volumetric dilated convolution

1 files changed, 103 insertions, 0 deletions

diff --git a/VolumetricDilatedConvolution.lua b/VolumetricDilatedConvolution.lua
new file mode 100644
index 0000000..fc7f037
--- /dev/null
+++ b/VolumetricDilatedConvolution.lua
@@ -0,0 +1,103 @@
+local THNN = require 'nn.THNN'
+local VolumetricDilatedConvolution, parent = torch.class('nn.VolumetricDilatedConvolution', 'nn.VolumetricConvolution')
+
+function VolumetricDilatedConvolution:__init(nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH, padT, padW, padH, dilationT, dilationW, dilationH)
+   parent.__init(self, nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH, padT, padW, padH)
+
+   self.dilationT = dilationT or 1
+   self.dilationW = dilationW or 1
+   self.dilationH = dilationH or 1
+end
+
+local function makeContiguous(self, input, gradOutput)
+   if not input:isContiguous() then
+      self._input = self._input or input.new()
+      self._input:resizeAs(input):copy(input)
+      input = self._input
+   end
+   if gradOutput then
+      if not gradOutput:isContiguous() then
+	 self._gradOutput = self._gradOutput or gradOutput.new()
+	 self._gradOutput:resizeAs(gradOutput):copy(gradOutput)
+	 gradOutput = self._gradOutput
+      end
+   end
+   return input, gradOutput
+end
+
+function VolumetricDilatedConvolution:updateOutput(input)
+   self.finput = self.finput or self.weight.new()
+   self.fgradInput = self.fgradInput or self.weight.new()
+   input = makeContiguous(self, input)
+   input.THNN.VolumetricDilatedConvolution_updateOutput(
+      input:cdata(),
+      self.output:cdata(),
+      self.weight:cdata(),
+      THNN.optionalTensor(self.bias),
+      self.finput:cdata(),
+      self.fgradInput:cdata(),
+      self.kT, self.kW, self.kH,
+      self.dT, self.dW, self.dH,
+      self.padT, self.padW, self.padH,
+      self.dilationT, self.dilationW, self.dilationH
+   )
+   return self.output
+end
+
+function VolumetricDilatedConvolution:updateGradInput(input, gradOutput)
+   if self.gradInput then
+      input, gradOutput = makeContiguous(self, input, gradOutput)
+      self.fgradInput = self.fgradInput or self.weight.new()
+      input.THNN.VolumetricDilatedConvolution_updateGradInput(
+         input:cdata(),
+         gradOutput:cdata(),
+         self.gradInput:cdata(),
+         self.weight:cdata(),
+         self.finput:cdata(),
+         self.kT, self.kW, self.kH,
+         self.dT, self.dW, self.dH,
+         self.padT, self.padW, self.padH,
+         self.dilationT, self.dilationW, self.dilationH
+      )
+      return self.gradInput
+   end
+end
+
+function VolumetricDilatedConvolution:accGradParameters(input, gradOutput, scale)
+   scale = scale or 1
+   input, gradOutput = makeContiguous(self, input, gradOutput)
+   self.fgradInput = self.fgradInput or self.weight.new()
+   input.THNN.VolumetricDilatedConvolution_accGradParameters(
+      input:cdata(),
+      gradOutput:cdata(),
+      self.gradWeight:cdata(),
+      THNN.optionalTensor(self.gradBias),
+      self.finput:cdata(),
+      self.fgradInput:cdata(),
+      self.kT, self.kW, self.kH,
+      self.dT, self.dW, self.dH,
+      self.padT, self.padW, self.padH,
+      self.dilationT, self.dilationW, self.dilationH,
+      scale
+   )
+end
+
+function VolumetricDilatedConvolution:__tostring__()
+   local s = string.format('%s(%d -> %d, %dx%dx%d', torch.type(self),
+         self.nInputPlane, self.nOutputPlane, self.kT, self.kW, self.kH)
+   if self.dT ~= 1 or self.dW ~= 1 or self.dH ~= 1
+   or self.padT ~= 0 or self.padW ~= 0 or self.padH ~= 0 then
+     s = s .. string.format(', %d,%d,%d', self.dT, self.dW, self.dH)
+   end
+   if (self.padT or self.padW or self.padH)
+   and (self.padT ~= 0 or self.padW ~= 0 or self.padH ~= 0) then
+     s = s .. ', ' .. self.padT .. ',' .. self.padW .. ',' .. self.padH
+   end
+   s = s .. ', ' .. self.dilationT .. ','
+       .. self.dilationW .. ',' .. self.dilationH
+   if self.bias then
+      return s .. ')'
+   else
+      return s .. ') without bias'
+   end
+end