pyadolla/cudnn.torch

Torch-7 FFI bindings for NVIDIA CuDNN

cudnn.torch

Torch7 FFI bindings for NVidia CuDNN kernels!

Modules are API compatible their nn equivalents. Fully unit-tested against nn implementations.

Installation

• Install CuDNN
• Have at least Cuda 6.5
• Have libcudnn.so in your library path (Install it from https://developer.nvidia.com/cuDNN )

Modules

-- All inputs have to be 3D or 4D(batch-mode), even for ReLU, SoftMax etc.
cudnn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
cudnn.SpatialMaxPooling(kW, kH, dW, dH, padW, padH)
cudnn.SpatialAveragePooling(kW, kH, dW, dH, padW, padH)
cudnn.ReLU()
cudnn.Tanh()
cudnn.Sigmoid()
-- SoftMax can be run in fast mode or accurate mode. Default is accurate mode.
cudnn.SoftMax(fastMode [= false])          -- SoftMax across each image (just like nn.SoftMax)
cudnn.SpatialSoftMax(fastMode [= false])   -- SoftMax across feature-maps (per spatial location)

-- Volumetric inputs (4D or 5D batched mode)
cudnn.VolumetricConvolution(nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH, padT, padW, padH)

I have no time to support these, so please don't expect a quick response to filed github issues.