diff options
Diffstat (limited to 'SpatialFullConvolution.lua')
| -rw-r--r-- | SpatialFullConvolution.lua | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/SpatialFullConvolution.lua b/SpatialFullConvolution.lua index e6019bc..d28579b 100644 --- a/SpatialFullConvolution.lua +++ b/SpatialFullConvolution.lua @@ -72,7 +72,7 @@ function SpatialFullConvolution:updateOutput(input) -- The input can be a table where the second element indicates the target -- output size, in which case the adj factors are computed automatically - if type(inputTensor) == 'table' then + if torch.type(inputTensor) == 'table' then inputTensor = input[1] local targetTensor = input[2] local tDims = targetTensor:dim() @@ -113,7 +113,7 @@ function SpatialFullConvolution:updateGradInput(input, gradOutput) -- The input can be a table where the second element indicates the target -- output size, in which case the adj factors are computed automatically - if type(inputTensor) == 'table' then + if torch.type(inputTensor) == 'table' then inputTensor = input[1] local targetTensor = input[2] local tDims = targetTensor:dim() @@ -122,7 +122,7 @@ function SpatialFullConvolution:updateGradInput(input, gradOutput) adjW = calculateAdj(tW, self.kW, self.padW, self.dW) adjH = calculateAdj(tH, self.kH, self.padH, self.dH) -- Momentarily extract the gradInput tensor - if type(self.gradInput) == 'table' then + if torch.type(self.gradInput) == 'table' then self.gradInput = self.gradInput[1] or inputTensor.new() end end @@ -139,7 +139,7 @@ function SpatialFullConvolution:updateGradInput(input, gradOutput) adjW, adjH ) - if type(input) == 'table' then + if torch.type(input) == 'table' then -- Create a zero tensor to be expanded and used as gradInput[2]. self.zeroScalar = self.zeroScalar or input[2].new(1):zero() self.ones:resize(input[2]:dim()):fill(1) @@ -162,7 +162,7 @@ function SpatialFullConvolution:accGradParameters(input, gradOutput, scale) -- The input can be a table where the second element indicates the target -- output size, in which case the adj factors are computed automatically - if type(inputTensor) == 'table' then + if torch.type(inputTensor) == 'table' then inputTensor = input[1] local targetTensor = input[2] local tDims = targetTensor:dim() |