Awesome
Shift Operation CUDA Implementation
created by Peter Jin
Tradeoffs and further analysis can be found in the paper. If you find this work useful for your research, please consider citing:
@inproceedings{shift,
Author = {Bichen Wu and Alvin Wan and Xiangyu Yue and Peter Jin and Sicheng Zhao and Noah Golmant and Amir Gholaminejad and Joseph Gonzalez and Kurt Keutzer},
Title = {Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions},
Journal = {arXiv:1711.08141},
Year = {2017}
}
Uses of Shift:
- ShiftResNet for CIFAR-10, CIFAR-100 classification
Installation
If you have included this
shift
repository as a submodule in a separate repository, feel free to skip down to step 5.
- If you have not already, setup a virtual environment with Python3, and activate it.
virtualenv shift --python=python3
source shift/bin/activate
Your prompt should now be prefaced with (shift)
, as in
(shift) [user@server:~]$
- Install
pytorch
andtorchvision
. Access pytorch.org, scroll down to the "Getting Started" section, and select the appropriate OS, package manager, Python, and CUDA build. For example, selecting Linux, pip, Python3.5, and CUDA 8 gives the following, as of the time of this writing
pip3 install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp35-cp35m-linux_x86_64.whl
pip3 install torchvision
- Clone this repository.
git clone git@github.com:peterhj/shiftnet_cuda_v2.git
cd
into the root of this repository.
cd shiftnet_cuda_v2
- Install the Python requirements for this package.
pip3 install -r requirements.txt
- Compile the Shift Layer implementation in C.
make
Getting
invalid_device_function
? Update the architecture code inmodels/shiftnet_cuda_v2/Makefile
, currently configured for a Titan X. e.g., A Tesla K80 issm-30
.
Your custom CUDA layer is now installed.
Test
To check that the build completed successfully, run the test script
python test_shiftnet.py
After ~3s, the script should output a number of different tensors, where the last tensor has non-zero values only in the first column.
Columns 13 to 17
89 0 0 0 0
107 0 0 0 0
125 0 0 0 0
143 0 0 0 0
161 0 0 0 0
179 0 0 0 0
197 0 0 0 0
215 0 0 0 0
233 0 0 0 0
251 0 0 0 0
269 0 0 0 0
287 0 0 0 0
305 0 0 0 0
323 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
[torch.FloatTensor of size 18x18]