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local TemporalConvolution, parent = torch.class('nn.TemporalConvolution', 'nn.Module')
function TemporalConvolution:__init(inputFrameSize, outputFrameSize, kW, dW)
parent.__init(self)
dW = dW or 1
self.inputFrameSize = inputFrameSize
self.outputFrameSize = outputFrameSize
self.kW = kW
self.dW = dW
self.weight = torch.Tensor(outputFrameSize, inputFrameSize*kW)
self.bias = torch.Tensor(outputFrameSize)
self.gradWeight = torch.Tensor(outputFrameSize, inputFrameSize*kW)
self.gradBias = torch.Tensor(outputFrameSize)
self:reset()
end
function TemporalConvolution:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1/math.sqrt(self.kW*self.inputFrameSize)
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)
end
end
function TemporalConvolution:updateOutput(input)
input.THNN.TemporalConvolution_updateOutput(
input:cdata(), self.output:cdata(),
self.weight:cdata(), self.bias:cdata(),
self.kW, self.dW,
self.inputFrameSize, self.outputFrameSize
)
return self.output
end
function TemporalConvolution:updateGradInput(input, gradOutput)
if self.gradInput then
input.THNN.TemporalConvolution_updateGradInput(
input:cdata(), gradOutput:cdata(),
self.gradInput:cdata(), self.weight:cdata(),
self.kW, self.dW
)
return self.gradInput
end
end
function TemporalConvolution:accGradParameters(input, gradOutput, scale)
scale = scale or 1
input.THNN.TemporalConvolution_accGradParameters(
input:cdata(), gradOutput:cdata(),
self.gradWeight:cdata(), self.gradBias:cdata(),
self.kW, self.dW, scale
)
end
function TemporalConvolution:sharedAccUpdateGradParameters(input, gradOutput, lr)
-- we do not need to accumulate parameters when sharing:
self:defaultAccUpdateGradParameters(input, gradOutput, lr)
end
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