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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,22 +1,22 @@ | ||
| local Power, parent = torch.class('nn.Power','nn.Module') | ||
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| function Power:__init(p) | ||
| parent.__init(self) | ||
| self.pow = p | ||
| if not p then | ||
| error('nn.Power(power)') | ||
| end | ||
| end | ||
|
|
||
| function Power:updateOutput(input) | ||
| self.output:resizeAs(input):copy(input) | ||
| self.output:pow(self.pow) | ||
| return self.output | ||
| end | ||
|
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||
| function Power:updateGradInput(input, gradOutput) | ||
| self.gradInput:resizeAs(input):copy(input) | ||
| self.gradInput:pow(self.pow - 1) | ||
| self.gradInput:cmul(gradOutput):mul(self.pow) | ||
| return self.gradInput | ||
| end | ||
| local Power, parent = torch.class('nn.Power','nn.Module') | ||
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|
||
| function Power:__init(p) | ||
| parent.__init(self) | ||
| self.pow = p | ||
| if not p then | ||
| error('nn.Power(power)') | ||
| end | ||
| end | ||
|
|
||
| function Power:updateOutput(input) | ||
| self.output:resizeAs(input):copy(input) | ||
| self.output:pow(self.pow) | ||
| return self.output | ||
| end | ||
|
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||
| function Power:updateGradInput(input, gradOutput) | ||
| self.gradInput:resizeAs(input):copy(input) | ||
| self.gradInput:pow(self.pow - 1) | ||
| self.gradInput:cmul(gradOutput):mul(self.pow) | ||
| return self.gradInput | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,58 +1,58 @@ | ||
| local SpatialUpSamplingNearest, parent = torch.class('nn.SpatialUpSamplingNearest', 'nn.Module') | ||
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| --[[ | ||
| Applies a 2D up-sampling over an input image composed of several input planes. | ||
| The upsampling is done using the simple nearest neighbor technique. | ||
| The Y and X dimensions are assumed to be the last 2 tensor dimensions. For | ||
| instance, if the tensor is 4D, then dim 3 is the y dimension and dim 4 is the x. | ||
| owidth = width*scale_factor | ||
| oheight = height*scale_factor | ||
| --]] | ||
|
|
||
| function SpatialUpSamplingNearest:__init(scale) | ||
| parent.__init(self) | ||
|
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| self.scale_factor = scale | ||
| if self.scale_factor < 1 then | ||
| error('scale_factor must be greater than 1') | ||
| end | ||
| if math.floor(self.scale_factor) ~= self.scale_factor then | ||
| error('scale_factor must be integer') | ||
| end | ||
| self.inputSize = torch.LongStorage(4) | ||
| self.outputSize = torch.LongStorage(4) | ||
| self.usage = nil | ||
| end | ||
|
|
||
| function SpatialUpSamplingNearest:updateOutput(input) | ||
| if input:dim() ~= 4 and input:dim() ~= 3 then | ||
| error('SpatialUpSamplingNearest only support 3D or 4D tensors') | ||
| end | ||
| -- Copy the input size | ||
| local xdim = input:dim() | ||
| local ydim = input:dim() - 1 | ||
| for i = 1, input:dim() do | ||
| self.inputSize[i] = input:size(i) | ||
| self.outputSize[i] = input:size(i) | ||
| end | ||
| self.outputSize[ydim] = self.outputSize[ydim] * self.scale_factor | ||
| self.outputSize[xdim] = self.outputSize[xdim] * self.scale_factor | ||
| -- Resize the output if needed | ||
| if input:dim() == 3 then | ||
| self.output:resize(self.outputSize[1], self.outputSize[2], | ||
| self.outputSize[3]) | ||
| else | ||
| self.output:resize(self.outputSize) | ||
| end | ||
| input.nn.SpatialUpSamplingNearest_updateOutput(self, input) | ||
| return self.output | ||
| end | ||
|
|
||
| function SpatialUpSamplingNearest:updateGradInput(input, gradOutput) | ||
| self.gradInput:resizeAs(input) | ||
| input.nn.SpatialUpSamplingNearest_updateGradInput(self, input, gradOutput) | ||
| return self.gradInput | ||
| end | ||
| local SpatialUpSamplingNearest, parent = torch.class('nn.SpatialUpSamplingNearest', 'nn.Module') | ||
|
|
||
| --[[ | ||
| Applies a 2D up-sampling over an input image composed of several input planes. | ||
| The upsampling is done using the simple nearest neighbor technique. | ||
| The Y and X dimensions are assumed to be the last 2 tensor dimensions. For | ||
| instance, if the tensor is 4D, then dim 3 is the y dimension and dim 4 is the x. | ||
| owidth = width*scale_factor | ||
| oheight = height*scale_factor | ||
| --]] | ||
|
|
||
| function SpatialUpSamplingNearest:__init(scale) | ||
| parent.__init(self) | ||
|
|
||
| self.scale_factor = scale | ||
| if self.scale_factor < 1 then | ||
| error('scale_factor must be greater than 1') | ||
| end | ||
| if math.floor(self.scale_factor) ~= self.scale_factor then | ||
| error('scale_factor must be integer') | ||
| end | ||
| self.inputSize = torch.LongStorage(4) | ||
| self.outputSize = torch.LongStorage(4) | ||
| self.usage = nil | ||
| end | ||
|
|
||
| function SpatialUpSamplingNearest:updateOutput(input) | ||
| if input:dim() ~= 4 and input:dim() ~= 3 then | ||
| error('SpatialUpSamplingNearest only support 3D or 4D tensors') | ||
| end | ||
| -- Copy the input size | ||
| local xdim = input:dim() | ||
| local ydim = input:dim() - 1 | ||
| for i = 1, input:dim() do | ||
| self.inputSize[i] = input:size(i) | ||
| self.outputSize[i] = input:size(i) | ||
| end | ||
| self.outputSize[ydim] = self.outputSize[ydim] * self.scale_factor | ||
| self.outputSize[xdim] = self.outputSize[xdim] * self.scale_factor | ||
| -- Resize the output if needed | ||
| if input:dim() == 3 then | ||
| self.output:resize(self.outputSize[1], self.outputSize[2], | ||
| self.outputSize[3]) | ||
| else | ||
| self.output:resize(self.outputSize) | ||
| end | ||
| input.nn.SpatialUpSamplingNearest_updateOutput(self, input) | ||
| return self.output | ||
| end | ||
|
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||
| function SpatialUpSamplingNearest:updateGradInput(input, gradOutput) | ||
| self.gradInput:resizeAs(input) | ||
| input.nn.SpatialUpSamplingNearest_updateGradInput(self, input, gradOutput) | ||
| return self.gradInput | ||
| end |
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