| import torch |
| from .Module import Module |
| from .utils import clear |
| |
| |
| class VolumetricDropout(Module): |
| |
| def __init__(self, p=0.5): |
| super(VolumetricDropout, self).__init__() |
| self.p = p |
| self.train = True |
| self.noise = torch.Tensor() |
| |
| def updateOutput(self, input): |
| self.output.resize_as_(input).copy_(input) |
| if self.train: |
| assert input.dim() == 5 |
| self.noise.resize_(input.size(0), input.size(1), 1, 1, 1) |
| |
| self.noise.bernoulli_(1 - self.p) |
| # We expand the random dropouts to the entire feature map because the |
| # features are likely correlated accross the map and so the dropout |
| # should also be correlated. |
| self.output.mul_(self.noise.expand_as(input)) |
| else: |
| self.output.mul_(1 - self.p) |
| |
| return self.output |
| |
| def updateGradInput(self, input, gradOutput): |
| if self.train: |
| self.gradInput.resize_as_(gradOutput).copy_(gradOutput) |
| self.gradInput.mul_(self.noise.expand_as(input)) # simply mask the gradients with the noise vector |
| else: |
| raise RuntimeError('backprop only defined while training') |
| |
| return self.gradInput |
| |
| def setp(self, p): |
| self.p = p |
| |
| def __repr__(self): |
| return super(VolumetricDropout, self).__repr__() + '({:.4f})'.format(self.p) |
| |
| def clearState(self): |
| clear(self, 'noise') |
| return super(VolumetricDropout, self).clearState() |
