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| --[[ | |
| Input: A length-2 table of length-K tables | |
| Input[1]: K tables | |
| Input[2]: K tables | |
| Output: A length-K table of length-2 tables | |
| Output[i]: {Input[1][i], input[2][i]} | |
| --]] | |
| local CrossMergeTable, parent = torch.class('nn.CrossMergeTable', 'nn.Module') | |
| function CrossMergeTable:__init() | |
| parent.__init(self) | |
| self.gradInput = {} | |
| end | |
| function CrossMergeTable:updateOutput(input) | |
| assert(#input == 2) | |
| assert(#input[1] == #input[2]) | |
| self.output = {} | |
| for i = 1, #input[1] do | |
| self.output[i] = {input[1][i], input[2][i]} | |
| end | |
| return self.output | |
| end | |
| function CrossMergeTable:updateGradInput(input, gradOutput) | |
| for i = 1, 2 do | |
| if not self.gradInput[i] then | |
| self.gradInput[i] = {} | |
| end | |
| end | |
| for i = 1, #gradOutput do | |
| self.gradInput[1][i] = gradOutput[i][1] | |
| self.gradInput[2][i] = gradOutput[i][2] | |
| end | |
| return self.gradInput | |
| end | |
| function CrossMergeTable:__tostring__() | |
| return string.format('%s()', torch.type(self)) | |
| end | |