Yolov5系列(3)-loss解析

Abstract

在yolov5中,loss在训练中起到了决定性的作用,同时,yolov5的loss又与大部分传统的方法不同,它是基于网格的.在网格上生成相应的anchor框和其对应的cls以及conf,同时,box loss还采用了CIOU的方式来进行回归,很好的提高了回归的速度.并且针对传统yolo中正样本不足的情况.它还采用了,它还利用了一种样本扩充的策略,很大的提高了其最终的检测精度

Introduction

1.yolov5划定正负样本

yolov5会将最终的输出划分为一个w*h的网格.

对于每一个gt框,首先会判断gt框的中心落在哪一个网格中,这个网格会被标记为正样本,同时,这个网格所对应的三个anchor就是正样本,并且如果anchor的长宽比于gt的长宽比相差过大,那么,这个anchor也会被过滤掉

然后进行扩充gt样本. 样本扩充的依据是判断gt的中心点落在的网格相对的上下左右偏移量.( 例如,gt的中心在网格中偏向左上角,那么gt所在gird的上方和左方的gird也会被扩充为gt样本.同时,扩充的gird所对应的gt框是由原始的gt框移动一个单位到其所在的gird中) ,下面这张图可以很好的解释这一过程

目标中心点为图中的红色点，红色点在本网格的左上，则让本网格、其左、其上网格负责该目标检测；
同理目标中心点如果在右下，则让本网格、其右、其下网格负责该目标检测。为什么这样做，据说是为了扩充正样本。

这种做法可以使yolov5的gt框数量增加,很好的缓解了正负样本的不平衡.然而这种通过将gt移动的方法进行扩充正样本,我觉得对于小目标来说,是存在很多的问题的

最后,会计算3个loss函数,(1) box loss,计算anchor与gt的ciou,作为box loss,(2) obj loss,计算网格的confidence是否正确,使用BCEWithLogitsLoss (3)cls loss 计算与gt相对应的anchor的分类是否正确. 使用BCEWithLogitsLoss

我们通过代码来仔细研究这一过程

2,loss.py代码解析

下面的这几种loss可供使用者选择,每个loss都有各自的优点,
我们可以在compute loss类中更换以下的loss

def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
    # return positive, negative label smoothing BCE targets
    return 1.0 - 0.5 * eps, 0.5 * eps


class BCEBlurWithLogitsLoss(nn.Module):
    # BCEwithLogitLoss() with reduced missing label effects.
    def __init__(self, alpha=0.05):
        super(BCEBlurWithLogitsLoss, self).__init__()
        self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')  # must be nn.BCEWithLogitsLoss()
        self.alpha = alpha

    def forward(self, pred, true):
        loss = self.loss_fcn(pred, true)
        pred = torch.sigmoid(pred)  # prob from logits
        dx = pred - true  # reduce only missing label effects
        # dx = (pred - true).abs()  # reduce missing label and false label effects
        alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
        loss *= alpha_factor
        return loss.mean()


class FocalLoss(nn.Module):
    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
        super(FocalLoss, self).__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = loss_fcn.reduction
        self.loss_fcn.reduction = 'none'  # required to apply FL to each element

    def forward(self, pred, true):
        loss = self.loss_fcn(pred, true)
        # p_t = torch.exp(-loss)
        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability

        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
        pred_prob = torch.sigmoid(pred)  # prob from logits
        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
        modulating_factor = (1.0 - p_t) ** self.gamma
        loss *= alpha_factor * modulating_factor

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        else:  # 'none'
            return loss


class QFocalLoss(nn.Module):
    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
        super(QFocalLoss, self).__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = loss_fcn.reduction
        self.loss_fcn.reduction = 'none'  # required to apply FL to each element

    def forward(self, pred, true):
        loss = self.loss_fcn(pred, true)

        pred_prob = torch.sigmoid(pred)  # prob from logits
        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
        modulating_factor = torch.abs(true - pred_prob) ** self.gamma
        loss *= alpha_factor * modulating_factor

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        else:  # 'none'
            return loss

这个类是用来计算最终的loss的,它会返回三个loss,分别是box loss,cls loss, obj loss

class ComputeLoss:
    # Compute losses
    def __init__(self, model, autobalance=False):
        super(ComputeLoss, self).__init__()
        #获取模型在cpu还是gpu上运行的.之后生成的临时变量也会在相应的设备上运行
        device = next(model.parameters()).device  # get model device
        #模型的参数
        h = model.hyp  # hyperparameters

        # Define criteria
        #定义cls loss和 obj loss
        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))

        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets

        # Focal loss默认不会使用这个
        g = h['fl_gamma']  # focal loss gamma
        if g > 0:
            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
            
		#获取模型的detect层
        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
        #用来实现obj,box,cls loss之间权重的平衡
        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
        #获取各个特征层的stride相关参数
        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
        #将各个loss加入到类的公共变量中
        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
        #参数获取
        for k in 'na', 'nc', 'nl', 'anchors':
            setattr(self, k, getattr(det, k))
	
	#loss计算
	#p为模型的最终输出
	#targets为gt框的信息
    def __call__(self, p, targets):  # predictions, targets, model
        device = targets.device
        #初始化loss
        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
        #建立targets目标,会在下面仔细讲解
        tcls, tbox, indices, anchors = self.build_targets(p, targets)  # targets
        #各个参数形状
        #tcls [[num]] 存放了gt框所对应的网格的cls
        #tbox [[x_offset,y_offset,w,h]] 存放了gt框所对应的网格的box,注意此处的x和y是相对于网格的偏移量
        #indices [[image, anchor, grid indices]] 存放了gt对应的gird的信息,包括:image对应batchsize的哪张图片,anchor,对应哪个尺度的anchor,以及所在的网格
        #anch [[num,2]]#anchor信息

        # Losses
        #遍历各个特征层,大小分别是80x80,40x40,20x20
        for i, pi in enumerate(p):  # layer index, layer predictions
        
            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
            #初始化target obj
            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
            #计算有多少个target
            n = b.shape[0]  # number of targets
            if n:#如果存在target的话
            	#首先获取target所在的网格模型预测的pred 信息
                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
                
                #对xywh进行解码工作
                # Regression
                pxy = ps[:, :2].sigmoid() * 2. - 0.5
                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
                pbox = torch.cat((pxy, pwh), 1)  # predicted box
                #计算ciou
                iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
                #计算box的ciouloss
                lbox += (1.0 - iou).mean()  # iou loss

                # Objectness
                #获取target所对应的obj,网格中存在gt目标的会被标记为iou与gt的交并比
                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio

                # Classification
                #如果类别数只有一个的话,将不会调用cls loss
                if self.nc > 1:  # cls loss (only if multiple classes)
                	#target所在的gird对应的cls的one hot格式
                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets
                    t[range(n), tcls[i]] = self.cp
                    #计算loss
                    lcls += self.BCEcls(ps[:, 5:], t)  # BCE

                # Append targets to text file
                # with open('targets.txt', 'a') as file:
                #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]

            obji = self.BCEobj(pi[..., 4], tobj)
            lobj += obji * self.balance[i]  # obj loss
            if self.autobalance:
                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()

        if self.autobalance:
            self.balance = [x / self.balance[self.ssi] for x in self.balance]
        #提高loss各自的权重,可以在配置文件中设置
        lbox *= self.hyp['box']
        lobj *= self.hyp['obj']
        lcls *= self.hyp['cls']
        bs = tobj.shape[0]  # batch size

        loss = lbox + lobj + lcls
        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
	#这段代码相对来说比较难以理解内部是怎么运行的
    def build_targets(self, p, targets):
        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
        
		#这里na为锚框种类数 nt为目标数 这里的na为3，nt也为3
        na, nt = self.na, targets.shape[0]  # number of anchors, targets
		#类别 边界盒 索引 锚框
        tcls, tbox, indices, anch = [], [], [], []
		#利用gain来计算目标在某一个特征图上的位置信息，初始化为1
        gain = torch.ones(7, device=targets.device)  # normalized to gridspace gain
		# ai.shape = (na, nt)，锚框的索引，三个目标，三种锚框，所以共9个元素
        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
		# targets.shape = (na, nt, 7)（3，3，7）给每个目标加上锚框索引
        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices

        g = 0.5  # bias
        
		#off偏移量
        off = torch.tensor([[0, 0],
                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
                            ], device=targets.device).float() * g  # offsets

        for i in range(self.nl):
        	#获取当前的锚框尺寸
            anchors = self.anchors[i]
            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
            
			# 将xywh映射到当前特征图，即乘以对应的特征图尺寸
            # Match targets to anchors
            t = targets * gain
            if nt:
                # Matches
                #r为目标wh和锚框wh的比值，比值在0.25到4即采用该种锚框预测目标
                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
                #将比值和预先设置的比例anchor_t对比，符合条件为True，反之False
                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
                #根据j筛选符合条件的情况
                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                t = t[j]  # filter

                # Offsets
                #得到相对于左上角的目标
                gxy = t[:, 2:4]  # grid xy
                #得到相对于右上角的目标
                gxi = gain[[2, 3]] - gxy  # inverse
                #这里是重点，也是比较难理解的部分，jk是判断gxy更偏向哪里，左？上？
                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
                #jk是判断gxi更偏向哪里，下？右？
                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
                j = torch.stack((torch.ones_like(j), j, k, l, m))
                #yolov5不仅用目标中心点所在的网格预测该目标，还采用了距目标中心点的最近两个网格
        		#所以有五种情况，网格本身，上下左右，这就是repeat函数第一个参数为5的原因
                t = t.repeat((5, 1, 1))[j]
                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
            else:
                t = targets[0]
                offsets = 0

            # Define
            b, c = t[:, :2].long().T  # image, class
            gxy = t[:, 2:4]  # grid xy
            gwh = t[:, 4:6]  # grid wh
            gij = (gxy - offsets).long()
            gi, gj = gij.T  # grid xy indices

            # Append
            a = t[:, 6].long()  # anchor indices
            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
            anch.append(anchors[a])  # anchors
            tcls.append(c)  # class
           

        return tcls, tbox, indices, anch

我们可以发现yolov5的代码具有很大的灵活性,从代码中更加不同的场景,不同的情况,选择不同的loss,可以达到非常好的效果.但是在样本扩充这一块的内容,我觉得对于小目标来说还是有所欠缺的,可以采用其他的策略来进行样本扩充