CLIP-ReID代码解读八——loss文件夹（make_loss.py）

make_loss.py

这段代码定义了一个函数 make_loss，它根据配置文件 cfg 和类别数量 num_classes 创建一个损失函数和中心损失（CenterLoss）准则。以下是代码的详细注释：

import torch.nn.functional as F
from .softmax_loss import CrossEntropyLabelSmooth, LabelSmoothingCrossEntropy
from .triplet_loss import TripletLoss
from .center_loss import CenterLoss

# 定义make_loss函数，用于根据配置文件和类别数量创建损失函数
def make_loss(cfg, num_classes):
    # 获取采样器类型
    sampler = cfg.DATALOADER.SAMPLER
    # 特征维度
    feat_dim = 2048
    # 创建CenterLoss实例
    center_criterion = CenterLoss(num_classes=num_classes, feat_dim=feat_dim, use_gpu=True)

    # 如果使用triplet损失
    if 'triplet' in cfg.MODEL.METRIC_LOSS_TYPE:
        if cfg.MODEL.NO_MARGIN:
            # 使用不带边界的TripletLoss
            triplet = TripletLoss()
            print("using soft triplet loss for training")
        else:
            # 使用带边界的TripletLoss
            triplet = TripletLoss(cfg.SOLVER.MARGIN)
            print("using triplet loss with margin:{}".format(cfg.SOLVER.MARGIN))
    else:
        print('expected METRIC_LOSS_TYPE should be triplet but got {}'.format(cfg.MODEL.METRIC_LOSS_TYPE))

    # 如果开启了标签平滑
    if cfg.MODEL.IF_LABELSMOOTH == 'on':
        # 创建CrossEntropyLabelSmooth实例
        xent = CrossEntropyLabelSmooth(num_classes=num_classes)
        print("label smooth on, numclasses:", num_classes)

    # 定义不同采样器的损失函数
    if sampler == 'softmax':
        def loss_func(score, feat, target):
            return F.cross_entropy(score, target)
    elif cfg.DATALOADER.SAMPLER == 'softmax_triplet':
        def loss_func(score, feat, target, target_cam, i2tscore=None):
            if cfg.MODEL.METRIC_LOSS_TYPE == 'triplet':
                if cfg.MODEL.IF_LABELSMOOTH == 'on':
                    if isinstance(score, list):
                        # 计算ID损失
                        ID_LOSS = [xent(scor, target) for scor in score[0:]]
                        ID_LOSS = sum(ID_LOSS)
                    else:
                        ID_LOSS = xent(score, target)

                    if isinstance(feat, list):
                        # 计算Triplet损失
                        TRI_LOSS = [triplet(feats, target)[0] for feats in feat[0:]]
                        TRI_LOSS = sum(TRI_LOSS)
                    else:
                        TRI_LOSS = triplet(feat, target)[0]

                    # 计算总损失
                    loss = cfg.MODEL.ID_LOSS_WEIGHT * ID_LOSS + cfg.MODEL.TRIPLET_LOSS_WEIGHT * TRI_LOSS

                    if i2tscore is not None:
                        # 计算I2T损失
                        I2TLOSS = xent(i2tscore, target)
                        loss = cfg.MODEL.I2T_LOSS_WEIGHT * I2TLOSS + loss

                    return loss
                else:
                    if isinstance(score, list):
                        ID_LOSS = [F.cross_entropy(scor, target) for scor in score[0:]]
                        ID_LOSS = sum(ID_LOSS)
                    else:
                        ID_LOSS = F.cross_entropy(score, target)

                    if isinstance(feat, list):
                        TRI_LOSS = [triplet(feats, target)[0] for feats in feat[0:]]
                        TRI_LOSS = sum(TRI_LOSS)
                    else:
                        TRI_LOSS = triplet(feat, target)[0]

                    loss = cfg.MODEL.ID_LOSS_WEIGHT * ID_LOSS + cfg.MODEL.TRIPLET_LOSS_WEIGHT * TRI_LOSS

                    if i2tscore is not None:
                        I2TLOSS = F.cross_entropy(i2tscore, target)
                        loss = cfg.MODEL.I2T_LOSS_WEIGHT * I2TLOSS + loss

                    return loss
            else:
                print('expected METRIC_LOSS_TYPE should be triplet but got {}'.format(cfg.MODEL.METRIC_LOSS_TYPE))
    else:
        print('expected sampler should be softmax, triplet, softmax_triplet or softmax_triplet_center but got {}'.format(cfg.DATALOADER.SAMPLER))

    return loss_func, center_criterion

主要功能和流程

1. 导入依赖：导入必要的函数和类，包括交叉熵损失、三元组损失和中心损失。
2. 定义 make_loss 函数：根据配置文件和类别数量创建损失函数。
3. 创建 CenterLoss 实例：中心损失用于衡量特征中心与各类样本特征的距离。
4. 创建 TripletLoss 实例：根据配置文件选择是否使用带边界的三元组损失。
5. 创建 CrossEntropyLabelSmooth 实例：如果开启了标签平滑，则创建标签平滑交叉熵损失实例。
6. 定义损失函数：根据不同的采样器类型定义相应的损失函数逻辑，包括交叉熵损失和三元组损失的组合。
7. 返回损失函数和中心损失：最终返回定义好的损失函数和中心损失实例。