paddlepaddle实现CS_CE Loss且并入PaddleClas

1、 环境

1.1 paddlepaddle版本

paddlepaddle-gpu     2.2.2

1.2 PaddleClas版本

* release/2.4

2、 CS_CE Loss

CS_CE Loss在CEloss上加入了对于类别数量的权重系数，增加模型对于样本数量少类别的回归能力
计算方式：
$$Loss(z,c)=-(\frac{N_{min}}{N_c}{)}^{\gamma }\ast CrossEntropy(z,c)$$
其中：

• $\gamma$为控制权重的超参数
• $N_{min}$为存在样本最少的类别的样本数量
• $N_c$为c类别样本数量

3、paddle代码

import paddle.nn as nn
import paddle
import numpy as np
import paddle.nn.functional as F


class CostSensitiveCE(nn.Layer):
    r""" Equation: Loss(z, c) = - (\frac{N_min}{N_c})^\gamma * CrossEntropy(z, c), where gamma is a hyper-parameter to control the weights, N_min is the number of images in the smallest class, and N_c is the number of images in the class c. The representative re-weighting methods, which assigns class-dependent weights to the loss function Args: gamma (float or double): to control the loss weights: (N_min/N_i)^gamma """

    def __init__(self, num_class_list, gamma):
        super(CostSensitiveCE, self).__init__()
        self.num_class_list = num_class_list
        self.csce_weight = paddle.to_tensor(np.array([(min(self.num_class_list) / N)**gamma for N in self.num_class_list], dtype=np.float32))

    def forward(self, x, label):
        if isinstance(x, dict):
            x = x["logits"]

        if label.shape[-1] == x.shape[-1]:
            label = F.softmax(label, axis=-1)
            soft_label = True
        else:
            soft_label = False
        cs_ce_loss = F.cross_entropy(x, label=label, soft_label=soft_label, weight=self.csce_weight)
        cs_ce_loss = cs_ce_loss.mean()
        return {
    "CS_CELoss":cs_ce_loss}

4、并入PaddleClas

4.1 loss代码

在PaddeClas/ppcls/loss目录下添加文件cs_celoss.py文件，并在PaddeClas/ppcls/loss/__init__.py中添加

from .cs_celoss import CostSensitiveCE

4.2 config 文件

Loss:
  Train:
    - CostSensitiveCE:
        gamma: 1
        num_class_list: [4400, 1520, 560, 1680] # 每个类别样本数量
        weight: 1 # 一个Classfier默认为1
  Eval:
    - CostSensitiveCE:
        gamma: 1
        num_class_list: [4400, 1520, 560, 1680] # 每个类别样本数量
        weight: 1

5、参考文献

• 论文：Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks
• 官方代码：https://github.com/zhangyongshun/BagofTricks-LT

本帖写于：2022年8月6号， 未经本人允许，禁止转载。