我们可以将Linear与BatchNorm参数融合吗

如何解决我们可以将Linear与BatchNorm参数融合吗

我想从模型中删除Batchnorm。因此，我考虑将其与Linear融合。我的模型结构如下：

• Linear -> ReLU -> Batchnorm -> Dropout -> Linear

我尝试融合Batchnorm -> Linear，但无法与可用代码融合。有什么方法可以将Batchnorm与以上任何一层融合。

解决方法

class DummyModule_1(nn.Module):
    def __init__(self):
        super(DummyModule_1,self).__init__()

    def forward(self,x):
        # print("Dummy,Dummy.")
        return x


def fuse_1(linear,bn):
    w = linear.weight
    print(w.size())
    mean = bn.running_mean
    var_sqrt = torch.sqrt(bn.running_var + bn.eps)

    beta = bn.weight
    gamma = bn.bias

    if linear.bias is not None:
        b = linear.bias
    else:
        b = mean.new_zeros(mean.shape)

    w = w.cuda()
    b = b.cuda()
    w = w * (beta / var_sqrt).reshape([4096,1])
    b = (b - mean)/var_sqrt * beta + gamma
    fused_linear = nn.Linear(linear.in_features,linear.out_features)
                                             
    fused_linear.weight = nn.Parameter(w)
    fused_linear.bias = nn.Parameter(b)
    return fused_linear


def fuse_module_1(m):
    children = list(m.named_children())
    c = None
    cn = None
    global c1
    global count
    global c18

    for name,child in children:
        print("name is",name,"child is",child)
       
         

        if name == 'linear':
          count = count+1 
          
          if count == 2:
            c18 = child
            print("c18 is",c18)

          else:
            fuse_module_1(child)

        if name =='2' and isinstance(child,nn.BatchNorm1d):
          print("child is",child)
          bc = fuse_1(c18,child)
          m.classifier[1].linear = bc
          m.classifier[2] = DummyModule_1(


        else:
            #fuse_module_1(child)
          fuse_module_1(child)```