part/dropout.py

import torch


class LockedDropout(torch.nn.Module):
    """
    Implementation of locked (or variational) dropout. 
    Randomly drops out entire parameters in embedding space.

    :param dropout_rate: represent the fraction of the input unit to be dropped. It will be from 0 to 1.
    :param batch_first: represent if the drop will perform in an ascending manner
    :param inplace: 
    """

    def __init__(self, dropout_rate=0.5, batch_first=True, inplace=False):
        super(LockedDropout, self).__init__()
        self.dropout_rate = dropout_rate
        self.batch_first = batch_first
        self.inplace = inplace

    def forward(self, x):
        if not self.training or not self.dropout_rate:
            return x

        if not self.batch_first:
            m = x.data.new(1, x.size(1), x.size(2)).bernoulli_(1 - self.dropout_rate)
        else:
            m = x.data.new(x.size(0), 1, x.size(2)).bernoulli_(1 - self.dropout_rate)

        mask = torch.autograd.Variable(m, requires_grad=False) / (1 - self.dropout_rate)
        mask = mask.expand_as(x)
        return mask * x

    def extra_repr(self):
        inplace_str = ", inplace" if self.inplace else ""
        return "p={}{}".format(self.dropout_rate, inplace_str)


class WordDropout(torch.nn.Module):
    """
    Implementation of word dropout. Randomly drops out entire words 
    (or characters) in embedding space.
    """

    def __init__(self, dropout_rate=0.05, inplace=False):
        super(WordDropout, self).__init__()
        self.dropout_rate = dropout_rate
        self.inplace = inplace

    def forward(self, x):
        if not self.training or not self.dropout_rate:
            return x

        m = x.data.new(x.size(0), x.size(1), 1).bernoulli_(1 - self.dropout_rate)

        mask = torch.autograd.Variable(m, requires_grad=False)
        return mask * x

    def extra_repr(self):
        inplace_str = ", inplace" if self.inplace else ""
        return "p={}{}".format(self.dropout_rate, inplace_str)