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local SpatialMatching, parent = torch.class('nn.SpatialMatching', 'nn.Module')
function SpatialMatching:__init(maxh, maxw, full_output)
-- 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.maxw = maxw or 11
self.maxh = maxh or 11
if full_output == nil then
full_output = false
end
self.full_output = full_output
self.gradInput1 = torch.Tensor()
self.gradInput2 = torch.Tensor()
end
function SpatialMatching: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, self.maxw)
input[1].nn.SpatialMatching_updateOutput(self, input[1], input[2])
return self.output
end
function SpatialMatching:updateGradInput(input, gradOutput)
self.gradInput1:resize(input[1]:size()):zero()
self.gradInput2:resize(input[2]:size()):zero()
input[1].nn.SpatialMatching_updateGradInput(self, input[1], input[2], gradOutput)
self.gradInput = {self.gradInput1, self.gradInput2}
return self.gradInput
end
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