Pytorch实现对卷积的可插拔reparameterization

标签：一个   else   卷积层   tor   module   item   解决   *args   prope   

需要实现对卷积层的重参数化reparameterization

但是代码里卷积前weight并没有hook，很难在原本的卷积类上用pure oo的方式实现

目前的解决方案是继承原本的卷积，挂载一个weight module替代原本的weight parameter。需要hack一下getattr

大致代码：

class ReparamLayer(nn.module):
    def __init__(self, weight:nn.Parameter):
        self.weight = weight

    def forward(self):
        reparam = self.weight
        # do something
        # reparam = fn(reparam)
        return reparam
    
    @property
    def data(self):
        return self.forward()  # hack

class ReparamConv2d(nn.Conv2d):
    def __init__(self, *args, **kwargs):
        self._inited = False
        super().__init__
        w = self.weight
        self._inited = True
        self._weight = ReparamLayer(w)  # reparam weights here
        del self._parameters['weight']
    
    def __getattr__(self, item):
        if self._inited and item == 'weight':
            return self._weight  # hack
        else:
            return super().__getattr__(item)

Pytorch实现对卷积的可插拔reparameterization

标签：一个   else   卷积层   tor   module   item   解决   *args   prope   

原文地址：https://www.cnblogs.com/MisTariano/p/12284037.html