From 4725c6b639f8dfc5d0440557c65e5dbc6fec1873 Mon Sep 17 00:00:00 2001
From: Jonathan Tompson <tompson@cims.nyu.edu>
Date: Thu, 26 Jun 2014 11:32:57 -0400
Subject: Added SpatialUpSamplingNearest module.

---
 SpatialUpSamplingNearest.lua | 58 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)
 create mode 100644 SpatialUpSamplingNearest.lua

(limited to 'SpatialUpSamplingNearest.lua')

diff --git a/SpatialUpSamplingNearest.lua b/SpatialUpSamplingNearest.lua
new file mode 100644
index 0000000..8288250
--- /dev/null
+++ b/SpatialUpSamplingNearest.lua
@@ -0,0 +1,58 @@
+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
+
+function SpatialUpSamplingNearest:updateGradInput(input, gradOutput)
+   self.gradInput:resizeAs(input)
+   input.nn.SpatialUpSamplingNearest_updateGradInput(self, input, gradOutput)
+   return self.gradInput
+end
-- 
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