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local SpatialRadialMatching, parent = torch.class('nn.SpatialRadialMatching', 'nn.Module')
function SpatialRadialMatching:__init(maxh)
-- If full_output is false, output is computed on elements of the first input
-- for which all the possible corresponding elements exist in the second input
-- In addition, if full_output is set to false, the pixel (1,1) of the first input
-- is supposed to correspond to the pixel (maxh/2, maxw/2) of the second one
parent.__init(self)
self.maxh = maxh
self.gradInput1 = torch.Tensor()
self.gradInput2 = torch.Tensor()
end
function SpatialRadialMatching:updateOutput(input)
-- input is a table of 2 inputs, each one being KxHxW
-- if not full_output, the 1st one is KxH1xW1 where H1 <= H-maxh+1, W1 <= W-maxw+1
self.output:resize(input[1]:size(2), input[1]:size(3), self.maxh)
--if input[3] == nil then
-- input[3] = torch.LongTensor(input[1]:size(2), input[1]:size(3)):fill(1)
--end
--input[1].nn.SpatialRadialMatching_updateOutput(self, input[1], input[2], input[3])
input[1].nn.SpatialRadialMatching_updateOutput(self, input[1], input[2])
return self.output
end
function SpatialRadialMatching:updateGradInput(input, gradOutput)
self.gradInput1:resize(input[1]:size()):zero()
self.gradInput2:resize(input[2]:size()):zero()
--input[1].nn.SpatialRadialMatching_updateGradInput(self,input[1],input[2],gradOutput,input[3])
input[1].nn.SpatialRadialMatching_updateGradInput(self,input[1],input[2],gradOutput)
self.gradInput = {self.gradInput1, self.gradInput2}
return self.gradInput
end
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