目标检测改进系列1:yolo v5网络中OTA损失函数替换

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标签分配(label assignment)

什么是标签分配

标签分配(Label Assignment)标签分配策略是对训练过程中各个Anchor划分正负属性,并分配各自学习目标的策略方法,在整体上通过标签是否是非负即正可以分为硬标签分配和软标签分配。其中,硬标签分配可以分成静态分配策略和动态分配策略两类。
动态

  • 静态分配策略
    静态标签分配方法主要基于距离、IOU等先验知识设置固定阈值去区分正负样本,如FCOS、两阶段标检测算法、RFLA等。
  • 动态分配策略
    动态标签分配方法则根据不同策略动态设置阈值选择正负样本,如ATSS、PAA、OTA、DSL、SimOTA等。

OTA损失函数介绍

OTA(Optimal Transport Assignment for Object Detection)出处为2021年 CVPR会议上的一篇论文。该论文的贡献主要有以下两点:

  1. 提出了一种基于优化策略的标签分配方式,将 gt 看做 label 供应商,anchor 看做 label 需求方。对于正样本,将分类和回归的 loss 加权和作为传输花费,对于负样本,传输花费就为分类 loss,通过最小化该花费,让网络自己学习最优的标签分配方式。
  2. 免去了手工选定参数的方式来实现标签分配,让网络自己选择每个 gt 对应的 anchor 数量,而非提前设定,也能够较好的解决模棱两可的 anchor 分配问题,提高网络对这部分 anchor 的处理效果

论文:Optimal Transport Assignment for Object Detection
代码:OTA代码

背景

Label assignment 在object detection领域中发挥着非常重要的作用,它能够分配每个 anchor 的正负。但是传统的方法具有一些缺点,具体体现为:不同大小、形状、遮挡程度的目标,其 positive/negative 的判定条件应该是不同的。针对这个缺点,就有一些方法使用了动态分配的策略来实现label assignment。

方法

这个方法可以看这个博客,讲的比较详细:博客文章来源地址https://www.toymoban.com/news/detail-649180.html

如何在yolo v5目标检测算法中改为OTA损失

步骤一、修改loss.py文件

import torch.nn.functional as F
from utils.metrics import box_iou
from utils.torch_utils import de_parallel
from utils.general import xywh2xyxy

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

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

        det = de_parallel(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
        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, 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   
        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)
        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

            n = b.shape[0]  # number of targets
            if n:
                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets

                # Regression
                grid = torch.stack([gi, gj], dim=1)
                pxy = ps[:, :2].sigmoid() * 2. - 0.5
                #pxy = ps[:, :2].sigmoid() * 3. - 1.
                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
                pbox = torch.cat((pxy, pwh), 1)  # predicted box
                selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
                selected_tbox[:, :2] -= grid
                iou = bbox_iou(pbox, selected_tbox, CIoU=True)  # iou(prediction, target)
                if type(iou) is tuple:
                    lbox += (iou[1].detach() * (1 - iou[0])).mean()
                    iou = iou[0]
                else:
                    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

                # 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
                    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]
        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)).detach()

    def build_targets(self, p, targets, imgs):
        indices, anch = self.find_3_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]):
        
            b_idx = targets[:, 0]==batch_idx
            this_target = targets[b_idx]
            if this_target.shape[0] == 0:
                continue
                
            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
            txyxy = xywh2xyxy(txywh)

            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]
                idx = (b == batch_idx)
                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]                
                p_obj.append(fg_pred[:, 4:5])
                p_cls.append(fg_pred[:, 5:])
                
                grid = torch.stack([gi, gj], dim=1)
                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
                #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
                pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
                pxywh = torch.cat([pxy, pwh], dim=-1)
                pxyxy = xywh2xyxy(pxywh)
                pxyxys.append(pxyxy)
            
            pxyxys = torch.cat(pxyxys, dim=0)
            if pxyxys.shape[0] == 0:
                continue
            p_obj = torch.cat(p_obj, dim=0)
            p_cls = torch.cat(p_cls, dim=0)
            from_which_layer = torch.cat(from_which_layer, dim=0)
            all_b = torch.cat(all_b, dim=0)
            all_a = torch.cat(all_a, dim=0)
            all_gj = torch.cat(all_gj, dim=0)
            all_gi = torch.cat(all_gi, dim=0)
            all_anch = torch.cat(all_anch, dim=0)
        
            pair_wise_iou = box_iou(txyxy, pxyxys)

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

            top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1)
            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=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)
            )

            num_gt = this_target.shape[0]
            cls_preds_ = (
                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
            )

            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)
            del cls_preds_
        
            cost = (
                pair_wise_cls_loss
                + 3.0 * pair_wise_iou_loss
            )

            matching_matrix = torch.zeros_like(cost, device=device)

            for gt_idx in range(num_gt):
                _, pos_idx = torch.topk(
                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
                )
                matching_matrix[gt_idx][pos_idx] = 1.0

            del top_k, dynamic_ks
            anchor_matching_gt = matching_matrix.sum(0)
            if (anchor_matching_gt > 1).sum() > 0:
                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
                matching_matrix[:, anchor_matching_gt > 1] *= 0.0
                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
            fg_mask_inboxes = (matching_matrix.sum(0) > 0.0).to(device)
            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
        
            from_which_layer = from_which_layer[fg_mask_inboxes]
            all_b = all_b[fg_mask_inboxes]
            all_a = all_a[fg_mask_inboxes]
            all_gj = all_gj[fg_mask_inboxes]
            all_gi = all_gi[fg_mask_inboxes]
            all_anch = all_anch[fg_mask_inboxes]
        
            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):
        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
        na, nt = self.na, targets.shape[0]  # number of anchors, targets
        indices, anch = [], []
        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices

        g = 0.5  # bias
        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):
            anchors = self.anchors[i]
            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain

            # Match targets to anchors
            t = targets * gain
            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
                # 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
                gxy = t[:, 2:4]  # grid xy
                gxi = gain[[2, 3]] - gxy  # inverse
                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
                j = torch.stack((torch.ones_like(j), j, k, l, m))
                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
            anch.append(anchors[a])  # anchors

        return indices, anch

步骤二、在train.py和val.py中修改conpute_loss

  • from utils.loss import ComputeLoss 改为ComputeLossOTA。
  • 将 compute_loss = ComputeLoss(model) 中的ComputeLoss方法改为 ComputeLossOTA。
  • loss, loss_items = compute_loss(pred, targets.to(device)) 括号内添加imgs。
  • 在val.py中做出和如上所示的修改。
  • 运行train.py即可进行训练过程。

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