基于BCEWithLogitsLoss样本不均衡的处理方案-巨人网络通讯

基于BCEWithLogitsLoss样本不均衡的处理方案

最近在做deepfake检测任务（可以将其视为二分类问题，label为1和0），遇到了正负样本不均衡的问题，正样本数目是负样本的5倍，这样会导致FP率较高。

尝试将正样本的loss权重增高，看BCEWithLogitsLoss的源码

Examples::
 
    >>> target = torch.ones([10, 64], dtype=torch.float32)  # 64 classes, batch size = 10
    >>> output = torch.full([10, 64], 0.999)  # A prediction (logit)
    >>> pos_weight = torch.ones([64])  # All weights are equal to 1
    >>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)
    >>> criterion(output, target)  # -log(sigmoid(0.999))
    tensor(0.3135)
 
Args:
    weight (Tensor, optional): a manual rescaling weight given to the loss
        of each batch element. If given, has to be a Tensor of size `nbatch`.
    size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
        the losses are averaged over each loss element in the batch. Note that for
        some losses, there are multiple elements per sample. If the field :attr:`size_average`
        is set to ``False``, the losses are instead summed for each minibatch. Ignored
        when reduce is ``False``. Default: ``True``
    reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
        losses are averaged or summed over observations for each minibatch depending
        on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
        batch element instead and ignores :attr:`size_average`. Default: ``True``
    reduction (string, optional): Specifies the reduction to apply to the output:
        ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
        ``'mean'``: the sum of the output will be divided by the number of
        elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
        and :attr:`reduce` are in the process of being deprecated, and in the meantime,
        specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
    pos_weight (Tensor, optional): a weight of positive examples.
            Must be a vector with length equal to the number of classes.

对其中的参数pos_weight的使用存在疑惑，BCEloss里的例子pos_weight = torch.ones([64]) # All weights are equal to 1，不懂为什么会有64个class，因为BCEloss是针对二分类问题的loss，后经过检索，得知还有多标签分类，

多标签分类就是多个标签，每个标签有两个label（0和1），这类任务同样可以使用BCEloss。

现在讲一下BCEWithLogitsLoss里的pos_weight使用方法

比如我们有正负两类样本，正样本数量为100个，负样本为400个，我们想要对正负样本的loss进行加权处理，将正样本的loss权重放大4倍，通过这样的方式缓解样本不均衡问题。

criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([4]))
 
# pos_weight (Tensor, optional): a weight of positive examples.
#            Must be a vector with length equal to the number of classes.

pos_weight里是一个tensor列表，需要和标签个数相同，比如我们现在是二分类，只需要将正样本loss的权重写上即可。

如果是多标签分类，有64个标签，则

Examples::
 
    >>> target = torch.ones([10, 64], dtype=torch.float32)  # 64 classes, batch size = 10
    >>> output = torch.full([10, 64], 0.999)  # A prediction (logit)
    >>> pos_weight = torch.ones([64])  # All weights are equal to 1
    >>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)
    >>> criterion(output, target)  # -log(sigmoid(0.999))
    tensor(0.3135)

补充：Pytorch —— BCEWithLogitsLoss()的一些问题

一、等价表达

1、pytorch：

torch.sigmoid() + torch.nn.BCELoss()

2、自己编写

def ce_loss(y_pred, y_train, alpha=1):
    
    p = torch.sigmoid(y_pred)
    # p = torch.clamp(p, min=1e-9, max=0.99)  
    loss = torch.sum(- alpha * torch.log(p) * y_train \

           - torch.log(1 - p) * (1 - y_train))/len(y_train)
    return loss~

3、验证

import torch
import torch.nn as nn
torch.cuda.manual_seed(300)       # 为当前GPU设置随机种子
torch.manual_seed(300)            # 为CPU设置随机种子
def ce_loss(y_pred, y_train, alpha=1):
   # 计算loss
   p = torch.sigmoid(y_pred)
   # p = torch.clamp(p, min=1e-9, max=0.99)
   loss = torch.sum(- alpha * torch.log(p) * y_train \

          - torch.log(1 - p) * (1 - y_train))/len(y_train)
   return loss
py_lossFun = nn.BCEWithLogitsLoss()
input = torch.randn((10000,1), requires_grad=True)
target = torch.ones((10000,1))
target.requires_grad_(True)
py_loss = py_lossFun(input, target)
py_loss.backward()
print("*********BCEWithLogitsLoss***********")
print("loss: ")
print(py_loss.item())
print("梯度: ")
print(input.grad)
input = input.detach()
input.requires_grad_(True)
self_loss = ce_loss(input, target)
self_loss.backward()
print("*********SelfCELoss***********")
print("loss: ")
print(self_loss.item())
print("梯度: ")
print(input.grad)

测试结果: