                 reduction='mean',
                 loss_weight=1.0):
        """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
        Args:
            use_sigmoid (bool, optional): Whether the prediction is
                used for sigmoid or softmax. Defaults to True.
            alpha (float, optional): A balance factor for the negative part of
                Varifocal Loss, which is different from the alpha of Focal
                Loss. Defaults to 0.75.
            gamma (float, optional): The gamma for calculating the modulating
                factor. Defaults to 2.0.
            iou_weighted (bool, optional): Whether to weight the loss of the
                positive examples with the iou target. Defaults to True.
            reduction (str, optional): The method used to reduce the loss into
                a scalar. Defaults to 'mean'. Options are "none", "mean" and
                "sum".
            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
        """
        super(VarifocalLoss, self).__init__()
        assert use_sigmoid is True, \
            'Only sigmoid varifocal loss supported now.'
        assert alpha >= 0.0
        self.use_sigmoid = use_sigmoid
        self.alpha = alpha
        self.gamma = gamma
        self.iou_weighted = iou_weighted
        self.reduction = reduction
        self.loss_weight = loss_weight
 
    def forward(self,
                pred,
                target,
                weight=None,
                avg_factor=None,
                reduction_override=None):
        """Forward function.
        Args:
            pred (torch.Tensor): The prediction.
            target (torch.Tensor): The learning target of the prediction.
            weight (torch.Tensor, optional): The weight of loss for each
                prediction. Defaults to None.
            avg_factor (int, optional): Average factor that is used to average
                the loss. Defaults to None.
            reduction_override (str, optional): The reduction method used to
                override the original reduction method of the loss.
                Options are "none", "mean" and "sum".
        Returns:
            torch.Tensor: The calculated loss
        """
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        if self.use_sigmoid:
            loss_cls = self.loss_weight * varifocal_loss(
                pred,
                target,
                weight,
                alpha=self.alpha,
                gamma=self.gamma,
                iou_weighted=self.iou_weighted,
                reduction=reduction,
                avg_factor=avg_factor)
        else:
            raise NotImplementedError
        return loss_cls

步骤二：在YOLOX-main/yolox/models/yolo_head.py中调用VarifocalLoss

（1）导入

from .varifocalloss import VarifocalLoss

（2）在init中实例化

self.varifocal = VarifocalLoss(reduction='none')

（3）替换原有的置信度预测损失loss_obj

# loss_iou:定位损失；loss_obj：置信度预测损失；loss_cls：预测损失
        loss_iou = (
            self.iou_loss(bbox_preds.view(-1, 4)[fg_masks], reg_targets)
        ).sum() / num_fg
        #loss_obj = (  
        #    self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets)
        #).sum() / num_fg
        loss_obj = (self.varifocal(obj_preds.view(-1, 1), obj_targets)
        ).sum() / num_fg
        loss_cls = (
            self.bcewithlog_loss(
                cls_preds.view(-1, self.num_classes)[fg_masks], cls_targets)
        ).sum() / num_fg

效果：根据个人数据集而定。FocalLoss与VariFocalLoss在我的数据集上均能提升，模型越大效果越明显。（但是在yolox-tiny上FocalLoss效果AP50会低于原来）

以上代码链接：
链接：https://pan.baidu.com/s/1ee1sQ9Eulz_mUdHTOnBe7w
提取码：8v8r

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原文链接：https://blog.csdn.net/weixin_45679938/article/details/122343945