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local Pointwise, parent = torch.class('cudnn._Pointwise','nn.Module')
local errcheck = cudnn.errcheck
function Pointwise:__init(inplace)
parent.__init(self)
self.inplace = inplace or false
end
function Pointwise:createIODescriptors(input)
assert(self.mode, 'mode is not set. (trying to use base class?)');
assert(input:isContiguous(), 'Non-contiguous inputs not supported yet');
local nElem = input:nElement()
self.nElem = self.nElem or nElem -- this goes to the second branch only once
if self.iDesc and nElem == self.nElem then return end
self.nElem = nElem
self.iDesc = cudnn.toDescriptor(input:view(1,1,1,nElem))
if not self.inplace then
self.gradInput:resizeAs(input)
self.output:resizeAs(input)
end
end
local one = torch.FloatTensor({1});
local zero = torch.FloatTensor({0});
function Pointwise:updateOutput(input)
self:createIODescriptors(input)
if self.inplace then self.output = input end
errcheck('cudnnActivationForward',
cudnn.getHandle(), self.mode,
one:data(),
self.iDesc[0], input:data(),
zero:data(),
self.iDesc[0], self.output:data());
return self.output
end
function Pointwise:updateGradInput(input, gradOutput)
if not gradOutput:isContiguous() then
self._gradOutput = self._gradOutput
or gradOutput.new():resizeAs(gradOutput)
self._gradOutput:copy(gradOutput)
gradOutput = self._gradOutput
end
self:createIODescriptors(input)
if self.inplace then self.output = input; self.gradInput = gradOutput end
errcheck('cudnnActivationBackward',
cudnn.getHandle(), self.mode,
one:data(),
self.iDesc[0], self.output:data(),
self.iDesc[0], gradOutput:data(),
self.iDesc[0], input:data(),
zero:data(),
self.iDesc[0], self.gradInput:data());
return self.gradInput
end
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