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>
77 lines
2.8 KiB
C++
77 lines
2.8 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 "test.h"
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#include "RawApiHelpers.h"
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namespace ml = Microsoft::AI::MachineLearning;
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void RunOnDevice(ml::learning_model& model, ml::learning_model_device& device, InputStrategy strategy) {
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const wchar_t input_name[] = L"data_0";
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const wchar_t output_name[] = L"softmaxout_1";
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std::unique_ptr<ml::learning_model_session> session = nullptr;
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WINML_EXPECT_NO_THROW(session = std::make_unique<ml::learning_model_session>(model, device));
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std::unique_ptr<ml::learning_model_binding> binding = nullptr;
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WINML_EXPECT_NO_THROW(binding = std::make_unique<ml::learning_model_binding>(*session.get()));
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auto input_shape = std::vector<ml::tensor_shape_type>{ 1, 3, 224, 224 };
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auto input_data = std::vector<float>(1 * 3 * 224 * 224);
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auto output_shape = std::vector<ml::tensor_shape_type>{ 1, 1000, 1, 1 };
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std::iota(begin(input_data), end(input_data), 0.f);
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if (strategy == InputStrategy::CopyInputs)
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{
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WINML_EXPECT_HRESULT_SUCCEEDED(
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binding->bind<float>(
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input_name, _countof(input_name) - 1,
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input_shape.data(), input_shape.size(),
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input_data.data(), input_data.size()));
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} else if (strategy == InputStrategy::BindAsReference)
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{
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WINML_EXPECT_HRESULT_SUCCEEDED(
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binding->bind_as_reference<float>(
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input_name, _countof(input_name) - 1,
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input_shape.data(), input_shape.size(),
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input_data.data(), input_data.size()));
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} else if (strategy == InputStrategy::BindWithMultipleReferences) {
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size_t channel_size = 224 * 224;
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auto channel_buffers_sizes = std::vector<size_t>{
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channel_size,
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channel_size,
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channel_size
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};
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auto channel_buffers_pointers = std::vector<float*>{
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&input_data.at(0),
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&input_data.at(0) + channel_buffers_sizes[0],
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&input_data.at(0) + channel_buffers_sizes[0] + +channel_buffers_sizes[1]
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};
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WINML_EXPECT_HRESULT_SUCCEEDED(
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binding->bind_as_references<float>(
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input_name, _countof(input_name) - 1,
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channel_buffers_pointers.data(), channel_buffers_sizes.data(),
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channel_buffers_sizes.size()));
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}
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WINML_EXPECT_HRESULT_SUCCEEDED(
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binding->bind<float>(
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output_name, _countof(output_name) - 1,
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output_shape.data(), output_shape.size()));
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ml::learning_model_results results = session->evaluate(*binding.get());
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float* p_buffer = nullptr;
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size_t buffer_size = 0;
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WINML_EXPECT_HRESULT_SUCCEEDED(
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0 == results.get_output(
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output_name,
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_countof(output_name) - 1,
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reinterpret_cast<void**>(&p_buffer),
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&buffer_size)
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);
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}
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