TF2.2 Сборка libtensorflow_ cc .so для C ++ API - PullRequest
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TF2.2 Сборка libtensorflow_ cc .so для C ++ API

1 голос
/ 19 июня 2020

Информация о системе :

  • Платформа и распространение ОС: Linux Ubuntu 18.04
  • Установленный источник TensorFlow: исходный код
  • Версия TensorFlow : 2.2.0 стабильная
  • Python версия: python3
  • Версия Bazel: использование Bazelisk с версией Bazel 2.0.0 в соответствии с требованиями tf-2.2.0
  • Версия GCC / компилятора (при компиляции из исходников): G CC -8
  • Версия CUDA / cuDNN: Нет CUDA (на данный момент)

Что у меня есть done :

Получил тензорный поток из github: 

git clone --recurse-submodules https://github.com/tensorflow/tensorflow.git

cd tensorflow

git checkout v2.2.0

Установлен и настроен Базелиск: (Используя это руководство: https://gist.github.com/philwo/f3a8144e46168f23e40f291ffe92e63c) 

$ sudo curl -Lo /usr/local/bin/bazel https://github.com/bazelbuild/bazelisk/releases/download/v1.1.0/bazelisk-linux-amd64
$ sudo chmod +x /usr/local/bin/bazel

$ grep -r _TF_MAX_BAZEL_VERSION .
./configure.py:_TF_MAX_BAZEL_VERSION = '2.0.0'

$ echo '2.0.0' > .bazelversion
$ bazel version

Начать построение тензорного потока с Bazel: 

$ ./configure

bazel --host_jvm_args=-Xmx30G build --jobs=8 --config=monolithic --config=v2 --config=opt --copt=-O3 --copt=-march=native --copt=-m64 --verbose_failures //tensorflow:tensorflow  //tensorflow:tensorflow_cc  //tensorflow:tensorflow_framework  //tensorflow/tools/lib_package:libtensorflow

Затем с помощью кода: 

#include <stdlib.h>

#include <fstream>
#include <iostream>
#include <string>
#include <vector>

#include "class_name.h"
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/default_device.h"
#include "tensorflow/core/graph/graph_def_builder.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/util/command_line_flags.h"

using namespace tensorflow;
using tensorflow::Flag;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::Tensor;

//Read the image file, apply appropriate decoding depending on type of image
int TensorFromFile(string filename, const int i_height, const int i_width, std::vector<Tensor>* o_tensors) {
  tensorflow::Status status;
  auto root = tensorflow::Scope::NewRootScope();
  using namespace ::tensorflow::ops;
  std::unique_ptr<tensorflow::Session> session(tensorflow::NewSession({}));
  tensorflow::GraphDef graph;

  auto reader = tensorflow::ops::ReadFile(root.WithOpName("img_reader"), filename);
  const int channels = 1;
  tensorflow::Output imgreader;

  if (tensorflow::str_util::EndsWith(filename, ".png")) {
    imgreader = DecodePng(root.WithOpName("png_reader"), reader, DecodePng::Channels(channels));
  } else if (tensorflow::str_util::EndsWith(filename, ".gif")) {
    imgreader = DecodeGif(root.WithOpName("gif_reader"), reader);
  } else {
    imgreader = DecodeJpeg(root.WithOpName("jpeg_reader"), reader, DecodeJpeg::Channels(channels));
  }

  auto f_caster = Cast(root.WithOpName("float_caster"), imgreader, tensorflow::DT_FLOAT);
  ExpandDims(root.WithOpName("output"), f_caster, 0);

  status = root.ToGraphDef(&graph);
  if (!status.ok()) {
    LOG(ERROR) << status.ToString();
    return -1;
  }

  status = session->Create(graph);
  if (!status.ok()) {
    LOG(ERROR) << status.ToString();
    return -1;
  }

  status = session->Run({}, {"output"}, {}, o_tensors);
  if (!status.ok()) {
    LOG(ERROR) << status.ToString();
    return -1;
  }

  return 0;
}

int main(int argc, char* argv[]) {
  using namespace ::tensorflow::ops;
  tensorflow::Status status;

  std::string delimiter = ".";
  std::string ofilename;
  std::vector<Tensor> inputs;
  std::vector<Tensor> outputs;

  std::string graph_path = "../../graphs/test0/";
  std::string image_path = "../../graphs/test0.png";

  std::string mdlpath(graph_path);
  std::string imgpath(image_path);
  int32 inputdim = 32;

  std::unique_ptr<tensorflow::Session> session(tensorflow::NewSession({}));
  tensorflow::GraphDef graph;

  //read model file
  status = ReadBinaryProto(Env::Default(), mdlpath, &graph);
  if (!status.ok()) {
    std::cout << status.ToString() << "\n";
    return -1;
  }

  //add graph to scope
  status = session->Create(graph);
  if (!status.ok()) {
    std::cout << status.ToString() << "\n";
    return -1;
  }

  //Read input image, assuming to be a sqaure image
  if (TensorFromFile(imgpath, inputdim, inputdim, &inputs)) {
    LOG(ERROR) << "Image reading failed"
               << "\n";
    return -1;
  }

  LOG(INFO) << "OK";

  std::cout << "input dimension of the image: " << inputs[0].DebugString() << std::endl;

  //get the appropriate input and out layer names from the graph/mode to execute
  auto inputlayer = graph.node(0).name();
  auto outputlayer = graph.node(graph.node_size() - 1).name();

  status = session->Run({{inputlayer, inputs[0]}}, {outputlayer}, {}, &outputs);
  if (!status.ok()) {
    LOG(ERROR) << status.ToString();
    return -1;
  }

  std::cout << "Output dimension of the image" << outputs[0].DebugString() << std::endl;

  //create filename
  ofilename.append(imgpath.substr(0, imgpath.find(delimiter)));
  ofilename.append("_mask.png");

  std::cout << "output filename: " << ofilename << std::endl;

  //Now write this to a image file
  //if (TensorToFile(ofilename, outputs, threshold)) return -1;

  session->Close();

  return 0;
}

Я попытался скомпилировать с: f I use theese flags: g++ -O3 -m64 -o test -I /opt/tpt/tensorflow_cpp_scratch/include/tensorflow/bazel-bin main.cpp -L /opt/tpt/tensorflow_cpp_scratch/lib -l tensorflow_cc

и Получил ошибку: 

/tmp/cc4xzZGr.o: In function `google::protobuf::RepeatedPtrField<tensorflow::NodeDef>::TypeHandler::WeakType const& google::protobuf::internal::RepeatedPtrFieldBase::Get<google::protobuf::RepeatedPtrField<tensorflow::NodeDef>::TypeHandler>(int) const':
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0x6a): undefined reference to `google::protobuf::internal::LogMessage::LogMessage(google::protobuf::LogLevel, char const*, int)'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0x79): undefined reference to `google::protobuf::internal::LogMessage::operator<<(char const*)'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0x86): undefined reference to `google::protobuf::internal::LogFinisher::operator=(google::protobuf::internal::LogMessage&)'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0x8e): undefined reference to `google::protobuf::internal::LogMessage::~LogMessage()'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0xb2): undefined reference to `google::protobuf::internal::LogMessage::LogMessage(google::protobuf::LogLevel, char const*, int)'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0xc1): undefined reference to `google::protobuf::internal::LogMessage::operator<<(char const*)'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0xce): undefined reference to `google::protobuf::internal::LogFinisher::operator=(google::protobuf::internal::LogMessage&)'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0xd6): undefined reference to `google::protobuf::internal::LogMessage::~LogMessage()'
main.cpp:(.text._ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi[_ZNK6google8protobuf8internal20RepeatedPtrFieldBase3GetINS0_16RepeatedPtrFieldIN10tensorflow7NodeDefEE11TypeHandlerEEERKNT_8WeakTypeEi]+0xf5): undefined reference to `google::protobuf::internal::LogMessage::~LogMessage()'
collect2: error: ld returned 1 exit status

Похоже, что отсутствует библиотека protobuf. Поэтому я добавил также -ltensorflow_framework Он компилируется без ошибок, НО я получаю эту ошибку: 

[libprotobuf ERROR external/com_google_protobuf/src/google/protobuf/descriptor_database.cc:118] File already exists in database: google/protobuf/any.proto
[libprotobuf FATAL external/com_google_protobuf/src/google/protobuf/descriptor.cc:1367] CHECK failed: GeneratedDatabase()->Add(encoded_file_descriptor, size): 
terminate called after throwing an instance of 'google::protobuf::FatalException'
  what():  CHECK failed: GeneratedDatabase()->Add(encoded_file_descriptor, size): 
Aborted (core dumped)

Почему я получаю эти ошибки?

Я уже читал это https://github.com/tensorflow/tensorflow/issues/14632 и это https://github.com/tensorflow/tensorflow/issues/40004, но безуспешно.

Thx

...