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local SpatialFullConvolution, parent =
torch.class('cudnn.SpatialFullConvolution', 'nn.SpatialFullConvolution')
local ffi = require 'ffi'
local find = require 'cudnn.find'
local errcheck = cudnn.errcheck
local checkedCall = find.checkedCall
local Convolution = cudnn.SpatialConvolution
function SpatialFullConvolution:__init(nInputPlane, nOutputPlane,
kW, kH, dW, dH, padW, padH, adjW, adjH, groups)
local delayedReset = self.reset
self.reset = function() end
parent.__init(self, nInputPlane, nOutputPlane,
kW, kH, dW, dH, padW, padH, adjW, adjH)
self.reset = delayedReset
self.groups = groups or 1
assert(nInputPlane % self.groups == 0,
'nInputPlane should be divisible by nGroups')
assert(nOutputPlane % self.groups == 0,
'nOutputPlane should be divisible by nGroups')
self.weight = torch.Tensor(nInputPlane, nOutputPlane/self.groups, kH, kW)
self.gradWeight = torch.Tensor(nInputPlane, nOutputPlane/self.groups, kH, kW)
self:reset()
-- should nil for serialization, the reset will still work
self.reset = nil
end
function SpatialFullConvolution:resetWeightDescriptors()
return Convolution.resetWeightDescriptors(self, {self.nInputPlane,
self.nOutputPlane/self.groups,
self.kH, self.kW})
end
function SpatialFullConvolution:fastest(mode)
return Convolution.fastest(self, mode)
end
function SpatialFullConvolution:setMode(fmode, bdmode, bwmode)
return Convolution.setMode(self, fmode, bdmode, bwmode)
end
function SpatialFullConvolution:resetMode()
return Convolution.resetMode(self)
end
function SpatialFullConvolution:noBias()
return Convolution.noBias(self)
end
function SpatialFullConvolution:createIODescriptors(input)
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());
self.iSize = self.iSize or torch.LongStorage(4):fill(0)
if Convolution.checkInputChanged(self, input) then
-- create input descriptor
local input_slice = input[{{},{1,self.nInputPlane},{},{}}]
self.iDesc = cudnn.toDescriptor(input_slice)
-- create conv descriptor
self.pad = {self.padH, self.padW}
self.stride = {self.dH, self.dW}
self.convDesc = cudnn.setConvolutionDescriptor({
padA = self.pad,
filterStrideA = self.stride,
dataType = cudnn.configmap(torch.type(self.weight)),
groupCount = self.groups
})
-- get output shape, resize output
local iwidth = input:size(4)
local iheight = input:size(3)
local owidth = (iwidth - 1) * self.dW - 2*self.padW + self.kW + self.adjW
local oheight = (iheight - 1) * self.dH - 2*self.padH + self.kH + self.adjH
local oSize = torch.IntTensor({input:size(1), self.nOutputPlane, oheight, owidth})
self.output:resize(oSize:long():storage())
-- create descriptor for output
local output_slice = self.output[{{},{1,self.nOutputPlane},{},{}}]
self.oDesc = cudnn.toDescriptor(output_slice)
self.oDescForBias = cudnn.toDescriptor(self.output)
self.input_offset = 0
self.output_offset = 0
self.weight_offset = 0
find:prepare(self, input_slice, output_slice)
if not batch then
self.output = self.output:view(self.output:size(2),
self.output:size(3),
self.output:size(4))
end
end
end
function SpatialFullConvolution:updateOutput(input)
self:backCompatibility()
if not self.weightDesc then self:resetWeightDescriptors() end
self:createIODescriptors(input)
local finder = find.get()
local bwdDataAlgo = finder:backwardDataAlgorithm(self, {self.weightDesc[0], self.weight,
self.iDesc[0],self.input_slice,
self.convDesc[0], self.oDesc[0], self.output_slice})
local extraBuffer, extraBufferSize = cudnn.getSharedWorkspace()
-- Because SpatialFullConvolution is performing the adjoint of the forward
-- convolution operator, we need to swap the forward and backward passes.
checkedCall(self,'cudnnConvolutionBackwardData', cudnn.getHandle(),
cudnn.scalar(input, 1),
self.weightDesc[0], self.weight:data(),
self.iDesc[0], input:data(),
self.convDesc[0], bwdDataAlgo,
extraBuffer, extraBufferSize,
cudnn.scalar(input, 0),
self.oDesc[0], self.output:data())
-- add bias
if self.bias then
errcheck('cudnnAddTensor', cudnn.getHandle(),
cudnn.scalar(input, 1), self.biasDesc[0], self.bias:data(),
cudnn.scalar(input, 1), self.oDescForBias[0], self.output:data())
end
return self.output
end
function SpatialFullConvolution:updateGradInput(input, gradOutput)
self:backCompatibility()
if not self.gradInput then return end
self.gradInput:resizeAs(input)
assert(gradOutput:dim() == 3 or gradOutput:dim() == 4, 'gradOutput has to be 3D or 4D');
assert(gradOutput:isContiguous(), 'gradOutput has to be contiguous')
self:createIODescriptors(input)
local finder = find.get()
local fwdAlgo = finder:forwardAlgorithm(self, {self.oDesc[0], self.output_slice,
self.weightDesc[0], self.weight,
self.convDesc[0], self.iDesc[0], self.input_slice})
local extraBuffer, extraBufferSize = cudnn.getSharedWorkspace()
checkedCall(self,'cudnnConvolutionForward', cudnn.getHandle(),
cudnn.scalar(input, 1),
self.oDesc[0], gradOutput:data(),
self.weightDesc[0], self.weight:data(),
self.convDesc[0],
fwdAlgo,
extraBuffer, extraBufferSize,
cudnn.scalar(input, 0),
self.iDesc[0], self.gradInput:data());
return self.gradInput
end
function SpatialFullConvolution:accGradParameters(input, gradOutput, scale)
self:backCompatibility()
self.scaleT = self.scaleT or self.weight.new(1)
-- this line forces this member to always be on CPU (needed for cudnn)
self.scaleT = torch.type(self.weight) == 'torch.CudaDoubleTensor'
and self.scaleT:double() or self.scaleT:float()
scale = scale or 1.0
self.scaleT[1] = scale
assert(gradOutput:dim() == 3 or gradOutput:dim() == 4,
'gradOutput has to be 3D or 4D');
assert(gradOutput:isContiguous(), 'gradOutput has to be contiguous')
self:createIODescriptors(input)
local finder = find.get()
local bwdFilterAlgo = finder:backwardFilterAlgorithm(self, {self.oDesc[0], self.output_slice,
self.iDesc[0], self.input_slice,
self.convDesc[0], self.weightDesc[0], self.weight})
-- gradBias
if self.bias then
errcheck('cudnnConvolutionBackwardBias', cudnn.getHandle(),
self.scaleT:data(),
self.oDescForBias[0], gradOutput:data(),
cudnn.scalar(input, 1),
self.biasDesc[0], self.gradBias:data())
end
local extraBuffer, extraBufferSize = cudnn.getSharedWorkspace()
-- gradWeight
checkedCall(self,'cudnnConvolutionBackwardFilter', cudnn.getHandle(),
self.scaleT:data(),
self.oDesc[0], gradOutput:data(),
self.iDesc[0], input:data(),
self.convDesc[0],
bwdFilterAlgo,
extraBuffer, extraBufferSize,
cudnn.scalar(input, 1),
self.weightDesc[0], self.gradWeight:data())
end
function SpatialFullConvolution:clearDesc()
return Convolution.clearDesc(self)
end
function SpatialFullConvolution:write(f)
self:clearDesc()
local var = {}
for k,v in pairs(self) do
var[k] = v
end
f:writeObject(var)
end
function SpatialFullConvolution:clearState()
return Convolution.clearState(self)
end
function SpatialFullConvolution:read(file, version)
parent.read(self, file)
self.adjW = self.adjW or 0
self.adjH = self.adjH or 0
end
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