Caffe on Mobile Devices

iOS/Android Caffe lib with demo APP (CPU_ONLY, NO_BACKWARD, NO_BOOST, NO_HDF5, NO_LEVELDB)

Screenshots

iPhone5s	Meizu M3 note

For iPhone Simulator

Step 1: Build Caffe-Mobile Lib with cmake

$ git clone --recursive https://github.com/solrex/caffe-mobile.git
$ cd caffe-mobile/third_party
$ ./build-protobuf-3.1.0.sh iPhoneSimulator
$ mkdir ../build
$ cd ../build
$ cmake .. -DCMAKE_TOOLCHAIN_FILE=../third_party/ios-cmake/toolchain/iOS.cmake \
  -DIOS_PLATFORM=SIMULATOR -DTHIRD_PARTY=1
$ make -j 4

Step 2: Build iOS App: CaffeSimple with Xcode

• For CaffeSimple to run, you need a pre-trained LeNet on MNIST caffe model and the weight file. Follow the instructions in Training LeNet on MNIST with Caffe to train your LeNet Model on MNIST. Then copy the model file caffe/examples/mnist/lenet.prototxt and the trained weight file caffe/examples/mnist/lenet_iter_10000.caffemodel to CaffeSimple app directory.

$ cp $CAFFE/examples/mnist/lenet.prototxt \
     $CAFFE_MOBILE/examples/ios/simple/CaffeSimple/data/net.prototxt
$ cp $CAFFE/examples/mnist/lenet_iter_10000.caffemodel \
     $CAFFE_MOBILE/examples/ios/simple/CaffeSimple/data/weight.caffemodel

• Check the batch size setting in net.prototxt, set it to 1 if needed.

$ diff $CAFFE/examples/mnist/lenet.prototxt \
       $CAFFE_MOBILE/examples/ios/simple/CaffeSimple/data/net.prototxt
6c6
<   input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
---
>   input_param { shape: { dim: 1 dim: 1 dim: 28 dim: 28 } }

• Load the Xcode project inside the $CAFFE_MOBILE/examples/ios/simple/ folder, and press Command-R to build and run it on the simulator.

For iPhone

Step 1: Build Caffe-Mobile Lib with cmake

$ git clone --recursive https://github.com/solrex/caffe-mobile.git
$ cd caffe-mobile/third_party
$ ./build-protobuf-3.1.0.sh iPhoneOS
$ mkdir ../build
$ cd ../build
$ cmake .. -DCMAKE_TOOLCHAIN_FILE=../third_party/ios-cmake/toolchain/iOS.cmake \
  -DIOS_PLATFORM=OS -DTHIRD_PARTY=1
$ make -j 4

Step 2: Build iOS App: CaffeSimple with Xcode

• For CaffeSimple to run, you need a pre-trained LeNet on MNIST caffe model and the weight file. Follow the instructions in Training LeNet on MNIST with Caffe to train your LeNet Model on MNIST. Then copy the model file caffe/examples/mnist/lenet.prototxt and the trained weight file caffe/examples/mnist/lenet_iter_10000.caffemodel to CaffeSimple app directory.

$ cp $CAFFE/examples/mnist/lenet.prototxt \
     $CAFFE_MOBILE/examples/ios/simple/CaffeSimple/data/net.prototxt
$ cp $CAFFE/examples/mnist/lenet_iter_10000.caffemodel \
     $CAFFE_MOBILE/examples/ios/simple/CaffeSimple/data/weight.caffemodel

• Check the batch size setting in net.prototxt, set it to 1 if needed.

$ diff $CAFFE/examples/mnist/lenet.prototxt \
       $CAFFE_MOBILE/examples/ios/simple/CaffeSimple/data/net.prototxt
6c6
<   input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
---
>   input_param { shape: { dim: 1 dim: 1 dim: 28 dim: 28 } }

• Load the Xcode project inside the $CAFFE_MOBILE/examples/ios/simple/ folder, and press Command-R to build and run it on your connected device.

For Android (arm64-v8a only)

Step 1: Build Caffe-Mobile Lib with cmake

$ export NDK_HOME=/path/to/your/ndk # TODO
$ git clone --recursive https://github.com/solrex/caffe-mobile.git
$ cd caffe-mobile/third_party
$ ./build-protobuf-3.1.0.sh Android
$ ./build-openblas.sh
$ mkdir ../build
$ cd ../build
$ cmake .. -DCMAKE_TOOLCHAIN_FILE=../third_party/android-cmake/android.toolchain.cmake \
-DANDROID_NDK=$NDK_HOME \
-DANDROID_ABI="arm64-v8a" \
-DANDROID_NATIVE_API_LEVEL=21 \
-DTHIRD_PARTY=1
$ make -j 4

Step 2: Copy Caffe-Mobile Lib to JniLib of CaffeSimple

$ mkdir -p ../examples/android/CaffeSimple/app/libs/arm64-v8a/
$ cp ../build/lib/libcaffe-jni.so ../examples/android/CaffeSimple/app/libs/arm64-v8a/

Step 3: Build Android App: CaffeSimple with Android Studio

• For CaffeSimple to run, you need a pre-trained LeNet on MNIST caffe model and the weight file. Follow the instructions in Training LeNet on MNIST with Caffe to train your LeNet Model on MNIST. Then copy the model file caffe/examples/mnist/lenet.prototxt and the trained weight file caffe/examples/mnist/lenet_iter_10000.caffemodel to the SD card root of your Android mobile phone.

$ adb push $CAFFE/examples/mnist/lenet.prototxt \
     /sdcard/net.prototxt
$ adb push $CAFFE/examples/mnist/lenet_iter_10000.caffemodel \
     /sdcard/weight.caffemodel
$ adb push $CAFFE_MOBILE/examples/ios/simple/CaffeSimple/data/test_image.png \
     /sdcard/test_image.png

• Check the batch size setting in net.prototxt, set it to 1 if needed.

$ diff $CAFFE/examples/mnist/lenet.prototxt \
       net.prototxt
6c6
<   input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
---
>   input_param { shape: { dim: 1 dim: 1 dim: 28 dim: 28 } }

• Load the Android studio project inside the $CAFFE_MOBILE/examples/android/CaffeSimple/ folder, and press Command-R to build and run it on your connected device.

For MacOSX & Ubuntu

Step 1: Install dependency

$ brew install protobuf # MacOSX
$ sudo apt install libprotobuf-dev protobuf-compiler libatlas-dev # Ubuntu

Step 2: Build Caffe-Mobile Lib with cmake

$ git clone --recursive https://github.com/solrex/caffe-mobile.git
$ mkdir build
$ cd ../build
$ cmake ..
$ make -j 4

Step 3: Build Caffe-bin with cmake

$ brew install gflags
$ cmake .. -DTOOLS
$ make -j 4

Thanks

• Based on https://github.com/BVLC/caffe
• Inspired by https://github.com/chyh1990/caffe-compact
• Use https://github.com/Yangqing/ios-cmake
• Use https://github.com/taka-no-me/android-cmake