1 files changed, 51 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/SpatialReplicationPadding.lua b/contrib/lua-torch/nn/SpatialReplicationPadding.lua
new file mode 100644
index 000000000..429763f9b
--- /dev/null
+++ b/contrib/lua-torch/nn/SpatialReplicationPadding.lua
@@ -0,0 +1,51 @@
+local SpatialReplicationPadding, parent =
+   torch.class('nn.SpatialReplicationPadding', 'nn.Module')
+
+function SpatialReplicationPadding:__init(pad_l, pad_r, pad_t, pad_b)
+   parent.__init(self)
+   self.pad_l = pad_l
+   self.pad_r = pad_r or self.pad_l
+   self.pad_t = pad_t or self.pad_l
+   self.pad_b = pad_b or self.pad_l
+end
+
+function SpatialReplicationPadding:updateOutput(input)
+   if input:dim() == 3 or input:dim() == 4 then
+      input.THNN.SpatialReplicationPadding_updateOutput(
+         input:cdata(), self.output:cdata(),
+         self.pad_l, self.pad_r, self.pad_t, self.pad_b)
+   else
+      error('input must be 3 or 4-dimensional')
+   end
+   return self.output
+end
+
+function SpatialReplicationPadding:updateGradInput(input, gradOutput)
+   if input:dim() == 3 and gradOutput:dim() == 3 then
+      assert(input:size(1) == gradOutput:size(1)
+             and input:size(2) + self.pad_t + self.pad_b == gradOutput:size(2)
+             and input:size(3) + self.pad_l + self.pad_r == gradOutput:size(3),
+             'input and gradOutput must be compatible in size')
+   elseif input:dim() == 4 and gradOutput:dim() == 4 then
+      assert(input:size(1) == gradOutput:size(1)
+             and input:size(2) == gradOutput:size(2)
+             and input:size(3) + self.pad_t + self.pad_b == gradOutput:size(3)
+             and input:size(4) + self.pad_l + self.pad_r == gradOutput:size(4),
+             'input and gradOutput must be compatible in size')
+   else
+      error(
+         [[input and gradOutput must be 3 or 4-dimensional
+         and have equal number of dimensions]]
+         )
+   end
+   input.THNN.SpatialReplicationPadding_updateGradInput(
+      input:cdata(), gradOutput:cdata(), self.gradInput:cdata(),
+      self.pad_l, self.pad_r, self.pad_t, self.pad_b)
+   return self.gradInput
+end
+
+function SpatialReplicationPadding:__tostring__()
+   return torch.type(self) ..
+   string.format('(l=%d, r=%d, t=%d, b=%d)', self.pad_l, self.pad_r,
+   self.pad_t, self.pad_b)
+end