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import torch
import torch.nn as nn
from torch.autograd import Variable
class LockedDropout(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, dropout=0.5):
if not self.training or not dropout:
return x
m = x.data.new(1, x.size(1), x.size(2)).bernoulli_(1 - dropout)
mask = Variable(m, requires_grad=False) / (1 - dropout)
mask = mask.expand_as(x)
return mask * x