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########################################## Torch7 provides a Matlab-like environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to an easy and fast scripting language (Lua) and a underlying C implementation.
Modified to be compiled and used with Android
- Loading of lua packages from the apk directly.
- This is done by writing a custom package.loader Reference: http://www.lua.org/manual/5.1/manual.html#pdf-package.loaders The loader is in torchandroid.cpp as loader_android
- torchandroid.h and torchandroid.cpp give lots of helper functions to make life easier
- Print function overriden to redirect to logcat (only handles strings for now)
- Function to get apk assets as bytes (very useful)
- Full support for ffi and shared libraries
torch.load now takes three additional modes: apkbinary32, apkbinary64, apkascii. One can store model files in the assets folder and use these modes to load them. If the model was saved on a 64-bit machine, use apkbinary64, if it was saved on a 32-bit machine, use apkbinary32.
For CUDA-enabled version: NVIDIA CodeWorks for Android: https://developer.nvidia.com/codeworks-android. For CPU-only version : Android NDK and Android SDK
- Three sample projects has been provided in demos/
- demos/android-demo/jni/torchdemo.cpp is a simple use-case
- demos/android-demo/assets/main.lua is the file that is run
- demos/android-demo-cifar showcases classifying Camera inputs (or images from gallery) into one of 10 CIFAR-10 categories.
- Vinayak Ghokale from e-lab Purdue (https://github.com/e-lab) contributed a face detector demo, which showcases a fuller use-case (demos/facedetector_e-lab ).
If on ubuntu, install the following packages: sudo apt-get install libx32gcc-4.8-dev libc6-dev-i386
Default is to build with CUDA - so make sure you installed NVIDIA CodeWorks for Android and its nvcc is in your PATH.
Otherwise, set WITH_CUDA=OFF in build.sh
- git submodule update --init --recursive
- Optionally, open build.sh and modify ARCH (to match your device architecture) and WITH_CUDA variables.
- run build script: 3 ./build.sh
You can use torch in your android apps. The relevant directories are
- install/include - include directories
- install/lib - static libs cross-compiled for armeabi-v7a
- install/share/lua - lua files
- Build Torch-Android atleast once using the steps above.
- [Optional] Connect your android phone in debugging mode, to automatically install the apk.
- Change directory into demos/android-demo folder.
- Run build script. $ ./build.sh
- Run the app TorchDemo on your phone.