Added missng file.

1 files changed, 68 insertions, 0 deletions

diff --git a/SpatialSparseCriterion.lua b/SpatialSparseCriterion.lua
new file mode 100644
index 0000000..abb2301
--- /dev/null
+++ b/SpatialSparseCriterion.lua
@@ -0,0 +1,68 @@
+local SpatialSparseCriterion, parent = torch.class('nn.SpatialSparseCriterion', 'nn.SparseCriterion')
+
+function SpatialSparseCriterion:__init(...)
+   parent.__init(self)
+
+   xlua.unpack_class(self, {...},
+      'nn.SpatialSparseCriterion',
+      'A spatial extension of the SparseCriterion class.\n'
+        ..' Provides a set of parameters to deal with spatial mini-batch training.',
+      {arg='nbGradients', type='number', help='number of gradients to backpropagate (-1:all, >=1:nb)', default=-1},
+      {arg='sizeAverage', type='number', help='if true, forward() returns an average instead of a sum of errors', default=true}
+   )
+end
+
+function SpatialSparseCriterion:forward(input)
+   self.fullOutput = self.fullOutput or torch.Tensor()
+   self.fullOutput:resize(input:size(2), input:size(3))
+   input.nn.SpatialSparseCriterion_forward(self, input)
+   if self.sizeAverage then
+      self.output = self.fullOutput:mean()
+   else
+      self.output = self.fullOutput:sum()
+   end
+   return self.output
+end
+
+function SpatialSparseCriterion:backward(input,target)
+   -- (1) retrieve adjusted target
+   target = self.target
+   -- (2) resize input gradient map
+   self.gradInput:resizeAs(input):zero()
+   -- (3) compute input gradients, based on the nbGradients param
+   if self.nbGradients == -1 then
+      -- dense gradients
+      input.nn.SpatialSparseCriterion_backward(self, input, self.gradInput)
+   elseif self.nbGradients == 1 then
+      -- only 1 gradient is computed, sampled in the center
+      self.fullGradInput = torch.Tensor() or self.fullGradInput
+      self.fullGradInput:resizeAs(input):zero()
+      input.nn.SpatialSparseCriterion_backward(self, input, self.fullGradInput)
+      local y = math.ceil(self.gradInput:size(2)/2)
+      local x = math.ceil(self.gradInput:size(3)/2)
+      self.gradInput:select(3,x):select(2,y):copy(self.fullGradInput:select(3,x):select(2,y))
+   else
+      -- only N gradients are computed, sampled in random locations
+      self.fullGradInput = torch.Tensor() or self.fullGradInput
+      self.fullGradInput:resizeAs(input):zero()
+      input.nn.SpatialSparseCriterion_backward(self, input, self.fullGradInput)
+      for i = 1,self.nbGradients do
+         local x = math.random(1,self.gradInput:size(1))
+         local y = math.random(1,self.gradInput:size(2))
+         self.gradInput:select(3,x):select(2,y):copy(self.fullGradInput:select(3,x):select(2,y))
+      end
+   end
+   return self.gradInput
+end
+
+function SpatialSparseCriterion:write(file)
+   parent.write(self, file)
+   file:writeDouble(self.resampleTarget)
+   file:writeInt(self.nbGradients)
+end
+
+function SpatialSparseCriterion:read(file)
+   parent.read(self, file)
+   self.resampleTarget= file:readDouble()
+   self.nbGradients = file:readInt()
+end