import torch import torch.nn as nn import torch.nn.functional as F from .matcher import TaskAlignedAssigner from utils.box_ops import bbox_iou class Criterion(object): def __init__(self, cfg, device, num_classes=80): self.cfg = cfg self.device = device self.num_classes = num_classes # loss self.cls_lossf = ClassificationLoss(cfg) self.reg_lossf = RegressionLoss(num_classes) # loss weight self.loss_cls_weight = cfg['loss_cls_weight'] self.loss_box_weight = cfg['loss_box_weight'] # matcher matcher_config = cfg['matcher'] self.matcher = TaskAlignedAssigner( topk=matcher_config['topk'], num_classes=num_classes, alpha=matcher_config['alpha'], beta=matcher_config['beta'] ) def __call__(self, outputs, targets, epoch=0): """ outputs['pred_cls']: List(Tensor) [B, M, C] outputs['pred_regs']: List(Tensor) [B, M, 4*(reg_max+1)] outputs['pred_boxs']: List(Tensor) [B, M, 4] outputs['anchors']: List(Tensor) [M, 2] outputs['strides']: List(Int) [8, 16, 32] output stride outputs['stride_tensor']: List(Tensor) [M, 1] targets: (List) [dict{'boxes': [...], 'labels': [...], 'orig_size': ...}, ...] """ bs = outputs['pred_cls'][0].shape[0] device = outputs['pred_cls'][0].device anchors = outputs['anchors'] anchors = torch.cat(anchors, dim=0) num_anchors = anchors.shape[0] # preds: [B, M, C] cls_preds = torch.cat(outputs['pred_cls'], dim=1) box_preds = torch.cat(outputs['pred_box'], dim=1) # label assignment gt_label_targets = [] gt_score_targets = [] gt_bbox_targets = [] fg_masks = [] for batch_idx in range(bs): tgt_labels = targets[batch_idx]["labels"].to(device) # [Mp,] tgt_boxs = targets[batch_idx]["boxes"].to(device) # [Mp, 4] # check target if len(tgt_labels) == 0 or tgt_boxs.max().item() == 0.: # There is no valid gt gt_label = cls_preds.new_full((1, num_anchors), self.num_classes).long() #[1, M,] gt_score = cls_preds.new_zeros((1, num_anchors, self.num_classes)) #[1, M, C] gt_box = cls_preds.new_zeros((1, num_anchors, 4)) #[1, M, 4] fg_mask = cls_preds.new_zeros(1, num_anchors).bool() #[1, M,] else: tgt_labels = tgt_labels[None, :, None] # [1, Mp, 1] tgt_boxs = tgt_boxs[None] # [1, Mp, 4] ( gt_label, #[1, M,] gt_box, #[1, M, 4] gt_score, #[1, M, C] fg_mask, #[1, M,] _ ) = self.matcher( pd_scores = cls_preds[batch_idx:batch_idx+1].detach().sigmoid(), pd_bboxes = box_preds[batch_idx:batch_idx+1].detach(), anc_points = anchors, gt_labels = tgt_labels, gt_bboxes = tgt_boxs ) gt_label_targets.append(gt_label) gt_score_targets.append(gt_score) gt_bbox_targets.append(gt_box) fg_masks.append(fg_mask) # List[B, 1, M, C] -> Tensor[B, M, C] -> Tensor[BM, C] fg_masks = torch.cat(fg_masks, 0).view(-1) # [BM,] gt_label_targets = torch.cat(gt_label_targets, 0).view(-1,) # [BM,] gt_score_targets = torch.cat(gt_score_targets, 0).view(-1, self.num_classes) # [BM, C] gt_bbox_targets = torch.cat(gt_bbox_targets, 0).view(-1, 4) # [BM, 4] num_fgs = max(gt_score_targets.sum(), 1) # cls loss cls_preds = cls_preds.view(-1, self.num_classes) loss_cls = self.cls_lossf(cls_preds, gt_label_targets, gt_score_targets) # reg loss bbox_weight = gt_score_targets[fg_masks].sum(-1, keepdim=True) # [BM, 1] box_preds = box_preds.view(-1, 4) # [BM, 4] loss_box = self.reg_lossf(box_preds, gt_bbox_targets, bbox_weight, fg_masks) # normalize loss loss_cls = loss_cls.sum() / num_fgs loss_box = loss_box.sum() / num_fgs # total loss losses = loss_cls * self.loss_cls_weight + \ loss_box * self.loss_box_weight loss_dict = dict( loss_cls = loss_cls, loss_box = loss_box, losses = losses ) return loss_dict class ClassificationLoss(nn.Module): def __init__(self, cfg): super(ClassificationLoss, self).__init__() self.cfg = cfg def quality_focal_loss(self, pred_cls, gt_label, gt_score, beta=2.0): # Quality FocalLoss """ pred_cls: (torch.Tensor): [N, C] gt_label: (torch.Tensor): [N,] gt_score: (torch.Tensor): [N, C] """ gt_label = gt_label.long() gt_score = gt_score[torch.arange(gt_label.shape[0]), gt_label] 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 = ((gt_label >= 0) & (gt_label < bg_class_ind)).nonzero().squeeze(1) pos_label = gt_label[pos].long() scale_factor = gt_score[pos] - pred_sigmoid[pos, pos_label] ce_loss[pos, pos_label] = F.binary_cross_entropy_with_logits( pred_cls[pos, pos_label], gt_score[pos], reduction='none') * scale_factor.abs().pow(beta) return ce_loss def binary_cross_entropy(self, pred_logits, gt_score): loss = F.binary_cross_entropy_with_logits( pred_logits, gt_score, reduction='none') return loss def forward(self, pred_logits, gt_label, gt_score): if self.cfg['cls_loss'] == 'bce': loss = self.binary_cross_entropy(pred_logits, gt_score) elif self.cfg['cls_loss'] == 'qfl': loss = self.quality_focal_loss(pred_logits, gt_label, gt_score) return loss class RegressionLoss(nn.Module): def __init__(self, num_classes): super(RegressionLoss, self).__init__() self.num_classes = num_classes def forward(self, pred_boxs, gt_boxs, bbox_weight, fg_masks): """ Input: pred_boxs: (Tensor) [BM, 4] gt_boxs: (Tensor) [BM, 4] bbox_weight: (Tensor) [BM, 1] fg_masks: (Tensor) [BM,] """ # select positive samples mask num_pos = fg_masks.sum() if num_pos > 0: pred_boxs_pos = pred_boxs[fg_masks] gt_boxs_pos = gt_boxs[fg_masks] # iou loss ious = bbox_iou(pred_boxs_pos, gt_boxs_pos, xywh=False, CIoU=True) loss_iou = (1.0 - ious) * bbox_weight else: loss_iou = pred_boxs.sum() * 0. return loss_iou def build_criterion(cfg, device, num_classes): criterion = Criterion( cfg=cfg, device=device, num_classes=num_classes ) return criterion if __name__ == "__main__": pass