mirror of
https://github.com/saymrwulf/onnxruntime.git
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671fa60327
* switch to work PC * back with iterable of buffers * add raw api tests * tensorization * last test * all tests pass! * small cleanup * whitespace * newline * whitespace * refactor common code into DisjointBufferHelpers * remove unused file * warning * skip gpu tests when hardware not available * Add error condition when createreference is invoked * add null check to cretereference * uncomment out check Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
108 lines
3.5 KiB
C++
108 lines
3.5 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "testPch.h"
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#include "RawApiTests.h"
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#include "RawApiHelpers.h"
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#include <fstream>
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#include <roapi.h>
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namespace ml = Microsoft::AI::MachineLearning;
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auto CreateModelAsBuffer(const wchar_t* model_path)
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{
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std::ifstream input_stream(model_path, std::ios::binary | std::ios::ate);
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std::streamsize size = input_stream.tellg();
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input_stream.seekg(0, std::ios::beg);
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std::vector<char> buffer(static_cast<std::vector<char>::size_type>(size));
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input_stream.read(buffer.data(), size);
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return std::make_pair(buffer, size);
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}
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static void RawApiTestsApiTestsClassSetup() {
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RoInitialize(RO_INIT_TYPE::RO_INIT_SINGLETHREADED);
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}
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static void CreateModelFromFilePath() {
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std::wstring model_path = L"model.onnx";
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std::unique_ptr<ml::learning_model> model = nullptr;
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WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size()));
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WINML_EXPECT_NO_THROW(model.reset());
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}
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static void CreateCpuDevice() {
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std::unique_ptr<ml::learning_model_device> device = nullptr;
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WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
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}
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static void Evaluate() {
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std::wstring model_path = L"model.onnx";
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std::unique_ptr<ml::learning_model> model = nullptr;
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WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size()));
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std::unique_ptr<ml::learning_model_device> device = nullptr;
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WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
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RunOnDevice(*model.get(), *device.get(), InputStrategy::CopyInputs);
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WINML_EXPECT_NO_THROW(model.reset());
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}
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static void EvaluateNoInputCopy() {
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std::wstring model_path = L"model.onnx";
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std::unique_ptr<ml::learning_model> model = nullptr;
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WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size()));
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std::unique_ptr<ml::learning_model_device> device = nullptr;
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WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
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RunOnDevice(*model.get(), *device.get(), InputStrategy::BindAsReference);
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WINML_EXPECT_NO_THROW(model.reset());
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}
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static void EvaluateManyBuffers() {
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std::wstring model_path = L"model.onnx";
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std::unique_ptr<ml::learning_model> model = nullptr;
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WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(model_path.c_str(), model_path.size()));
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std::unique_ptr<ml::learning_model_device> device = nullptr;
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WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
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RunOnDevice(*model.get(), *device.get(), InputStrategy::BindWithMultipleReferences);
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WINML_EXPECT_NO_THROW(model.reset());
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}
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static void EvaluateFromModelFromBuffer() {
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std::wstring model_path = L"model.onnx";
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auto [buffer, size] = CreateModelAsBuffer(model_path.c_str());
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std::unique_ptr<ml::learning_model> model = nullptr;
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WINML_EXPECT_NO_THROW(model = std::make_unique<ml::learning_model>(buffer.data(), static_cast<size_t>(size)));
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std::unique_ptr<ml::learning_model_device> device = nullptr;
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WINML_EXPECT_NO_THROW(device = std::make_unique<ml::learning_model_device>());
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RunOnDevice(*model.get(), *device.get(), InputStrategy::CopyInputs);
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WINML_EXPECT_NO_THROW(model.reset());
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}
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const RawApiTestsApi& getapi() {
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static constexpr RawApiTestsApi api = {
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RawApiTestsApiTestsClassSetup,
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CreateModelFromFilePath,
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CreateCpuDevice,
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Evaluate,
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EvaluateNoInputCopy,
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EvaluateManyBuffers,
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EvaluateFromModelFromBuffer,
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};
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return api;
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}
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