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import torch
import torch.nn as nn
# from torch.autograd import Function
# from torch.autograd import Variable
import torch.nn.init as init
class L2Norm(nn.Module):
def __init__(self,n_channels, scale):
super(L2Norm,self).__init__()
self.n_channels = n_channels
self.gamma = scale or None
self.eps = 1e-10
self.weight = nn.Parameter(torch.Tensor(self.n_channels))
self.reset_parameters()
def reset_parameters(self):
init.constant_(self.weight,self.gamma)
def forward(self, x):
norm = x.pow(2).sum(dim=1, keepdim=True).sqrt()+self.eps
#x /= norm
x = torch.div(x,norm)
out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x
return out
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