onnxruntime/winml/test/api/RawApiTests.cpp
Sheil Kumar 43a828f0a2
Add tests for WinRT Projection Raw ABI consumption (#3718)
Add tests for WinRT Projection Raw ABI consumption
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
2020-05-02 00:33:17 -07:00

96 lines
2.9 KiB
C++

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include "testPch.h"
#include "RawApiTests.h"
#include "RawApiHelpers.h"
#include <fstream>
#include <roapi.h>
namespace ml = Microsoft::AI::MachineLearning;
auto CreateModelAsBuffer(const wchar_t* model_path)
{
std::ifstream input_stream(model_path, std::ios::binary | std::ios::ate);
std::streamsize size = input_stream.tellg();
input_stream.seekg(0, std::ios::beg);
std::vector<char> buffer(size);
input_stream.read(buffer.data(), size);
return std::make_pair(buffer, size);
}
static void RawApiTestsApiTestsClassSetup() {
RoInitialize(RO_INIT_TYPE::RO_INIT_SINGLETHREADED);
}
static void CreateModelFromFilePath() {
std::wstring model_path = L"model.onnx";
std::unique_ptr<ml::learning_model> model = nullptr;
WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size()));
WINML_EXPECT_NO_THROW(model.reset());
}
static void CreateCpuDevice() {
std::unique_ptr<ml::learning_model_device> device = nullptr;
WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
}
static void Evaluate() {
std::wstring model_path = L"model.onnx";
std::unique_ptr<ml::learning_model> model = nullptr;
WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size()));
std::unique_ptr<ml::learning_model_device> device = nullptr;
WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
RunOnDevice(*model.get(), *device.get(), true);
WINML_EXPECT_NO_THROW(model.reset());
}
static void EvaluateNoInputCopy() {
std::wstring model_path = L"model.onnx";
std::unique_ptr<ml::learning_model> model = nullptr;
WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size()));
std::unique_ptr<ml::learning_model_device> device = nullptr;
WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
RunOnDevice(*model.get(), *device.get(), false);
WINML_EXPECT_NO_THROW(model.reset());
}
static void EvaluateFromModelFromBuffer() {
std::wstring model_path = L"model.onnx";
size_t size;
std::vector<char> buffer;
std::tie(buffer, size) = CreateModelAsBuffer(model_path.c_str());
std::unique_ptr<ml::learning_model> model = nullptr;
WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(buffer.data(), size));
std::unique_ptr<ml::learning_model_device> device = nullptr;
WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
RunOnDevice(*model.get(), *device.get(), true);
WINML_EXPECT_NO_THROW(model.reset());
}
const RawApiTestsApi& getapi() {
static constexpr RawApiTestsApi api = {
RawApiTestsApiTestsClassSetup,
CreateModelFromFilePath,
CreateCpuDevice,
Evaluate,
EvaluateNoInputCopy,
EvaluateFromModelFromBuffer,
};
return api;
}