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Merge pull request torch#659 from fbesse/volumetric_full_fix
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Fixed VolumetricFullConvolution.
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@soumith
soumith committed Feb 23, 2016
2 parents 85831db + 7e8c4be commit 00a6096
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Showing 5 changed files with 547 additions and 322 deletions.
59 changes: 31 additions & 28 deletions THNN.lua
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Original file line number Diff line number Diff line change
Expand Up @@ -877,36 +877,39 @@ TH_API void THNN_(VolumetricConvolutionMM_accGradParameters)(
real scale);
TH_API void THNN_(VolumetricFullConvolution_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THTensor *weight,
THTensor *bias,
THTensor *finput,
THTensor *fgradInput,
int dT, int dW, int dH,
int pT, int pW, int pH);
THNNState *state, // library state
THTensor *input, // 4D or 5D (batch) tensor
THTensor *output, // [OUT] volumetric convolution output
THTensor *weight, // weight tensor (nInputPlane x nOutputPlane x kT x kH x kW)
THTensor *bias, // gradBias tensor (nOutputPlane)
THTensor *finput, // [OUT] internal columns buffer
THTensor *fgradInput, // [OUT] internal ones buffer
int dT, int dW, int dH, // stride of the convolution
int pT, int pW, int pH, // padding
int aT, int aW, int aH); // extra output adjustment
TH_API void THNN_(VolumetricFullConvolution_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THTensor *weight,
THTensor *finput,
THTensor *fgradInput,
int dT, int dW, int dH,
int pT, int pW, int pH);
THNNState *state, // library state
THTensor *input, // 4D or 5D (batch) tensor
THTensor *gradOutput, // gradient w.r.t. output
THTensor *gradInput, // [OUT] gradient w.r.t. input
THTensor *weight, // weight tensor (nInputPlane x nOutputPlane x kT x kH x kW)
THTensor *finput, // internal columns buffer
THTensor *fgradInput, // internal ones buffer
int dT, int dW, int dH, // stride
int pT, int pW, int pH, // padding
int aT, int aW, int aH); // extra output adjustment
TH_API void THNN_(VolumetricFullConvolution_accGradParameters)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradWeight,
THTensor *gradBias,
THTensor *finput,
THTensor *fgradInput,
int dT, int dW, int dH,
int pT, int pW, int pH,
real scale);
THNNState *state, // library state
THTensor *input, // 4D or 5D (batch) tensor
THTensor *gradOutput, // gradient w.r.t. output
THTensor *gradWeight, // gradWeight tensor (nInputPlane x nOutputPlane x kT x kH x kW)
THTensor *gradBias, // gradBias tensor (nOutputPlane)
THTensor *finput, // internal columns buffer
THTensor *fgradInput, // internal ones buffer
int dT, int dW, int dH, // stride
int pT, int pW, int pH, // padding
int aT, int aW, int aH, // extra output adjustment
real scale); // scaling factor
TH_API void THNN_(VolumetricMaxPooling_updateOutput)(
THNNState *state,
Expand Down
138 changes: 104 additions & 34 deletions VolumetricFullConvolution.lua
View file
Original file line number Diff line number Diff line change
@@ -1,58 +1,97 @@
local VolumetricFullConvolution, parent = torch.class('nn.VolumetricFullConvolution', 'nn.Module')
local VolumetricFullConvolution, parent = torch.class('nn.VolumetricFullConvolution','nn.Module')

function VolumetricFullConvolution:__init(nInputPlane, nOutputPlane, kT, kH, kW, dT, dH, dW, pT, pH, pW)
function VolumetricFullConvolution:__init(nInputPlane, nOutputPlane,
kT, kW, kH, -- kernel size
dT, dW, dH, -- stride
padT, padW, padH, -- padding
adjT, adjW, adjH) -- extra output adjustment
parent.__init(self)

dT = dT or 1
dW = dW or 1
dH = dH or 1

pT = pT or 0
pW = pW or 0
pH = pH or 0
dT = dT or 1

self.nInputPlane = nInputPlane
self.nOutputPlane = nOutputPlane
self.kT = kT
self.kW = kW
self.kH = kH
self.dT = dT
self.kT = kT
self.dW = dW
self.dH = dH
self.pT = pT
self.pW = pW
self.pH = pH

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.dT = dT
self.padW = padW or 0
self.padH = padH or 0
self.padT = padT or 0
self.adjW = adjW or 0
self.adjH = adjH or 0
self.adjT = adjT or 0

if self.adjW > self.dW - 1 or self.adjH > self.dH - 1 or self.adjT > self.dT - 1 then
error('adjW, adjH and adjT must be smaller than self.dW - 1,' ..
' self.dH - 1 and self.dT - 1 respectively')
end

self.weight = torch.Tensor(nInputPlane, nOutputPlane, kT, kH, kW)
self.gradWeight = torch.Tensor(nInputPlane, nOutputPlane, kT, kH, kW)
self.bias = torch.Tensor(self.nOutputPlane)
self.gradBias = torch.Tensor(self.nOutputPlane)

self.finput = torch.Tensor()
self.fgradInput = torch.Tensor()

self:reset()
end

function VolumetricFullConvolution:reset(stdv)
-- initialization of parameters
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1/math.sqrt(self.kT*self.kW*self.kH*self.nInputPlane)
local nInputPlane = self.nInputPlane
local kT = self.kT
local kH = self.kH
local kW = self.kW
stdv = 1/math.sqrt(kW*kH*kT*nInputPlane)
end
if nn.oldSeed then
self.weight:apply(function()
return torch.uniform(-stdv, stdv)
end)
self.bias:apply(function()
return torch.uniform(-stdv, stdv)
end)
else
self.weight:uniform(-stdv, stdv)
self.bias:uniform(-stdv, stdv)
self.weight:uniform(-stdv, stdv)
self.bias:uniform(-stdv, stdv)
end

local function makeContiguous(self, input, gradOutput)
if not input:isContiguous() then
self._input = self._input or input.new()
self._input:resizeAs(input):copy(input)
input = self._input
end
if gradOutput then
if not gradOutput:isContiguous() then
self._gradOutput = self._gradOutput or gradOutput.new()
self._gradOutput:resizeAs(gradOutput):copy(gradOutput)
gradOutput = self._gradOutput
end
end
return input, gradOutput
end

function VolumetricFullConvolution:backCompatibility()
-- Transpose the weight when loading from an old version
if not self.adjW then
self.weight = self.weight:transpose(1, 2):contiguous()
end

-- Rename the padding when loading from an old version
self.padW = self.padW or self.pW
self.padH = self.padH or self.pH
self.padT = self.padT or self.pT

self.adjW = self.adjW or 0
self.adjH = self.adjH or 0
self.adjT = self.adjT or 0
end

function VolumetricFullConvolution:updateOutput(input)
self.finput = self.finput or input.new()
self.fgradInput = self.fgradInput or input.new()
self:backCompatibility()

input = makeContiguous(self, input)
input.THNN.VolumetricFullConvolution_updateOutput(
input:cdata(),
self.output:cdata(),
Expand All @@ -61,12 +100,17 @@ function VolumetricFullConvolution:updateOutput(input)
self.finput:cdata(),
self.fgradInput:cdata(),
self.dT, self.dW, self.dH,
self.pT, self.pW, self.pH
self.padT, self.padW, self.padH,
self.adjT, self.adjW, self.adjH
)

return self.output
end

function VolumetricFullConvolution:updateGradInput(input, gradOutput)
self:backCompatibility()

input, gradOutput = makeContiguous(self, input, gradOutput)
input.THNN.VolumetricFullConvolution_updateGradInput(
input:cdata(),
gradOutput:cdata(),
Expand All @@ -75,12 +119,16 @@ function VolumetricFullConvolution:updateGradInput(input, gradOutput)
self.finput:cdata(),
self.fgradInput:cdata(),
self.dT, self.dW, self.dH,
self.pT, self.pW, self.pH
self.padT, self.padW, self.padH,
self.adjT, self.adjW, self.adjH
)
return self.gradInput
end

function VolumetricFullConvolution:accGradParameters(input, gradOutput, scale)
self:backCompatibility()

input, gradOutput = makeContiguous(self, input, gradOutput)
input.THNN.VolumetricFullConvolution_accGradParameters(
input:cdata(),
gradOutput:cdata(),
Expand All @@ -89,7 +137,29 @@ function VolumetricFullConvolution:accGradParameters(input, gradOutput, scale)
self.finput:cdata(),
self.fgradInput:cdata(),
self.dT, self.dW, self.dH,
self.pT, self.pW, self.pH,
self.padT, self.padW, self.padH,
self.adjT, self.adjW, self.adjH,
scale or 1
)
end

function VolumetricFullConvolution:type(type, tensorCache)
self.finput = torch.Tensor()
self.fgradInput = torch.Tensor()
return parent.type(self, type, tensorCache)
end

function VolumetricFullConvolution:__tostring__()
local s = string.format('%s(%d -> %d, %dx%dx%d', torch.type(self),
self.nInputPlane, self.nOutputPlane, self.kW, self.kH, self.kT)
if self.dW ~= 1 or self.dH ~= 1 or self.dT ~= 1 or self.padW ~= 0 or self.padH ~= 0 or self.padT ~= 0 then
s = s .. string.format(', %d,%d,%d', self.dW, self.dH, self.dT)
end
if (self.padW or self.padH or self.padT) and (self.padW ~= 0 or self.padH ~= 0 or self.padT ~= 0) then
s = s .. ', ' .. self.padW .. ',' .. self.padH .. ',' .. self.padT
end
if (self.adjW or self.adjH or self.adjT) and (self.adjW ~= 0 or self.adjH ~= 0 or self.adjT ~= 0) then
s = s .. ', ' .. self.adjW .. ',' .. self.adjH .. ',' .. self.adjT
end
return s .. ')'
end
59 changes: 31 additions & 28 deletions lib/THNN/generic/THNN.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -829,36 +829,39 @@ TH_API void THNN_(VolumetricConvolutionMM_accGradParameters)(
real scale);

TH_API void THNN_(VolumetricFullConvolution_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output,
THTensor *weight,
THTensor *bias,
THTensor *finput,
THTensor *fgradInput,
int dT, int dW, int dH,
int pT, int pW, int pH);
THNNState *state, // library state
THTensor *input, // 4D or 5D (batch) tensor
THTensor *output, // [OUT] volumetric convolution output
THTensor *weight, // weight tensor (nInputPlane x nOutputPlane x kT x kH x kW)
THTensor *bias, // gradBias tensor (nOutputPlane)
THTensor *finput, // [OUT] internal columns buffer
THTensor *fgradInput, // [OUT] internal ones buffer
int dT, int dW, int dH, // stride of the convolution
int pT, int pW, int pH, // padding
int aT, int aW, int aH); // extra output adjustment
TH_API void THNN_(VolumetricFullConvolution_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THTensor *weight,
THTensor *finput,
THTensor *fgradInput,
int dT, int dW, int dH,
int pT, int pW, int pH);
THNNState *state, // library state
THTensor *input, // 4D or 5D (batch) tensor
THTensor *gradOutput, // gradient w.r.t. output
THTensor *gradInput, // [OUT] gradient w.r.t. input
THTensor *weight, // weight tensor (nInputPlane x nOutputPlane x kT x kH x kW)
THTensor *finput, // internal columns buffer
THTensor *fgradInput, // internal ones buffer
int dT, int dW, int dH, // stride
int pT, int pW, int pH, // padding
int aT, int aW, int aH); // extra output adjustment
TH_API void THNN_(VolumetricFullConvolution_accGradParameters)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradWeight,
THTensor *gradBias,
THTensor *finput,
THTensor *fgradInput,
int dT, int dW, int dH,
int pT, int pW, int pH,
real scale);
THNNState *state, // library state
THTensor *input, // 4D or 5D (batch) tensor
THTensor *gradOutput, // gradient w.r.t. output
THTensor *gradWeight, // gradWeight tensor (nInputPlane x nOutputPlane x kT x kH x kW)
THTensor *gradBias, // gradBias tensor (nOutputPlane)
THTensor *finput, // internal columns buffer
THTensor *fgradInput, // internal ones buffer
int dT, int dW, int dH, // stride
int pT, int pW, int pH, // padding
int aT, int aW, int aH, // extra output adjustment
real scale); // scaling factor

TH_API void THNN_(VolumetricMaxPooling_updateOutput)(
THNNState *state,
Expand Down
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