YOLO-Tutorial-v2/yolo/models/yolov3/loss.py

import torch
import torch.nn.functional as F
from .matcher import Yolov3Matcher
from utils.box_ops import get_ious
from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized


class SetCriterion(object):
    def __init__(self, cfg):
        self.cfg = cfg
        self.num_classes = cfg.num_classes
        # loss weight
        self.loss_obj_weight = cfg.loss_obj
        self.loss_cls_weight = cfg.loss_cls
        self.loss_box_weight = cfg.loss_box

        # matcher
        self.matcher = Yolov3Matcher(self.num_classes, 3, cfg.anchor_size, cfg.iou_thresh)

    def loss_objectness(self, pred_obj, gt_obj):
        loss_obj = F.binary_cross_entropy_with_logits(pred_obj, gt_obj, reduction='none')

        return loss_obj
    
    def loss_classes(self, pred_cls, gt_label):
        loss_cls = F.binary_cross_entropy_with_logits(pred_cls, gt_label, reduction='none')

        return loss_cls

    def loss_bboxes(self, pred_box, gt_box):
        # regression loss
        ious = get_ious(pred_box,
                        gt_box,
                        box_mode="xyxy",
                        iou_type='giou')
        loss_box = 1.0 - ious

        return loss_box, ious

    def __call__(self, outputs, targets):
        # label assignment
        (
            gt_objectness, 
            gt_classes, 
            gt_bboxes,
            ) = self.matcher(fmp_sizes   = outputs['fmp_sizes'], 
                             fpn_strides = outputs['strides'], 
                             targets     = targets)
        # List[B, M, C] -> [B, M, C] -> [BM, C]
        pred_obj = torch.cat(outputs['pred_obj'], dim=1).view(-1)                      # [BM,]
        pred_cls = torch.cat(outputs['pred_cls'], dim=1).view(-1, self.num_classes)    # [BM, C]
        pred_box = torch.cat(outputs['pred_box'], dim=1).view(-1, 4)                   # [BM, 4]
        device = pred_box.device
       
        gt_objectness = gt_objectness.view(-1).to(device).float()               # [BM,]
        gt_classes = gt_classes.view(-1, self.num_classes).to(device).float()   # [BM, C]
        gt_bboxes = gt_bboxes.view(-1, 4).to(device).float()                    # [BM, 4]

        pos_masks = (gt_objectness > 0)
        num_fgs = pos_masks.sum()

        if is_dist_avail_and_initialized():
            torch.distributed.all_reduce(num_fgs)
        num_fgs = (num_fgs / get_world_size()).clamp(1.0)

        # box loss
        pred_box_pos = pred_box[pos_masks]
        gt_bboxes_pos = gt_bboxes[pos_masks]
        loss_box, ious = self.loss_bboxes(pred_box_pos, gt_bboxes_pos)
        loss_box = loss_box.sum() / num_fgs
        
        # cls loss
        pred_cls_pos = pred_cls[pos_masks]
        gt_classes_pos = gt_classes[pos_masks] * ious.unsqueeze(-1).clamp(0.)
        loss_cls = self.loss_classes(pred_cls_pos, gt_classes_pos)
        loss_cls = loss_cls.sum() / num_fgs

        # obj loss
        loss_obj = self.loss_objectness(pred_obj, gt_objectness)
        loss_obj = loss_obj.sum() / num_fgs

        # total loss
        losses = self.loss_obj_weight * loss_obj + \
                 self.loss_cls_weight * loss_cls + \
                 self.loss_box_weight * loss_box

        loss_dict = dict(
                loss_obj = loss_obj,
                loss_cls = loss_cls,
                loss_box = loss_box,
                losses = losses
        )

        return loss_dict

    
if __name__ == "__main__":
    pass