YOLO-Tutorial-v2/yolo/models/yolov10/loss.py

import torch
import torch.nn as nn
import torch.nn.functional as F

from utils.box_ops import bbox2dist, bbox_iou
from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized

from .matcher import TaskAlignedAssigner


class SetCriterion(object):
    def __init__(self, cfg):
        # --------------- Basic parameters ---------------
        self.cfg = cfg
        self.reg_max = cfg.reg_max
        self.num_classes = cfg.num_classes
        # --------------- Loss config ---------------
        self.loss_cls_weight = cfg.loss_cls
        self.loss_box_weight = cfg.loss_box
        self.loss_dfl_weight = cfg.loss_dfl
        # --------------- Matcher config ---------------
        self.matcher = TaskAlignedAssigner(num_classes     = cfg.num_classes,
                                           topk_candidates = cfg.tal_topk_candidates,
                                           alpha           = cfg.tal_alpha,
                                           beta            = cfg.tal_beta
                                           )

    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_bboxes(self, pred_box, gt_box, bbox_weight):
        # regression loss
        ious = bbox_iou(pred_box, gt_box, xywh=False, CIoU=True)
        loss_box = (1.0 - ious.squeeze(-1)) * bbox_weight

        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.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.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.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 compute_loss(self, outputs, targets):        
        """
            outputs['pred_cls']: List(Tensor) [B, M, C]
            outputs['pred_reg']: List(Tensor) [B, M, 4*(reg_max+1)]
            outputs['pred_box']: 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': ...}, ...]
        """
        # 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)
        bs, num_anchors = cls_preds.shape[:2]
        device = cls_preds.device
        anchors = torch.cat(outputs['anchors'], dim=0)
        
        # --------------- label assignment ---------------
        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
                fg_mask  = cls_preds.new_zeros(1, num_anchors).bool()               #[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]
            else:
                tgt_labels = tgt_labels[None, :, None]      # [1, Mp, 1]
                tgt_boxs = tgt_boxs[None]                   # [1, Mp, 4]
                (
                    _,
                    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_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_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 = gt_score_targets.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)

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

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

        # ------------------ Distribution focal loss  ------------------
        ## process anchors
        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.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_pos, anchors_pos, strides_pos, bbox_weight)
        loss_dfl = loss_dfl.sum() / num_fgs

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

        return loss_dict

    def __call__(self, outputs, targets):
        loss_o2o = self.compute_loss(outputs["outputs_o2o"], targets)
        loss_o2m = self.compute_loss(outputs["outputs_o2m"], targets)

        loss_dict = {}
        for k in loss_o2o:
            loss_dict[k+"_o2o"] = loss_o2o[k]

        for k in loss_o2m:
            loss_dict[k+"_o2m"] = loss_o2m[k]

        loss_dict["losses"] = loss_o2o["losses"] + loss_o2m["losses"]

        return loss_dict


if __name__ == "__main__":
    pass