# Copyright 2021 AlQuraishi Laboratory # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn as nn from functools import partialmethod from typing import Union, List class Dropout(nn.Module): """ Implementation of dropout with the ability to share the dropout mask along a particular dimension. If not in training mode, this module computes the identity function. """ def __init__(self, r: float, batch_dim: Union[int, List[int]]): """ Args: r: Dropout rate batch_dim: Dimension(s) along which the dropout mask is shared """ super(Dropout, self).__init__() self.r = r if type(batch_dim) == int: batch_dim = [batch_dim] self.batch_dim = batch_dim self.dropout = nn.Dropout(self.r) def forward(self, x: torch.Tensor) -> torch.Tensor: """ Args: x: Tensor to which dropout is applied. Can have any shape compatible with self.batch_dim """ shape = list(x.shape) if self.batch_dim is not None: for bd in self.batch_dim: shape[bd] = 1 mask = x.new_ones(shape) mask = self.dropout(mask) x *= mask return x class DropoutRowwise(Dropout): """ Convenience class for rowwise dropout as described in subsection 1.11.6. """ __init__ = partialmethod(Dropout.__init__, batch_dim=-3) class DropoutColumnwise(Dropout): """ Convenience class for columnwise dropout as described in subsection 1.11.6. """ __init__ = partialmethod(Dropout.__init__, batch_dim=-2)