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author | Narumol Prangnawarat <narumol.prangnawarat@arm.com> | 2020-08-14 11:51:12 +0100 |
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committer | Jim Flynn <jim.flynn@arm.com> | 2020-08-16 14:47:00 +0000 |
commit | b8d771ac2e6f847a64b3b203591c5b1c3e198d3a (patch) | |
tree | 289b769e179e495e45f2d2c2f9374703be32f9c2 /src/backends/neon/test/NeonFallbackTests.cpp | |
parent | 9e132f57e3fc9d1cd12e3bca2dd3eb82549d7d84 (diff) | |
download | armnn-b8d771ac2e6f847a64b3b203591c5b1c3e198d3a.tar.gz |
IVGCVSW-5012 Enable zero copy for Neon
* Allow memory import if padding is not required in Neon
* AddMockImportBackend for fallback tests
* Refactor GraphUtils
* Memory import unit tests
* Fallback unit tests
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: Ic2e141e12774bf6d915e77745b6f6d2d83d9b82d
Diffstat (limited to 'src/backends/neon/test/NeonFallbackTests.cpp')
-rw-r--r-- | src/backends/neon/test/NeonFallbackTests.cpp | 547 |
1 files changed, 547 insertions, 0 deletions
diff --git a/src/backends/neon/test/NeonFallbackTests.cpp b/src/backends/neon/test/NeonFallbackTests.cpp new file mode 100644 index 0000000000..cf4d91b119 --- /dev/null +++ b/src/backends/neon/test/NeonFallbackTests.cpp @@ -0,0 +1,547 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <backendsCommon/test/CommonTestUtils.hpp> +#include <backendsCommon/test/mockBackend/MockImportBackend.hpp> + +#include <test/GraphUtils.hpp> + +#include <boost/test/unit_test.hpp> + +BOOST_AUTO_TEST_SUITE(NeonFallback) + +std::vector<armnn::BackendId> defaultBackends = { armnn::Compute::CpuAcc }; + +BOOST_AUTO_TEST_CASE(FallbackImportToCpuAcc) +{ + using namespace armnn; + + // Create a mock backend object + MockImportBackendInitialiser initialiser; // Register the Mock Backend + auto backendObjPtr = CreateBackendObject(MockImportBackendId()); + BOOST_TEST((backendObjPtr != nullptr)); + + BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); + if (backendIds.find("MockRef") == backendIds.end()) + { + std::string message = "Cannot load MockRef"; + BOOST_FAIL(message); + } + + // Create runtime in which test will run and allow fallback to CpuRef. + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); + IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); + IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); + IConnectableLayer* add = net->AddAdditionLayer("add"); + IConnectableLayer* sub = net->AddSubtractionLayer("sub"); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + + input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); + add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); + sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); + + input0->GetOutputSlot(0).SetTensorInfo(info); + input1->GetOutputSlot(0).SetTensorInfo(info); + input2->GetOutputSlot(0).SetTensorInfo(info); + add->GetOutputSlot(0).SetTensorInfo(info); + sub->GetOutputSlot(0).SetTensorInfo(info); + + // optimize the network + std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + OptimizedNetwork* optNetObjPtr = PolymorphicDowncast<OptimizedNetwork*>(optNet.get()); + Graph& graph = optNetObjPtr->GetGraph(); + + armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); + armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); + armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); + armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); + armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); + armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); + armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); + + // Checks order is valid. + BOOST_TEST(CheckOrder(graph, layer0, layer1)); + BOOST_TEST(CheckOrder(graph, layer1, layer2)); + BOOST_TEST(CheckOrder(graph, layer2, layer3)); + BOOST_TEST(CheckOrder(graph, layer3, layer4)); + BOOST_TEST(CheckOrder(graph, layer4, layer5)); + BOOST_TEST(CheckOrder(graph, layer5, layer6)); + + // Load it into the runtime. It should pass. + NetworkId netId; + std::string ignoredErrorMessage; + INetworkProperties networkProperties(true, true); + + runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); + + // Creates structures for input & output + std::vector<float> inputData0 + { + 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f + }; + std::vector<float> inputData1 + { + 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f + }; + std::vector<float> inputData2 + { + 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f + }; + + std::vector<float> outputData(12); + + std::vector<float> expectedOutput + { + 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f + }; + + InputTensors inputTensors + { + { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, + { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, + { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } + }; + OutputTensors outputTensors + { + { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } + }; + + runtime->GetProfiler(netId)->EnableProfiling(true); + + // Do the inference + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Retrieve the Profiler.Print() output to get the workload execution + ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); + std::stringstream ss; + profilerManager.GetProfiler()->Print(ss);; + std::string dump = ss.str(); + + // Contains ImportMemGeneric + std::size_t found = dump.find("ImportMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Contains SyncMemGeneric + found = dump.find("SyncMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Does not contain CopyMemGeneric + found = dump.find("CopyMemGeneric"); + BOOST_TEST(found == std::string::npos); + + // Use memory import between backends + BOOST_TEST((layer4->GetType() == LayerType::MemImport)); + + // Check output is as expected + BOOST_TEST(outputData == expectedOutput); +} + +BOOST_AUTO_TEST_CASE(FallbackPaddingCopyToCpuAcc) +{ + using namespace armnn; + + // Create a mock backend object + MockImportBackendInitialiser initialiser; // Register the Mock Backend + auto backendObjPtr = CreateBackendObject(MockImportBackendId()); + BOOST_TEST((backendObjPtr != nullptr)); + + BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); + if (backendIds.find("MockRef") == backendIds.end()) + { + std::string message = "Cannot load MockRef"; + BOOST_FAIL(message); + } + + // Create runtime in which test will run and allow fallback to CpuRef. + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + Pooling2dDescriptor desc; + + IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); + IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); + IConnectableLayer* add = net->AddAdditionLayer("add"); + IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + + input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); + pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); + TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); + + input0->GetOutputSlot(0).SetTensorInfo(info); + input1->GetOutputSlot(0).SetTensorInfo(info); + add->GetOutputSlot(0).SetTensorInfo(info); + pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); + + // optimize the network + std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + OptimizedNetwork* optNetObjPtr = PolymorphicDowncast<OptimizedNetwork*>(optNet.get()); + Graph& graph = optNetObjPtr->GetGraph(); + + armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); + armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); + armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "add"); + armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "[ add (0) -> pooling (0) ]"); + armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "pooling"); + armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "output"); + + // Checks order is valid. + BOOST_TEST(CheckOrder(graph, layer0, layer1)); + BOOST_TEST(CheckOrder(graph, layer1, layer2)); + BOOST_TEST(CheckOrder(graph, layer2, layer3)); + BOOST_TEST(CheckOrder(graph, layer3, layer4)); + BOOST_TEST(CheckOrder(graph, layer4, layer5)); + + // Load it into the runtime. It should pass. + NetworkId netId; + std::string ignoredErrorMessage; + INetworkProperties networkProperties(true, true); + + runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); + + // Creates structures for input & output + std::vector<float> inputData0 + { + 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f + }; + std::vector<float> inputData1 + { + 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f + }; + + std::vector<float> outputData(2); + + std::vector<float> expectedOutput + { + 6.0f, 12.0f + }; + + InputTensors inputTensors + { + { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, + { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) } + }; + OutputTensors outputTensors + { + { 0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } + }; + + runtime->GetProfiler(netId)->EnableProfiling(true); + + // Do the inference + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Retrieve the Profiler.Print() output to get the workload execution + ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); + std::stringstream ss; + profilerManager.GetProfiler()->Print(ss);; + std::string dump = ss.str(); + + // Contains CopyMemGeneric between the backends + std::size_t found = dump.find("CopyMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Contains SyncMemGeneric for the output + found = dump.find("SyncMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Does not contain ImportMemGeneric + found = dump.find("ImportMemGeneric"); + BOOST_TEST(found == std::string::npos); + + // Use memory import between backends + BOOST_TEST((layer3->GetType() == LayerType::MemCopy)); + + // Check output is as expected + BOOST_TEST(outputData == expectedOutput); +} + +BOOST_AUTO_TEST_CASE(FallbackImportFromCpuAcc) +{ + using namespace armnn; + + // Create a mock backend object + MockImportBackendInitialiser initialiser; // Register the Mock Backend + auto backendObjPtr = CreateBackendObject(MockImportBackendId()); + BOOST_TEST((backendObjPtr != nullptr)); + + BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); + if (backendIds.find("MockRef") == backendIds.end()) + { + std::string message = "Cannot load MockRef"; + BOOST_FAIL(message); + } + + // Create runtime in which test will run and allow fallback to CpuRef. + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); + IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); + IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); + IConnectableLayer* sub = net->AddSubtractionLayer("sub"); + IConnectableLayer* add = net->AddAdditionLayer("add"); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + + input0->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); + input1->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); + input2->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + sub->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); + + input0->GetOutputSlot(0).SetTensorInfo(info); + input1->GetOutputSlot(0).SetTensorInfo(info); + input2->GetOutputSlot(0).SetTensorInfo(info); + sub->GetOutputSlot(0).SetTensorInfo(info); + add->GetOutputSlot(0).SetTensorInfo(info); + + // optimize the network + std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + OptimizedNetwork* optNetObjPtr = PolymorphicDowncast<OptimizedNetwork*>(optNet.get()); + Graph& graph = optNetObjPtr->GetGraph(); + + armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); + armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); + armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); + armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "sub"); + armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ sub (0) -> add (1) ]"); + armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "add"); + armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); + + // Checks order is valid. + BOOST_TEST(CheckOrder(graph, layer0, layer1)); + BOOST_TEST(CheckOrder(graph, layer1, layer2)); + BOOST_TEST(CheckOrder(graph, layer2, layer3)); + BOOST_TEST(CheckOrder(graph, layer3, layer4)); + BOOST_TEST(CheckOrder(graph, layer4, layer5)); + BOOST_TEST(CheckOrder(graph, layer5, layer6)); + + // Load it into the runtime. It should pass. + NetworkId netId; + std::string ignoredErrorMessage; + INetworkProperties networkProperties(true, true); + + runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); + + // Creates structures for input & output + std::vector<float> inputData0 + { + 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f + }; + std::vector<float> inputData1 + { + 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f + }; + std::vector<float> inputData2 + { + 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f + }; + + std::vector<float> outputData(12); + + std::vector<float> expectedOutput + { + 13.0f, 11.0f, 11.0f, 9.0f, 7.0f, 7.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, -5.0f + }; + + InputTensors inputTensors + { + { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, + { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, + { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } + }; + OutputTensors outputTensors + { + { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } + }; + + runtime->GetProfiler(netId)->EnableProfiling(true); + + // Do the inference + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Retrieve the Profiler.Print() output to get the workload execution + ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); + std::stringstream ss; + profilerManager.GetProfiler()->Print(ss);; + std::string dump = ss.str(); + + // Contains ImportMemGeneric + std::size_t found = dump.find("ImportMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Contains SyncMemGeneric + found = dump.find("SyncMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Does not contain CopyMemGeneric + found = dump.find("CopyMemGeneric"); + BOOST_TEST(found == std::string::npos); + + // Use memory import between backends + BOOST_TEST((layer4->GetType() == LayerType::MemImport)); + + // Check output is as expected + BOOST_TEST(outputData == expectedOutput); +} + +BOOST_AUTO_TEST_CASE(FallbackPaddingCopyFromCpuAcc) +{ + using namespace armnn; + + // Create a mock backend object + MockImportBackendInitialiser initialiser; // Register the Mock Backend + auto backendObjPtr = CreateBackendObject(MockImportBackendId()); + BOOST_TEST((backendObjPtr != nullptr)); + + BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); + if (backendIds.find("MockRef") == backendIds.end()) + { + std::string message = "Cannot load MockRef"; + BOOST_FAIL(message); + } + + // Create runtime in which test will run and allow fallback to CpuRef. + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + Pooling2dDescriptor desc; + + IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); + IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); + IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); + IConnectableLayer* add = net->AddAdditionLayer("add"); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + + input0->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); + input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + pooling->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + TensorInfo inputInfo = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); + TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); + + input0->GetOutputSlot(0).SetTensorInfo(inputInfo); + input1->GetOutputSlot(0).SetTensorInfo(poolingInfo); + pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); + add->GetOutputSlot(0).SetTensorInfo(poolingInfo); + + // optimize the network + std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + OptimizedNetwork* optNetObjPtr = PolymorphicDowncast<OptimizedNetwork*>(optNet.get()); + Graph& graph = optNetObjPtr->GetGraph(); + + armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); + armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); + armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "pooling"); + armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "[ pooling (0) -> add (0) ]"); + armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "add"); + armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "output"); + + // Checks order is valid. + BOOST_TEST(CheckOrder(graph, layer0, layer1)); + BOOST_TEST(CheckOrder(graph, layer1, layer2)); + BOOST_TEST(CheckOrder(graph, layer2, layer3)); + BOOST_TEST(CheckOrder(graph, layer3, layer4)); + BOOST_TEST(CheckOrder(graph, layer4, layer5)); + + // Load it into the runtime. It should pass. + NetworkId netId; + std::string ignoredErrorMessage; + INetworkProperties networkProperties(true, true); + + runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); + + // Creates structures for input & output + std::vector<float> inputData0 + { + 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f + }; + std::vector<float> inputData1 + { + -1.0f, 3.0f + }; + + std::vector<float> outputData(2); + + std::vector<float> expectedOutput + { + 5.0f, 15.0f + }; + + InputTensors inputTensors + { + { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, + { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) } + }; + OutputTensors outputTensors + { + { 0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } + }; + + runtime->GetProfiler(netId)->EnableProfiling(true); + + // Do the inference + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Retrieve the Profiler.Print() output to get the workload execution + ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); + std::stringstream ss; + profilerManager.GetProfiler()->Print(ss);; + std::string dump = ss.str(); + + // Contains CopyMemGeneric between the backends + std::size_t found = dump.find("CopyMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Contains SyncMemGeneric for the output + found = dump.find("SyncMemGeneric"); + BOOST_TEST(found != std::string::npos); + + // Does not contain ImportMemGeneric + found = dump.find("ImportMemGeneric"); + BOOST_TEST(found == std::string::npos); + + // Use memory import between backends + BOOST_TEST((layer3->GetType() == LayerType::MemCopy)); + + // Check output is as expected + BOOST_TEST(outputData == expectedOutput); +} + +BOOST_AUTO_TEST_SUITE_END() |