Yolov5系列(3) |
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Yolov5系列(3)
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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 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] 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. 1.)).T #jk是判断gxi更偏向哪里，下？右？ l, m = ((gxi % 1. 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 |
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。【本文地址】