TensorFlowLite_Demo

TensorFlow Lite 的demo。iOS使用TensorFlow Lite配置教程

TensorFlow Lite for iOS

Building

为编译TensorFlow Lite的iOS版静态库, 需要用到MacOS上的终端. 如果还没达标, 那么须先安装 Xcode 8 or later and the tools using xcode-select:

xcode-select --install

•第一次安装，需要打开Xcode，按照提示授权信任.

(还需要安装 Homebrew installed.)

•另外两个必要工具 automake/libtool:

brew install automake
brew install libtool

•接下来，是运行脚本，下载所需要的依赖，但是先不要立马执行，请看完tips:

tensorflow/contrib/lite/download_dependencies.sh

•这个脚本会联网下载所需依赖包，并放到到这个目录： tensorflow/contrib/lite/downloads.

•demo中需要的label和.tflite模型文件，则下载并解压到： tensorflow/contrib/lite/example/ios/camera/data.

•tips: 对于国内开发者，可能直接执行脚本，会有几个依赖包下载失败或者 出现问题导致即便下载依赖包步骤通过，但是编译阶段出错，所以我们直接在这里做一些修改：

EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v bazel-mirror | head -n1)"
GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)"
GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz"
ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_PATH}" | head -n1)"
NEON_2_SSE_URL="https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip"
FARMHASH_URL="https://mirror.bazel.build/github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz"
FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/master.zip"
# 下面这两个的意思是联网下载。 我们就不要用脚本下载了，这里先注释掉
# MODELS_URL="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_ios_lite_float_2017_11_08.zip"
# QUANTIZED_MODELS_URL="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip"

download_and_extract "${EIGEN_URL}" "${DOWNLOADS_DIR}/eigen"
download_and_extract "${GEMMLOWP_URL}" "${DOWNLOADS_DIR}/gemmlowp"
download_and_extract "${GOOGLETEST_URL}" "${DOWNLOADS_DIR}/googletest"
download_and_extract "${ABSL_URL}" "${DOWNLOADS_DIR}/absl"
download_and_extract "${NEON_2_SSE_URL}" "${DOWNLOADS_DIR}/neon_2_sse"
download_and_extract "${FARMHASH_URL}" "${DOWNLOADS_DIR}/farmhash"
download_and_extract "${FLATBUFFERS_URL}" "${DOWNLOADS_DIR}/flatbuffers"
# 下面这两个的意思是下载完依赖包，解压到的路径，同理，也注释掉
# download_and_extract "${MODELS_URL}" "${DOWNLOADS_DIR}/models"
# download_and_extract "${QUANTIZED_MODELS_URL}" "${DOWNLOADS_DIR}/quantized_models"

•现在，可以放心的跑脚本了（都在你的TensorFlow的根目录下执行即可）

tensorflow/contrib/lite/download_dependencies.sh

•下载完成，别忘了，上面我们注释掉了两个包的下载，现在需要根据脚本里这两个的下载链接 MODELS_URL、 QUANTIZED_MODELS_URL， 自己去下载并解压。下载完成后，根据注释掉的解压路径，分别把两个文件放到对应的路径下面tensorflow/contrib/lite/downloads/models、tensorflow/contrib/lite/downloads/quantized_models ， 缺少的文件夹自己创建。

•所需依赖全部放置完毕，接下来就是编译iOS所需要的静态库:

tensorflow/contrib/lite/build_ios_universal_lib.sh

不出意外就是等待编译完成。

最后，编译出的结果放在tensorflow/contrib/lite/gen/lib目录下，有各个环境对应的静态库，还有一个lipo合并后的 tensorflow/contrib/lite/gen/lib/libtensorflow-lite.a.我们用这个即可。

集成到iOS项目中

1、安装 CocoaPods ，这里不做赘述

2、在 tensorflow/contrib/lite/examples/ios/camera路径下 运行 pod install.

3、配置4项（重要）：

•Target ***->General->Linked Frameworks and Libraries:

点击＋，添加tensorflow/contrib/lite/gen/lib路径下的libtensorflow-lite.a

•Target ***->Build Settings->Library Search Paths：

点击＋，添加/Users/xiaoqiang/6TensorFlowlite/tensorflow-master/tensorflow/contrib/lite/gen/lib。

这个路径我用了绝对路径，开发者根据自己的项目，确保路径链接到此即可。

•Target ***->Build Settings->Header Search Paths：

点击＋，逐个添加

'${SRCROOT}/Pods/TensorFlow-experimental/Frameworks/tensorflow_experimental.framework/Headers'

'${SRCROOT}/Pods/TensorFlow-experimental/Frameworks/tensorflow_experimental.framework/Headers/third_party/eigen3'

$(inherited)

"${PODS_ROOT}/Headers/Public"

"${PODS_ROOT}/Headers/Public/TensorFlow-experimental"

"$(SRCROOT)/../../../6TensorFlowlite/tensorflow-master"

"$(SRCROOT)/../../../6TensorFlowlite/tensorflow-master/tensorflow/contrib/lite/downloads"

"$(SRCROOT)/../../../6TensorFlowlite/tensorflow-master/tensorflow/contrib/lite/downloads/flatbuffers/include"

后三条也使用了我的绝对路径，开发者视自己情况而定，同样确保能最终链接到这些路径。

•Target ***->Build Settings->Other Linker Flags：

点击＋，逐个添加

$(inherited)

-L

${SRCROOT}/Pods/TensorFlow-experimental/Frameworks/tensorflow_experimental.framework

-ObjC

-l"c++"

-l"protobuf_experimental"

-framework

"Accelerate"

-framework

"tensorflow_experimental"

-force_load

${SRCROOT}/Pods/TensorFlow-experimental/Frameworks/tensorflow_experimental.framework/tensorflow_experimental

4、打开 tflite_camera_example.xcworkspace, 跑起来吧. 注意：

• C++11 support (or later) should be enabled by setting C++ Language Dialect to GNU++11 (or GNU++14), and C++ Standard Library to libc++.