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local Pointwise, parent = torch.class('cudnn._Pointwise','nn.Module')
local errcheck = cudnn.errcheck
function Pointwise:__init()
parent.__init(self)
self.iSize = torch.LongStorage(4):fill(0)
end
function Pointwise:createIODescriptors(input)
assert(self.mode, 'mode is not set. (trying to use base class?)');
local batch = true
if input:dim() == 3 then
input = input:view(1, input:size(1), input:size(2), input:size(3))
batch = false
end
assert(input:dim() == 4 and input:isContiguous());
if not self.iDesc or not self.oDesc or
input:size(1) ~= self.iSize[1] or input:size(2) ~= self.iSize[2]
or input:size(3) ~= self.iSize[3] or input:size(4) ~= self.iSize[4] then
self.iSize = input:size()
self.gradInput:resizeAs(input)
self.output:resizeAs(input)
self.iDesc = cudnn.toDescriptor(input)
self.oDesc = cudnn.toDescriptor(self.output)
if not batch then
self.gradInput = self.gradInput:view(self.gradInput:size(2),
self.gradInput:size(3),
self.gradInput:size(4))
self.output = self.output:view(self.output:size(2),
self.output:size(3),
self.output:size(4))
end
end
end
local one = torch.FloatTensor({1});
local zero = torch.FloatTensor({0});
function Pointwise:updateOutput(input)
self:createIODescriptors(input)
errcheck('cudnnActivationForward',
cudnn.handle[cutorch.getDevice()-1], self.mode,
one:data(),
self.iDesc[0], input:data(),
zero:data(),
self.oDesc[0], self.output:data());
return self.output
end
function Pointwise:updateGradInput(input, gradOutput)
assert((gradOutput:dim() == 4 or gradOutput:dim() == 3));
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)
errcheck('cudnnActivationBackward',
cudnn.handle[cutorch.getDevice()-1], self.mode,
one:data(),
self.oDesc[0], self.output:data(),
self.oDesc[0], gradOutput:data(),
self.iDesc[0], input:data(),
zero:data(),
self.iDesc[0], self.gradInput:data());
return self.gradInput
end
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