在上一篇中已經成功用tensorflow C++測試過好幾個例子，說明tensorflow c++安裝編譯運行已經沒有問題。
這次不是加載.pb模型而是要加載如下圖所示的模型：
這是python版本：
import tensorflow as tf
import numpy as np
from skimage import io, transform
import cv2
sess = tf.Session()
new_saver = tf.train.import_meta_graph(r'D:\CNN_test_result_sizhuangzao_model\v2_101_ziji_9\model\model.meta')
new_saver.restore(sess,tf.train.latest_checkpoint(r'D:\CNN_test_result_sizhuangzao_model\v2_101_ziji_9\model'))
graph = tf.get_default_graph()
input_x = graph.get_tensor_by_name("input:0")
is_training_x=graph.get_tensor_by_name("is_training:0")
#print (sess.run("input:0"))
jpg_path=r"D:\CNN_test_result_sizhuangzao\chaodacesiji1\pos.txt"
file = open(jpg_path)
lines = file.readlines()
num=0
for line in lines:
img0 = io.imread(line.strip())
l=img0.shape
k=l[0]
c=l[1]
num+=1
print(num)
if c/k<5:
continue
img = transform.resize(img0, (48,160, 1))
feed_dict={input_x:np.reshape(img, [-1, 48, 160, 1]),is_training_x:False}
prob_op = graph.get_operation_by_name('output')
out_softmax = graph.get_tensor_by_name("output:0")
img_out_softmax = sess.run(out_softmax,feed_dict)
prediction_labels = np.argmax(img_out_softmax,1)
同事說在python下可以正常運行預測。
然後我在C++下是仿照
https://blog.csdn.net/seniusen/article/details/82972734
這個例子寫的：
#include "tensorflow/core/framework/graph.pb.h"
#include <tensorflow/core/public/session_options.h>
#include <tensorflow/core/protobuf/meta_graph.pb.h>
#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;
typedef std::vector<std::pair<std::string, tensorflow::Tensor>> tensor_dict;
using tensorflow::Status;
static Status ReadtheFile(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 ReadImageFile(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(ReadtheFile(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::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);
const std::string graph_fn = "/media/root/Ubuntu311/projects/Ecology_projects/tensorflowtest/model-0617/model.meta";
MetaGraphDef graphdef;
Status status_load = ReadBinaryProto(Env::Default(), graph_fn, &graphdef); //從meta文件中讀取圖模型;
if (!status_load.ok()) {
std::cout << "ERROR: Loading model failed..." << graph_fn << std::endl;
std::cout << status_load.ToString() << "\n";
return -1;
}
Status status_create = session->Create(graphdef.graph_def()); //將模型導入會話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.Load model successfully!"<< endl;
// 讀入預先訓練好的模型的權重
const std::string checkpointPath = "/media/root/Ubuntu311/projects/Ecology_projects/tensorflowtest/model-0617/model";
Tensor checkpointPathTensor(DT_STRING, TensorShape());
checkpointPathTensor.scalar<std::string>()() = checkpointPath;
status = session->Run(
{{ graphdef.saver_def().filename_tensor_name(), checkpointPathTensor },},
{},{graphdef.saver_def().restore_op_name()},nullptr);
if (!status.ok())
{
throw runtime_error("Error loading checkpoint from " + checkpointPath + ": " + status.ToString());
}
cout << "Load weights successfully!"<< endl;
//read image for prediction...
string image_path= "/media/root/Ubuntu311/projects/Ecology_projects/copy/cnn-imgs/AABW.jpg";
int input_height =48;
int input_width=160;
int input_mean=0;
int input_std=1;
std::vector<Tensor> resized_tensors;
Status read_tensor_status =
ReadImageFile(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 <<"Read image successfully: "<< resized_tensor.DebugString()<<endl;
std::string Input1Name = "input";
std::string Input2Name = "is_training";
vector<std::pair<string, Tensor> > inputs;
inputs.push_back(std::make_pair(Input1Name, resized_tensor));
inputs.push_back(std::make_pair(Input2Name, resized_tensor));
vector<tensorflow::Tensor> outputs;
string output2="out_softmax";
Status status_run = session->Run(inputs, {output2}, {}, &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 <<"result: "<<i<<" :"<< outputs[i].DebugString()<<endl;
}
cout << "Prediction successfully!"<< endl;
return 0;
}
運行時報錯信息如下：
2019-07-03 14:46:51.826344: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-07-03 14:46:51.857340: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3407945000 Hz
2019-07-03 14:46:51.858724: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x109c780 executing computations on platform Host. Devices:
2019-07-03 14:46:51.858753: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2019-07-03 14:46:52.337448: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 0. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
2019-07-03 14:46:52.627237: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 1. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
Session successfully created.Load model successfully!
2019-07-03 14:46:53.305255: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 2. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
2019-07-03 14:46:53.440792: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1337] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
2019-07-03 14:46:53.459186: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 3. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
Load weights successfully!
2019-07-03 14:46:54.499491: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 0. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
2019-07-03 14:46:54.499908: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 1. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
2019-07-03 14:46:54.505033: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 2. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
2019-07-03 14:46:54.506574: I tensorflow/core/common_runtime/optimization_registry.cc:35] Running all optimization passes in grouping 3. If you see this a lot, you might be extending the graph too many times (which means you modify the graph many times before execution). Try reducing graph modifications or using SavedModel to avoid any graph modification
Read image successfully: Tensor<type: float shape: [1,48,160,1] values: [[[0.592156887][0.603921592][0.603921592]]]...>
2019-07-03 14:47:02.087086: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:486] model_pruner failed: Invalid argument: Invalid input graph.
我知道這樣寫是錯誤的，因爲官網和谷歌上都沒找到同時有"input:0"和"is_training:0"的例子，所以我不知道怎麼將python部分轉到c++。