These instructions are written using an Apple Mac Pro M1.
bazelisk is recommended for building the project, which can be done with brew:
$ brew install bazelisk- Change directory into
tensorflowand run:./configure
- I just use the default Python options, but for these other options I select no:
You have bazel 5.1.1 installed.
Please specify the location of python. [Default is /Users/tallamjr/mambaforge/envs/main/bin/python3]:
Found possible Python library paths:
/Users/tallamjr/github/tallamjr/origin/astronet
/Users/tallamjr/mambaforge/envs/main/lib/python3.10/site-packages
/opt/homebrew/Cellar/apache-spark/3.2.1/libexec/python
Please input the desired Python library path to use. Default is [/Users/tallamjr/github/tallamjr/origin/astronet]
Do you wish to build TensorFlow with ROCm support? [y/N]: N
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: N
No CUDA support will be enabled for TensorFlow.
Do you wish to download a fresh release of clang? (Experimental) [y/N]: N
Clang will not be downloaded.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: N
Not configuring the WORKSPACE for Android builds.
Do you wish to build TensorFlow with iOS support? [y/N]: N- Now build with:
$ bazel build -c opt //tensorflow/lite:libtensorflowlite.dylib- Check TFLite library file
$ file tensorflow/bazel-bin/tensorflow/lite/libtensorflowlite.dylib
bazel-bin/tensorflow/lite/libtensorflowlite.dylib: Mach-O 64-bit dynamically linked shared library arm64From here, we simply move the necessary files into a directory that we will eventually "deploy". So,
for this to work we need to ensure we have the compiled binary: centernet_detection, the TFLite
model: centernet_mobilenetv2_fpn_kpts.tflite and the sample input data: coco_labels.txt
(although even this is not required since we could construct our application to take input from the
user at runtime)
$ cp -r tensorflow/bazel-bin/external/flatbuffers/_virtual_includes/flatbuffers minitflite-example/include/
$ cp -r tensorflow/tensorflow/lite minitflite-example/include/tensorflow/
$ cp tensorflow/bazel-bin/tensorflow/lite/libtensorflowlite.dylib minitflite-example/libs/$ tree minitflite-example -L 3 -I "CMakeCache.txt|CMakeFiles"
minitflite-example
├── CMakeLists.txt
├── bin
│ ├── Makefile
│ ├── centernet_detection
│ └── cmake_install.cmake
├── centernet_detector.cpp
├── centernet_detector.h
├── include
│ ├── flatbuffers
│ │ └── flatbuffers
│ └── tensorflow
│ └── lite
├── inputs
│ └── coco_labels.txt
├── libs
│ └── libtensorflowlite.dylib
├── main.cpp
└── models
├── centernet_mobilenetv2_fpn_kpts.tflite
└── centernet_mobilenetv2_fpn_od.tflite
9 directories, 11 files$ cd build
$ cmake ..
$ makeWARNING -- this demo uses your camera
./minitflite-example/bin/centernet_detection \
./minitflite-example/models/centernet_mobilenetv2_fpn_kpts.tflite \
--label=./minitflite-example/labels/coco_labels.txt
I am ignoring the submodule of tensorflow since what's really used for deployment will be in
minitflite-example folder. A guide to reducing the binary size can be found here: Reduce TensorFlow Lite binary size
$ du -sh */** | grep -v "tensorflow|assets" | sort -rh
52M minitflite-example/include
18M minitflite-example/models
3.6M minitflite-example/libs
676K minitflite-example/bin
12K minitflite-example/main.cpp
8.0K minitflite-example/centernet_detector.cpp
4.0K minitflite-example/inputs
4.0K minitflite-example/centernet_detector.h
4.0K minitflite-example/CMakeLists.txt
This repo and working demo is really down to some amazing online resources: