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local TemporalSubSampling, parent = torch.class('nn.TemporalSubSampling', 'nn.Module')
function TemporalSubSampling:__init(inputFrameSize, kW, dW)
parent.__init(self)
dW = dW or 1
self.inputFrameSize = inputFrameSize
self.kW = kW
self.dW = dW
self.weight = torch.Tensor(inputFrameSize)
self.bias = torch.Tensor(inputFrameSize)
self.gradWeight = torch.Tensor(inputFrameSize)
self.gradBias = torch.Tensor(inputFrameSize)
self:reset()
end
function TemporalSubSampling:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1/math.sqrt(self.kW)
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 TemporalSubSampling:updateOutput(input)
return input.nn.TemporalSubSampling_updateOutput(self, input)
end
function TemporalSubSampling:updateGradInput(input, gradOutput)
if self.gradInput then
return input.nn.TemporalSubSampling_updateGradInput(self, input, gradOutput)
end
end
function TemporalSubSampling:accGradParameters(input, gradOutput, scale)
return input.nn.TemporalSubSampling_accGradParameters(self, input, gradOutput, scale)
end
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