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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../TestUtils.hpp"
#include <Optimizer.hpp>
#include <Half.hpp>
#include <doctest/doctest.h>
using namespace armnn;
TEST_SUITE("Optimizer")
{
using namespace armnn::optimizations;
TEST_CASE("ConvertConstantsFloatToHalfTest")
{
armnn::Graph graph;
const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::Float16);
// Create const tensor from fp32 data
unsigned int dims[] = { 4, 1, 1, 1 };
std::vector<float> floatWeights{ 1.0f, 2.0f, 3.0f, 4.0f };
armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true), floatWeights);
// Create 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));
// Check tensor data type before conversion
CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
// Run the optimizer
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf()));
// Check tensor data type after conversion
CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
// Check whether data matches expected fp16 data
const Half* data = fc->m_Weight->GetConstTensor<Half>();
CHECK(data[0] == Half(1.0f));
CHECK(data[1] == Half(2.0f));
CHECK(data[2] == Half(3.0f));
CHECK(data[3] == Half(4.0f));
}
}
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