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local VolumetricDropout, Parent = torch.class('nn.VolumetricDropout', 'nn.Module')
function VolumetricDropout:__init(p,stochasticInference)
Parent.__init(self)
self.p = p or 0.5
self.train = true
self.stochastic_inference = stochasticInference or false
self.noise = torch.Tensor()
end
function VolumetricDropout:updateOutput(input)
self.output:resizeAs(input):copy(input)
if self.train or self.stochastic_inference then
if input:dim() == 5 then
self.noise:resize(input:size(1), input:size(2), 1, 1, 1)
elseif input:dim() == 4 then
self.noise:resize(input:size(1), 1, 1, 1)
else
error('Input must be 5D (nbatch, nfeat, t, h, w) or 4D (nfeat, t, h, w)')
end
self.noise:bernoulli(1-self.p)
-- We expand the random dropouts to the entire feature map because the
-- features are likely correlated across the map and so the dropout
-- should also be correlated.
self.output:cmul(torch.expandAs(self.noise, input))
else
self.output:mul(1-self.p)
end
return self.output
end
function VolumetricDropout:updateGradInput(input, gradOutput)
if self.train then
self.gradInput:resizeAs(gradOutput):copy(gradOutput)
self.gradInput:cmul(torch.expandAs(self.noise, input)) -- simply mask the gradients with the noise vector
else
error('backprop only defined while training')
end
return self.gradInput
end
function VolumetricDropout:setp(p)
self.p = p
end
function VolumetricDropout:__tostring__()
return string.format('%s(%f)', torch.type(self), self.p)
end
function VolumetricDropout:clearState()
if self.noise then
self.noise:set()
end
return Parent.clearState(self)
end
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