#CMakeLists.txt
cmake_minimum_required (VERSION 2.8.8)
project (tf_example)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -std=c++11 -W")
#link_directories(./lib)
include_directories(
/home/u/tf1.13/tensorflow-GPU
/home/u/tf1.13/tensorflow-GPU/bazel-genfiles
/home/u/tf1.13/tensorflow-GPU/bazel-bin/tensorflow
/home/u/tf1.13/tensorflow-GPU/tensorflow/contrib/makefile/downloads/nsync/public
/home/u/tf1.13/tensorflow-GPU/tensorflow/contrib/makefile/gen/protobuf/include
/usr/local/include/eigen3
/home/u/tf1.13/tensorflow-GPU/tensorflow/contrib/makefile/downloads/absl
)
add_executable(tf_test tf.cpp)
#link_directories(./lib)
#target_link_libraries(tf_test tensorflow_cc tensorflow_framework)
target_link_libraries(tf_test /home/u/tf1.13/tensorflow-GPU/bazel-bin/tensorflow/libtensorflow_cc.so.1 /home/u/tf1.13/tensorflow-GPU/bazel-bin/tensorflow/libtensorflow_framework.so.1)
//tf.cpp
/*
* test tensorflow_cc c++ successfully
* load mnist.pb model successfully
* conference:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image
* */
//@wp conference:https://blog.csdn.net/wd1603926823/article/details/92843830
#include <fstream>
#include <utility>
#include <vector>
#include <Eigen/Core>
#include <Eigen/Dense>
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/image_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/graph.pb.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 std;
using namespace tensorflow;
using namespace tensorflow::ops;
using tensorflow::Flag;
using tensorflow::Tensor;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::int32;
static Status ReadEntireFile(tensorflow::Env* env, const string& filename,
Tensor* output) {
tensorflow::uint64 file_size = 0;
TF_RETURN_IF_ERROR(env->GetFileSize(filename, &file_size));
string contents;
contents.resize(file_size);
std::unique_ptr<tensorflow::RandomAccessFile> file;
TF_RETURN_IF_ERROR(env->NewRandomAccessFile(filename, &file));
tensorflow::StringPiece data;
TF_RETURN_IF_ERROR(file->Read(0, file_size, &data, &(contents)[0]));
if (data.size() != file_size) {
return tensorflow::errors::DataLoss("Truncated read of '", filename,
"' expected ", file_size, " got ",
data.size());
}
// output->scalar<string>()() = data.ToString();
output->scalar<string>()() = string(data);
return Status::OK();
}
Status ReadTensorFromImageFile(const string& file_name, const int input_height,
const int input_width, const float input_mean,
const float input_std,
std::vector<Tensor>* out_tensors) {
auto root = tensorflow::Scope::NewRootScope();
using namespace ::tensorflow::ops;
string input_name = "file_reader";
string output_name = "normalized";
// read file_name into a tensor named input
Tensor input(tensorflow::DT_STRING, tensorflow::TensorShape());
TF_RETURN_IF_ERROR(
ReadEntireFile(tensorflow::Env::Default(), file_name, &input));
// use a placeholder to read input data
auto file_reader =
Placeholder(root.WithOpName("input"), tensorflow::DataType::DT_STRING);
std::vector<std::pair<string, tensorflow::Tensor>> inputs = {
{"input", input},
};
// Now try to figure out what kind of file it is and decode it.
const int wanted_channels = 1;
// tensorflow::Output image_reader;
// if (tensorflow::StringPiece(file_name).ends_with(".png")) {
// image_reader = DecodePng(root.WithOpName("png_reader"), file_reader,
// DecodePng::Channels(wanted_channels));
// } else if (tensorflow::StringPiece(file_name).ends_with(".gif")) {
// // gif decoder returns 4-D tensor, remove the first dim
// image_reader =
// Squeeze(root.WithOpName("squeeze_first_dim"),
// DecodeGif(root.WithOpName("gif_reader"), file_reader));
// } else if (tensorflow::StringPiece(file_name).ends_with(".bmp")) {
// image_reader = DecodeBmp(root.WithOpName("bmp_reader"), file_reader);
// } else {
// // Assume if it's neither a PNG nor a GIF then it must be a JPEG.
// image_reader = DecodeJpeg(root.WithOpName("jpeg_reader"), file_reader,
// DecodeJpeg::Channels(wanted_channels));
// }
tensorflow::Output image_reader;
if (tensorflow::str_util::EndsWith(file_name, ".png")) {
image_reader = DecodePng(root.WithOpName("png_reader"), file_reader,
DecodePng::Channels(wanted_channels));
} else if (tensorflow::str_util::EndsWith(file_name, ".gif")) {
// gif decoder returns 4-D tensor, remove the first dim
image_reader =
Squeeze(root.WithOpName("squeeze_first_dim"),
DecodeGif(root.WithOpName("gif_reader"), file_reader));
} else if (tensorflow::str_util::EndsWith(file_name, ".bmp")) {
image_reader = DecodeBmp(root.WithOpName("bmp_reader"), file_reader);
} else {
// Assume if it's neither a PNG nor a GIF then it must be a JPEG.
image_reader = DecodeJpeg(root.WithOpName("jpeg_reader"), file_reader,
DecodeJpeg::Channels(wanted_channels));
}
// Now cast the image data to float so we can do normal math on it.
auto float_caster =
Cast(root.WithOpName("float_caster"), image_reader, tensorflow::DT_FLOAT);
auto dims_expander = ExpandDims(root.WithOpName("expand"), float_caster, 0);
float input_max = 255;
Div(root.WithOpName("div"),dims_expander,input_max);
tensorflow::GraphDef graph;
TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));
std::unique_ptr<tensorflow::Session> session(
tensorflow::NewSession(tensorflow::SessionOptions()));
TF_RETURN_IF_ERROR(session->Create(graph));
// std::vector<Tensor> out_tensors;
// TF_RETURN_IF_ERROR(session->Run({}, {output_name + ":0", output_name + ":1"},
// {}, &out_tensors));
TF_RETURN_IF_ERROR(session->Run({inputs}, {"div"}, {}, out_tensors));
return Status::OK();
}
int main()
{
Session* session;
Status status = NewSession(SessionOptions(), &session);//创建新会话Session
string model_path="model.pb";
GraphDef graphdef; //Graph Definition for current model
Status status_load = ReadBinaryProto(Env::Default(), model_path, &graphdef); //从pb文件中读取图模型;
if (!status_load.ok()) {
std::cout << "ERROR: Loading model failed..." << model_path << std::endl;
std::cout << status_load.ToString() << "\n";
return -1;
}
Status status_create = session->Create(graphdef); //将模型导入会话Session中;
if (!status_create.ok()) {
std::cout << "ERROR: Creating graph in session failed..." << status_create.ToString() << std::endl;
return -1;
}
cout << "Session successfully created."<< endl;
string image_path= "/home/u/tf1.13/tensorflow-c-mnist/digit.jpg";
int input_height =28;
int input_width=28;
int input_mean=0;
int input_std=1;
std::vector<Tensor> resized_tensors;
Status read_tensor_status =
ReadTensorFromImageFile(image_path, input_height, input_width, input_mean,
input_std, &resized_tensors);
if (!read_tensor_status.ok()) {
LOG(ERROR) << read_tensor_status;
cout<<"resing error"<<endl;
return -1;
}
const Tensor& resized_tensor = resized_tensors[0];
std::cout << resized_tensor.DebugString()<<endl;
vector<tensorflow::Tensor> outputs;
string output_node = "softmax";
Status status_run = session->Run({{"inputs", resized_tensor}}, {output_node}, {}, &outputs);
if (!status_run.ok()) {
std::cout << "ERROR: RUN failed..." << std::endl;
std::cout << status_run.ToString() << "\n";
return -1;
}
//Fetch output value
std::cout << "Output tensor size:" << outputs.size() << std::endl;
for (std::size_t i = 0; i < outputs.size(); i++) {
std::cout << outputs[i].DebugString()<<endl;
}
Tensor t = outputs[0]; // Fetch the first tensor
int ndim2 = t.shape().dims(); // Get the dimension of the tensor
auto tmap = t.tensor<float, 2>(); // Tensor Shape: [batch_size, target_class_num]
int output_dim = t.shape().dim_size(1); // Get the target_class_num from 1st dimension
std::vector<double> tout;
// Argmax: Get Final Prediction Label and Probability
int output_class_id = -1;
double output_prob = 0.0;
for (int j = 0; j < output_dim; j++)
{
std::cout << "Class " << j << " prob:" << tmap(0, j) << "," << std::endl;
if (tmap(0, j) >= output_prob) {
output_class_id = j;
output_prob = tmap(0, j);
}
}
std::cout << "Final class id: " << output_class_id << std::endl;
std::cout << "Final class prob: " << output_prob << std::endl;
return 0;
}
u@u160406:~/tf1.13/tensorflow-c-mnist/build$ cmake .. -- The C compiler identification is GNU 5.4.0 -- The CXX compiler identification is GNU 5.4.0 -- Check for working C compiler: /usr/bin/cc -- Check for working C compiler: /usr/bin/cc -- works -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Detecting C compile features -- Detecting C compile features - done -- Check for working CXX compiler: /usr/bin/c++ -- Check for working CXX compiler: /usr/bin/c++ -- works -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Detecting CXX compile features -- Detecting CXX compile features - done -- Configuring done -- Generating done -- Build files have been written to: /home/u/tf1.13/tensorflow-c-mnist/build u@u160406:~/tf1.13/tensorflow-c-mnist/build$ make Scanning dependencies of target tf_test [ 50%] Building CXX object CMakeFiles/tf_test.dir/tf.cpp.o [100%] Linking CXX executable tf_test [100%] Built target tf_test u@u160406:~/tf1.13/tensorflow-c-mnist/build$ ./tf_test 2019-10-15 10:43:40.577836: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2019-10-15 10:43:40.599739: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:40.600269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.65 pciBusID: 0000:01:00.0 2019-10-15 10:43:40.600401: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0 2019-10-15 10:43:40.601114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0 2019-10-15 10:43:40.601796: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0 2019-10-15 10:43:40.601986: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0 2019-10-15 10:43:40.603086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0 2019-10-15 10:43:40.603734: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0 2019-10-15 10:43:40.605733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2019-10-15 10:43:40.605793: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:40.606283: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:40.606722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 2019-10-15 10:43:40.606738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0 2019-10-15 10:43:40.697274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-10-15 10:43:40.697295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 2019-10-15 10:43:40.697302: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N 2019-10-15 10:43:40.697444: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:40.697830: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:40.698271: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:40.698632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10138 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5) Session successfully created. 2019-10-15 10:43:41.417655: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:41.419321: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.65 pciBusID: 0000:01:00.0 2019-10-15 10:43:41.419390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0 2019-10-15 10:43:41.419423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0 2019-10-15 10:43:41.419448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0 2019-10-15 10:43:41.419471: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0 2019-10-15 10:43:41.419497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0 2019-10-15 10:43:41.419523: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0 2019-10-15 10:43:41.419549: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2019-10-15 10:43:41.419667: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:41.421127: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:41.422501: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 2019-10-15 10:43:41.422566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-10-15 10:43:41.422592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 2019-10-15 10:43:41.422613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N 2019-10-15 10:43:41.423170: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:41.424636: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-10-15 10:43:41.426045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10138 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5) Tensor<type: float shape: [1,28,28,1] values: [[[0.992156923][0.0823529437][0]]]...> 2019-10-15 10:43:44.755529: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0 2019-10-15 10:43:45.821366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 Output tensor size:1 Tensor<type: float shape: [1,10] values: [0.000222602132 0.951202273 0.00674963091...]...> Class 0 prob:0.000222602, Class 1 prob:0.951202, Class 2 prob:0.00674963, Class 3 prob:0.00721128, Class 4 prob:0.0029518, Class 5 prob:0.0100965, Class 6 prob:0.0119467, Class 7 prob:0.00218653, Class 8 prob:0.00189467, Class 9 prob:0.00553806, Final class id: 1 Final class prob: 0.951202 u@u160406:~/tf1.13/tensorflow-c-mnist/build$
@https://github.com/zhangcliff/tensorflow-c-mnist 会报错：
参照@https://blog.csdn.net/wd1603926823/article/details/92843830解决问题。
”“”“找了很久原因没解决。因为我去看string_view的源码，真的没有报错的这几个成员函数。所以可能是我用错了版本还是这几个函数已经被废弃。
今天终于解决了。原来上面这个例程是按照 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc 仿照这个来写的，而外国人进行了更新，国内很多人以前的博客都是老版本，所以我使用会出问题。 ”“”“