# Loss functions import torch import torch.nn as nn from utils.metrics import bbox_iou from utils.torch_utils import is_parallel def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 # return positive, negative label smoothing BCE targets 标签平滑，https://wenku.baidu.com/view/27fdf1deadf8941ea76e58fafab069dc51224773.html #机器学习样本中少量错误标签，影响预测效果，训练时假设可能存在错误，避免过分相信。如果是交叉熵，可以简单实现，成为标签平滑。 return 1.0 - 0.5 * eps, 0.5 * eps class BCEBlurWithLogitsLoss(nn.Module): # BCEwithLogitLoss() with reduced missing label effects. #BCEWithLogitsLoss这个loss类将sigmoid操作和BCELoss（二进制交叉熵损失）集合到了一个类 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 #loss乘以alpha_factor这个系数， 考虑到图片中有目标但是没有做标签的情况，false negative return loss.mean() class FocalLoss(nn.Module): # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) #FocalLoss主要是为了解决one-stage的目标检测中正负样本比例严重失衡的问题，损失函数降低了大量简单负样本在训练过程中的比例 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 #计算损失=（分类损失+置信度损失+框坐标回归损失） class ComputeLoss: # Compute losses def __init__(self, model, autobalance=False): super(ComputeLoss, self).__init__() self.sort_obj_iou = False device = next(model.parameters()).device # get model device h = model.hyp # hyperparameters 获得超参数！！！ # Define criteria 定义类别及目标性得分损失函数 BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) #将'cls_pw'这两个参数传进来，在hyp.scratch.yaml里 BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) #将'obj_pw'这两个参数传进来，在hyp.scratch.yaml里 # 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 #这里g>0才考虑focal loss if g > 0: BCEcls, BCEobj = QFocalLoss(BCEcls, g), QFocalLoss(BCEobj, g) det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module #设置3个特征图对应的损失函数的损失系数 80x80、40x40、20x20有相应的系数 显然80特征图系数最大 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': setattr(self, k, getattr(det, k)) def __call__(self, p, targets): # predictions, targets, model __call__可以实例化对象名后直接调用这个函数，格式是：实例化对象名（参数） #p是网络的输出，targets是这个batch中所有图片标注的目标框信息 #获取设备，用的是cuda device = targets.device #初始化各部分损失 #类别损失、box回归损失、目标性得分损失（即置信度） lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) #获得标签分类信息、边框信息（不同尺度上的预测框）、索引信息、anchors tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets # Losses #遍历每一个预测输出 for i, pi in enumerate(p): # layer index, layer predictions 在每一层特征图上迭代，比如先80x80，再40x40，最后20x20 #根据indices获取索引，方便找到对应网格的输出 b, a, gj, gi = indices[i] # image, anchor, gridy, gridx tobj = torch.zeros_like(pi[..., 0], device=device) # target obj tobj初始化为0 n = b.shape[0] # number of targets if n: #找到对应网格的输出，取出对应位置的预测值。若pi里为80x80，则那个维度数据对应此特征图上，下面pxy和pwh进行框微调，有专门公式 ps = pi[b, a, gj, gi] # prediction subset corresponding to targets # Regression #对输出的xywh做反算 #想计算预测框的xy，这里是微调？ pxy = ps[:, :2].sigmoid() * 2. - 0.5 #想计算预测框的wh，这里是微调？ 通过偏移值，求出这个框真正的xywh pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] pbox = torch.cat((pxy, pwh), 1) # predicted box #计算边框损失，注意这个CIoU=True，计算的是是CIoU，bbox_iou可以选择传参呢！。 注意：tbox[i]里面是groundtruth iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) lbox += (1.0 - iou).mean() # iou loss 损失函数在这里了，通过iou算出iou的loss。求了mean，就变成一个均值。 # Objectness score_iou = iou.detach().clamp(0).type(tobj.dtype) if self.sort_obj_iou: sort_id = torch.argsort(score_iou) b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id] #根据model.gr设置objectness的标签值，有目标的conf分支权重。 #不同anchor和gt bbox匹配度不一样，预测框和gt bbox的匹配度也不一样，如果权重设置一样肯定不是最优的 #故将预测框和bbox的iou作为权重乘到conf分支，用于表征预测质量 tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou # iou ratio # Classification #如果类别数>1，才计算分类损失（即多类别损失） if self.nc > 1: # cls loss (only if multiple classes) t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets t[range(n), tcls[i]] = self.cp lcls += self.BCEcls(ps[:, 5:], t) # BCE对每个类别单独计算loss # 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)] #计算objectness的损失 obji = self.BCEobj(pi[..., 4], tobj) lobj += obji * self.balance[i] # obj loss 考虑了balance的值，即不同的特征图大小考虑不同的权重！！！ 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'] #求最后总的损失时，还进行了加权 lobj *= self.hyp['obj'] #超参里面有设置 lcls *= self.hyp['cls'] #超参里面有设置 bs = tobj.shape[0] # batch size #总的loss=lbox + lobj + lcls，再乘以batchsize，返回 return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() def build_targets(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 tcls, tbox, indices, anch = [], [], [], [] gain = torch.ones(7, device=targets.device) # 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 tbox.append(torch.cat((gxy - gij, gwh), 1)) # box anch.append(anchors[a]) # anchors tcls.append(c) # class return tcls, tbox, indices, anch