Added SpatialMaxSampling module, for flexible competitive resampling.

1 files changed, 26 insertions, 0 deletions

diff --git a/SpatialMaxSampling.lua b/SpatialMaxSampling.lua
new file mode 100644
index 0000000..670d44b
--- /dev/null
+++ b/SpatialMaxSampling.lua
@@ -0,0 +1,26 @@
+local SpatialMaxSampling, parent = torch.class('nn.SpatialMaxSampling', 'nn.Module')
+
+function SpatialMaxSampling:__init(...)
+   parent.__init(self)
+   xlua.unpack_class(
+      self, {...}, 'nn.SpatialMaxSampling',
+      'resample an image using max selection',
+      {arg='rwidth', type='number', help='ratio: owidth/iwidth'},
+      {arg='rheight', type='number', help='ratio: oheight/iheight'},
+      {arg='owidth', type='number', help='output width'},
+      {arg='oheight', type='number', help='output height'}
+   )
+   self.indices = torch.Tensor()
+end
+
+function SpatialMaxSampling:forward(input)
+   self.oheight = self.oheight or self.rheight*input:size(2)
+   self.owidth = self.owidth or self.rwidth*input:size(3)
+   input.nn.SpatialMaxSampling_forward(self, input)
+   return self.output
+end
+
+function SpatialMaxSampling:backward(input, gradOutput)
+   input.nn.SpatialMaxSampling_backward(self, input, gradOutput)
+   return self.gradInput
+end