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78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
"""
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BSD 3-Clause License
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Copyright (c) 2017,
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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"""
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import torch
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from torch.nn import Parameter
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from functools import wraps
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class WeightDrop(torch.nn.Module):
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def __init__(self, module, weights, dropout=0, variational=False):
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super().__init__()
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self.module = module
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self.weights = weights
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self.dropout = dropout
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self.variational = variational
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self._setup()
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def null_function(*args, **kwargs):
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# We need to replace flatten_parameters with a nothing function
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return
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def _setup(self):
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# Terrible temporary solution to an issue regarding compacting weights re: CUDNN RNN
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if issubclass(type(self.module), torch.nn.RNNBase):
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self.module.flatten_parameters = self.null_function
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for name_w in self.weights:
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print('Applying weight drop of {} to {}'.format(self.dropout, name_w))
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w = getattr(self.module, name_w)
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del self.module._parameters[name_w]
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self.module.register_parameter(name_w + '_raw', Parameter(w.data))
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def _setweights(self):
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for name_w in self.weights:
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raw_w = getattr(self.module, name_w + '_raw')
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w = None
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if self.variational:
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mask = torch.autograd.Variable(torch.ones(raw_w.size(0), 1))
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if raw_w.is_cuda: mask = mask.cuda()
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mask = torch.nn.functional.dropout(mask, p=self.dropout, training=True)
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w = mask.expand_as(raw_w) * raw_w
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else:
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w = torch.nn.functional.dropout(raw_w, p=self.dropout, training=self.training)
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setattr(self.module, name_w, w)
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def forward(self, *args):
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self._setweights()
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return self.module.forward(*args)
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