IVGCVSW-5790 Merge async prototype
* Added thread safe execution mechanism for armnn
* Removed duplicate function bool Compare(T a, T b, float tolerance)
* Added StridedSliceAsyncEndToEndTest
* Fixed memory leak
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I2d367fc77ee7c01b8953138543e76af5e691211f
diff --git a/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp b/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp
new file mode 100644
index 0000000..2ccd2b1
--- /dev/null
+++ b/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp
@@ -0,0 +1,178 @@
+//
+// 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
+