YOLOV7算法(三)损失函数ComputeLossOTA学习记录

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YOLOV7正负样本策略及ComputeLossOTA学习笔记

class ComputeLossOTA:
    # Compute losses
    def __init__(self, model, autobalance=False):
        super(ComputeLossOTA, self).__init__()
        device = next(model.parameters()).device  # get model device
        h = model.hyp  # hyperparameters

        # Define criteria
        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, 1.0, 0.0
        
        # Focal loss
        g = h['fl_gamma']  # focal loss gamma, 0.0
        if g > 0:
            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)

        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7, [4.0, 1.0, 0.4]
        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index, 0
        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
        for k in 'na', 'nc', 'nl', 'anchors', 'stride':
            setattr(self, k, getattr(det, k))

    def __call__(self, p, targets, imgs):  # predictions, targets, model
        import sys
        device = targets.device
        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
        bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)  # 返回匹配到的image索引, anchor索引, gj, gi, GT, anchor
        # pre_gen_gains=[tensor([80, 80, 80, 80], device='cuda:0'), tensor([40, 40, 40, 40], device='cuda:0'), tensor([20, 20, 20, 20], device='cuda:0')]
        pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p]

        # Losses
        for i, pi in enumerate(p):  # layer index, layer predictions
            b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i]  # image, anchor, gridy, gridx
            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj, tobj.shape=torch.Size([bs, 3, 80, 80]) or torch.Size([bs, 3, 40, 40]) or torch.Size([bs, 3, 20, 20])

            n = b.shape[0]  # number of targets
            if n:
                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets, 设匹配到GT的正样本数为p, ps.shape=torch.Size([p, 85])

                # Regression
                grid = torch.stack([gi, gj], dim=1)  # grid.shape=torch.Size([p, 2])
                pxy = ps[:, :2].sigmoid() * 2. - 0.5  # pxy.shape=torch.Size([p, 2])
                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]  # pwh.shape=torch.Size([p, 2])
                pbox = torch.cat((pxy, pwh), 1)  # predicted box, pbox.shape=torch.Size([p, 4])
                selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]  # selected_tbox.shape=torch.Size([p, 4])
                selected_tbox[:, :2] -= grid  # 将选中的tbox减去网格坐标,得到偏移量
                iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
                lbox += (1.0 - iou).mean()  # iou loss

                # Objectness
                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio, tobj[b, a, gj, gi]=iou.detach().clamp(0).type(tobj.dtype)

                # Classification
                selected_tcls = targets[i][:, 1].long()
                if self.nc > 1:  # cls loss (only if multiple classes)
                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets
                    t[range(n), selected_tcls] = self.cp  # t相当于one-hot编码, 里面的元素仅在所属类别那一列为1, 其余为0 
                    lcls += self.BCEcls(ps[:, 5:], t)  # BCE

            obji = self.BCEobj(pi[..., 4], tobj)
            lobj += obji * self.balance[i]  # obj loss, self.balance=[4.0, 1.0, 0.4]
            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]
        lbox *= self.hyp['box']  # self.hyp['box']=0.05
        lobj *= self.hyp['obj']  # self.hyp['obj']=0.7
        lcls *= self.hyp['cls']  # self.hyp['cls']=0.3
        bs = tobj.shape[0]  # batch size

        loss = lbox + lobj + lcls
        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()

    def build_targets(self, p, targets, imgs):
        #indices, anch = self.find_positive(p, targets)
        indices, anch = self.find_3_positive(p, targets)  # 正样本
        #indices, anch = self.find_4_positive(p, targets)
        #indices, anch = self.find_5_positive(p, targets)
        #indices, anch = self.find_9_positive(p, targets)
        device = torch.device(targets.device)
        matching_bs = [[] for pp in p]
        matching_as = [[] for pp in p]
        matching_gjs = [[] for pp in p]
        matching_gis = [[] for pp in p]
        matching_targets = [[] for pp in p]
        matching_anchs = [[] for pp in p]
        
        nl = len(p)    
    
        for batch_idx in range(p[0].shape[0]):  # 遍历batch size中的每一张图片
        
            b_idx = targets[:, 0]==batch_idx  # 找出targets中与batch_idx相等的索引
            this_target = targets[b_idx]  # 根据索引找出对应的GT
            if this_target.shape[0] == 0:  # 如果GT数量为0,则处理下一张图片
                continue
                
            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]  # 将GT的坐标由0~1映射到与输入图片大小匹配的数值
            txyxy = xywh2xyxy(txywh)  # 将坐标由[cx,cy,w,h]转换为[x1,y1,x2,y2](左上角及右下角坐标)

            pxyxys = []
            p_cls = []
            p_obj = []
            from_which_layer = []
            all_b = []
            all_a = []
            all_gj = []
            all_gi = []
            all_anch = []
            
            for i, pi in enumerate(p):
                
                b, a, gj, gi = indices[i]  # image, anchor, grid indices, 对gj、gi进行截断,不能超出特征图的范围
                idx = (b == batch_idx)  # 从b中找出与batch_idx相等的目标
                b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]
                all_b.append(b)
                all_a.append(a)
                all_gj.append(gj)
                all_gi.append(gi)
                all_anch.append(anch[i][idx])
                from_which_layer.append((torch.ones(size=(len(b),)) * i).to(device))
                
                fg_pred = pi[b, a, gj, gi]  # 选出相应的预测值,假设数量为n
                p_obj.append(fg_pred[:, 4:5])  # obj预测值
                p_cls.append(fg_pred[:, 5:])  # cls预测值
                
                grid = torch.stack([gi, gj], dim=1)
                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i]  # 预测的cx、cy
                pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i]  # 预测的w、h
                pxywh = torch.cat([pxy, pwh], dim=-1)  # 预测的cx、cy、w、h
                pxyxy = xywh2xyxy(pxywh)  # 将cx、cy、w、h转换为x1、y1、x2、y2
                pxyxys.append(pxyxy)
            
            pxyxys = torch.cat(pxyxys, dim=0)  # pxyxys.shape=torch.Size([n, 4])
            if pxyxys.shape[0] == 0:
                continue
            p_obj = torch.cat(p_obj, dim=0)  # p_obj.shape=torch.Size([n, 1])
            p_cls = torch.cat(p_cls, dim=0)  # p_cls.shape=torch.Size([n, 80])
            from_which_layer = torch.cat(from_which_layer, dim=0)
            all_b = torch.cat(all_b, dim=0)  # torch.Size([n])
            all_a = torch.cat(all_a, dim=0)  # torch.Size([n])
            all_gj = torch.cat(all_gj, dim=0)  # torch.Size([n])
            all_gi = torch.cat(all_gi, dim=0)  # torch.Size([n])
            all_anch = torch.cat(all_anch, dim=0)  # torch.Size([n, 2])
        
            pair_wise_iou = box_iou(txyxy, pxyxys)  # 计算GT与预测边界框之间的iou

            pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)  # iou loss

            top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1)  # 从大到小对iou进行排序,取前10个iou
            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)  # 对topk进行求和、取整,将该数值作为一个GT需要匹配到的正样本数,匹配的正样本数不能小于1

            gt_cls_per_image = (
                F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
                .float()
                .unsqueeze(1)
                .repeat(1, pxyxys.shape[0], 1)
            )  # 将GT类别转换成one-hot编码

            num_gt = this_target.shape[0]  # GT数量, 假设为t
            cls_preds_ = (
                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
            )  # 各类别的预测分数

            y = cls_preds_.sqrt_()
            pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
               torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
            ).sum(-1)  # cls loss
            del cls_preds_
        
            cost = (
                pair_wise_cls_loss
                + 3.0 * pair_wise_iou_loss
            )

            matching_matrix = torch.zeros_like(cost, device=device)  # torch.Size([t, n])

            for gt_idx in range(num_gt):
                _, pos_idx = torch.topk(
                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
                )  # 对每个GT的loss由小到大排序,取排在前dynamic_ks个数值的索引
                matching_matrix[gt_idx][pos_idx] = 1.0  # 按照索引,给matching_matrix的相应元素置1

            del top_k, dynamic_ks
            anchor_matching_gt = matching_matrix.sum(0)  # torch.Size([n]), 对所有正样本匹配的GT数进行求和
            if (anchor_matching_gt > 1).sum() > 0:  # 如果大于0, 则认为一个正样本匹配多个GT
                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)  # 如果同一个正样本匹配到的GT数量大于1,则比较多个GT,取cost小作为正样本,其他的舍去
                matching_matrix[:, anchor_matching_gt > 1] *= 0.0  # 首先将大于1的列的元素置0
                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0  # 再根据cost_argmin,将对应位置的元素置1
            fg_mask_inboxes = (matching_matrix.sum(0) > 0.0).to(device)  # 找出与GT成功匹配的正样本
            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)  # 根据fg_mask_inboxes找出符合条件的列,再求每列的argmax。这步操作目的在于找出与GT匹配的正样本的索引值
        
            from_which_layer = from_which_layer[fg_mask_inboxes]
            all_b = all_b[fg_mask_inboxes]  # 为匹配到正样本对应的图像索引
            all_a = all_a[fg_mask_inboxes]  # 为匹配到正样本对应的anchor索引
            all_gj = all_gj[fg_mask_inboxes]  # 为匹配到正样本对应的gj
            all_gi = all_gi[fg_mask_inboxes]  # 为匹配到正样本对应的gi
            all_anch = all_anch[fg_mask_inboxes]  # 为匹配到正样本对应的anchor

            this_target = this_target[matched_gt_inds]
        
            for i in range(nl):
                layer_idx = from_which_layer == i
                matching_bs[i].append(all_b[layer_idx])
                matching_as[i].append(all_a[layer_idx])
                matching_gjs[i].append(all_gj[layer_idx])
                matching_gis[i].append(all_gi[layer_idx])
                matching_targets[i].append(this_target[layer_idx])
                matching_anchs[i].append(all_anch[layer_idx])

        for i in range(nl):
            if matching_targets[i] != []:
                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
                matching_as[i] = torch.cat(matching_as[i], dim=0)
                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
            else:
                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)

        return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs           

    def find_3_positive(self, p, targets):
        # p为预测值(p[0].shape=torch.Size([8, 3, 80, 80, 85]), p[1].shape=torch.Size([8, 3, 40, 40, 85]), p[2].shape=torch.Size([8, 3, 20, 20, 85])), targets=(image,class,x,y,w,h)
        na, nt = self.na, targets.shape[0]  # anchors的数量, GT的数量; na=3, nt=n
        indices, anch = [], []
        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain, gain=tensor([1, 1, 1, 1, 1, 1, 1])
        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt), ai.shape=torch.Size([3, n])
        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices, targets.shape=torch.Size([3, n, 7])

        g = 0.5  # bias
        off = torch.tensor([[0, 0],
                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
                            ], device=targets.device).float() * g  # offsets, [0,0]为中间网格,[1, 0]、[0, 1]、[-1, 0]、[0, -1]为相邻右、下、左、上的网格
                
        for i in range(self.nl):  # self.nl=3
            anchors = self.anchors[i]
            '''
            anchors=tensor([[1.50000, 2.00000],
                            [2.37500, 4.50000],
                            [5.00000, 3.50000]], device='cuda:0')
            '''
            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain, gain[2:6]=tensor([80, 80, 80, 80]) or tensor([40, 40, 40, 40]) or tensor([20, 20, 20, 20])
            # gain = tensor([1, 1, 80, 80, 80, 80, 1]) or tensor([1, 1, 40, 40, 40, 40, 1]) or tensor([1, 1, 20, 20, 20, 20, 1])

            # Match targets to anchors
            t = targets * gain  # 将targets里的坐标从0~1映射到与特征图大小匹配的坐标
            if nt:
                # Matches
                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
                t = t[j]  # filter, 去掉大于self.hyp['anchor_t']的GT, 设余下数量为m

                # Offsets
                gxy = t[:, 2:4]  # grid xy, GT中心点坐标(以左上角为参考点)
                gxi = gain[[2, 3]] - gxy  # inverse, GT中心点坐标(以右下角为参考点)
                j, k = ((gxy % 1. < g) & (gxy > 1.)).T  # 对gxy取余,也就是把坐标的小数提出来与0.5做对比,对坐标做近似值处理
                l, m = ((gxi % 1. < g) & (gxi > 1.)).T  # 原理与上面的一致,l为横坐标,m为纵坐标
                j = torch.stack((torch.ones_like(j), j, k, l, m))  # j.shape=torch.Size([5, m])
                '''
                复制5个t, 选其中的3个, 第一个torch.ones_like(j)必选, 在剩下的4个相邻网格中选2个(j、l互斥, 点只能落在左右两边的其中一边; k、m互斥; 点只能落在上下两边的其中一边),
                因此总共选择了3个网格作为正样本的中心点。每个layer分配了3个anchor, 理论上一个GT最多可以匹配9个正样本。YOLOV7的输出有3个layer, 则一个GT最多可以匹配27个正样本。
                '''
                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()  # 坐标减去偏移量
            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, 对gj、gi进行截断,不能超出特征图的范围
            anch.append(anchors[a])  # anchors

        return indices, anch
YOLOV7算法(三)损失函数ComputeLossOTA学习记录

假设图中蓝色的点为GT的中心点,则YOLOV7中的ComputeLossOTA会把3个黄色的框视为正样本(对应着ComputeLossOTA类中的find_3_positive函数),而ComputeLossAuxOTA会把黄色框以及橙色框,总共5个框视为正样本(对应着ComputeLossAuxOTA类中的find_5_positive函数)。

参考:

https://zhuanlan.zhihu.com/p/543160484文章来源地址https://www.toymoban.com/news/detail-511034.html

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