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# Copyright (c) 2017,
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# Original implementation:
# https://github.com/salesforce/awd-lstm-lm/blob/master/locked_dropout.py
import torch.nn as nn
[docs]class LockedDropout(nn.Module):
""" LockedDropout applies the same dropout mask to every time step.
**Thank you** to Sales Force for their initial implementation of :class:`WeightDrop`. Here is
their `License
<https://github.com/salesforce/awd-lstm-lm/blob/master/LICENSE>`__.
Args:
p (float): Probability of an element in the dropout mask to be zeroed.
"""
def __init__(self, p=0.5):
self.p = p
super().__init__()
[docs] def forward(self, x):
"""
Args:
x (:class:`torch.FloatTensor` [sequence length, batch size, rnn hidden size]): Input to
apply dropout too.
"""
if not self.training or not self.p:
return x
x = x.clone()
mask = x.new_empty(1, x.size(1), x.size(2), requires_grad=False).bernoulli_(1 - self.p)
mask = mask.div_(1 - self.p)
mask = mask.expand_as(x)
return x * mask
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'p=' + str(self.p) + ')'