246 lines
13 KiB
Python
246 lines
13 KiB
Python
# Loss functions
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import torch
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import torch.nn as nn
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from utils.metrics import bbox_iou
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from utils.torch_utils import is_parallel
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def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
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# return positive, negative label smoothing BCE targets 标签平滑，https://wenku.baidu.com/view/27fdf1deadf8941ea76e58fafab069dc51224773.html
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#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。
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return 1.0 - 0.5 * eps, 0.5 * eps
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class BCEBlurWithLogitsLoss(nn.Module):
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# BCEwithLogitLoss() with reduced missing label effects.
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#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
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def __init__(self, alpha=0.05):
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super(BCEBlurWithLogitsLoss, self).__init__()
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self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
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self.alpha = alpha
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def forward(self, pred, true):
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loss = self.loss_fcn(pred, true)
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pred = torch.sigmoid(pred) # prob from logits
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dx = pred - true # reduce only missing label effects
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# dx = (pred - true).abs() # reduce missing label and false label effects
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alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
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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
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return loss.mean()
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class FocalLoss(nn.Module):
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# Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
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#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
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def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
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super(FocalLoss, self).__init__()
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self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
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self.gamma = gamma
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self.alpha = alpha
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self.reduction = loss_fcn.reduction
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self.loss_fcn.reduction = 'none' # required to apply FL to each element
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def forward(self, pred, true):
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loss = self.loss_fcn(pred, true)
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# p_t = torch.exp(-loss)
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# loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
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# TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
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pred_prob = torch.sigmoid(pred) # prob from logits
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p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
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alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
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modulating_factor = (1.0 - p_t) ** self.gamma
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loss *= alpha_factor * modulating_factor
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if self.reduction == 'mean':
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return loss.mean()
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elif self.reduction == 'sum':
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return loss.sum()
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else: # 'none'
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return loss
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class QFocalLoss(nn.Module):
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# Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
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def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
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super(QFocalLoss, self).__init__()
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self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
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self.gamma = gamma
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self.alpha = alpha
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self.reduction = loss_fcn.reduction
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self.loss_fcn.reduction = 'none' # required to apply FL to each element
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def forward(self, pred, true):
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loss = self.loss_fcn(pred, true)
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pred_prob = torch.sigmoid(pred) # prob from logits
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alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
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modulating_factor = torch.abs(true - pred_prob) ** self.gamma
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loss *= alpha_factor * modulating_factor
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if self.reduction == 'mean':
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return loss.mean()
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elif self.reduction == 'sum':
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return loss.sum()
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else: # 'none'
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return loss
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#计算损失=（分类损失+置信度损失+框坐标回归损失）
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class ComputeLoss:
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# Compute losses
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def __init__(self, model, autobalance=False):
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super(ComputeLoss, self).__init__()
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self.sort_obj_iou = False
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device = next(model.parameters()).device # get model device
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h = model.hyp # hyperparameters 获得超参数！！！
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# Define criteria 定义类别及目标性得分损失函数
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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
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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
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# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
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self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
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# Focal loss
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g = h['fl_gamma'] # focal loss gamma
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#这里g>0才考虑focal loss
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if g > 0:
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BCEcls, BCEobj = QFocalLoss(BCEcls, g), QFocalLoss(BCEobj, g)
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det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
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#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
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self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
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self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index
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self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
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for k in 'na', 'nc', 'nl', 'anchors':
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setattr(self, k, getattr(det, k))
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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）
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#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
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#获取设备，用的是cuda
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device = targets.device
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#初始化各部分损失
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#类别损失、box回归损失、目标性得分损失（即置信度）
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lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
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#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
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tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
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# Losses
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#遍历每一个预测输出
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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
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#根据indices获取索引，方便找到对应网格的输出
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b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
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tobj = torch.zeros_like(pi[..., 0], device=device) # target obj tobj初始化为0
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n = b.shape[0] # number of targets
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if n:
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#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
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ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
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# Regression
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#对输出的xywh做反算
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#想计算预测框的xy，这里是微调？
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pxy = ps[:, :2].sigmoid() * 2. - 0.5
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#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
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pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
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pbox = torch.cat((pxy, pwh), 1) # predicted box
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#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
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iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
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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。
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# Objectness
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score_iou = iou.detach().clamp(0).type(tobj.dtype)
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if self.sort_obj_iou:
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sort_id = torch.argsort(score_iou)
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b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id]
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#根据model.gr设置objectness的标签值，有目标的conf分支权重。
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#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
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#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
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tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou # iou ratio
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# Classification
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#如果类别数>1，才计算分类损失（即多类别损失）
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if self.nc > 1: # cls loss (only if multiple classes)
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t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets
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t[range(n), tcls[i]] = self.cp
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lcls += self.BCEcls(ps[:, 5:], t) # BCE对每个类别单独计算loss
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# Append targets to text file
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# with open('targets.txt', 'a') as file:
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# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
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#计算objectness的损失
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obji = self.BCEobj(pi[..., 4], tobj)
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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！！！
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if self.autobalance:
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self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
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if self.autobalance:
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self.balance = [x / self.balance[self.ssi] for x in self.balance]
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lbox *= self.hyp['box'] #求最后总的损失时，还进行了加权
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lobj *= self.hyp['obj'] #超参里面有设置
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lcls *= self.hyp['cls'] #超参里面有设置
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bs = tobj.shape[0] # batch size
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#总的loss=lbox + lobj + lcls，再乘以batchsize，返回
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return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
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def build_targets(self, p, targets):
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# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
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na, nt = self.na, targets.shape[0] # number of anchors, targets
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tcls, tbox, indices, anch = [], [], [], []
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gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
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ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
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targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
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g = 0.5 # bias
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off = torch.tensor([[0, 0],
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[1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
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# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
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], device=targets.device).float() * g # offsets
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for i in range(self.nl):
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anchors = self.anchors[i]
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gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
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# Match targets to anchors
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t = targets * gain
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if nt:
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# Matches
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r = t[:, :, 4:6] / anchors[:, None] # wh ratio
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j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare
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# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
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t = t[j] # filter
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# Offsets
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gxy = t[:, 2:4] # grid xy
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gxi = gain[[2, 3]] - gxy # inverse
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j, k = ((gxy % 1. < g) & (gxy > 1.)).T
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l, m = ((gxi % 1. < g) & (gxi > 1.)).T
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j = torch.stack((torch.ones_like(j), j, k, l, m))
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t = t.repeat((5, 1, 1))[j]
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offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
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else:
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t = targets[0]
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offsets = 0
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# Define
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b, c = t[:, :2].long().T # image, class
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gxy = t[:, 2:4] # grid xy
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gwh = t[:, 4:6] # grid wh
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gij = (gxy - offsets).long()
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gi, gj = gij.T # grid xy indices
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# Append
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a = t[:, 6].long() # anchor indices
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indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
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tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
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anch.append(anchors[a]) # anchors
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tcls.append(c) # class
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return tcls, tbox, indices, anch
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