修改C++示例代码

Src/Classifier.cpp

@@ -40,10 +40,9 @@ ErrorCode Classifier::Initialize(InitializationParameterOfClassifier initializat
     OrtMIGraphXProviderOptions migraphx_options;
     migraphx_options.device_id = 0;
     migraphx_options.migraphx_fp16_enable = 1;
-    migraphx_options.dynamic_model = 0; 
     sessionOptions.AppendExecutionProvider_MIGraphX(migraphx_options);
 
-    session = new Ort::Session(env, modelPath.c_str(), sessionOptions);
+    dcu_session = new Ort::Session(env, modelPath.c_str(), sessionOptions);
     return SUCCESS;
 }
 
@@ -91,24 +90,42 @@ ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector
     image2BlobParams.swapRB=false;
     blobFromImagesWithParams(image,inputBlob,image2BlobParams);
 
-    // 设置onnx的输入和输出名
-    std::vector<const char*> input_node_names = {"data"};
-    std::vector<const char*> output_node_names = {"resnetv24_dense0_fwd"};
+    // 获取模型输入输出信息
+    Ort::AllocatorWithDefaultOptions allocator;
+    std::vector<Ort::AllocatedStringPtr> inputNamesPtr;
+    std::vector<Ort::AllocatedStringPtr> outputNamesPtr;
+    for ( size_t i=0; i<dcu_session->GetInputCount(); i++)
+    {
+        auto input_name = dcu_session->GetInputNameAllocated(i , allocator);
+        inputNamesPtr.push_back(std::move(input_name));
+    }
+    for ( size_t i=0; i<dcu_session->GetOutputCount(); i++)
+    {
+        auto out_name = dcu_session->GetOutputNameAllocated(i , allocator);
+        outputNamesPtr.push_back(std::move(out_name));
+    }
+    std::vector<const char *> inputNames = {inputNamesPtr.data()->get()};
+    std::vector<const char *> outputNames = {outputNamesPtr.data()->get()};
 
-    // 初始化输入数据
-    std::array<int64_t, 4> inputShape{1, 3, 224, 224};
-    auto memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
-    std::array<float, 3 * 224 * 224> input_image{};
-    float* input_test = (float*)inputBlob.data;
-    Ort::Value inputTensor = Ort::Value::CreateTensor<float>(memoryInfo, input_test, input_image.size(), inputShape.data(), inputShape.size());
-    std::vector<Ort::Value> intput_tensors;
-    intput_tensors.push_back(std::move(inputTensor));
+    float* input_data = (float*)inputBlob.data;
+    std::array<float, 3 * 224 * 224> input_data_len{};
+
+    Ort::MemoryInfo memoryInfo =Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
+
+    std::vector<Ort::Value> inputTensors;
+    for(size_t i=0; i<inputNames.size(); i++)
+    {
+        Ort::TypeInfo inputTypeInfo = dcu_session->GetInputTypeInfo(i);
+        auto inputTensorInfo = inputTypeInfo.GetTensorTypeAndShapeInfo();
+        std::vector<int64_t> inputDims = inputTensorInfo.GetShape();
+        inputTensors.push_back(Ort::Value::CreateTensor<float>(memoryInfo,input_data,input_data_len.size(), inputDims.data(), inputDims.size()));
+    }
     
     // 进行推理
-    auto output_tensors = session->Run(Ort::RunOptions{nullptr}, input_node_names.data(), intput_tensors.data(), 1, output_node_names.data(), 1);
+    auto output = dcu_session->Run(Ort::RunOptions{nullptr}, inputNames.data(), inputTensors.data(), inputNames.size(), outputNames.data(), outputNames.size());
     
     // 解析输出结果
-    const float* pdata = output_tensors[0].GetTensorMutableData<float>();
+    const float* pdata = output[0].GetTensorMutableData<float>();
     int numberOfClasses = 1000 ;
     for(int i=0;i<srcImages.size();++i)
     {

Src/Classifier.h

@@ -19,9 +19,7 @@ public:
 
 private:
     cv::FileStorage configurationFile;
-    Ort::Session *session;
-    cv::Size inputSize;
-    std::string inputName;
+    Ort::Session *dcu_session;
     Ort::Env env = Ort::Env(ORT_LOGGING_LEVEL_ERROR, "ONNXRuntime");
     Ort::SessionOptions sessionOptions = Ort::SessionOptions();
 };