diff options
| author | Gregory Chanan <gchanan@fb.com> | 2016-12-01 00:40:24 +0300 |
|---|---|---|
| committer | Gregory Chanan <gchanan@fb.com> | 2016-12-01 00:45:16 +0300 |
| commit | 2a64e82da91cddc4b791d6c0763c2a38f1320e25 (patch) | |
| tree | 2e7e218c27c8920507417743c2b221ec791a945f /VolumetricDilatedConvolution.lua | |
| parent | 8dff3df9676caae7fd87551ee2ac24626f4a0852 (diff) | |
Move make contiguous code from lua to C.
Exceptions are:
1) SparseLinear
requires additional parameters to be passed in (e.g. nbatches),
so it's not clear it's worth moving to C since it won't really simplify the binding
code logic.
2) BatchNormalization
requires "makeBatch", which isn't a trivial translation to C.
3) LookupTable
requires "view" in C, which is already a TODO
4) SpatialUpSamplingBilinear
requires "view" in C, which is already TODO
Diffstat (limited to 'VolumetricDilatedConvolution.lua')
| -rw-r--r-- | VolumetricDilatedConvolution.lua | 19 |
1 files changed, 0 insertions, 19 deletions
diff --git a/VolumetricDilatedConvolution.lua b/VolumetricDilatedConvolution.lua index fc7f037..f1337eb 100644 --- a/VolumetricDilatedConvolution.lua +++ b/VolumetricDilatedConvolution.lua @@ -9,26 +9,9 @@ function VolumetricDilatedConvolution:__init(nInputPlane, nOutputPlane, kT, kW, 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(), @@ -46,7 +29,6 @@ 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(), @@ -65,7 +47,6 @@ 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(), |