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| --[[ | |
| Input: A length-2 Table | |
| Input[1]: Responses, batchSize x nChannels x iH x iW | |
| Input[2]: Pairs (minW, minH), batchSize x nFeatures x 2 | |
| Output: Cropped responses, batchSize x (nFeatures x nChannels) x oH x oW | |
| --]] | |
| local Crop, parent = torch.class('nn.Crop', 'nn.Module') | |
| function Crop:__init(iW, iH, oW, oH) | |
| parent.__init(self) | |
| self.iW = iW | |
| self.iH = iH | |
| self.oW = oW | |
| self.oH = oH | |
| self.gradInput = {} | |
| --self.vis = false | |
| end | |
| function Crop:getCoordinates() | |
| return self.coords | |
| end | |
| function Crop:updateOutput(input) | |
| assert(#input == 2) | |
| assert(input[1]:size(1) == input[2]:size(1)) | |
| assert(input[1]:size(3) == self.iH) | |
| assert(input[1]:size(4) == self.iW) | |
| local batchSize = input[1]:size(1) | |
| local nChannels = input[1]:size(2) | |
| local nFeatures = input[2]:size(2) | |
| --[[if self.vis then | |
| for k = 1, math.min(5, nChannels) do | |
| image.save('/home/jiajunwu/public_html/vis_results_11feat/CAS/crop_input_'..k..'.png', input[1][{1, k, {}, {}}]:double()) | |
| end | |
| end | |
| --]] | |
| self.output:resize(batchSize, nChannels * nFeatures, self.oH, self.oW) | |
| for p = 1, batchSize do | |
| for q = 1, nFeatures do | |
| local idxSt = (q - 1) * nChannels + 1 | |
| local idxEd = q * nChannels | |
| self.output[{p, {idxSt, idxEd}, {}, {}}]:copy(input[1][{p, {}, | |
| {input[2][{p, q, 1}], input[2][{p, q, 1}] + self.oH - 1}, | |
| {input[2][{p, q, 2}], input[2][{p, q, 2}] + self.oW - 1}}]) | |
| end | |
| end | |
| --[[ | |
| if self.vis then | |
| for r = 1, nFeatures do | |
| for k = 1, math.min(5, nChannels) do | |
| image.save('/home/jiajunwu/public_html/vis_results_11feat/CAS/crop_output_'..r..'chn'..k..'.png', self.output[{1, (r - 1) * nChannels + k, {}, {}}]:double()) | |
| end | |
| end | |
| end | |
| self.vis = false | |
| --]] | |
| return self.output | |
| end | |
| function Crop:updateGradInput(input, gradOutput) | |
| for i = 1, #input do | |
| if self.gradInput[i] == nil then | |
| self.gradInput[i] = input[i].new() | |
| end | |
| self.gradInput[i]:resizeAs(input[i]):zero() | |
| end | |
| local batchSize = input[1]:size(1) | |
| local nChannels = input[1]:size(2) | |
| local nFeatures = input[2]:size(2) | |
| for p = 1, batchSize do | |
| for q = 1, nFeatures do | |
| local idxSt = (q - 1) * nChannels + 1 | |
| local idxEd = q * nChannels | |
| self.gradInput[1][{p, {}, {input[2][{p, q, 1}], input[2][{p, q, 1}] + self.oH - 1}, | |
| {input[2][{p, q, 2}], input[2][{p, q, 2}] + self.oW - 1}}]:add(gradOutput[{p, {idxSt, idxEd}, {}, {}}]) | |
| end | |
| end | |
| return self.gradInput | |
| end | |
| function Crop:__tostring__() | |
| return string.format('%s()', torch.type(self)) | |
| end | |