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local SpatialSubSampling, parent = torch.class('nn.SpatialSubSampling', 'nn.Module')
function SpatialSubSampling:__init(nInputPlane, kW, kH, dW, dH)
parent.__init(self)
dW = dW or 1
dH = dH or 1
self.nInputPlane = nInputPlane
self.kW = kW
self.kH = kH
self.dW = dW
self.dH = dH
self.weight = torch.Tensor(nInputPlane)
self.bias = torch.Tensor(nInputPlane)
self.gradWeight = torch.Tensor(nInputPlane)
self.gradBias = torch.Tensor(nInputPlane)
self:reset()
end
function SpatialSubSampling:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1/math.sqrt(self.kW*self.kH)
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 SpatialSubSampling:updateOutput(input)
input.THNN.SpatialSubSampling_updateOutput(
input:cdata(),
self.output:cdata(),
self.weight:cdata(),
self.bias:cdata(),
self.kW, self.kH,
self.dW, self.dH
)
return self.output
end
function SpatialSubSampling:updateGradInput(input, gradOutput)
if self.gradInput then
input.THNN.SpatialSubSampling_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.weight:cdata(),
self.kW, self.kH,
self.dW, self.dH
)
return self.gradInput
end
end
function SpatialSubSampling:accGradParameters(input, gradOutput, scale)
scale = scale or 1
input.THNN.SpatialSubSampling_accGradParameters(
input:cdata(),
gradOutput:cdata(),
self.gradWeight:cdata(),
self.gradBias:cdata(),
self.kW, self.kH,
self.dW, self.dH,
scale
)
end
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