Handwritten digits recognition via TensorFlow based on Windows MFC (III) - Inter-process Communication framework (I)

As I mentioned in the first post, it's planned to have the inference run as a standalone process, so we feed it with image data and obtain the predicted result. We need some Inter-communication framework to do so. (I later realize probably DLL is enough, so I maybe fallback to DLL someday).

All things are done under Windows, please follow me patiently.

First as suggested by official tutorial of bazel and tensorflow, create a folder named tools under root directory like C:\ and install bazel there. Also some dependency like swigwin.


Second git clone tensorflow source code from github.

Third, under the directory C:\tensorflow\tensorflow\cc, create folder mnist. all of our work goes into this folder.

To utilize inter-process communication, I create two files MnistComm.h and MnistComm.cpp, I will paste the content in the next post. I also create another file MnistModel.cc to load the model and run inference.

Fourth, let's create files related to the build process.

1. BUILD file:
# Description:
# TensorFlow is a computational framework, primarily for use in machine
# learning applications.

package(
    default_visibility = ["//visibility:public"],
)

licenses(["notice"])  # Apache 2.0

exports_files(["LICENSE"])

load(":mnist.bzl", "mnist_copts")

cc_library(
    name = "MnistComm",
    srcs = ["MnistComm.cpp"],
    hdrs = ["MnistComm.h"],
    copts = ["/Zc:wchar_t", "/D_UNICODE", "/DUNICODE"],
    linkopts = [],
    deps = [
    ],
)

cc_binary(
    name = "MnistModel",
    srcs = ["MnistModel.cc"],
    copts = mnist_copts(["/Zc:wchar_t", "/D_UNICODE", "/DUNICODE"]),
    deps = [
        ":MnistComm",
        "//tensorflow/core:tensorflow",
    ],
)

2. a tiny extension file named mnist.bzl:

def mnist_copts(fs):

    cflags = fs + ["-DEIGEN_AVOID_STL_ARRAY",

        "-Iexternal/gemmlowp",

        "-Wno-sign-compare",

        "-fno-exceptions"] + \

        select({

            "//tensorflow:windows": [

                "/DLANG_CXX11",

                "/D__VERSION__=\\\"MSVC\\\"",

            ],

            "//conditions:default": ["-pthread"]})

    

    return cflags

Then how to build these targets?

1. Open the msys2 console, set the environment variables:

cd c:/tensorflow

export JAVA_HOME="$(ls -d C:/Program\ Files/Java/jdk* | sort | tail -n 1)"

export BAZEL_SH=c:/tools/msys64/usr/bin/bash.exe

export BAZEL_VS="C:/Program Files (x86)/Microsoft Visual Studio 14.0"

export BAZEL_PYTHON="C:/Program Files/Python35/python.exe"

export PATH=$PATH:/c/tools/swigwin-3.0.10:/c/tools/bazel:/c/Program\ Files/Python35

Adjust these variables properly.

2. Configure and build:

./configure

bazel build -c opt --cpu=x64_windows_msvc --host_cpu=x64_windows_msvc //tensorflow/cc/mnist:MnistComm --verbose_failures

bazel build -c opt --cpu=x64_windows_msvc --host_cpu=x64_windows_msvc //tensorflow/cc/mnist:MnistModel --verbose_failures