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local SpatialConvolution, parent =
torch.class('cudnn.SpatialConvolution', 'nn.SpatialConvolution')
local ffi = require 'ffi'
local errcheck = cudnn.errcheck
function SpatialConvolution:__init(nInputPlane, nOutputPlane,
kW, kH, dW, dH, padW, padH, groups)
parent.__init(self, nInputPlane, nOutputPlane, kW, kH, dW, dH)
self.padW = padW or 0
self.padH = padH or 0
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(nOutputPlane, nInputPlane/self.groups, kW, kH)
self.gradWeight = torch.Tensor(nOutputPlane, nInputPlane/self.groups, kW, kH)
self:reset()
self.iSize = torch.LongStorage(4):fill(0)
end
-- if you change the configuration of the module manually, call this
function SpatialConvolution:resetWeightDescriptors()
assert(torch.typename(self.weight) == 'torch.CudaTensor',
'Only Cuda supported duh!')
assert(torch.typename(self.bias) == 'torch.CudaTensor',
'Only Cuda supported duh!')
-- for compatibility
self.groups = self.groups or 1
-- create filterDescriptor for weight
self.weightDesc = ffi.new('struct cudnnFilterStruct*[1]')
errcheck('cudnnCreateFilterDescriptor', self.weightDesc)
local desc = torch.IntTensor({self.nOutputPlane/self.groups,
self.nInputPlane/self.groups,
self.kH, self.kW})
errcheck('cudnnSetFilterNdDescriptor', self.weightDesc[0],
'CUDNN_DATA_FLOAT', 4,
desc:data());
local function destroyWDesc(d)
errcheck('cudnnDestroyFilterDescriptor', d[0]);
end
ffi.gc(self.weightDesc, destroyWDesc)
-- create descriptor for bias
local bias_slice = {{}, {1,self.nOutputPlane/self.groups}, {}, {}}
self.biasDesc = cudnn.toDescriptor(self.bias:view(1, self.nOutputPlane,1,1)[bias_slice])
end
function SpatialConvolution:fastest(mode)
if mode == nil then mode = true end
self.fastest_mode = mode
return self
end
function SpatialConvolution: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());
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()
-- resize gradInput
if self.gradInput then self.gradInput:resizeAs(input); end
assert(self.nInputPlane == input:size(2), 'input has to contain: '
.. self.nInputPlane
.. ' feature maps, but received input of size: '
.. input:size(1) .. ' x ' .. input:size(2) ..
' x ' .. input:size(3) .. ' x ' .. input:size(4))
-- create input descriptor
local input_slice = {{},{1,self.nInputPlane/self.groups},{},{}}
self.iDesc = cudnn.toDescriptor(input[input_slice])
-- create conv descriptor
self.convDesc = ffi.new('struct cudnnConvolutionStruct*[1]')
errcheck('cudnnCreateConvolutionDescriptor', self.convDesc)
local pad = torch.IntTensor({self.padH, self.padW})
local stride = torch.IntTensor({self.dH, self.dW})
local upscale = torch.IntTensor({1,1})
errcheck('cudnnSetConvolutionNdDescriptor', self.convDesc[0],
2, pad:data(),
stride:data(), upscale:data(), 'CUDNN_CROSS_CORRELATION');
local function destroyConvDesc(d)
errcheck('cudnnDestroyConvolutionDescriptor', d[0]);
end
ffi.gc(self.convDesc, destroyConvDesc)
-- create output descriptor and resize output
local oSize = torch.IntTensor(4)
local oSizeD = oSize:data()
errcheck('cudnnGetConvolutionNdForwardOutputDim',
self.convDesc[0], self.iDesc[0],
self.weightDesc[0], 4, oSizeD)
oSize[2] = oSize[2] * self.groups
self.output:resize(oSize:long():storage())
-- create descriptor for output
local output_slice = {{},{1,self.nOutputPlane/self.groups},{},{}}
self.oDesc = cudnn.toDescriptor(self.output[output_slice])
-- create forwardAlgorithm descriptors for
local algType = ffi.new("cudnnConvolutionFwdAlgo_t[?]", 1)
local algSearchMode = 'CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT'
local algWorkspaceLimit = self.nInputPlane * self.kH * self.kW * 4 -- 4 = sizeof int.
if self.fastest_mode then algSearchMode = 'CUDNN_CONVOLUTION_FWD_PREFER_FASTEST' end
errcheck('cudnnGetConvolutionForwardAlgorithm',
cudnn.getHandle(),
self.iDesc[0], self.weightDesc[0],
self.convDesc[0], self.oDesc[0],
algSearchMode, algWorkspaceLimit, algType)
self.algType = algType
local bufSize = torch.LongTensor(1)
errcheck('cudnnGetConvolutionForwardWorkspaceSize',
cudnn.getHandle(),
self.iDesc[0], self.weightDesc[0],
self.convDesc[0], self.oDesc[0],
algType[0], bufSize:data())
self.extraBuffer = self.extraBuffer or input.new(1)
if bufSize[1] ~= 0 then self.extraBuffer:resize(bufSize[1]) end
-- create offsets for groups
self.input_offset = self.nInputPlane/self.groups*input:size(3)*input:size(4)
self.output_offset = self.nOutputPlane/self.groups*oSize[3]*oSize[4]
self.weight_offset =
self.nInputPlane/self.groups*self.nOutputPlane/self.groups*self.kW*self.kH
self.bias_offset = self.nOutputPlane/self.groups
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 SpatialConvolution:updateOutput(input)
if not self.weightDesc then self:resetWeightDescriptors() end
self:createIODescriptors(input)
for g=0,self.groups-1 do
errcheck('cudnnConvolutionForward', cudnn.getHandle(),
one:data(),
self.iDesc[0], input:data() + g*self.input_offset,
self.weightDesc[0], self.weight:data() + g*self.weight_offset,
self.convDesc[0], self.algType[0],
self.extraBuffer:data(), self.extraBuffer:nElement(),
zero:data(),
self.oDesc[0], self.output:data() + g*self.output_offset);
errcheck('cudnnAddTensor', cudnn.getHandle(),
'CUDNN_ADD_SAME_C',
one:data(), self.biasDesc[0], self.bias:data() + g*self.bias_offset,
one:data(), self.oDesc[0], self.output:data() + g*self.output_offset);
end
return self.output
end
function SpatialConvolution:updateGradInput(input, gradOutput)
if not self.gradInput then return end
assert((gradOutput:dim() == 3 or gradOutput:dim() == 4)
and gradOutput:isContiguous());
if not self.weightDesc then self:resetWeightDescriptors() end
self:createIODescriptors(input)
for g=0,self.groups-1 do
errcheck('cudnnConvolutionBackwardData', cudnn.getHandle(),
one:data(),
self.weightDesc[0], self.weight:data() + g*self.weight_offset,
self.oDesc[0], gradOutput:data() + g*self.output_offset,
self.convDesc[0],
zero:data(),
self.iDesc[0], self.gradInput:data() + g*self.input_offset);
end
return self.gradInput
end
function SpatialConvolution:accGradParameters(input, gradOutput, scale)
self.scaleT = self.scaleT or torch.FloatTensor(1):fill(1.0)
-- this line forces this member to always be on CPU (needed for cudnn)
self.scaleT = self.scaleT:float()
scale = scale or 1.0
self.scaleT[1] = scale
assert((gradOutput:dim() == 3 or gradOutput:dim() == 4)
and gradOutput:isContiguous());
self:createIODescriptors(input)
if not self.weightDesc then self:resetWeightDescriptors() end
for g=0,self.groups-1 do
-- gradBias
errcheck('cudnnConvolutionBackwardBias', cudnn.getHandle(),
self.scaleT:data(),
self.oDesc[0], gradOutput:data() + g*self.output_offset,
one:data(),
self.biasDesc[0], self.gradBias:data() + g*self.bias_offset);
-- gradWeight
errcheck('cudnnConvolutionBackwardFilter', cudnn.getHandle(),
self.scaleT:data(),
self.iDesc[0], input:data() + g*self.input_offset,
self.oDesc[0], gradOutput:data() + g*self.output_offset,
self.convDesc[0],
one:data(),
self.weightDesc[0], self.gradWeight:data() + g*self.weight_offset);
end
end
--[[
function SpatialConvolution:zeroGradParameters()
-- gradWeight, gradBias to zero
errcheck('cudnnSetTensor', cudnn.getHandle(),
self.weightDesc, self.gradWeight:data(), zero:data());
errcheck('cudnnSetTensor', cudnn.getHandle(),
self.biasDesc, self.gradBias:data(), zero:data());
end
]]--
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