YOLO-Tutorial-v2/yolo/models/yolof/loss.py

import torch
import torch.nn.functional as F

from utils.box_ops import get_ious
from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized

from .matcher import SimOtaMatcher


class SetCriterion(object):
    def __init__(self, cfg):
        self.cfg = cfg
        self.num_classes = cfg.num_classes
        # --------------- Loss config ---------------
        self.loss_cls_weight = cfg.loss_cls
        self.loss_box_weight = cfg.loss_box
        # --------------- Matcher config ---------------
        self.matcher = SimOtaMatcher(soft_center_radius = cfg.ota_soft_center_radius,
                                     topk_candidates    = cfg.ota_topk_candidates,
                                     num_classes        = cfg.num_classes,
                                     )

    def loss_classes(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)
        if pos.shape[0] > 0:
            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, bbox_weight=None):
        ious = get_ious(pred_box, gt_box, box_mode="xyxy", iou_type='giou')
        loss_box = 1.0 - ious

        if bbox_weight is not None:
            loss_box = loss_box.squeeze(-1) * bbox_weight

        return loss_box

    def __call__(self, outputs, targets):        
        """
            outputs['pred_cls']: List(Tensor) [B, M, C]
            outputs['pred_reg']: 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'].shape[0]
        device      = outputs['pred_cls'].device
        anchors     = outputs['anchors']
        stride      = outputs['stride']
        # preds: [B, M, C]
        cls_preds = outputs['pred_cls']
        box_preds = outputs['pred_box']
        
        # --------------- 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(stride=stride,
                                           anchors=anchors[..., :2],
                                           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()
        bbox_weight = assign_metrics[pos_inds]

        # ------------------ Classification loss ------------------
        cls_preds = cls_preds.view(-1, self.num_classes)
        loss_cls = self.loss_classes(cls_preds, (cls_targets, assign_metrics))
        loss_cls = loss_cls.sum() / num_fgs

        # ------------------ Regression loss ------------------
        box_preds_pos = box_preds.view(-1, 4)[pos_inds]
        box_targets_pos = box_targets[pos_inds]
        loss_box = self.loss_bboxes(box_preds_pos, box_targets_pos, bbox_weight)
        loss_box = loss_box.sum() / num_fgs

        # total loss
        losses = self.loss_cls_weight * loss_cls + \
                 self.loss_box_weight * loss_box
        loss_dict = dict(
                loss_cls = loss_cls,
                loss_box = loss_box,
                losses = losses
        )

        return loss_dict