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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "armnn/INetwork.hpp"
#include "armnnUtils/QuantizeHelper.hpp"
#include "ElementwiseBinaryEndToEndTestImpl.hpp"
#include "Optimizer.hpp"
#include <CommonTestUtils.hpp>
#include <ResolveType.hpp>
#include <doctest/doctest.h>
namespace
{
using namespace armnn;
armnn::INetworkPtr CreateBroadcastToNetwork(BroadcastToDescriptor& descriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo)
{
INetworkPtr network(INetwork::Create());
IConnectableLayer* inputLayer = network->AddInputLayer(0, "input");
IConnectableLayer* broadcastLayer = network->AddBroadcastToLayer(descriptor, "broadcast_to");
IConnectableLayer* outputLayer = network->AddOutputLayer(0, "output");
Connect(inputLayer, broadcastLayer, inputInfo, 0, 0);
Connect(broadcastLayer, outputLayer, outputInfo, 0, 0);
return network;
}
armnn::INetworkPtr CreateBroadcastToNetworkWithElementWiseBinary(BroadcastToDescriptor& descriptor,
const ElementwiseBinaryDescriptor&
elementWiseDescriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& inputInfoElementWise,
const armnn::TensorInfo& outputInfo)
{
INetworkPtr network(INetwork::Create());
IConnectableLayer* inputLayer = network->AddInputLayer(0, "input");
IConnectableLayer* inputLayerElementWise = network->AddInputLayer(1, "inputElementWiseBinary");
IConnectableLayer* broadcastLayer = network->AddBroadcastToLayer(descriptor, "broadcast_to");
IConnectableLayer* multiplicationLayer =
network->AddElementwiseBinaryLayer(elementWiseDescriptor,
"multiplication");
IConnectableLayer* outputLayer = network->AddOutputLayer(0, "output");
Connect(inputLayer, broadcastLayer, inputInfo, 0, 0);
Connect(inputLayerElementWise, multiplicationLayer,
inputInfoElementWise, 0, 1);
Connect(broadcastLayer, multiplicationLayer, inputInfo, 0, 0);
Connect(multiplicationLayer, outputLayer, outputInfo, 0, 0);
return network;
}
template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void BroadcastToEndToEnd(const std::vector<BackendId>& backends)
{
float qScale = 1.0f;
int32_t qOffset = 0;
bool qConst = true;
const TensorShape inputTensorShape = { {1, 4} };
const TensorShape outputTensorShape = { {4, 4} };
TensorInfo inputInfo (inputTensorShape, ArmnnType, qScale,
qOffset, qConst);
TensorInfo outputInfo (outputTensorShape, ArmnnType,qScale,
qOffset);
std::vector<T> inputData = armnnUtils::QuantizedVector<T>({
65, 144, 91, 161
}, qScale, qOffset);
std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>({
65, 144, 91, 161,
65, 144, 91, 161,
65, 144, 91, 161,
65, 144, 91, 161
}, qScale, qOffset);
auto descriptor = armnn::BroadcastToDescriptor(armnn::TensorShape({ 4, 4 }));
CHECK(descriptor.m_BroadcastToShape == outputTensorShape);
INetworkPtr network = CreateBroadcastToNetwork(descriptor, inputInfo, outputInfo);
std::map<int, std::vector<T>> inputTensor = { { 0, inputData } };
std::map<int, std::vector<T>> expectedOutputTensor = { { 0, expectedOutputData } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),inputTensor,
expectedOutputTensor, backends);
}
template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void BroadcastToEndToEndElementWiseBinary(const std::vector<BackendId>& backends,
const ElementwiseBinaryDescriptor& elementWiseDescriptor)
{
float qScale = 1.0f;
int32_t qOffset = 0;
bool qConst = true;
const TensorShape inputTensorShape = { {1, 4} };
const TensorShape outputTensorShape = { {4, 4} };
const TensorInfo inputInfo (inputTensorShape, ArmnnType, qScale,
qOffset, qConst);
const TensorInfo inputInfoElementWise (outputTensorShape, ArmnnType, qScale,
qOffset, qConst);
const TensorInfo outputInfo (outputTensorShape, ArmnnType,qScale,
qOffset);
std::vector<T> inputData = armnnUtils::QuantizedVector<T>({
65, 144, 91, 161
}, qScale, qOffset);
std::vector<T> inputDataElementWise = armnnUtils::QuantizedVector<T>({
1, 1, 1, 1,
1, 1, 1, 1,
1, 1, 1, 1,
1, 1, 1, 1
}, qScale, qOffset);
std::vector<T> expectedOutputData;
if (elementWiseDescriptor.m_Operation == BinaryOperation::Mul ||
elementWiseDescriptor.m_Operation == BinaryOperation::Div) {
expectedOutputData = armnnUtils::QuantizedVector<T>({
65, 144, 91, 161,
65, 144, 91, 161,
65, 144, 91, 161,
65, 144, 91, 161
}, qScale, qOffset);
}
else if (elementWiseDescriptor.m_Operation == BinaryOperation::Add)
{
expectedOutputData = armnnUtils::QuantizedVector<T>({
66, 145, 92, 162,
66, 145, 92, 162,
66, 145, 92, 162,
66, 145, 92, 162
}, qScale, qOffset);
}
else if (elementWiseDescriptor.m_Operation == BinaryOperation::Sub)
{
expectedOutputData = armnnUtils::QuantizedVector<T>({
64, 143, 90, 160,
64, 143, 90, 160,
64, 143, 90, 160,
64, 143, 90, 160
}, qScale, qOffset);
}
auto descriptor = armnn::BroadcastToDescriptor(armnn::TensorShape({ 4, 4 }));
CHECK(descriptor.m_BroadcastToShape == outputTensorShape);
INetworkPtr network = CreateBroadcastToNetworkWithElementWiseBinary(descriptor,
elementWiseDescriptor,
inputInfo,
inputInfoElementWise,
outputInfo);
// Create ArmNN runtime
IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions());
// Optimise ArmNN network
IOptimizedNetworkPtr optNet = Optimize(*network, {Compute::CpuRef},
run->GetDeviceSpec());
Graph& graph = GetGraphForTesting(optNet.get());
Optimizer::Pass(graph,
armnn::MakeOptimizations(armnn::optimizations::BroadcastToOptimizationLayer()));
std::map<int, std::vector<T>> inputTensor = { { 0, inputData }, {1, inputDataElementWise} };
std::map<int, std::vector<T>> expectedOutputTensor = { { 0, expectedOutputData } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),inputTensor,
expectedOutputTensor, backends);
}
} // anonymous namespace
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