# Loss functions import torch import torch.nn as nn import torch.nn.functional as F from utils.general import bbox_iou, bbox_alpha_iou, box_iou, box_giou, box_diou, box_ciou, xywh2xyxy 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 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 SigmoidBin(nn.Module): stride = None # strides computed during build export = False # onnx export def __init__(self, bin_count=10, min=0.0, max=1.0, reg_scale = 2.0, use_loss_regression=True, use_fw_regression=True, BCE_weight=1.0, smooth_eps=0.0): super(SigmoidBin, self).__init__() self.bin_count = bin_count self.length = bin_count + 1 self.min = min self.max = max self.scale = float(max - min) self.shift = self.scale / 2.0 self.use_loss_regression = use_loss_regression self.use_fw_regression = use_fw_regression self.reg_scale = reg_scale self.BCE_weight = BCE_weight start = min + (self.scale/2.0) / self.bin_count end = max - (self.scale/2.0) / self.bin_count step = self.scale / self.bin_count self.step = step #print(f" start = {start}, end = {end}, step = {step} ") bins = torch.range(start, end + 0.0001, step).float() self.register_buffer('bins', bins) self.cp = 1.0 - 0.5 * smooth_eps self.cn = 0.5 * smooth_eps self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight])) self.MSELoss = nn.MSELoss() def get_length(self): return self.length def forward(self, pred): assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale/2.0) * self.step pred_bin = pred[..., 1:(1+self.bin_count)] _, bin_idx = torch.max(pred_bin, dim=-1) bin_bias = self.bins[bin_idx] if self.use_fw_regression: result = pred_reg + bin_bias else: result = bin_bias result = result.clamp(min=self.min, max=self.max) return result def training_loss(self, pred, target): assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) assert pred.shape[0] == target.shape[0], 'pred.shape=%d is not equal to the target.shape=%d' % (pred.shape[0], target.shape[0]) device = pred.device pred_reg = (pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale/2.0) * self.step pred_bin = pred[..., 1:(1+self.bin_count)] diff_bin_target = torch.abs(target[..., None] - self.bins) _, bin_idx = torch.min(diff_bin_target, dim=-1) bin_bias = self.bins[bin_idx] bin_bias.requires_grad = False result = pred_reg + bin_bias target_bins = torch.full_like(pred_bin, self.cn, device=device) # targets n = pred.shape[0] target_bins[range(n), bin_idx] = self.cp loss_bin = self.BCEbins(pred_bin, target_bins) # BCE if self.use_loss_regression: loss_regression = self.MSELoss(result, target) # MSE loss = loss_bin + loss_regression else: loss = loss_bin out_result = result.clamp(min=self.min, max=self.max) return loss, out_result 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 class RankSort(torch.autograd.Function): @staticmethod def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): classification_grads=torch.zeros(logits.shape).cuda() #Filter fg logits fg_labels = (targets > 0.) fg_logits = logits[fg_labels] fg_targets = targets[fg_labels] fg_num = len(fg_logits) #Do not use bg with scores less than minimum fg logit #since changing its score does not have an effect on precision threshold_logit = torch.min(fg_logits)-delta_RS relevant_bg_labels=((targets==0) & (logits>=threshold_logit)) relevant_bg_logits = logits[relevant_bg_labels] relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() sorting_error=torch.zeros(fg_num).cuda() ranking_error=torch.zeros(fg_num).cuda() fg_grad=torch.zeros(fg_num).cuda() #sort the fg logits order=torch.argsort(fg_logits) #Loops over each positive following the order for ii in order: # Difference Transforms (x_ij) fg_relations=fg_logits-fg_logits[ii] bg_relations=relevant_bg_logits-fg_logits[ii] if delta_RS > 0: fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1) bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1) else: fg_relations = (fg_relations >= 0).float() bg_relations = (bg_relations >= 0).float() # Rank of ii among pos and false positive number (bg with larger scores) rank_pos=torch.sum(fg_relations) FP_num=torch.sum(bg_relations) # Rank of ii among all examples rank=rank_pos+FP_num # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7) ranking_error[ii]=FP_num/rank # Current sorting error of example ii. (Eq. 7) current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos #Find examples in the target sorted order for example ii iou_relations = (fg_targets >= fg_targets[ii]) target_sorted_order = iou_relations * fg_relations #The rank of ii among positives in sorted order rank_pos_target = torch.sum(target_sorted_order) #Compute target sorting error. (Eq. 8) #Since target ranking error is 0, this is also total target error target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target #Compute sorting error on example ii sorting_error[ii] = current_sorting_error - target_sorting_error #Identity Update for Ranking Error if FP_num > eps: #For ii the update is the ranking error fg_grad[ii] -= ranking_error[ii] #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num) relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num)) #Find the positives that are misranked (the cause of the error) #These are the ones with smaller IoU but larger logits missorted_examples = (~ iou_relations) * fg_relations #Denominotor of sorting pmf sorting_pmf_denom = torch.sum(missorted_examples) #Identity Update for Sorting Error if sorting_pmf_denom > eps: #For ii the update is the sorting error fg_grad[ii] -= sorting_error[ii] #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom) fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom)) #Normalize gradients by number of positives classification_grads[fg_labels]= (fg_grad/fg_num) classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num) ctx.save_for_backward(classification_grads) return ranking_error.mean(), sorting_error.mean() @staticmethod def backward(ctx, out_grad1, out_grad2): g1, =ctx.saved_tensors return g1*out_grad1, None, None, None class aLRPLoss(torch.autograd.Function): @staticmethod def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): classification_grads=torch.zeros(logits.shape).cuda() #Filter fg logits fg_labels = (targets == 1) fg_logits = logits[fg_labels] fg_num = len(fg_logits) #Do not use bg with scores less than minimum fg logit #since changing its score does not have an effect on precision threshold_logit = torch.min(fg_logits)-delta #Get valid bg logits relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) relevant_bg_logits=logits[relevant_bg_labels] relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() rank=torch.zeros(fg_num).cuda() prec=torch.zeros(fg_num).cuda() fg_grad=torch.zeros(fg_num).cuda() max_prec=0 #sort the fg logits order=torch.argsort(fg_logits) #Loops over each positive following the order for ii in order: #x_ij s as score differences with fgs fg_relations=fg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with fgs fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) #Discard i=j in the summation in rank_pos fg_relations[ii]=0 #x_ij s as score differences with bgs bg_relations=relevant_bg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with bgs bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) #Compute the rank of the example within fgs and number of bgs with larger scores rank_pos=1+torch.sum(fg_relations) FP_num=torch.sum(bg_relations) #Store the total since it is normalizer also for aLRP Regression error rank[ii]=rank_pos+FP_num #Compute precision for this example to compute classification loss prec[ii]=rank_pos/rank[ii] #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads if FP_num > eps: fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii] relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num)) #aLRP with grad formulation fg gradient classification_grads[fg_labels]= fg_grad #aLRP with grad formulation bg gradient classification_grads[relevant_bg_labels]= relevant_bg_grad classification_grads /= (fg_num) cls_loss=1-prec.mean() ctx.save_for_backward(classification_grads) return cls_loss, rank, order @staticmethod def backward(ctx, out_grad1, out_grad2, out_grad3): g1, =ctx.saved_tensors return g1*out_grad1, None, None, None, None class APLoss(torch.autograd.Function): @staticmethod def forward(ctx, logits, targets, delta=1.): classification_grads=torch.zeros(logits.shape).cuda() #Filter fg logits fg_labels = (targets == 1) fg_logits = logits[fg_labels] fg_num = len(fg_logits) #Do not use bg with scores less than minimum fg logit #since changing its score does not have an effect on precision threshold_logit = torch.min(fg_logits)-delta #Get valid bg logits relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) relevant_bg_logits=logits[relevant_bg_labels] relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() rank=torch.zeros(fg_num).cuda() prec=torch.zeros(fg_num).cuda() fg_grad=torch.zeros(fg_num).cuda() max_prec=0 #sort the fg logits order=torch.argsort(fg_logits) #Loops over each positive following the order for ii in order: #x_ij s as score differences with fgs fg_relations=fg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with fgs fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) #Discard i=j in the summation in rank_pos fg_relations[ii]=0 #x_ij s as score differences with bgs bg_relations=relevant_bg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with bgs bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) #Compute the rank of the example within fgs and number of bgs with larger scores rank_pos=1+torch.sum(fg_relations) FP_num=torch.sum(bg_relations) #Store the total since it is normalizer also for aLRP Regression error rank[ii]=rank_pos+FP_num #Compute precision for this example current_prec=rank_pos/rank[ii] #Compute interpolated AP and store gradients for relevant bg examples if (max_prec<=current_prec): max_prec=current_prec relevant_bg_grad += (bg_relations/rank[ii]) else: relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec))) #Store fg gradients fg_grad[ii]=-(1-max_prec) prec[ii]=max_prec #aLRP with grad formulation fg gradient classification_grads[fg_labels]= fg_grad #aLRP with grad formulation bg gradient classification_grads[relevant_bg_labels]= relevant_bg_grad classification_grads /= fg_num cls_loss=1-prec.mean() ctx.save_for_backward(classification_grads) return cls_loss @staticmethod def backward(ctx, out_grad1): g1, =ctx.saved_tensors return g1*out_grad1, None, None class ComputeLoss: # Compute losses def __init__(self, model, autobalance=False): super(ComputeLoss, 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 = 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 #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05]) # P3-P7 #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1]) # 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, model.gr, h, autobalance for k in 'na', 'nc', 'nl', 'anchors': setattr(self, k, getattr(det, k)) def __call__(self, p, targets): # predictions, targets, model device = targets.device lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets # Losses for i, pi in enumerate(p): # layer index, layer predictions b, a, gj, gi = indices[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 pxy = ps[:, :2].sigmoid() * 2. - 0.5 pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] pbox = torch.cat((pxy, pwh), 1) # predicted box iou = bbox_iou(pbox.T, tbox[i], 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 # Classification 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 #t[t==self.cp] = iou.detach().clamp(0).type(t.dtype) 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, loss)).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 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 = 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 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, 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 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.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 # 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, loss)).detach() def build_targets(self, p, targets, imgs): #indices, anch = self.find_positive(p, targets) indices, anch = self.find_3_positive(p, targets) #indices, anch = self.find_4_positive(p, targets) #indices, anch = self.find_5_positive(p, targets) #indices, anch = self.find_9_positive(p, targets) 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) 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) 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 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): 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) 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) # 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 class ComputeLossBinOTA: # Compute losses def __init__(self, model, autobalance=False): super(ComputeLossBinOTA, 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)) #MSEangle = nn.MSELoss().to(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 = 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 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, model.gr, h, autobalance for k in 'na', 'nc', 'nl', 'anchors', 'stride', 'bin_count': setattr(self, k, getattr(det, k)) #xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device) wh_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0, use_loss_regression=False).to(device) #angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device) self.wh_bin_sigmoid = wh_bin_sigmoid 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 obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 # x,y, w-bce, h-bce # xy_bin_sigmoid.get_length()*2 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) selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] selected_tbox[:, :2] -= grid #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 #x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0]) #y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1]) w_loss, pw = self.wh_bin_sigmoid.training_loss(ps[..., 2:(3+self.bin_count)], selected_tbox[..., 2] / anchors[i][..., 0]) h_loss, ph = self.wh_bin_sigmoid.training_loss(ps[..., (3+self.bin_count):obj_idx], selected_tbox[..., 3] / anchors[i][..., 1]) pw *= anchors[i][..., 0] ph *= anchors[i][..., 1] px = ps[:, 0].sigmoid() * 2. - 0.5 py = ps[:, 1].sigmoid() * 2. - 0.5 lbox += w_loss + h_loss # + x_loss + y_loss #print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n") pbox = torch.cat((px.unsqueeze(1), py.unsqueeze(1), pw.unsqueeze(1), ph.unsqueeze(1)), 1).to(device) # predicted box 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 # Classification selected_tcls = targets[i][:, 1].long() if self.nc > 1: # cls loss (only if multiple classes) t = torch.full_like(ps[:, (1+obj_idx):], self.cn, device=device) # targets t[range(n), selected_tcls] = self.cp lcls += self.BCEcls(ps[:, (1+obj_idx):], 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[..., obj_idx], 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, loss)).detach() def build_targets(self, p, targets, imgs): #indices, anch = self.find_positive(p, targets) indices, anch = self.find_3_positive(p, targets) #indices, anch = self.find_4_positive(p, targets) #indices, anch = self.find_5_positive(p, targets) #indices, anch = self.find_9_positive(p, targets) 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): obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 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) fg_pred = pi[b, a, gj, gi] p_obj.append(fg_pred[:, obj_idx:(obj_idx+1)]) p_cls.append(fg_pred[:, (obj_idx+1):]) grid = torch.stack([gi, gj], dim=1) pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. #pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. pw = self.wh_bin_sigmoid.forward(fg_pred[..., 2:(3+self.bin_count)].sigmoid()) * anch[i][idx][:, 0] * self.stride[i] ph = self.wh_bin_sigmoid.forward(fg_pred[..., (3+self.bin_count):obj_idx].sigmoid()) * anch[i][idx][:, 1] * self.stride[i] pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], 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) 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 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): 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) 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) # 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