onnxruntime/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/InferenceTestCapi.cpp

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
//
#include "CppUnitTest.h"
#include <assert.h>
#include <core/session/onnxruntime_c_api.h>

wchar_t* GetWideString(const char* c) {
  const size_t cSize = strlen(c) + 1;
  wchar_t* wc = new wchar_t[cSize];
  mbstowcs(wc, c, cSize);

  return wc;
}

#define ORT_ABORT_ON_ERROR(expr)                         \
  {                                                      \
    OrtStatus* onnx_status = (expr);                     \
    if (onnx_status != NULL) {                           \
      const char* msg = OrtGetErrorMessage(onnx_status); \
      fprintf(stderr, "%s\n", msg);                      \
      OrtReleaseStatus(onnx_status);                     \
      wchar_t* wmsg = GetWideString(msg);                \
      Assert::Fail(L"Failed on ORT_ABORT_ON_ERROR");     \
      free(wmsg);                                        \
    }                                                    \
  }

using namespace Microsoft::VisualStudio::CppUnitTestFramework;

namespace UnitTest1
{		
    TEST_CLASS(UnitTest1)
    {
    public:
        
        int run_inference(OrtSession* session) {
            size_t input_height = 224;
            size_t input_width = 224;
            float* model_input = (float *) malloc (sizeof (float) * 224 * 224 * 3);
            size_t model_input_ele_count = 224 * 224 * 3;

            // initialize to values between 0.0 and 1.0
            for (unsigned int i = 0; i < model_input_ele_count; i++)
                model_input[i] = (float)i / (float)(model_input_ele_count + 1);

            OrtAllocatorInfo* allocator_info;
            ORT_ABORT_ON_ERROR(OrtCreateCpuAllocatorInfo(OrtArenaAllocator, OrtMemTypeDefault, &allocator_info));
            const size_t input_shape[] = { 1, 3, 224, 224 };
            const size_t input_shape_len = sizeof(input_shape) / sizeof(input_shape[0]);
            const size_t model_input_len = model_input_ele_count * sizeof(float);

            OrtValue* input_tensor = NULL;
            ORT_ABORT_ON_ERROR(OrtCreateTensorWithDataAsOrtValue(allocator_info, model_input, model_input_len, input_shape, input_shape_len, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT, &input_tensor));
            assert(input_tensor != NULL);
            assert(OrtIsTensor(input_tensor));
            OrtReleaseAllocatorInfo(allocator_info);
            const char* input_names[] = { "data_0" };
            const char* output_names[] = { "softmaxout_1" };
            OrtValue* output_tensor = NULL;
            ORT_ABORT_ON_ERROR(OrtRun(session, NULL, input_names, (const OrtValue* const*)&input_tensor, 1, output_names, 1, &output_tensor));
            assert(output_tensor != NULL);
            assert(OrtIsTensor(output_tensor));

            OrtReleaseValue(output_tensor);
            OrtReleaseValue(input_tensor);
            free(model_input);
            return 0;
        }

        int test()
        {
            const wchar_t * model_path = L"squeezenet.onnx";
            OrtEnv* env;
            ORT_ABORT_ON_ERROR(OrtCreateEnv(ORT_LOGGING_LEVEL_WARNING, "test", &env));
            OrtSessionOptions* session_option = OrtCreateSessionOptions();
            OrtSession* session;
            ORT_ABORT_ON_ERROR(OrtCreateSession(env, model_path, session_option, &session));

            OrtSetSessionThreadPoolSize(session_option, 1);
            return run_inference(session);
        }

        TEST_METHOD(TestMethod1)
        {
            int res = test();
            Assert::AreEqual(res, 0);
        }
    };
}