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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <ResolveType.hpp>
#include <armnn/IWorkingMemHandle.hpp>
#include <armnn/INetwork.hpp>
#include <backendsCommon/test/CommonTestUtils.hpp>
#include <boost/test/unit_test.hpp>
#include <vector>
namespace armnn
{
namespace experimental
{
template<DataType ArmnnIType, DataType ArmnnOType,
typename TInput = ResolveType <ArmnnIType>, typename TOutput = ResolveType <ArmnnOType>>
void AsyncEndToEndTestImpl(INetworkPtr network,
const std::map<int, std::vector<TInput>>& inputTensorData,
const std::map<int, std::vector<TOutput>>& expectedOutputData,
std::vector<BackendId> backends,
float tolerance = 0.000001f)
{
// Create Runtime in which test will run
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
// Optimize the Network
IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec());
// Creates AsyncNetwork
NetworkId networkId = 0;
std::string errorMessage;
const INetworkProperties networkProperties;
auto asyncNetwork = runtime->CreateAsyncNetwork(networkId, std::move(optNet), errorMessage, networkProperties);
InputTensors inputTensors;
inputTensors.reserve(inputTensorData.size());
for (auto&& it : inputTensorData)
{
inputTensors.push_back({it.first,
ConstTensor(asyncNetwork->GetInputTensorInfo(it.first), it.second.data())});
}
OutputTensors outputTensors;
outputTensors.reserve(expectedOutputData.size());
std::map<int, std::vector<TOutput>> outputStorage;
for (auto&& it : expectedOutputData)
{
std::vector<TOutput> out(it.second.size());
outputStorage.emplace(it.first, out);
outputTensors.push_back({it.first,
Tensor(asyncNetwork->GetOutputTensorInfo(it.first),
outputStorage.at(it.first).data())});
}
// Create WorkingMemHandle for this async network
std::unique_ptr<IWorkingMemHandle> workingMemHandle = asyncNetwork->CreateWorkingMemHandle();
IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get();
// Run the async network
asyncNetwork->Execute(inputTensors, outputTensors, workingMemHandleRef);
// Checks the results.
for (auto&& it : expectedOutputData)
{
std::vector<TOutput> out = outputStorage.at(it.first);
for (unsigned int i = 0; i < out.size(); ++i)
{
BOOST_CHECK(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == true);
}
}
}
template<typename armnn::DataType DataType>
INetworkPtr CreateStridedSliceNetwork(const TensorShape& inputShape,
const TensorShape& outputShape,
const std::vector<int>& beginData,
const std::vector<int>& endData,
const std::vector<int>& stridesData,
int beginMask = 0,
int endMask = 0,
int shrinkAxisMask = 0,
int ellipsisMask = 0,
int newAxisMask = 0,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
using namespace armnn;
// Builds up the structure of the network.
INetworkPtr net(INetwork::Create());
TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset);
TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
armnn::StridedSliceDescriptor stridedSliceDescriptor;
stridedSliceDescriptor.m_Begin = beginData;
stridedSliceDescriptor.m_End = endData;
stridedSliceDescriptor.m_Stride = stridesData;
stridedSliceDescriptor.m_BeginMask = beginMask;
stridedSliceDescriptor.m_EndMask = endMask;
stridedSliceDescriptor.m_ShrinkAxisMask = shrinkAxisMask;
stridedSliceDescriptor.m_EllipsisMask = ellipsisMask;
stridedSliceDescriptor.m_NewAxisMask = newAxisMask;
IConnectableLayer* input = net->AddInputLayer(0, "Input_Layer");
IConnectableLayer* stridedSlice = net->AddStridedSliceLayer(stridedSliceDescriptor, "splitter");
IConnectableLayer* output = net->AddOutputLayer(0);
Connect(input, stridedSlice, inputTensorInfo, 0, 0);
Connect(stridedSlice, output, outputTensorInfo, 0, 0);
return net;
}
template<armnn::DataType ArmnnType>
void StridedSlicedEndToEndTest(const std::vector<BackendId>& backends)
{
using namespace armnn;
using T = ResolveType<ArmnnType>;
const TensorShape& inputShape = {3, 2, 3, 1};
const TensorShape& outputShape = {1, 2, 3, 1};
const std::vector<int>& beginData = {1, 0, 0, 0};
const std::vector<int>& endData = {2, 2, 3, 1};
const std::vector<int>& stridesData = {1, 1, 1, 1};
int beginMask = 0;
int endMask = 0;
int shrinkAxisMask = 0;
int ellipsisMask = 0;
int newAxisMask = 0;
// Builds up the structure of the network
INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape,
outputShape,
beginData,
endData,
stridesData,
beginMask,
endMask,
shrinkAxisMask,
ellipsisMask,
newAxisMask);
BOOST_TEST_CHECKPOINT("create a network");
// Creates structures for input & output.
std::vector<T> inputData{
1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f
};
std::vector<T> outputExpected{
3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f
};
std::map<int, std::vector<T>> inputTensorData = {{0, inputData}};
std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}};
AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
}
} // experimental namespace
} // armnn namespace
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