Merge pull request #1259 from wickedfoo/feature_lp_pooling

CPU implementation of L_p feature pooling

Author: soumith

FeatureLPPooling.lua

@@ -0,0 +1,74 @@
+
+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

init.lua

@@ -160,6 +160,8 @@ require('nn.VolumetricAveragePooling')
 require('nn.VolumetricBatchNormalization')
 require('nn.VolumetricReplicationPadding')
 
+require('nn.FeatureLPPooling')
+
 require('nn.GPU')
 
 require('nn.ParallelTable')