Add ROIWarping layer described in the winning solution of ILSVRC & MSCOCO 2015 competition (http://arxiv.org/abs/1512.04412).

.gitignore

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+*.sw*
+*.bak*
+build
+build/*

ROIWarping.lua

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+local ROIWarping,parent = torch.class('inn.ROIWarping', 'nn.Module')
+local C = inn.C
+
+--function ROIWarping:__init(W,H,spatial_scale)
+function ROIWarping:__init(H,W)
+  parent.__init(self)
+  assert(W and H, 'W and H have to be provided')
+  self.W = W
+  self.H = H
+  --self.spatial_scale = spatial_scale or 1
+
+  self.grid_gen = inn.ROIWarpingGridGenerator(self.H, self.W)
+  self.sample  = inn.ROIWarpingBilinearSample(self.H, self.W)
+
+  self.gradInput = {}
+end
+
+--function ROIWarping:setSpatialScale(scale)
+--  self.spatial_scale = scale
+--  return self
+--end
+
+function ROIWarping:updateOutput(input)
+  assert(#input == 2 or #input == 3)
+  local data = input[1]
+  local rois = input[2]
+  local delta_rois 
+  if #input == 3 then
+    delta_rois = input[3]
+  else -- #input == 2 
+    self.delta_rois = self.delta_rois or rois.new()
+    self.delta_rois:resizeAs(rois):zero()
+    self.delta_rois[{{}, 1}] = rois[{{}, 1}] 
+    delta_rois = self.delta_rois
+  end
+
+  if torch.type(data) == 'torch.CudaTensor' then 
+    self.grid_gen:cuda()
+    self.sample:cuda()
+  end
+
+  self.grid_gen:updateOutput({rois, delta_rois})
+  self.sample:updateOutput({data, self.grid_gen.output_tmp[1], self.grid_gen.output_tmp[2], self.grid_gen.output_tmp[3]})
+
+  self.output = self.sample.output
+
+  return self.output
+end
+
+function ROIWarping:updateGradInput(input,gradOutput)
+  local data = input[1]
+  local rois = input[2]
+  local delta_rois
+  if #input == 3 then
+    delta_rois = input[3]
+  else -- #input == 2
+    self.delta_rois = self.delta_rois or data.new()
+    self.delta_rois:resizeAs(rois):zero()
+    self.delta_rois[{{}, 1}] = rois[{{}, 1}]
+    delta_rois = self.delta_rois
+  end
+
+  if torch.type(data) == 'torch.CudaTensor' then
+    self.grid_gen:cuda()
+    self.sample:cuda()
+  end
+
+  self.sample:updateGradInput({data, self.grid_gen.output_tmp[1], self.grid_gen.output_tmp[2], self.grid_gen.output_tmp[3]}, gradOutput)
+  self.grid_gen:updateGradInput({rois, delta_rois}, {self.sample.gradInput[2], self.sample.gradInput[3]})
+
+  self.gradInput = {self.sample.gradInput[1], self.grid_gen.gradInput[1], self.grid_gen.gradInput[2]}
+
+  return self.gradInput
+end