import torch import torch.nn.functional as F try: from .loss_utils import get_ious, get_world_size, is_dist_avail_and_initialized from .matcher import AlignedSimOtaMatcher except: from loss_utils import get_ious, get_world_size, is_dist_avail_and_initialized from matcher import AlignedSimOtaMatcher class Criterion(object): def __init__(self, cfg, num_classes=80): # ------------ Basic parameters ------------ self.cfg = cfg self.num_classes = num_classes # --------------- Matcher config --------------- self.matcher_hpy = cfg['matcher_hpy'] self.matcher = AlignedSimOtaMatcher(soft_center_radius = self.matcher_hpy['soft_center_radius'], topk_candidates = self.matcher_hpy['topk_candidates'], num_classes = num_classes, ) # ------------- Loss weight ------------- self.weight_dict = {'loss_cls': cfg['loss_coeff']['class'], 'loss_box': cfg['loss_coeff']['bbox'], 'loss_giou': cfg['loss_coeff']['giou']} def loss_classes(self, pred_cls, target, num_gts, 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) losses = {} losses['loss_cls'] = ce_loss.sum() / num_gts return losses def loss_bboxes(self, pred_reg, pred_box, gt_box, anchors, stride_tensors, num_gts): # --------------- Compute L1 loss --------------- ## xyxy -> cxcy&bwbh gt_cxcy = (gt_box[..., :2] + gt_box[..., 2:]) * 0.5 gt_bwbh = gt_box[..., 2:] - gt_box[..., :2] ## Encode gt box gt_cxcy_encode = (gt_cxcy - anchors) / stride_tensors gt_bwbh_encode = torch.log(gt_bwbh / stride_tensors) gt_box_encode = torch.cat([gt_cxcy_encode, gt_bwbh_encode], dim=-1) # L1 loss loss_box = F.l1_loss(pred_reg, gt_box_encode, reduction='none') # --------------- Compute GIoU loss --------------- gious = get_ious(pred_box, gt_box, box_mode="xyxy", iou_type='giou') loss_giou = 1.0 - gious losses = {} losses['loss_box'] = loss_box.sum() / num_gts losses['loss_giou'] = loss_giou.sum() / num_gts return losses def __call__(self, outputs, targets): """ outputs['pred_cls']: List(Tensor) [B, M, C] outputs['pred_box']: List(Tensor) [B, M, 4] 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 anchors = outputs['anchors'] fpn_strides = outputs['strides'] stride_tensors = outputs['stride_tensors'] losses = dict() # preds: [B, M, C] cls_preds = torch.cat(outputs['pred_cls'], dim=1) box_preds = torch.cat(outputs['pred_box'], dim=1) reg_preds = torch.cat(outputs['pred_reg'], 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] 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']) # List[B, M, C] -> Tensor[BM, C] 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() # ------------------ Classification loss ------------------ cls_preds = cls_preds.view(-1, self.num_classes) loss_dict = self.loss_classes(cls_preds, (cls_targets, assign_metrics), num_fgs) loss_dict = {k: loss_dict[k] * self.weight_dict[k] for k in loss_dict if k in self.weight_dict} losses.update(loss_dict) # ------------------ Regression loss ------------------ box_targets_pos = box_targets[pos_inds] ## positive predictions box_preds_pos = box_preds.view(-1, 4)[pos_inds] reg_preds_pos = reg_preds.view(-1, 4)[pos_inds] ## anchor tensors anchors_tensors = torch.cat(anchors, dim=0)[None].repeat(bs, 1, 1) anchors_tensors_pos = anchors_tensors.view(-1, 2)[pos_inds] ## stride tensors stride_tensors = torch.cat(stride_tensors, dim=0)[None].repeat(bs, 1, 1) stride_tensors_pos = stride_tensors.view(-1, 1)[pos_inds] ## aux loss loss_dict = self.loss_bboxes(reg_preds_pos, box_preds_pos, box_targets_pos, anchors_tensors_pos, stride_tensors_pos, num_fgs) loss_dict = {k: loss_dict[k] * self.weight_dict[k] for k in loss_dict if k in self.weight_dict} losses.update(loss_dict) return losses def build_criterion(cfg, num_classes): criterion = Criterion(cfg, num_classes) return criterion