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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../TestUtils.hpp"
#include <Optimizer.hpp>
#include <doctest/doctest.h>
TEST_SUITE("Optimizer")
{
using namespace armnn::optimizations;
TEST_CASE("ConvertConstantsHalfToFloatTest")
{
armnn::Graph graph;
const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::Float32);
// Create the half precision input data
unsigned int dims[] = { 4, 1, 1, 1 };
std::vector<float> convWeightsData{ 1.f, 2.f, 3.f, 4.f };
std::vector<uint16_t> halfWeights(4);
armnnUtils::FloatingPointConverter::ConvertFloat32To16(convWeightsData.data(), convWeightsData.size(),
halfWeights.data());
armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float16, 0.0f, 0, true), halfWeights);
//Create the simple test network
auto input = graph.AddLayer<armnn::InputLayer>(0, "input");
input->GetOutputSlot().SetTensorInfo(info);
auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc");
fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights);
fc->GetOutputSlot().SetTensorInfo(info);
auto output = graph.AddLayer<armnn::OutputLayer>(1, "output");
//Connect up the layers
input->GetOutputSlot().Connect(fc->GetInputSlot(0));
fc->GetOutputSlot().Connect(output->GetInputSlot(0));
//Test the tensor info is correct.
CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
// Run the optimizer
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsHalfToFloat()));
//Test the tensor info is correct.
CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
// Now test the data matches float32 data
const float* data = fc->m_Weight->GetConstTensor<float>();
CHECK(1.0f == data[0]);
CHECK(2.0f == data[1]);
CHECK(3.0f == data[2]);
CHECK(4.0f == data[3]);
}
}
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