import torch import torch.nn.functional as F from utils.box_ops import bbox2dist, get_ious from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized from .matcher import build_matcher # ----------------------- Criterion for training ----------------------- class Criterion(object): def __init__(self, args, cfg, device, num_classes=80): self.cfg = cfg self.args = args self.device = device self.num_classes = num_classes self.max_epoch = args.max_epoch self.no_aug_epoch = args.no_aug_epoch self.use_ema_update = cfg['ema_update'] self.loss_box_aux = cfg['loss_box_aux'] # ---------------- Loss weight ---------------- loss_weights = cfg['loss_weights'][cfg['matcher']] self.loss_cls_weight = loss_weights['loss_cls_weight'] self.loss_box_weight = loss_weights['loss_box_weight'] self.loss_dfl_weight = loss_weights['loss_dfl_weight'] # ---------------- Matcher ---------------- ## Aligned SimOTA assigner self.matcher = build_matcher(cfg, num_classes) def ema_update(self, name: str, value, initial_value, momentum=0.9): if hasattr(self, name): old = getattr(self, name) else: old = initial_value new = old * momentum + value * (1 - momentum) setattr(self, name, new) return new # ----------------- Loss functions ----------------- def loss_classes(self, pred_cls, gt_score): # compute bce loss loss_cls = F.binary_cross_entropy_with_logits(pred_cls, gt_score, reduction='none') return loss_cls def loss_classes_qfl(self, pred_cls, target, beta=2.0): # Quality FocalLoss """ pred_cls: (torch.Tensor): [N, C]。 target: (tuple([torch.Tensor], [torch.Tensor])): label -> (N,), score -> (N,) """ label, score = target pred_sigmoid = pred_cls.sigmoid() scale_factor = pred_sigmoid zerolabel = scale_factor.new_zeros(pred_cls.shape) ce_loss = F.binary_cross_entropy_with_logits( pred_cls, zerolabel, reduction='none') * scale_factor.pow(beta) bg_class_ind = pred_cls.shape[-1] pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1) pos_label = label[pos].long() scale_factor = score[pos] - pred_sigmoid[pos, pos_label] ce_loss[pos, pos_label] = F.binary_cross_entropy_with_logits( pred_cls[pos, pos_label], score[pos], reduction='none') * scale_factor.abs().pow(beta) return ce_loss def loss_bboxes(self, pred_box, gt_box): # regression loss ious = get_ious(pred_box, gt_box, 'xyxy', 'giou') loss_box = 1.0 - ious return loss_box def loss_dfl(self, pred_reg, gt_box, anchor, stride, bbox_weight=None): # rescale coords by stride gt_box_s = gt_box / stride anchor_s = anchor / stride # compute deltas gt_ltrb_s = bbox2dist(anchor_s, gt_box_s, self.cfg['reg_max'] - 1) gt_left = gt_ltrb_s.to(torch.long) gt_right = gt_left + 1 weight_left = gt_right.to(torch.float) - gt_ltrb_s weight_right = 1 - weight_left # loss left loss_left = F.cross_entropy( pred_reg.view(-1, self.cfg['reg_max']), gt_left.view(-1), reduction='none').view(gt_left.shape) * weight_left # loss right loss_right = F.cross_entropy( pred_reg.view(-1, self.cfg['reg_max']), gt_right.view(-1), reduction='none').view(gt_left.shape) * weight_right loss_dfl = (loss_left + loss_right).mean(-1) if bbox_weight is not None: loss_dfl *= bbox_weight return loss_dfl def loss_bboxes_aux(self, pred_delta, gt_box, anchors, stride_tensors): gt_delta_tl = (anchors - gt_box[..., :2]) / stride_tensors gt_delta_rb = (gt_box[..., 2:] - anchors) / stride_tensors gt_delta = torch.cat([gt_delta_tl, gt_delta_rb], dim=1) loss_box_aux = F.l1_loss(pred_delta, gt_delta, reduction='none') return loss_box_aux # ----------------- Main process ----------------- def loss_simota(self, outputs, targets, epoch=0): bs = outputs['pred_cls'][0].shape[0] device = outputs['pred_cls'][0].device fpn_strides = outputs['strides'] anchors = outputs['anchors'] num_anchors = sum([ab.shape[0] for ab in anchors]) # preds: [B, M, C] cls_preds = torch.cat(outputs['pred_cls'], dim=1) reg_preds = torch.cat(outputs['pred_reg'], dim=1) box_preds = torch.cat(outputs['pred_box'], dim=1) # --------------- label assignment --------------- cls_targets = [] box_targets = [] fg_masks = [] for batch_idx in range(bs): tgt_labels = targets[batch_idx]["labels"].to(device) tgt_bboxes = targets[batch_idx]["boxes"].to(device) # check target if len(tgt_labels) == 0 or tgt_bboxes.max().item() == 0.: # There is no valid gt cls_target = cls_preds.new_zeros((num_anchors, self.num_classes)) box_target = cls_preds.new_zeros((0, 4)) fg_mask = cls_preds.new_zeros(num_anchors).bool() else: ( fg_mask, assigned_labels, assigned_ious, assigned_indexs ) = self.matcher( fpn_strides = fpn_strides, anchors = anchors, pred_cls = cls_preds[batch_idx], pred_box = box_preds[batch_idx], tgt_labels = tgt_labels, tgt_bboxes = tgt_bboxes ) # prepare cls targets assigned_labels = F.one_hot(assigned_labels.long(), self.num_classes) assigned_labels = assigned_labels * assigned_ious.unsqueeze(-1) cls_target = assigned_labels.new_zeros((num_anchors, self.num_classes)) cls_target[fg_mask] = assigned_labels # prepare box targets box_target = tgt_bboxes[assigned_indexs] cls_targets.append(cls_target) box_targets.append(box_target) fg_masks.append(fg_mask) cls_targets = torch.cat(cls_targets, 0) box_targets = torch.cat(box_targets, 0) fg_masks = torch.cat(fg_masks, 0) num_fgs = fg_masks.sum() # average loss normalizer across all the GPUs if is_dist_avail_and_initialized(): torch.distributed.all_reduce(num_fgs) num_fgs = (num_fgs / get_world_size()).clamp(1.0) # update loss normalizer with EMA if self.use_ema_update: normalizer = self.ema_update("loss_normalizer", max(num_fgs, 1), 100) else: normalizer = num_fgs # ------------------ Classification loss ------------------ cls_preds = cls_preds.view(-1, self.num_classes) loss_cls = self.loss_classes(cls_preds, cls_targets) loss_cls = loss_cls.sum() / normalizer # ------------------ Regression loss ------------------ box_preds_pos = box_preds.view(-1, 4)[fg_masks] loss_box = self.loss_bboxes(box_preds_pos, box_targets) loss_box = loss_box.sum() / normalizer # ------------------ Distribution focal loss ------------------ ## process anchors anchors = torch.cat(anchors, dim=0) anchors = anchors[None].repeat(bs, 1, 1).view(-1, 2) ## process stride tensors strides = torch.cat(outputs['stride_tensor'], dim=0) strides = strides.unsqueeze(0).repeat(bs, 1, 1).view(-1, 1) ## fg preds reg_preds_pos = reg_preds.view(-1, 4*self.cfg['reg_max'])[fg_masks] anchors_pos = anchors[fg_masks] strides_pos = strides[fg_masks] ## compute dfl loss_dfl = self.loss_dfl(reg_preds_pos, box_targets, anchors_pos, strides_pos) loss_dfl = loss_dfl.sum() / normalizer # total loss losses = self.loss_cls_weight * loss_cls + \ self.loss_box_weight * loss_box + \ self.loss_dfl_weight * loss_dfl loss_dict = dict( loss_cls = loss_cls, loss_box = loss_box, loss_dfl = loss_dfl, losses = losses ) # ------------------ Aux regression loss ------------------ if epoch >= (self.max_epoch - self.no_aug_epoch - 1) and self.loss_box_aux: ## delta_preds delta_preds = torch.cat(outputs['pred_delta'], dim=1) delta_preds_pos = delta_preds.view(-1, 4)[fg_masks] ## aux loss loss_box_aux = self.loss_bboxes_aux(delta_preds_pos, box_targets, anchors_pos, strides_pos) loss_box_aux = loss_box_aux.sum() / normalizer losses += loss_box_aux loss_dict['loss_box_aux'] = loss_box_aux return loss_dict def loss_aligned_simota(self, outputs, targets, epoch=0): """ outputs['pred_cls']: List(Tensor) [B, M, C] outputs['pred_box']: List(Tensor) [B, M, 4] outputs['strides']: List(Int) [8, 16, 32] output stride targets: (List) [dict{'boxes': [...], 'labels': [...], 'orig_size': ...}, ...] """ bs = outputs['pred_cls'][0].shape[0] device = outputs['pred_cls'][0].device fpn_strides = outputs['strides'] anchors = outputs['anchors'] # preds: [B, M, C] cls_preds = torch.cat(outputs['pred_cls'], dim=1) reg_preds = torch.cat(outputs['pred_reg'], dim=1) box_preds = torch.cat(outputs['pred_box'], dim=1) # --------------- label assignment --------------- cls_targets = [] box_targets = [] assign_metrics = [] for batch_idx in range(bs): tgt_labels = targets[batch_idx]["labels"].to(device) # [N,] tgt_bboxes = targets[batch_idx]["boxes"].to(device) # [N, 4] # label assignment assigned_result = self.matcher(fpn_strides=fpn_strides, anchors=anchors, pred_cls=cls_preds[batch_idx].detach(), pred_box=box_preds[batch_idx].detach(), gt_labels=tgt_labels, gt_bboxes=tgt_bboxes ) cls_targets.append(assigned_result['assigned_labels']) box_targets.append(assigned_result['assigned_bboxes']) assign_metrics.append(assigned_result['assign_metrics']) cls_targets = torch.cat(cls_targets, dim=0) box_targets = torch.cat(box_targets, dim=0) assign_metrics = torch.cat(assign_metrics, dim=0) # FG cat_id: [0, num_classes -1], BG cat_id: num_classes bg_class_ind = self.num_classes pos_inds = ((cls_targets >= 0) & (cls_targets < bg_class_ind)).nonzero().squeeze(1) num_fgs = assign_metrics.sum() if is_dist_avail_and_initialized(): torch.distributed.all_reduce(num_fgs) num_fgs = (num_fgs / get_world_size()).clamp(1.0).item() # update loss normalizer with EMA if self.use_ema_update: normalizer = self.ema_update("loss_normalizer", max(num_fgs, 1), 100) else: normalizer = num_fgs # ---------------------------- Classification loss ---------------------------- cls_preds = cls_preds.view(-1, self.num_classes) loss_cls = self.loss_classes_qfl(cls_preds, (cls_targets, assign_metrics)) loss_cls = loss_cls.sum() / normalizer # ---------------------------- Regression loss ---------------------------- box_preds_pos = box_preds.view(-1, 4)[pos_inds] box_targets_pos = box_targets[pos_inds] box_weight_pos = assign_metrics[pos_inds] loss_box = self.loss_bboxes(box_preds_pos, box_targets_pos) loss_box *= box_weight_pos loss_box = loss_box.sum() / normalizer # ------------------ Distribution focal loss ------------------ ## process anchors anchors = torch.cat(anchors, dim=0) anchors = anchors[None].repeat(bs, 1, 1).view(-1, 2) ## process stride tensors strides = torch.cat(outputs['stride_tensor'], dim=0) strides = strides.unsqueeze(0).repeat(bs, 1, 1).view(-1, 1) ## fg preds reg_preds_pos = reg_preds.view(-1, 4*self.cfg['reg_max'])[pos_inds] anchors_pos = anchors[pos_inds] strides_pos = strides[pos_inds] ## compute dfl loss_dfl = self.loss_dfl(reg_preds_pos, box_targets_pos, anchors_pos, strides_pos) loss_dfl *= box_weight_pos loss_dfl = loss_dfl.sum() / normalizer # total loss losses = self.loss_cls_weight * loss_cls + \ self.loss_box_weight * loss_box + \ self.loss_dfl_weight * loss_dfl loss_dict = dict( loss_cls = loss_cls, loss_box = loss_box, loss_dfl = loss_dfl, losses = losses ) # ------------------ Aux regression loss ------------------ if epoch >= (self.max_epoch - self.no_aug_epoch - 1) and self.loss_box_aux: ## delta_preds delta_preds = torch.cat(outputs['pred_delta'], dim=1) delta_preds_pos = delta_preds.view(-1, 4)[pos_inds] ## aux loss loss_box_aux = self.loss_bboxes_aux(delta_preds_pos, box_targets_pos, anchors_pos, strides_pos) loss_box_aux = loss_box_aux.sum() / normalizer losses += loss_box_aux loss_dict['loss_box_aux'] = loss_box_aux return loss_dict def __call__(self, outputs, targets, epoch=0): if self.cfg['matcher'] == "simota": return self.loss_simota(outputs, targets, epoch) elif self.cfg['matcher'] == "aligned_simota": return self.loss_aligned_simota(outputs, targets, epoch) def build_criterion(args, cfg, device, num_classes): criterion = Criterion( args=args, cfg=cfg, device=device, num_classes=num_classes ) return criterion if __name__ == "__main__": pass