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local THNN = require 'nn.THNN'
local VolumetricConvolution, parent = torch.class('nn.VolumetricConvolution', 'nn.Module')
function VolumetricConvolution:__init(nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH, padT, padW, padH)
parent.__init(self)
dT = dT or 1
dW = dW or 1
dH = dH or 1
self.nInputPlane = nInputPlane
self.nOutputPlane = nOutputPlane
self.kT = kT
self.kW = kW
self.kH = kH
self.dT = dT
self.dW = dW
self.dH = dH
self.padT = padT or 0
self.padW = padW or self.padT
self.padH = padH or self.padW
self.weight = torch.Tensor(nOutputPlane, nInputPlane, kT, kH, kW)
self.bias = torch.Tensor(nOutputPlane)
self.gradWeight = torch.Tensor(nOutputPlane, nInputPlane, kT, kH, kW)
self.gradBias = torch.Tensor(nOutputPlane)
self:reset()
end
function VolumetricConvolution:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1/math.sqrt(self.kT*self.kW*self.kH*self.nInputPlane)
end
if nn.oldSeed then
self.weight:apply(function()
return torch.uniform(-stdv, stdv)
end)
if self.bias then
self.bias:apply(function()
return torch.uniform(-stdv, stdv)
end)
end
else
self.weight:uniform(-stdv, stdv)
if self.bias then
self.bias:uniform(-stdv, stdv)
end
end
end
function VolumetricConvolution:noBias()
self.bias = nil
self.gradBias = nil
return self
end
function VolumetricConvolution:updateOutput(input)
self.finput = self.finput or input.new()
self.fgradInput = self.fgradInput or input.new()
if torch.typename(input):find('torch%.Cuda.*Tensor') then
input.THNN.VolumetricConvolution_updateOutput(
input:cdata(),
self.output:cdata(),
self.weight:cdata(),
THNN.optionalTensor(self.bias),
self.finput:cdata(),
self.fgradInput:cdata(),
self.dT, self.dW, self.dH,
self.padT, self.padW, self.padH
)
else
input.THNN.VolumetricConvolutionMM_updateOutput(
input:cdata(),
self.output:cdata(),
self.weight:cdata(),
THNN.optionalTensor(self.bias),
self.finput:cdata(),
self.kT, self.kW, self.kH,
self.dT, self.dW, self.dH,
self.padT, self.padW, self.padH
)
end
return self.output
end
function VolumetricConvolution:updateGradInput(input, gradOutput)
if torch.typename(input):find('torch%.Cuda.*Tensor') then
input.THNN.VolumetricConvolution_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.weight:cdata(),
self.finput:cdata(),
self.dT, self.dW, self.dH,
self.padT, self.padW, self.padH
)
return self.gradInput
else
if self.gradInput then
input.THNN.VolumetricConvolutionMM_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.weight:cdata(),
self.finput:cdata(),
self.fgradInput:cdata(),
self.kT, self.kW, self.kH,
self.dT, self.dW, self.dH,
self.padT, self.padW, self.padH
)
return self.gradInput
end
end
end
function VolumetricConvolution:accGradParameters(input, gradOutput, scale)
if torch.typename(input):find('torch%.Cuda.*Tensor') then
input.THNN.VolumetricConvolution_accGradParameters(
input:cdata(),
gradOutput:cdata(),
self.gradWeight:cdata(),
THNN.optionalTensor(self.gradBias),
self.finput:cdata(),
self.fgradInput:cdata(),
self.dT, self.dW, self.dH,
self.padT, self.padW, self.padH,
scale or 1
)
else
input.THNN.VolumetricConvolutionMM_accGradParameters(
input:cdata(),
gradOutput:cdata(),
self.gradWeight:cdata(),
THNN.optionalTensor(self.gradBias),
self.finput:cdata(),
self.kT, self.kW, self.kH,
self.dT, self.dW, self.dH,
self.padT, self.padW, self.padH,
scale or 1
)
end
end
function VolumetricConvolution:type(type, tensorCache)
if self.finput then self.finput:set() end
if self.fgradInput then self.fgradInput:set() end
return parent.type(self, type, tensorCache)
end
function VolumetricConvolution:clearState()
nn.utils.clear(self, 'finput', 'fgradInput', '_input', '_gradOutput')
return parent.clearState(self)
end
function VolumetricConvolution:__tostring__()
local s = string.format('%s(%d -> %d, %dx%dx%d', torch.type(self),
self.nInputPlane, self.nOutputPlane, self.kT, self.kW, self.kH)
if self.dT ~= 1 or self.dW ~= 1 or self.dH ~= 1 or
self.padT ~= 0 or self.padW ~= 0 or self.padH ~= 0 then
s = s .. string.format(', %d,%d,%d', self.dT, self.dW, self.dH)
end
if (self.padT or self.padW or self.padH) and
(self.padT ~=0 or self.padW ~= 0 or self.padH ~= 0) then
s = s .. ', ' .. self.padT .. ',' .. self.padW .. ',' .. self.padH
end
return s .. ')'
end
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