Add SpatialDownSampling module, a simple down-sampling averaging the pixels

1 files changed, 39 insertions, 0 deletions

diff --git a/SpatialDownSampling.lua b/SpatialDownSampling.lua
new file mode 100644
index 0000000..b18849f
--- /dev/null
+++ b/SpatialDownSampling.lua
@@ -0,0 +1,39 @@
+local SpatialDownSampling, parent = torch.class('nn.SpatialDownSampling', 'nn.Module')
+
+local help_desc = [[
+Applies a 2D down-sampling over an input image composed of
+several input planes. The input tensor in forward(input) is
+expected to be a 3D tensor (nInputPlane x width x height).
+The number of output planes will be the same as nInputPlane.
+
+The downsampling is done using the simple average
+technique. For interpolated (bicubic) downsampling, use 
+nn.SpatialReSampling().
+
+If the input image is a 3D tensor nInputPlane x width x height,
+the output image size will be nInputPlane x owidth x oheight where
+
+owidth  = floor(width/rW)
+oheight  = floor(height/rH) ]]
+
+function SpatialDownSampling:__init(...)
+   parent.__init(self)
+
+   -- get args
+   xlua.unpack_class(self, {...}, 'nn.SpatialDownSampling',  help_desc,
+                     {arg='rW', type='number', help='ratio width', req=true},
+                     {arg='rH', type='number', help='ratio height', req=true})
+end
+
+function SpatialDownSampling:updateOutput(input)
+   self.output:resize(input:size(1), math.floor(input:size(2) / self.rH),
+		      math.floor(input:size(3) / self.rW))
+   input.nn.SpatialDownSampling_updateOutput(self, input)
+   return self.output
+end
+
+function SpatialDownSampling:updateGradInput(input, gradOutput)
+   self.gradInput:resizeAs(input)
+   input.nn.SpatialDownSampling_updateGradInput(self, gradOutput)
+   return self.gradInput
+end