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local FeatureLPPooling, parent =
torch.class('nn.FeatureLPPooling', 'nn.Module')
--[[
Possible inputs that we handle:
#### `batch_mode = false`
The dimensionality of the input chooses between the following modes:
```
[feature dim]
[feature dim][opt dim 1]
[feature dim][opt dim 1][opt dim 2]
```
#### `batch_mode = true`
The dimensionality of the input chooses between the following modes:
```
[batch dim][feature dim]
[batch dim][feature dim][opt dim 1]
[batch dim][feature dim][opt dim 1][opt dim 2]
```
The output has the same number of dimensions as the input, except the feature
dimension size is reduced to ((`input` - `width`) / `stride`) + 1
]]
function FeatureLPPooling:__init(width, stride, power, batch_mode)
parent.__init(self)
if (width < 2 or width > 16) then
error('width must be within 2 to 16')
end
if (stride < 1 or stride > 4) then
error('stride must be within 1 to 4')
end
self.width = width
self.stride = stride
self.power = power
self.batch_mode = batch_mode
self.output = torch.Tensor()
self.gradInput = torch.Tensor()
end
function FeatureLPPooling:updateOutput(input)
input.THNN.FeatureLPPooling_updateOutput(input:cdata(),
self.output:cdata(),
self.power,
self.width,
self.stride,
self.batch_mode)
return self.output
end
function FeatureLPPooling:updateGradInput(input, gradOutput)
input.THNN.FeatureLPPooling_updateGradInput(gradOutput:cdata(),
input:cdata(),
self.output:cdata(),
self.gradInput:cdata(),
self.power,
self.width,
self.stride,
self.batch_mode)
return self.gradInput
end
function FeatureLPPooling:__tostring__()
return string.format('%s(w%d s%d power %d batch %d',
torch.type(self),
self.width, self.stride, self.power, self.batch_mode)
end
|
