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//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "CommonTestUtils.hpp"
#include <armnn/INetwork.hpp>
#include <ResolveType.hpp>
#include <doctest/doctest.h>
namespace{
armnn::INetworkPtr CreateGatherNetwork(const armnn::TensorInfo& paramsInfo,
const armnn::TensorInfo& indicesInfo,
const armnn::TensorInfo& outputInfo,
const std::vector<int32_t>& indicesData)
{
armnn::INetworkPtr net(armnn::INetwork::Create());
armnn::GatherDescriptor descriptor;
armnn::IConnectableLayer* paramsLayer = net->AddInputLayer(0);
armnn::IConnectableLayer* indicesLayer = net->AddConstantLayer(armnn::ConstTensor(indicesInfo, indicesData));
armnn::IConnectableLayer* gatherLayer = net->AddGatherLayer(descriptor, "gather");
armnn::IConnectableLayer* outputLayer = net->AddOutputLayer(0, "output");
Connect(paramsLayer, gatherLayer, paramsInfo, 0, 0);
Connect(indicesLayer, gatherLayer, indicesInfo, 0, 1);
Connect(gatherLayer, outputLayer, outputInfo, 0, 0);
return net;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void GatherEndToEnd(const std::vector<BackendId>& backends)
{
armnn::TensorInfo paramsInfo({ 8 }, ArmnnType);
armnn::TensorInfo indicesInfo({ 3 }, armnn::DataType::Signed32);
armnn::TensorInfo outputInfo({ 3 }, ArmnnType);
paramsInfo.SetQuantizationScale(1.0f);
paramsInfo.SetQuantizationOffset(0);
outputInfo.SetQuantizationScale(1.0f);
outputInfo.SetQuantizationOffset(0);
// Creates structures for input & output.
std::vector<T> paramsData{
1, 2, 3, 4, 5, 6, 7, 8
};
std::vector<int32_t> indicesData{
7, 6, 5
};
std::vector<T> expectedOutput{
8, 7, 6
};
// Builds up the structure of the network
armnn::INetworkPtr net = CreateGatherNetwork(paramsInfo, indicesInfo, outputInfo, indicesData);
CHECK(net);
std::map<int, std::vector<T>> inputTensorData = {{ 0, paramsData }};
std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }};
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void GatherMultiDimEndToEnd(const std::vector<BackendId>& backends)
{
armnn::TensorInfo paramsInfo({ 3, 2, 3}, ArmnnType);
armnn::TensorInfo indicesInfo({ 2, 3 }, armnn::DataType::Signed32);
armnn::TensorInfo outputInfo({ 2, 3, 2, 3 }, ArmnnType);
paramsInfo.SetQuantizationScale(1.0f);
paramsInfo.SetQuantizationOffset(0);
outputInfo.SetQuantizationScale(1.0f);
outputInfo.SetQuantizationOffset(0);
// Creates structures for input & output.
std::vector<T> paramsData{
1, 2, 3,
4, 5, 6,
7, 8, 9,
10, 11, 12,
13, 14, 15,
16, 17, 18
};
std::vector<int32_t> indicesData{
1, 2, 1,
2, 1, 0
};
std::vector<T> expectedOutput{
7, 8, 9,
10, 11, 12,
13, 14, 15,
16, 17, 18,
7, 8, 9,
10, 11, 12,
13, 14, 15,
16, 17, 18,
7, 8, 9,
10, 11, 12,
1, 2, 3,
4, 5, 6
};
// Builds up the structure of the network
armnn::INetworkPtr net = CreateGatherNetwork(paramsInfo, indicesInfo, outputInfo, indicesData);
std::map<int, std::vector<T>> inputTensorData = {{ 0, paramsData }};
std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }};
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
}
} // anonymous namespace
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