tensorflow c++ API预测多张图片batch inference
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经过前几篇的折腾与各种查资料后，单张预测代码：
//one image prediction ---single image
int mainsingle()
{
Session* session;
Status status = NewSession(SessionOptions(), &session);
const std::string graph_fn = "/media/root/Ubuntu311/projects/Ecology_projects/JPMVCNN_AlgaeAnalysisMathTestDemo/model-0723/model.meta";
MetaGraphDef graphdef;
Status status_load = ReadBinaryProto(Env::Default(), graph_fn, &graphdef); //从meta文件中读取图模型;
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()); //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.Load model successfully!"<< endl;
// 读入预先训练好的模型的权重
const std::string checkpointPath = "/media/root/Ubuntu311/projects/Ecology_projects/JPMVCNN_AlgaeAnalysisMathTestDemo/model-0723/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...
char srcfile[200];
double alltime=0.0;
for(int numingroup=0;numingroup<1326;numingroup++)
{
sprintf(srcfile, "/media/root/Ubuntu311/projects/Ecology_projects/copy/cnn-imgs96224/%d.JPG",numingroup);
cv::Mat srcimg=cv::imread(srcfile,0);
if(!srcimg.data)
{
continue;
}
Tensor resized_tensor(DT_FLOAT, TensorShape({1,96,224,1}));
float *imgdata = resized_tensor.flat<float>().data();
cv::Mat cameraImg(96, 224, CV_32FC1, imgdata);
srcimg.convertTo(cameraImg, CV_32FC1);
//对图像做预处理
cameraImg=cameraImg/255;
std::cout <<"Read image successfully: "<< resized_tensor.DebugString()<<endl;
vector<std::pair<string, Tensor> > inputs;
std::string Input1Name = "input";
inputs.push_back(std::make_pair(Input1Name, resized_tensor));
Tensor is_training_val(DT_BOOL,TensorShape());
is_training_val.scalar<bool>()()=false;
std::string Input2Name = "is_training";
inputs.push_back(std::make_pair(Input2Name, is_training_val));
vector<tensorflow::Tensor> outputs;
string output="output";
cv::TickMeter timer;
timer.start();
Status status_run = session->Run(inputs, {output}, {}, &outputs);
if (!status_run.ok()) {
std::cout << "ERROR: RUN failed..." << std::endl;
std::cout << status_run.ToString() << "\n";
return -1;
}
timer.stop();
cout<<"single image inference time is: "<<timer.getTimeSec()<<" s."<<endl;
alltime+=(timer.getTimeSec());
timer.reset();
Tensor t = outputs[0];
int ndim2 = t.shape().dims();
auto tmap = t.tensor<float, 2>(); // Tensor Shape: [batch_size, target_class_num]
int output_dim = t.shape().dim_size(1);
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;
}
cout<<"all image have been predicted and time is: "<<alltime<<endl;
return 0;
}
我测了下预测时间每张图几乎0.02秒：
下面是分成多个batch进行预测：
//batch inference...
int mainbatchinference()
{
Session* session;
Status status = NewSession(SessionOptions(), &session);
const std::string graph_fn = "/media/root/Ubuntu311/projects/Ecology_projects/JPMVCNN_AlgaeAnalysisMathTestDemo/model-0723/model.meta";
MetaGraphDef graphdef;
Status status_load = ReadBinaryProto(Env::Default(), graph_fn, &graphdef); //从meta文件中读取图模型;
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()); //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.Load model successfully!"<< endl;
// 读入预先训练好的模型的权重
const std::string checkpointPath = "/media/root/Ubuntu311/projects/Ecology_projects/JPMVCNN_AlgaeAnalysisMathTestDemo/model-0723/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;
int cnnrows=96;
int cnncols=224;
//read image for prediction...
char srcfile[200];
const int imgnum=1326;
const int batch=32;
double alltime=0.0;
//all image inference...
for(int imgind=0;imgind<imgnum/batch;imgind++)
{
//a batch inference...
tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({ batch, cnnrows, cnncols, 1 }));
auto input_tensor_mapped = input_tensor.tensor<float, 4>();
int batchind=0;
int imgrealind=imgind*batch;
for(;batchind!=batch;batchind++)
{
sprintf(srcfile, "/media/root/Ubuntu311/projects/Ecology_projects/copy/cnn-imgs96224/%d.JPG",imgrealind);
cv::Mat srcimg=cv::imread(srcfile,0);
if(!srcimg.data)
{
continue;
}
cv::Mat cameraImg(cnnrows, cnncols, CV_32FC1);
srcimg.convertTo(cameraImg, CV_32FC1);
cameraImg=cameraImg/255;
//convert batch cv image to tensor
for (int y = 0; y < cnnrows; ++y)
{
const float* source_row = (float*)cameraImg.data + (y * cnncols);
for (int x = 0; x < cnncols; ++x)
{
const float* source_pixel = source_row + x;
input_tensor_mapped(batchind, y, x, 0) = *source_pixel;
}
}
imgrealind++;
//a batch image transfer done...
}
vector<std::pair<string, Tensor> > inputs;
std::string Input1Name = "input";
inputs.push_back(std::make_pair(Input1Name, input_tensor));
Tensor is_training_val(DT_BOOL,TensorShape());
is_training_val.scalar<bool>()()=false;
std::string Input2Name = "is_training";
inputs.push_back(std::make_pair(Input2Name, is_training_val));
vector<tensorflow::Tensor> outputs;
string output="output";
cv::TickMeter timer;
timer.start();
Status status_run = session->Run(inputs, {output}, {}, &outputs);
if (!status_run.ok()) {
std::cout << "ERROR: RUN failed..." << std::endl;
std::cout << status_run.ToString() << "\n";
return -1;
}
timer.stop();
cout<<"time of this batch inference is: "<<timer.getTimeSec()<<" s."<<endl;
alltime+=(timer.getTimeSec());
timer.reset();
auto finalOutputTensor = outputs[0].tensor<float, 2>();
int output_dim = outputs[0].shape().dim_size(1);
for(int b=0; b<batch;b++)
{
for(int i=0; i<output_dim; i++)
{
cout << b << "the probability for class "<<i<<" is "<< finalOutputTensor(b, i) <<endl;
}
}
//all images inference done...
}
cout<<"all image have been predicted and time is: "<<alltime<<endl;
return 0;
}
batch inference的时间是：
已对比测试过，多张预测batch inference与single image inference预测结果一致，证明代码正确。
但是之前stackoverflow上有人说batch inference比single image inference快，所以我才尝试batch inference的，但是我测出来并不快！！！
他说他single inference是0.02秒，batch=1560的inference只要0.03秒，提速了1560X0.02/0.03=几乎1000倍！！！但是我这里并没有什么提速的效果，难道是batch设置太小了？
关于预测时间这个问题我已在 https://stackoverflow.com/questions/57460782/batch-inference-is-as-slow-as-single-image-inference-in-tensorflow-c 和 https://github.com/tensorflow/tensorflow/issues/31572 上提问了，目前没有有效答复。
依旧放一张小不点的照片镇楼
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