今天自定义不可导函数的时候遇到了一个大坑。
首先我需要自定义一个函数：sign_f

import torch
from torch.autograd import Function
import torch.nn as nn


class sign_f(Function):
    @staticmethod
    def forward(ctx, inputs):
        output = inputs.new(inputs.size())
        output[inputs >= 0.] = 1
        output[inputs < 0.] = -1
        ctx.save_for_backward(inputs)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input_, = ctx.saved_tensors
        grad_output[input_>1.] = 0
        grad_output[input_<-1.] = 0
        return grad_output

然后我需要把它封装为一个module 类型，就像 nn.Conv2d 模块 封装 f.conv2d 一样，于是

import torch
from torch.autograd import Function
import torch.nn as nn
class sign_(nn.Module):
	# 我需要的module
    def __init__(self, *kargs, **kwargs):
        super(sign_, self).__init__(*kargs, **kwargs)
        
    def forward(self, inputs):
    	# 使用自定义函数
        outs = sign_f(inputs)
        return outs


class sign_f(Function):
    @staticmethod
    def forward(ctx, inputs):
        output = inputs.new(inputs.size())
        output[inputs >= 0.] = 1
        output[inputs < 0.] = -1
        ctx.save_for_backward(inputs)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input_, = ctx.saved_tensors
        grad_output[input_>1.] = 0
        grad_output[input_<-1.] = 0
        return grad_output

结果报错

TypeError: backward() missing 2 required positional arguments: 'ctx' and 'grad_output'

---

我试了半天，发现自定义函数后面要加 apply ，详细见下面

import torch
from torch.autograd import Function
import torch.nn as nn
class sign_(nn.Module):

    def __init__(self, *kargs, **kwargs):
        super(sign_, self).__init__(*kargs, **kwargs)
        self.r = sign_f.apply ### <-----注意此处
        
    def forward(self, inputs):
        outs = self.r(inputs)
        return outs


class sign_f(Function):
    @staticmethod
    def forward(ctx, inputs):
        output = inputs.new(inputs.size())
        output[inputs >= 0.] = 1
        output[inputs < 0.] = -1
        ctx.save_for_backward(inputs)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input_, = ctx.saved_tensors
        grad_output[input_>1.] = 0
        grad_output[input_<-1.] = 0
        return grad_output

问题解决了