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//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <ResolveType.hpp>
#include <armnn/INetwork.hpp>
#include <doctest/doctest.h>
#include <CommonTestUtils.hpp>
namespace
{
template<typename armnn::DataType DataType>
armnn::INetworkPtr CreateBatchMatMulNetwork(const armnn::TensorShape& inputXShape,
const armnn::TensorShape& inputYShape,
const armnn::TensorShape& outputShape,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
using namespace armnn;
INetworkPtr network(INetwork::Create());
TensorInfo inputXTensorInfo(inputXShape, DataType, qScale, qOffset, true);
TensorInfo inputYTensorInfo(inputYShape, DataType, qScale, qOffset, true);
TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
BatchMatMulDescriptor batchMatMulDesc;
batchMatMulDesc.m_TransposeX = false;
batchMatMulDesc.m_TransposeY = true;
IConnectableLayer* batchMatMul = network->AddBatchMatMulLayer(batchMatMulDesc, "batchMatMul");
IConnectableLayer* inputX = network->AddInputLayer(0, "inputX");
IConnectableLayer* inputY = network->AddInputLayer(1, "inputY");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(inputX, batchMatMul, inputXTensorInfo, 0, 0);
Connect(inputY, batchMatMul, inputYTensorInfo, 0, 1);
Connect(batchMatMul, output, outputTensorInfo, 0, 0);
return network;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void BatchMatMulEndToEnd(const std::vector<armnn::BackendId>& backends)
{
using namespace armnn;
const TensorShape& inputXShape = { 2, 2, 2 };
const TensorShape& inputYShape = { 2, 2, 2 };
const TensorShape& outputShape = { 2, 2, 2 };
constexpr float qScale = 1.0f;
constexpr int32_t qOffset = 0;
INetworkPtr network = CreateBatchMatMulNetwork<ArmnnType>(inputXShape, inputYShape, outputShape, qScale, qOffset);
CHECK(network);
std::vector<float> floatInputXData{ 1., 2.,
3., 4.,
9., 10.,
11., 12. };
std::vector<T> inputXData = armnnUtils::QuantizedVector<T>(floatInputXData, qScale, qOffset);
std::vector<float> floatInputYData{ 5., 7.,
6., 8.,
13., 15.,
14., 16. };
std::vector<T> inputYData = armnnUtils::QuantizedVector<T>(floatInputYData, qScale, qOffset);
std::vector<float> floatExpectedOutputData{ 19., 22.,
43., 50.,
267., 286.,
323., 346. };
std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData, qScale, qOffset);
std::map<int, std::vector<T>> inputTensor = {{ 0, inputXData }, {1, inputYData}};
std::map<int, std::vector<T>> expectedOutput = { { 0, expectedOutputData } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensor, expectedOutput, backends);
}
} // anonymous namespace
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