loss.py 文件源码

def forward(self, loc_preds, loc_targets, cls_preds, cls_targets):
        '''Compute loss between (loc_preds, loc_targets) and (cls_preds, cls_targets).

        Args:
          loc_preds: (tensor) predicted locations, sized [batch_size, #anchors, 4].
          loc_targets: (tensor) encoded target locations, sized [batch_size, #anchors, 4].
          cls_preds: (tensor) predicted class confidences, sized [batch_size, #anchors, #classes].
          cls_targets: (tensor) encoded target labels, sized [batch_size, #anchors].

        loss:
          (tensor) loss = SmoothL1Loss(loc_preds, loc_targets) + FocalLoss(cls_preds, cls_targets).
        '''
        batch_size, num_boxes = cls_targets.size()
        pos = cls_targets > 0  # [N,#anchors]
        num_pos = pos.data.long().sum()

        ################################################################
        # loc_loss = SmoothL1Loss(pos_loc_preds, pos_loc_targets)
        ################################################################
        mask = pos.unsqueeze(2).expand_as(loc_preds)       # [N,#anchors,4]
        masked_loc_preds = loc_preds[mask].view(-1,4)      # [#pos,4]
        masked_loc_targets = loc_targets[mask].view(-1,4)  # [#pos,4]
        loc_loss = F.smooth_l1_loss(masked_loc_preds, masked_loc_targets, size_average=False)

        ################################################################
        # cls_loss = FocalLoss(loc_preds, loc_targets)
        ################################################################
        pos_neg = cls_targets > -1  # exclude ignored anchors
        mask = pos_neg.unsqueeze(2).expand_as(cls_preds)
        masked_cls_preds = cls_preds[mask].view(-1,self.num_classes)
        cls_loss = self.focal_loss_alt(masked_cls_preds, cls_targets[pos_neg])

        print('loc_loss: %.3f | cls_loss: %.3f' % (loc_loss.data[0]/num_pos, cls_loss.data[0]/num_pos), end=' | ')
        loss = (loc_loss+cls_loss)/num_pos
        return loss