IVGCVSW-2073: Move remaining backend-specific tests from armnn to backends
Change-Id: I45fd5b6dd32c435b78a54dc377a623e60978ce13
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5cdc07d..257a49d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -337,7 +337,6 @@
src/armnn/test/UnitTests.hpp
src/armnn/test/EndToEndTest.cpp
src/armnn/test/UtilsTests.cpp
- src/armnn/test/JsonPrinterTests.cpp
src/armnn/test/GraphTests.cpp
src/armnn/test/OptimizerTests.cpp
src/armnn/test/ProfilerTests.cpp
@@ -366,6 +365,8 @@
src/backends/test/Conv2dTestImpl.hpp
src/backends/test/ActivationTestImpl.hpp
src/backends/test/ActivationFixture.hpp
+ src/backends/test/EndToEndTestImpl.hpp
+ src/backends/test/JsonPrinterTestImpl.hpp
src/backends/test/Pooling2dTestImpl.hpp
src/backends/test/ReshapeTestImpl.hpp
src/backends/test/PermuteTestImpl.hpp
@@ -373,6 +374,7 @@
src/backends/test/SplitterTestImpl.hpp
src/backends/test/NormTestImpl.hpp
src/backends/test/BatchNormTestImpl.hpp
+ src/backends/test/OptimizedNetworkTests.cpp
src/backends/test/WorkloadTestUtils.hpp
src/backends/test/QuantizeHelper.hpp)
diff --git a/src/armnn/test/EndToEndTest.cpp b/src/armnn/test/EndToEndTest.cpp
index d34bf69..4f202f1 100644
--- a/src/armnn/test/EndToEndTest.cpp
+++ b/src/armnn/test/EndToEndTest.cpp
@@ -2,14 +2,15 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
-#include <boost/test/unit_test.hpp>
#include <armnn/Descriptors.hpp>
#include <armnn/IRuntime.hpp>
#include <armnn/INetwork.hpp>
#include <backends/test/QuantizeHelper.hpp>
+
#include <boost/core/ignore_unused.hpp>
+#include <boost/test/unit_test.hpp>
#include <set>
@@ -17,12 +18,14 @@
namespace
{
+
template<typename T>
bool IsFloatIterFunc(T iter)
{
boost::ignore_unused(iter);
return IsFloatingPointIterator<T>::value;
}
+
} //namespace
BOOST_AUTO_TEST_CASE(QuantizedHelper)
@@ -44,381 +47,6 @@
BOOST_TEST(IsFloatIterFunc(&ints[0]) == false);
}
-BOOST_AUTO_TEST_CASE(Unsigned8)
-{
- using namespace armnn;
-
- // Create runtime in which test will run
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- // Builds up the structure of the network.
- armnn::INetworkPtr net(INetwork::Create());
-
- IConnectableLayer* input = net->AddInputLayer(0, "input");
- IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax");
- IConnectableLayer* output = net->AddOutputLayer(0, "output");
-
- input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0));
- softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- // Sets the tensors in the network.
- TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
- inputTensorInfo.SetQuantizationOffset(100);
- inputTensorInfo.SetQuantizationScale(10000.0f);
- input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
- TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
- outputTensorInfo.SetQuantizationOffset(0);
- outputTensorInfo.SetQuantizationScale(1.0f/255.0f);
- softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
-
- // optimize the network
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
-
- // Loads it into the runtime.
- NetworkId netId;
- auto error = runtime->LoadNetwork(netId, std::move(optNet));
- BOOST_TEST(error == Status::Success);
-
- // Creates structures for input & output.
- std::vector<uint8_t> inputData
- {
- 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax.
- };
- std::vector<uint8_t> outputData(5);
-
- armnn::InputTensors inputTensors
- {
- {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
- };
- armnn::OutputTensors outputTensors
- {
- {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
- };
-
- // Does the inference.
- runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
- // Checks the results.
- BOOST_TEST(outputData[0] == 0);
- BOOST_TEST(outputData[1] == 0);
- BOOST_TEST(outputData[2] == 0);
- BOOST_TEST(outputData[3] == 255); // softmax has been saturated.
- BOOST_TEST(outputData[4] == 0);
-}
-
-template <typename T>
-void ConstantUsageTest(const std::vector<armnn::BackendId>& computeDevice,
- const armnn::TensorInfo& commonTensorInfo,
- const std::vector<T>& inputData,
- const std::vector<T>& constantData,
- const std::vector<T>& expectedOutputData)
-{
- using namespace armnn;
-
- // Create runtime in which test will run
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- // Builds up the structure of the network.
- INetworkPtr net(INetwork::Create());
-
- IConnectableLayer* input = net->AddInputLayer(0);
- IConnectableLayer* constant = net->AddConstantLayer(ConstTensor(commonTensorInfo, constantData));
- IConnectableLayer* add = net->AddAdditionLayer();
- IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(add->GetInputSlot(0));
- constant->GetOutputSlot(0).Connect(add->GetInputSlot(1));
- add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- // Sets the tensors in the network.
- input->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
- constant->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
- add->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
-
- // optimize the network
- IOptimizedNetworkPtr optNet = Optimize(*net, computeDevice, runtime->GetDeviceSpec());
-
- // Loads it into the runtime.
- NetworkId netId;
- runtime->LoadNetwork(netId, std::move(optNet));
-
- // Creates structures for input & output.
- std::vector<T> outputData(inputData.size());
-
- InputTensors inputTensors
- {
- {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
- };
- OutputTensors outputTensors
- {
- {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
- };
-
- // Does the inference.
- runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
- // Checks the results.
- BOOST_TEST(outputData == expectedOutputData);
-}
-
-static void ConstantUsageFloat32Test(const std::vector<armnn::BackendId>& computeDevice)
-{
- const armnn::TensorInfo commonTensorInfo({ 2, 3 }, armnn::DataType::Float32);
-
- ConstantUsageTest(computeDevice,
- commonTensorInfo,
- std::vector<float>{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, // Input.
- std::vector<float>{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, // Const input.
- std::vector<float>{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f } // Expected output.
- );
-}
-
-static void ConstantUsageUint8Test(const std::vector<armnn::BackendId>& computeDevice)
-{
- armnn::TensorInfo commonTensorInfo({ 2, 3 }, armnn::DataType::QuantisedAsymm8);
-
- const float scale = 0.023529f;
- const int8_t offset = -43;
-
- commonTensorInfo.SetQuantizationScale(scale);
- commonTensorInfo.SetQuantizationOffset(offset);
-
- ConstantUsageTest(computeDevice,
- commonTensorInfo,
- QuantizedVector<uint8_t>(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input.
- QuantizedVector<uint8_t>(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input.
- QuantizedVector<uint8_t>(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }) // Expected output.
- );
-}
-
-BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32)
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- ConstantUsageFloat32Test(backends);
-}
-
-#if ARMCOMPUTENEON_ENABLED
-BOOST_AUTO_TEST_CASE(ConstantUsage_Neon_Float32)
-{
- ConstantUsageFloat32Test({armnn::Compute::CpuAcc});
-}
-#endif
-
-#if ARMCOMPUTECL_ENABLED
-BOOST_AUTO_TEST_CASE(ConstantUsage_Cl_Float32)
-{
- ConstantUsageFloat32Test({armnn::Compute::GpuAcc});
-}
-#endif
-
-BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8)
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- ConstantUsageUint8Test(backends);
-}
-
-BOOST_AUTO_TEST_CASE(TrivialAdd)
-{
- // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp.
-
- using namespace armnn;
-
- // Create runtime in which test will run
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- // Builds up the structure of the network.
- armnn::INetworkPtr net(INetwork::Create());
-
- IConnectableLayer* input1 = net->AddInputLayer(0);
- IConnectableLayer* input2 = net->AddInputLayer(1);
- IConnectableLayer* add = net->AddAdditionLayer();
- IConnectableLayer* output = net->AddOutputLayer(0);
-
- input1->GetOutputSlot(0).Connect(add->GetInputSlot(0));
- input2->GetOutputSlot(0).Connect(add->GetInputSlot(1));
- add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- // Sets the tensors in the network.
- TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32);
- input1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- input2->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- add->GetOutputSlot(0).SetTensorInfo(tensorInfo);
-
- // optimize the network
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
-
- // Loads it into the runtime.
- NetworkId netId;
- runtime->LoadNetwork(netId, std::move(optNet));
-
- // Creates structures for input & output - matching android nn test.
- std::vector<float> input1Data
- {
- 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f
- };
- std::vector<float> input2Data
- {
- 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f
- };
- std::vector<float> outputData(12);
-
- InputTensors inputTensors
- {
- {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())},
- {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())}
- };
- OutputTensors outputTensors
- {
- {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
- };
-
- // Does the inference.
- runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
- // Checks the results
- BOOST_TEST(outputData[0] == 101);
- BOOST_TEST(outputData[1] == 202);
- BOOST_TEST(outputData[2] == 303);
- BOOST_TEST(outputData[3] == 404);
- BOOST_TEST(outputData[4] == 505);
- BOOST_TEST(outputData[5] == 606);
- BOOST_TEST(outputData[6] == 707);
- BOOST_TEST(outputData[7] == 808);
- BOOST_TEST(outputData[8] == 909);
- BOOST_TEST(outputData[9] == 1010);
- BOOST_TEST(outputData[10] == 1111);
- BOOST_TEST(outputData[11] == 1212);
-}
-
-BOOST_AUTO_TEST_CASE(MultipleOutputs)
-{
- using namespace armnn;
-
- // Create runtime in which test will run
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- // Builds up the structure of the network.
- INetworkPtr net(INetwork::Create());
-
- IConnectableLayer* input = net->AddInputLayer(0);
-
- // ReLu1
- ActivationDescriptor activation1Descriptor;
- activation1Descriptor.m_Function = ActivationFunction::BoundedReLu;
- activation1Descriptor.m_A = 1.f;
- activation1Descriptor.m_B = -1.f;
- IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor);
-
- // ReLu6
- ActivationDescriptor activation2Descriptor;
- activation2Descriptor.m_Function = ActivationFunction::BoundedReLu;
- activation2Descriptor.m_A = 6.0f;
- IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor);
-
- // BoundedReLu(min=2, max=5)
- ActivationDescriptor activation3Descriptor;
- activation3Descriptor.m_Function = ActivationFunction::BoundedReLu;
- activation3Descriptor.m_A = 5.0f;
- activation3Descriptor.m_B = 2.0f;
- IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor);
-
- IConnectableLayer* output1 = net->AddOutputLayer(0);
- IConnectableLayer* output2 = net->AddOutputLayer(1);
- IConnectableLayer* output3 = net->AddOutputLayer(2);
-
- input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0));
- input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0));
- input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0));
-
- activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0));
- activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0));
- activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0));
-
- // Sets the tensors in the network.
- TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32);
- input->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo);
-
- // optimize the network
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
-
- // Loads it into the runtime.
- NetworkId netId;
- runtime->LoadNetwork(netId, std::move(optNet));
-
- // Creates structures for input & output.
- const std::vector<float> inputData{ 3.f, 5.f, 2.f, 3.f, 7.f, 0.f, -2.f, -1.f, 3.f, 3.f };
-
- std::vector<float> output1Data(inputData.size());
- std::vector<float> output2Data(inputData.size());
- std::vector<float> output3Data(inputData.size());
-
- InputTensors inputTensors
- {
- {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
- };
- OutputTensors outputTensors
- {
- {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())},
- {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())},
- {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())}
- };
-
- // Does the inference.
- runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
- // Checks the results.
- BOOST_TEST(output1Data == std::vector<float>({ 1.f, 1.f, 1.f, 1.f, 1.f, 0.f, -1.f, -1.f, 1.f, 1.f })); // ReLu1
- BOOST_TEST(output2Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 6.f, 0.f, 0.f, 0.f, 3.f, 3.f })); // ReLu6
- BOOST_TEST(output3Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 5.f, 2.f, 2.f, 2.f, 3.f, 3.f })); // [2, 5]
-}
-
-#if ARMCOMPUTENEON_ENABLED
-BOOST_AUTO_TEST_CASE(FallbackToCpuRef)
-{
- using namespace armnn;
-
- // 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* input = net->AddInputLayer(0);
-
- // This layer configuration isn't supported by CpuAcc but we allow fallback to CpuRef so it shoud pass.
- NormalizationDescriptor descriptor;
- IConnectableLayer* pooling = net->AddNormalizationLayer(descriptor);
-
- IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(pooling->GetInputSlot(0));
- pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
- pooling->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
-
- // optimize the network
- std::vector<BackendId> backends = {Compute::CpuAcc, Compute::CpuRef};
- IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
-
- // Load it into the runtime. It should pass.
- NetworkId netId;
- BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet)) == Status::Success);
-}
-#endif // ARMCOMPUTENEON_ENABLED
-
BOOST_AUTO_TEST_CASE(ErrorOnLoadNetwork)
{
using namespace armnn;
diff --git a/src/armnn/test/GraphUtils.hpp b/src/armnn/test/GraphUtils.hpp
index 4d385de..3325405 100644
--- a/src/armnn/test/GraphUtils.hpp
+++ b/src/armnn/test/GraphUtils.hpp
@@ -4,7 +4,8 @@
//
#pragma once
-#include "Graph.hpp"
+#include <armnn/Graph.hpp>
+
#include <string>
namespace
diff --git a/src/armnn/test/NetworkTests.cpp b/src/armnn/test/NetworkTests.cpp
index 4f8dd7e..91ff7fa 100644
--- a/src/armnn/test/NetworkTests.cpp
+++ b/src/armnn/test/NetworkTests.cpp
@@ -2,17 +2,14 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
-#include <boost/test/unit_test.hpp>
-
-#include <armnn/ArmNN.hpp>
-#include <Network.hpp>
-#include <Graph.hpp>
-#include <backends/reference/RefWorkloadFactory.hpp>
-#include <backends/neon/NeonWorkloadFactory.hpp>
-#include <backends/cl/ClWorkloadFactory.hpp>
#include "GraphUtils.hpp"
+#include <armnn/ArmNN.hpp>
+#include <armnn/Network.hpp>
+
+#include <boost/test/unit_test.hpp>
+
namespace
{
@@ -43,54 +40,6 @@
BOOST_TEST(inputId != outputId);
}
-BOOST_AUTO_TEST_CASE(SerializeToDot)
-{
- armnn::Network net;
-
- //Defines layers.
- auto input = net.AddInputLayer(0);
- auto add = net.AddAdditionLayer();
- auto output = net.AddOutputLayer(0);
-
- // Connects layers.
- input->GetOutputSlot(0).Connect(add->GetInputSlot(0));
- input->GetOutputSlot(0).Connect(add->GetInputSlot(1));
- add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- armnn::TensorShape shape({4});
- armnn::TensorInfo info(shape, armnn::DataType::Float32);
- input->GetOutputSlot(0).SetTensorInfo(info);
- add->GetOutputSlot(0).SetTensorInfo(info);
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
-
- std::ostringstream ss;
- optimizedNet->SerializeToDot(ss);
-
- auto inputId = input->GetGuid();
- auto addId = add->GetGuid();
- auto outputId = output->GetGuid();
-
- std::stringstream expected;
- expected <<
- "digraph Optimized {\n"
- " node [shape=\"record\"];\n"
- " edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"
- " " << inputId << " [label=\"{Input}\"];\n"
- " " << addId << " [label=\"{Addition}\"];\n"
- " " << outputId << " [label=\"{Output}\"];\n"
- " " << inputId << " -> " << addId << " [label=< [4] >];\n"
- " " << inputId << " -> " << addId << " [label=< [4] >];\n"
- " " << addId << " -> " << outputId << " [label=< [4] >];\n"
- "}\n";
-
- BOOST_TEST(ss.str() == expected.str());
-}
-
BOOST_AUTO_TEST_CASE(NetworkBasic)
{
armnn::Network net;
@@ -417,585 +366,4 @@
prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
}
-BOOST_AUTO_TEST_CASE(OptimizeValidateCpuRefWorkloads)
-{
- const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
-
- armnn::Network net;
-
- armnn::NormalizationDescriptor nmDesc;
- armnn::ActivationDescriptor acDesc;
-
- // in
- // |
- // nm
- // / |
- // ac |
- // \ |
- // ml
- // |
- // sm
- // |
- // ot
- armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
-
- layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
- normLayer->GetOutputSlot(0).SetTensorInfo(desc);
-
- layer = net.AddActivationLayer(acDesc, "ac");
-
- normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- armnn::IConnectableLayer* prevLayer = layer;
- layer = net.AddMultiplicationLayer("ml");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- prevLayer = layer;
- armnn::SoftmaxDescriptor softmaxDescriptor;
- layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- prevLayer = layer;
- layer = net.AddOutputLayer(0, "ot");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
- static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph().AllocateDynamicBuffers();
- BOOST_CHECK(optNet);
-
- // Validates workloads.
- armnn::RefWorkloadFactory fact;
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- BOOST_CHECK_NO_THROW(
- layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
- }
-}
-
-#if ARMCOMPUTENEON_ENABLED
-BOOST_AUTO_TEST_CASE(OptimizeValidateCpuAccDeviceSupportLayerNoFallback)
-{
- // build up the structure of the network
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
- armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
- BOOST_CHECK(optNet);
- // validate workloads
- armnn::NeonWorkloadFactory fact;
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
- BOOST_CHECK_NO_THROW(
- layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
- }
-}
-#endif // ARMCOMPUTENEON_ENABLED
-
-#if ARMCOMPUTECL_ENABLED
-BOOST_AUTO_TEST_CASE(OptimizeValidateGpuDeviceSupportLayerNoFallback)
-{
- // build up the structure of the network
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
- armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
- BOOST_CHECK(optNet);
- // validate workloads
- armnn::ClWorkloadFactory fact;
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc);
- BOOST_CHECK_NO_THROW(
- layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
- }
-}
-#endif // ARMCOMPUTECL_ENABLED
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback)
-{
- // build up the structure of the network
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
- // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null.
- armnn::NormalizationDescriptor descriptor;
- armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
-
- armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
- normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
- normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
- BOOST_CHECK(!optNet);
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerWithFallback)
-{
- // build up the structure of the network
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
- // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.
- armnn::NormalizationDescriptor descriptor;
- armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
-
- armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
- normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
- normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, armnn::Compute::CpuRef };
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
- BOOST_REQUIRE(optNet);
-
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- // If NEON is enabled, Input and Output layers are supported by CpuAcc,
- // the other layers are supported by CpuRef.
- // If NEON is not enabled, all layers are supported by CpuRef.
-#if ARMCOMPUTENEON_ENABLED
- if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
- }
- else if (layer->GetType() == armnn::LayerType::Normalization)
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
- }
-#else
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-#endif
- }
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDevice)
-{
- const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
-
- armnn::Network net;
-
- armnn::NormalizationDescriptor nmDesc;
- armnn::ActivationDescriptor acDesc;
-
- // in
- // |
- // nm
- // / |
- // ac |
- // \ |
- // ml
- // |
- // sm
- // |
- // ot
- armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
-
- layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
- normLayer->GetOutputSlot(0).SetTensorInfo(desc);
-
- layer = net.AddActivationLayer(acDesc, "ac");
-
- normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- armnn::IConnectableLayer* prevLayer = layer;
- layer = net.AddMultiplicationLayer("ml");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- prevLayer = layer;
- armnn::SoftmaxDescriptor softmaxDescriptor;
- layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- prevLayer = layer;
- layer = net.AddOutputLayer(0, "ot");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::Undefined };
-
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
- BOOST_CHECK(!optNet);
-
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDeviceWithFallback)
-{
- const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
-
- armnn::Network net;
-
- armnn::NormalizationDescriptor nmDesc;
- armnn::ActivationDescriptor acDesc;
-
- // in
- // |
- // nm
- // / |
- // ac |
- // \ |
- // ml
- // |
- // sm
- // |
- // ot
- armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
-
- layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
- normLayer->GetOutputSlot(0).SetTensorInfo(desc);
-
- layer = net.AddActivationLayer(acDesc, "ac");
-
- normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- armnn::IConnectableLayer* prevLayer = layer;
- layer = net.AddMultiplicationLayer("ml");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- prevLayer = layer;
- armnn::SoftmaxDescriptor softmaxDescriptor;
- layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- layer->GetOutputSlot(0).SetTensorInfo(desc);
-
- prevLayer = layer;
- layer = net.AddOutputLayer(0, "ot");
-
- prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::Undefined, armnn::Compute::CpuRef };
-
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
- BOOST_CHECK(optNet);
-
- // validate workloads
- armnn::RefWorkloadFactory fact;
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
- BOOST_CHECK_NO_THROW(
- layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
- }
-}
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback)
-{
- // build up the structure of the network
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
- // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.
- armnn::NormalizationDescriptor descriptor;
- armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
-
- armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
- normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
- normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc,
- armnn::Compute::GpuAcc,
- armnn::Compute::CpuRef };
-
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
- BOOST_REQUIRE(optNet);
-
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- // If NEON is enabled, Input and Output layers are supported by CpuAcc,
- // the other layers are supported by CpuRef.
- // If only CL is enabled, Input and Output layers are supported by GpuAcc,
- // the other layers are supported by CpuRef.
- // If neither NEON, nor CL is enabled, all layers are supported by CpuRef.
-#if ARMCOMPUTENEON_ENABLED
- if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
- }
- else if (layer->GetType() == armnn::LayerType::Normalization)
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
- }
-#elif ARMCOMPUTECL_ENABLED
- if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc);
- }
- else if (layer->GetType() == armnn::LayerType::Normalization)
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
- }
-#else
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-#endif
- }
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefPermuteLayer)
-{
- // Create runtime in which test will run
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
-
- // build up the structure of the network
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
- armnn::PermuteDescriptor descriptor({0, 2, 3, 1});
- armnn::IConnectableLayer* permute = net->AddPermuteLayer(descriptor);
-
- armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));
- permute->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
- permute->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 4, 1, 4 }, armnn::DataType::Float32));
-
- // optimize the network
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
-
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
- }
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefMeanLayer)
-{
- // Create runtime in which test will run
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
-
- // build up the structure of the network
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
- armnn::MeanDescriptor descriptor({ 0, 1 }, false);
- armnn::IConnectableLayer* meanLayer = net->AddMeanLayer(descriptor);
-
- armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
- input->GetOutputSlot(0).Connect(meanLayer->GetInputSlot(0));
- meanLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 4, 3, 2 }, armnn::DataType::Float32));
- meanLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 2 }, armnn::DataType::Float32));
-
- // optimize the network
- armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
-
- for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
- {
- BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
- }
-}
-
-BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnCpuRef)
-{
- // Test to check when FP16 Turbo mode set
- // it converts the FP32 network to FP16 Network
- // add FP32ToFP16 conversion layer after the InputLayer
- // add FP16ToFP32 conversion layer after the OutputLayer
- // checks the other layers if they are supported in FP16
- // if they are not put the conversion layers before and after
- // if they are not supported in FP16 use FP32 instead
- // if there are inverse conversion layers remove them with optimization
- // at the moment FloorLayer is not supported in FP16 so it rolls back to FP32
- // and inverse conversion layers are removed by the optimizer
- armnn::Network net;
-
- // Defines layers.
- auto input = net.AddInputLayer(0);
- auto floor = net.AddFloorLayer();
- auto output = net.AddOutputLayer(0);
-
- // Connects layers.
- input->GetOutputSlot(0).Connect(floor->GetInputSlot(0));
- floor->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- armnn::TensorShape shape({4});
- armnn::TensorInfo info(shape, armnn::DataType::Float32);
- input->GetOutputSlot(0).SetTensorInfo(info);
- floor->GetOutputSlot(0).SetTensorInfo(info);
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
-
- armnn::OptimizerOptions optimizerOptions;
- optimizerOptions.m_ReduceFp32ToFp16 = true;
-
- armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec(),
- optimizerOptions);
-
- std::ostringstream ss;
- optimizedNet->SerializeToDot(ss);
-
- auto inputId = input->GetGuid();
- auto floorId = floor->GetGuid();
- auto outputId = output->GetGuid();
-
- std::stringstream expected;
- expected <<
- "digraph Optimized {\n"
- " node [shape=\"record\"];\n"
- " edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"
- " " << inputId << " [label=\"{Input}\"];\n"
- " " << floorId << " [label=\"{Floor}\"];\n"
- " " << outputId << " [label=\"{Output}\"];\n"
- " " << inputId << " -> " << floorId << " [label=< [4] >];\n"
- " " << floorId << " -> " << outputId << " [label=< [4] >];\n"
- "}\n";
-
- BOOST_TEST(ss.str() == expected.str());
-}
-
-#if ARMCOMPUTECL_ENABLED
-BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnGpuAcc)
-{
- // Test to check when Fp16 Turbo mode set
- // it converts the Fp32 network to Fp16 Network
- // add Fp32ToFp16 conversion layer after the InputLayer
- // add Fp16ToFp32 conversion layer after the OutputLayer
- // checks the other layers if they are supported in Fp16
- // if they are not put the conversion layers before and after
- // if they are not supported in Fp16 use Fp32 instead
- // if there are inverse conversion layers remove them with optimization
- // at the moment FloorLayer is not supported in Fp16 so it rolls back to Fp32
- // and inverse conversion layers are removed by the optimizer
- armnn::Network net;
-
- // Defines layers.
- auto input = net.AddInputLayer(0, "input layer");
- // ReLu1
- armnn::ActivationDescriptor activation1Descriptor;
- activation1Descriptor.m_Function = armnn::ActivationFunction::BoundedReLu;
- activation1Descriptor.m_A = 1.f;
- activation1Descriptor.m_B = -1.f;
- auto activation = net.AddActivationLayer(activation1Descriptor, "activation layer");
- auto output = net.AddOutputLayer(0, "output layer");
-
- // Connects layers.
- input->GetOutputSlot(0).Connect(activation->GetInputSlot(0));
- activation->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
- armnn::TensorShape shape({4});
- armnn::TensorInfo info(shape, armnn::DataType::Float32);
- input->GetOutputSlot(0).SetTensorInfo(info);
- activation->GetOutputSlot(0).SetTensorInfo(info);
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
-
- armnn::OptimizerOptions optimizerOptions;
- optimizerOptions.m_ReduceFp32ToFp16 = true;
-
- armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(
- net, backends, runtime->GetDeviceSpec(), optimizerOptions);
-
- const armnn::Graph& graph = static_cast<armnn::OptimizedNetwork*>(optimizedNet.get())->GetGraph();
-
- // Tests that all layers are present in the graph.
- BOOST_TEST(graph.GetNumLayers() == 5);
-
- // Tests that the vertices exist and have correct names.
- BOOST_TEST(GraphHasNamedLayer(graph, "input layer"));
- BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp32_to_fp16-0-input layer"));
- BOOST_TEST(GraphHasNamedLayer(graph, "activation layer"));
- BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp16_to_fp32-0-output layer"));
- BOOST_TEST(GraphHasNamedLayer(graph, "output layer"));
-}
-#endif
-
BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/cl/backend.mk b/src/backends/cl/backend.mk
index 1f89f3b..996db3f 100644
--- a/src/backends/cl/backend.mk
+++ b/src/backends/cl/backend.mk
@@ -44,9 +44,12 @@
BACKEND_TEST_SOURCES := \
test/ClCreateWorkloadTests.cpp \
+ test/ClEndToEndTests.cpp \
+ test/ClJsonPrinterTests.cpp \
test/ClLayerSupportTests.cpp \
test/ClLayerTests.cpp \
test/ClMemCopyTests.cpp \
+ test/ClOptimizedNetworkTests.cpp \
test/ClRuntimeTests.cpp \
test/Fp16SupportTest.cpp \
test/OpenClTimerTest.cpp
diff --git a/src/backends/cl/test/CMakeLists.txt b/src/backends/cl/test/CMakeLists.txt
index 69aa08d..c017377 100644
--- a/src/backends/cl/test/CMakeLists.txt
+++ b/src/backends/cl/test/CMakeLists.txt
@@ -6,9 +6,12 @@
list(APPEND armnnClBackendUnitTests_sources
ClContextControlFixture.hpp
ClCreateWorkloadTests.cpp
+ ClEndToEndTests.cpp
+ ClJsonPrinterTests.cpp
ClLayerSupportTests.cpp
ClLayerTests.cpp
ClMemCopyTests.cpp
+ ClOptimizedNetworkTests.cpp
ClRuntimeTests.cpp
OpenClTimerTest.cpp
)
diff --git a/src/backends/cl/test/ClEndToEndTests.cpp b/src/backends/cl/test/ClEndToEndTests.cpp
new file mode 100644
index 0000000..d6fd887
--- /dev/null
+++ b/src/backends/cl/test/ClEndToEndTests.cpp
@@ -0,0 +1,18 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <backends/test/EndToEndTestImpl.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(ClEndToEnd)
+
+BOOST_AUTO_TEST_CASE(ConstantUsage_Cl_Float32)
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ ConstantUsageFloat32Test(backends);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/cl/test/ClJsonPrinterTests.cpp b/src/backends/cl/test/ClJsonPrinterTests.cpp
new file mode 100644
index 0000000..f0b4b7a
--- /dev/null
+++ b/src/backends/cl/test/ClJsonPrinterTests.cpp
@@ -0,0 +1,23 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/BackendId.hpp>
+
+#include <backends/cl/test/ClContextControlFixture.hpp>
+#include <backends/test/JsonPrinterTestImpl.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+#include <vector>
+
+BOOST_FIXTURE_TEST_SUITE(ClJsonPrinter, ClProfilingContextControlFixture)
+
+BOOST_AUTO_TEST_CASE(SoftmaxProfilerJsonPrinterGpuAccTest)
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(backends);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/backends/cl/test/ClOptimizedNetworkTests.cpp b/src/backends/cl/test/ClOptimizedNetworkTests.cpp
new file mode 100644
index 0000000..b39a4b1
--- /dev/null
+++ b/src/backends/cl/test/ClOptimizedNetworkTests.cpp
@@ -0,0 +1,101 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Network.hpp>
+
+#include <armnn/test/GraphUtils.hpp>
+
+#include <backends/cl/ClWorkloadFactory.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(ClOptimizedNetwork)
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateGpuDeviceSupportLayerNoFallback)
+{
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+ BOOST_CHECK(optNet);
+ // validate workloads
+ armnn::ClWorkloadFactory fact;
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc);
+ BOOST_CHECK_NO_THROW(
+ layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
+ }
+}
+
+BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnGpuAcc)
+{
+ // Test to check when Fp16 Turbo mode set
+ // it converts the Fp32 network to Fp16 Network
+ // add Fp32ToFp16 conversion layer after the InputLayer
+ // add Fp16ToFp32 conversion layer after the OutputLayer
+ // checks the other layers if they are supported in Fp16
+ // if they are not put the conversion layers before and after
+ // if they are not supported in Fp16 use Fp32 instead
+ // if there are inverse conversion layers remove them with optimization
+ // at the moment FloorLayer is not supported in Fp16 so it rolls back to Fp32
+ // and inverse conversion layers are removed by the optimizer
+ armnn::Network net;
+
+ // Defines layers.
+ auto input = net.AddInputLayer(0, "input layer");
+ // ReLu1
+ armnn::ActivationDescriptor activation1Descriptor;
+ activation1Descriptor.m_Function = armnn::ActivationFunction::BoundedReLu;
+ activation1Descriptor.m_A = 1.f;
+ activation1Descriptor.m_B = -1.f;
+ auto activation = net.AddActivationLayer(activation1Descriptor, "activation layer");
+ auto output = net.AddOutputLayer(0, "output layer");
+
+ // Connects layers.
+ input->GetOutputSlot(0).Connect(activation->GetInputSlot(0));
+ activation->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ armnn::TensorShape shape({4});
+ armnn::TensorInfo info(shape, armnn::DataType::Float32);
+ input->GetOutputSlot(0).SetTensorInfo(info);
+ activation->GetOutputSlot(0).SetTensorInfo(info);
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+
+ armnn::OptimizerOptions optimizerOptions;
+ optimizerOptions.m_ReduceFp32ToFp16 = true;
+
+ armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(
+ net, backends, runtime->GetDeviceSpec(), optimizerOptions);
+
+ const armnn::Graph& graph = static_cast<armnn::OptimizedNetwork*>(optimizedNet.get())->GetGraph();
+
+ // Tests that all layers are present in the graph.
+ BOOST_TEST(graph.GetNumLayers() == 5);
+
+ // Tests that the vertices exist and have correct names.
+ BOOST_TEST(GraphHasNamedLayer(graph, "input layer"));
+ BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp32_to_fp16-0-input layer"));
+ BOOST_TEST(GraphHasNamedLayer(graph, "activation layer"));
+ BOOST_TEST(GraphHasNamedLayer(graph, "convert_fp16_to_fp32-0-output layer"));
+ BOOST_TEST(GraphHasNamedLayer(graph, "output layer"));
+}
+
+BOOST_AUTO_TEST_SUITE_END();
\ No newline at end of file
diff --git a/src/backends/neon/backend.mk b/src/backends/neon/backend.mk
index a4e6db9..8f7e72b 100644
--- a/src/backends/neon/backend.mk
+++ b/src/backends/neon/backend.mk
@@ -41,8 +41,11 @@
BACKEND_TEST_SOURCES := \
test/NeonCreateWorkloadTests.cpp \
+ test/NeonEndToEndTests.cpp \
+ test/NeonJsonPrinterTests.cpp \
test/NeonLayerSupportTests.cpp \
test/NeonLayerTests.cpp \
test/NeonMemCopyTests.cpp \
+ test/NeonOptimizedNetworkTests.cpp \
test/NeonRuntimeTests.cpp \
test/NeonTimerTest.cpp
diff --git a/src/backends/neon/test/CMakeLists.txt b/src/backends/neon/test/CMakeLists.txt
index e6a2859..999bd4f 100644
--- a/src/backends/neon/test/CMakeLists.txt
+++ b/src/backends/neon/test/CMakeLists.txt
@@ -5,9 +5,12 @@
list(APPEND armnnNeonBackendUnitTests_sources
NeonCreateWorkloadTests.cpp
+ NeonEndToEndTests.cpp
+ NeonJsonPrinterTests.cpp
NeonLayerSupportTests.cpp
NeonLayerTests.cpp
NeonMemCopyTests.cpp
+ NeonOptimizedNetworkTests.cpp
NeonRuntimeTests.cpp
NeonTimerTest.cpp
)
diff --git a/src/backends/neon/test/NeonEndToEndTests.cpp b/src/backends/neon/test/NeonEndToEndTests.cpp
new file mode 100644
index 0000000..f9aa8a5
--- /dev/null
+++ b/src/backends/neon/test/NeonEndToEndTests.cpp
@@ -0,0 +1,52 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <backends/test/EndToEndTestImpl.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(NeonEndToEnd)
+
+BOOST_AUTO_TEST_CASE(ConstantUsage_Neon_Float32)
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ BOOST_TEST(ConstantUsageFloat32Test(backends));
+}
+
+BOOST_AUTO_TEST_CASE(FallbackToCpuRef)
+{
+ using namespace armnn;
+
+ // 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* input = net->AddInputLayer(0);
+
+ // This layer configuration isn't supported by CpuAcc but we allow fallback to CpuRef so it shoud pass.
+ NormalizationDescriptor descriptor;
+ IConnectableLayer* pooling = net->AddNormalizationLayer(descriptor);
+
+ IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(pooling->GetInputSlot(0));
+ pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
+ pooling->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
+
+ // optimize the network
+ std::vector<BackendId> backends = {Compute::CpuAcc, Compute::CpuRef};
+ IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
+
+ // Load it into the runtime. It should pass.
+ NetworkId netId;
+ BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet)) == Status::Success);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/neon/test/NeonJsonPrinterTests.cpp b/src/backends/neon/test/NeonJsonPrinterTests.cpp
new file mode 100644
index 0000000..6213c14
--- /dev/null
+++ b/src/backends/neon/test/NeonJsonPrinterTests.cpp
@@ -0,0 +1,22 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/BackendId.hpp>
+
+#include <backends/test/JsonPrinterTestImpl.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+#include <vector>
+
+BOOST_AUTO_TEST_SUITE(NeonJsonPrinter)
+
+BOOST_AUTO_TEST_CASE(SoftmaxProfilerJsonPrinterCpuAccTest)
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(backends);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/backends/neon/test/NeonOptimizedNetworkTests.cpp b/src/backends/neon/test/NeonOptimizedNetworkTests.cpp
new file mode 100644
index 0000000..ae657ba
--- /dev/null
+++ b/src/backends/neon/test/NeonOptimizedNetworkTests.cpp
@@ -0,0 +1,70 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Graph.hpp>
+#include <armnn/Network.hpp>
+
+#include <backends/neon/NeonWorkloadFactory.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(NeonOptimizedNetwork)
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateCpuAccDeviceSupportLayerNoFallback)
+{
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+ BOOST_CHECK(optNet);
+ // validate workloads
+ armnn::NeonWorkloadFactory fact;
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
+ BOOST_CHECK_NO_THROW(
+ layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
+ }
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback)
+{
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+
+ // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null.
+ armnn::NormalizationDescriptor descriptor;
+ armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
+
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
+ normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+ normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+ BOOST_CHECK(!optNet);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/backends/reference/backend.mk b/src/backends/reference/backend.mk
index 455ab46..007efce 100644
--- a/src/backends/reference/backend.mk
+++ b/src/backends/reference/backend.mk
@@ -65,6 +65,9 @@
BACKEND_TEST_SOURCES := \
test/RefCreateWorkloadTests.cpp \
+ test/RefEndToEndTests.cpp \
+ test/RefJsonPrinterTests.cpp \
test/RefLayerSupportTests.cpp \
test/RefLayerTests.cpp \
+ test/RefOptimizedNetworkTests.cpp \
test/RefRuntimeTests.cpp
diff --git a/src/backends/reference/test/CMakeLists.txt b/src/backends/reference/test/CMakeLists.txt
index dea0ef6..1eec594 100644
--- a/src/backends/reference/test/CMakeLists.txt
+++ b/src/backends/reference/test/CMakeLists.txt
@@ -5,8 +5,11 @@
list(APPEND armnnRefBackendUnitTests_sources
RefCreateWorkloadTests.cpp
+ RefEndToEndTests.cpp
+ RefJsonPrinterTests.cpp
RefLayerSupportTests.cpp
RefLayerTests.cpp
+ RefOptimizedNetworkTests.cpp
RefRuntimeTests.cpp
)
diff --git a/src/backends/reference/test/RefEndToEndTests.cpp b/src/backends/reference/test/RefEndToEndTests.cpp
new file mode 100644
index 0000000..8938d6f
--- /dev/null
+++ b/src/backends/reference/test/RefEndToEndTests.cpp
@@ -0,0 +1,251 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <backends/test/EndToEndTestImpl.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(RefEndToEnd)
+
+BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32)
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ BOOST_TEST(ConstantUsageFloat32Test(backends));
+}
+
+BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8)
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ BOOST_TEST(ConstantUsageUint8Test(backends));
+}
+
+BOOST_AUTO_TEST_CASE(Unsigned8)
+{
+ using namespace armnn;
+
+ // Create runtime in which test will run
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ // Builds up the structure of the network.
+ armnn::INetworkPtr net(INetwork::Create());
+
+ IConnectableLayer* input = net->AddInputLayer(0, "input");
+ IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax");
+ IConnectableLayer* output = net->AddOutputLayer(0, "output");
+
+ input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0));
+ softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ // Sets the tensors in the network.
+ TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
+ inputTensorInfo.SetQuantizationOffset(100);
+ inputTensorInfo.SetQuantizationScale(10000.0f);
+ input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+
+ TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
+ outputTensorInfo.SetQuantizationOffset(0);
+ outputTensorInfo.SetQuantizationScale(1.0f/255.0f);
+ softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+ // optimize the network
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
+
+ // Loads it into the runtime.
+ NetworkId netId;
+ auto error = runtime->LoadNetwork(netId, std::move(optNet));
+ BOOST_TEST(error == Status::Success);
+
+ // Creates structures for input & output.
+ std::vector<uint8_t> inputData
+ {
+ 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax.
+ };
+ std::vector<uint8_t> outputData(5);
+
+ armnn::InputTensors inputTensors
+ {
+ {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
+ };
+ armnn::OutputTensors outputTensors
+ {
+ {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
+ };
+
+ // Does the inference.
+ runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
+
+ // Checks the results.
+ BOOST_TEST(outputData[0] == 0);
+ BOOST_TEST(outputData[1] == 0);
+ BOOST_TEST(outputData[2] == 0);
+ BOOST_TEST(outputData[3] == 255); // softmax has been saturated.
+ BOOST_TEST(outputData[4] == 0);
+}
+
+BOOST_AUTO_TEST_CASE(TrivialAdd)
+{
+ // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp.
+
+ using namespace armnn;
+
+ // Create runtime in which test will run
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ // Builds up the structure of the network.
+ armnn::INetworkPtr net(INetwork::Create());
+
+ IConnectableLayer* input1 = net->AddInputLayer(0);
+ IConnectableLayer* input2 = net->AddInputLayer(1);
+ IConnectableLayer* add = net->AddAdditionLayer();
+ IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input1->GetOutputSlot(0).Connect(add->GetInputSlot(0));
+ input2->GetOutputSlot(0).Connect(add->GetInputSlot(1));
+ add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ // Sets the tensors in the network.
+ TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32);
+ input1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ input2->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ add->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+ // optimize the network
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
+
+ // Loads it into the runtime.
+ NetworkId netId;
+ runtime->LoadNetwork(netId, std::move(optNet));
+
+ // Creates structures for input & output - matching android nn test.
+ std::vector<float> input1Data
+ {
+ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f
+ };
+ std::vector<float> input2Data
+ {
+ 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f
+ };
+ std::vector<float> outputData(12);
+
+ InputTensors inputTensors
+ {
+ {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())},
+ {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())}
+ };
+ OutputTensors outputTensors
+ {
+ {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
+ };
+
+ // Does the inference.
+ runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
+
+ // Checks the results
+ BOOST_TEST(outputData[0] == 101);
+ BOOST_TEST(outputData[1] == 202);
+ BOOST_TEST(outputData[2] == 303);
+ BOOST_TEST(outputData[3] == 404);
+ BOOST_TEST(outputData[4] == 505);
+ BOOST_TEST(outputData[5] == 606);
+ BOOST_TEST(outputData[6] == 707);
+ BOOST_TEST(outputData[7] == 808);
+ BOOST_TEST(outputData[8] == 909);
+ BOOST_TEST(outputData[9] == 1010);
+ BOOST_TEST(outputData[10] == 1111);
+ BOOST_TEST(outputData[11] == 1212);
+}
+
+BOOST_AUTO_TEST_CASE(MultipleOutputs)
+{
+ using namespace armnn;
+
+ // Create runtime in which test will run
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ // Builds up the structure of the network.
+ INetworkPtr net(INetwork::Create());
+
+ IConnectableLayer* input = net->AddInputLayer(0);
+
+ // ReLu1
+ ActivationDescriptor activation1Descriptor;
+ activation1Descriptor.m_Function = ActivationFunction::BoundedReLu;
+ activation1Descriptor.m_A = 1.f;
+ activation1Descriptor.m_B = -1.f;
+ IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor);
+
+ // ReLu6
+ ActivationDescriptor activation2Descriptor;
+ activation2Descriptor.m_Function = ActivationFunction::BoundedReLu;
+ activation2Descriptor.m_A = 6.0f;
+ IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor);
+
+ // BoundedReLu(min=2, max=5)
+ ActivationDescriptor activation3Descriptor;
+ activation3Descriptor.m_Function = ActivationFunction::BoundedReLu;
+ activation3Descriptor.m_A = 5.0f;
+ activation3Descriptor.m_B = 2.0f;
+ IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor);
+
+ IConnectableLayer* output1 = net->AddOutputLayer(0);
+ IConnectableLayer* output2 = net->AddOutputLayer(1);
+ IConnectableLayer* output3 = net->AddOutputLayer(2);
+
+ input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0));
+ input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0));
+ input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0));
+
+ activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0));
+ activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0));
+ activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0));
+
+ // Sets the tensors in the network.
+ TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32);
+ input->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+ // optimize the network
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
+
+ // Loads it into the runtime.
+ NetworkId netId;
+ runtime->LoadNetwork(netId, std::move(optNet));
+
+ // Creates structures for input & output.
+ const std::vector<float> inputData{ 3.f, 5.f, 2.f, 3.f, 7.f, 0.f, -2.f, -1.f, 3.f, 3.f };
+
+ std::vector<float> output1Data(inputData.size());
+ std::vector<float> output2Data(inputData.size());
+ std::vector<float> output3Data(inputData.size());
+
+ InputTensors inputTensors
+ {
+ {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
+ };
+ OutputTensors outputTensors
+ {
+ {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())},
+ {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())},
+ {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())}
+ };
+
+ // Does the inference.
+ runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
+
+ // Checks the results.
+ BOOST_TEST(output1Data == std::vector<float>({ 1.f, 1.f, 1.f, 1.f, 1.f, 0.f, -1.f, -1.f, 1.f, 1.f })); // ReLu1
+ BOOST_TEST(output2Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 6.f, 0.f, 0.f, 0.f, 3.f, 3.f })); // ReLu6
+ BOOST_TEST(output3Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 5.f, 2.f, 2.f, 2.f, 3.f, 3.f })); // [2, 5]
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/backends/reference/test/RefJsonPrinterTests.cpp b/src/backends/reference/test/RefJsonPrinterTests.cpp
new file mode 100644
index 0000000..ee668a2
--- /dev/null
+++ b/src/backends/reference/test/RefJsonPrinterTests.cpp
@@ -0,0 +1,22 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/BackendId.hpp>
+
+#include <backends/test/JsonPrinterTestImpl.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+#include <vector>
+
+BOOST_AUTO_TEST_SUITE(RefJsonPrinter)
+
+BOOST_AUTO_TEST_CASE(SoftmaxProfilerJsonPrinterCpuRefTest)
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(backends);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/backends/reference/test/RefOptimizedNetworkTests.cpp b/src/backends/reference/test/RefOptimizedNetworkTests.cpp
new file mode 100644
index 0000000..63615e6
--- /dev/null
+++ b/src/backends/reference/test/RefOptimizedNetworkTests.cpp
@@ -0,0 +1,212 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Graph.hpp>
+#include <armnn/Network.hpp>
+
+#include <backends/reference/RefWorkloadFactory.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(RefOptimizedNetwork)
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateCpuRefWorkloads)
+{
+ const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
+
+ armnn::Network net;
+
+ armnn::NormalizationDescriptor nmDesc;
+ armnn::ActivationDescriptor acDesc;
+
+ // in
+ // |
+ // nm
+ // / |
+ // ac |
+ // \ |
+ // ml
+ // |
+ // sm
+ // |
+ // ot
+ armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
+
+ layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
+ normLayer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ layer = net.AddActivationLayer(acDesc, "ac");
+
+ normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ armnn::IConnectableLayer* prevLayer = layer;
+ layer = net.AddMultiplicationLayer("ml");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ prevLayer = layer;
+ armnn::SoftmaxDescriptor softmaxDescriptor;
+ layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ prevLayer = layer;
+ layer = net.AddOutputLayer(0, "ot");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
+ static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph().AllocateDynamicBuffers();
+ BOOST_CHECK(optNet);
+
+ // Validates workloads.
+ armnn::RefWorkloadFactory fact;
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ BOOST_CHECK_NO_THROW(
+ layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
+ }
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefPermuteLayer)
+{
+ // Create runtime in which test will run
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+
+ armnn::PermuteDescriptor descriptor({0, 2, 3, 1});
+ armnn::IConnectableLayer* permute = net->AddPermuteLayer(descriptor);
+
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));
+ permute->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+ permute->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 4, 1, 4 }, armnn::DataType::Float32));
+
+ // optimize the network
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+ }
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefMeanLayer)
+{
+ // Create runtime in which test will run
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+
+ armnn::MeanDescriptor descriptor({ 0, 1 }, false);
+ armnn::IConnectableLayer* meanLayer = net->AddMeanLayer(descriptor);
+
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(meanLayer->GetInputSlot(0));
+ meanLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 4, 3, 2 }, armnn::DataType::Float32));
+ meanLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 2 }, armnn::DataType::Float32));
+
+ // optimize the network
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+ }
+}
+
+BOOST_AUTO_TEST_CASE(FP16TurboModeTestOnCpuRef)
+{
+ // Test to check when FP16 Turbo mode set
+ // it converts the FP32 network to FP16 Network
+ // add FP32ToFP16 conversion layer after the InputLayer
+ // add FP16ToFP32 conversion layer after the OutputLayer
+ // checks the other layers if they are supported in FP16
+ // if they are not put the conversion layers before and after
+ // if they are not supported in FP16 use FP32 instead
+ // if there are inverse conversion layers remove them with optimization
+ // at the moment FloorLayer is not supported in FP16 so it rolls back to FP32
+ // and inverse conversion layers are removed by the optimizer
+ armnn::Network net;
+
+ // Defines layers.
+ auto input = net.AddInputLayer(0);
+ auto floor = net.AddFloorLayer();
+ auto output = net.AddOutputLayer(0);
+
+ // Connects layers.
+ input->GetOutputSlot(0).Connect(floor->GetInputSlot(0));
+ floor->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ armnn::TensorShape shape({4});
+ armnn::TensorInfo info(shape, armnn::DataType::Float32);
+ input->GetOutputSlot(0).SetTensorInfo(info);
+ floor->GetOutputSlot(0).SetTensorInfo(info);
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+
+ armnn::OptimizerOptions optimizerOptions;
+ optimizerOptions.m_ReduceFp32ToFp16 = true;
+
+ armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec(),
+ optimizerOptions);
+
+ std::ostringstream ss;
+ optimizedNet->SerializeToDot(ss);
+
+ auto inputId = input->GetGuid();
+ auto floorId = floor->GetGuid();
+ auto outputId = output->GetGuid();
+
+ std::stringstream expected;
+ expected <<
+ "digraph Optimized {\n"
+ " node [shape=\"record\"];\n"
+ " edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"
+ " " << inputId << " [label=\"{Input}\"];\n"
+ " " << floorId << " [label=\"{Floor}\"];\n"
+ " " << outputId << " [label=\"{Output}\"];\n"
+ " " << inputId << " -> " << floorId << " [label=< [4] >];\n"
+ " " << floorId << " -> " << outputId << " [label=< [4] >];\n"
+ "}\n";
+
+ BOOST_TEST(ss.str() == expected.str());
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/test/EndToEndTestImpl.hpp b/src/backends/test/EndToEndTestImpl.hpp
new file mode 100644
index 0000000..5f17f78
--- /dev/null
+++ b/src/backends/test/EndToEndTestImpl.hpp
@@ -0,0 +1,102 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include <armnn/ArmNN.hpp>
+
+#include <backends/test/QuantizeHelper.hpp>
+
+#include <vector>
+
+namespace
+{
+
+using namespace armnn;
+
+template<typename T>
+bool ConstantUsageTest(const std::vector<BackendId>& computeDevice,
+ const TensorInfo& commonTensorInfo,
+ const std::vector<T>& inputData,
+ const std::vector<T>& constantData,
+ const std::vector<T>& expectedOutputData)
+{
+ // Create runtime in which test will run
+ IRuntime::CreationOptions options;
+ IRuntimePtr runtime(IRuntime::Create(options));
+
+ // Builds up the structure of the network.
+ INetworkPtr net(INetwork::Create());
+
+ IConnectableLayer* input = net->AddInputLayer(0);
+ IConnectableLayer* constant = net->AddConstantLayer(ConstTensor(commonTensorInfo, constantData));
+ IConnectableLayer* add = net->AddAdditionLayer();
+ IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(add->GetInputSlot(0));
+ constant->GetOutputSlot(0).Connect(add->GetInputSlot(1));
+ add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ // Sets the tensors in the network.
+ input->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
+ constant->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
+ add->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
+
+ // optimize the network
+ IOptimizedNetworkPtr optNet = Optimize(*net, computeDevice, runtime->GetDeviceSpec());
+
+ // Loads it into the runtime.
+ NetworkId netId;
+ runtime->LoadNetwork(netId, std::move(optNet));
+
+ // Creates structures for input & output.
+ std::vector<T> outputData(inputData.size());
+
+ InputTensors inputTensors
+ {
+ {0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
+ };
+ OutputTensors outputTensors
+ {
+ {0, Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
+ };
+
+ // Does the inference.
+ runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
+
+ // Checks the results.
+ return outputData == expectedOutputData;
+}
+
+inline bool ConstantUsageFloat32Test(const std::vector<BackendId>& backends)
+{
+ const TensorInfo commonTensorInfo({ 2, 3 }, DataType::Float32);
+
+ return ConstantUsageTest(backends,
+ commonTensorInfo,
+ std::vector<float>{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, // Input.
+ std::vector<float>{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, // Const input.
+ std::vector<float>{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f } // Expected output.
+ );
+}
+
+inline bool ConstantUsageUint8Test(const std::vector<BackendId>& backends)
+{
+ TensorInfo commonTensorInfo({ 2, 3 }, DataType::QuantisedAsymm8);
+
+ const float scale = 0.023529f;
+ const int8_t offset = -43;
+
+ commonTensorInfo.SetQuantizationScale(scale);
+ commonTensorInfo.SetQuantizationOffset(offset);
+
+ return ConstantUsageTest(backends,
+ commonTensorInfo,
+ QuantizedVector<uint8_t>(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input.
+ QuantizedVector<uint8_t>(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input.
+ QuantizedVector<uint8_t>(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }) // Expected output.
+ );
+}
+
+} // anonymous namespace
\ No newline at end of file
diff --git a/src/armnn/test/JsonPrinterTests.cpp b/src/backends/test/JsonPrinterTestImpl.hpp
similarity index 86%
rename from src/armnn/test/JsonPrinterTests.cpp
rename to src/backends/test/JsonPrinterTestImpl.hpp
index 93f32cc..47e0ec7 100644
--- a/src/armnn/test/JsonPrinterTests.cpp
+++ b/src/backends/test/JsonPrinterTestImpl.hpp
@@ -2,29 +2,27 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
-#include <boost/test/unit_test.hpp>
-#include <boost/algorithm/string.hpp>
-#include <boost/lexical_cast.hpp>
-#include <stack>
-#include <string>
-#include <vector>
-#include <sstream>
-#include <Profiling.hpp>
#include <armnn/Descriptors.hpp>
#include <armnn/IRuntime.hpp>
#include <armnn/INetwork.hpp>
-#include <backends/cl/test/ClContextControlFixture.hpp>
-#include <backends/cl/ClWorkloadFactory.hpp>
+#include <armnn/Profiling.hpp>
-BOOST_FIXTURE_TEST_SUITE(JsonPrinterTests, ClProfilingContextControlFixture)
+#include <boost/test/unit_test.hpp>
+#include <boost/algorithm/string.hpp>
+#include <boost/lexical_cast.hpp>
-bool AreMatchingPair(const char opening, const char closing)
+#include <sstream>
+#include <stack>
+#include <string>
+#include <vector>
+
+inline bool AreMatchingPair(const char opening, const char closing)
{
return (opening == '{' && closing == '}') || (opening == '[' && closing == ']');
}
-bool AreParenthesesMatching(const std::string& exp)
+inline bool AreParenthesesMatching(const std::string& exp)
{
std::stack<char> expStack;
for (size_t i = 0; i < exp.length(); ++i)
@@ -48,7 +46,7 @@
return expStack.empty();
}
-std::vector<double> ExtractMeasurements(const std::string& exp)
+inline std::vector<double> ExtractMeasurements(const std::string& exp)
{
std::vector<double> numbers;
bool inArray = false;
@@ -95,7 +93,7 @@
return numbers;
}
-std::vector<std::string> ExtractSections(const std::string& exp)
+inline std::vector<std::string> ExtractSections(const std::string& exp)
{
std::vector<std::string> sections;
@@ -117,7 +115,7 @@
return sections;
}
-std::string SoftmaxProfilerTestSetupHelper(const std::vector<armnn::BackendId>& backends)
+inline std::string SoftmaxProfilerTestSetupHelper(const std::vector<armnn::BackendId>& backends)
{
using namespace armnn;
@@ -193,7 +191,7 @@
return ss.str();
}
-void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& testData)
+inline void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& testData)
{
// ensure all measurements are greater than zero
std::vector<double> measurementsVector = ExtractMeasurements(result);
@@ -238,7 +236,7 @@
BOOST_CHECK(AreParenthesesMatching(result));
}
-void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult(
+inline void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(
const std::vector<armnn::BackendId>& backends)
{
// setup the test fixture and obtain JSON Printer result
@@ -354,25 +352,3 @@
// validate the JSON Printer result
SoftmaxProfilerTestValidationHelper(result, testData);
}
-
-BOOST_AUTO_TEST_CASE(SoftmaxProfilerJSONPrinterCpuRefTest)
-{
- SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult({armnn::Compute::CpuRef});
-}
-
-
-#if ARMCOMPUTENEON_ENABLED
-BOOST_AUTO_TEST_CASE(SoftmaxProfilerJSONPrinterCpuAccTest)
-{
- SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult({armnn::Compute::CpuAcc});
-}
-#endif
-
-#if ARMCOMPUTECL_ENABLED
-BOOST_AUTO_TEST_CASE(SoftmaxProfilerJSONPrinterGpuAccTest)
-{
- SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult({armnn::Compute::GpuAcc});
-}
-#endif
-
-BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/test/OptimizedNetworkTests.cpp b/src/backends/test/OptimizedNetworkTests.cpp
new file mode 100644
index 0000000..72a35f9
--- /dev/null
+++ b/src/backends/test/OptimizedNetworkTests.cpp
@@ -0,0 +1,329 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/ArmNN.hpp>
+#include <armnn/Graph.hpp>
+#include <armnn/Network.hpp>
+
+#include <backends/reference/RefWorkloadFactory.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+BOOST_AUTO_TEST_SUITE(OptimizedNetwork)
+
+BOOST_AUTO_TEST_CASE(SerializeToDot)
+{
+ armnn::Network net;
+
+ //Defines layers.
+ auto input = net.AddInputLayer(0);
+ auto add = net.AddAdditionLayer();
+ auto output = net.AddOutputLayer(0);
+
+ // Connects layers.
+ input->GetOutputSlot(0).Connect(add->GetInputSlot(0));
+ input->GetOutputSlot(0).Connect(add->GetInputSlot(1));
+ add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ armnn::TensorShape shape({4});
+ armnn::TensorInfo info(shape, armnn::DataType::Float32);
+ input->GetOutputSlot(0).SetTensorInfo(info);
+ add->GetOutputSlot(0).SetTensorInfo(info);
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
+
+ std::ostringstream ss;
+ optimizedNet->SerializeToDot(ss);
+
+ auto inputId = input->GetGuid();
+ auto addId = add->GetGuid();
+ auto outputId = output->GetGuid();
+
+ std::stringstream expected;
+ expected <<
+ "digraph Optimized {\n"
+ " node [shape=\"record\"];\n"
+ " edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"
+ " " << inputId << " [label=\"{Input}\"];\n"
+ " " << addId << " [label=\"{Addition}\"];\n"
+ " " << outputId << " [label=\"{Output}\"];\n"
+ " " << inputId << " -> " << addId << " [label=< [4] >];\n"
+ " " << inputId << " -> " << addId << " [label=< [4] >];\n"
+ " " << addId << " -> " << outputId << " [label=< [4] >];\n"
+ "}\n";
+
+ BOOST_TEST(ss.str() == expected.str());
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback)
+{
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+
+ // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null.
+ armnn::NormalizationDescriptor descriptor;
+ armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
+
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
+ normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+ normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+ BOOST_CHECK(!optNet);
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerWithFallback)
+{
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+
+ // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.
+ armnn::NormalizationDescriptor descriptor;
+ armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
+
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
+ normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+ normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, armnn::Compute::CpuRef };
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+ BOOST_REQUIRE(optNet);
+
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ // If NEON is enabled, Input and Output layers are supported by CpuAcc,
+ // the other layers are supported by CpuRef.
+ // If NEON is not enabled, all layers are supported by CpuRef.
+#if ARMCOMPUTENEON_ENABLED
+ if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
+ }
+ else if (layer->GetType() == armnn::LayerType::Normalization)
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+ }
+#else
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+#endif
+ }
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDevice)
+{
+ const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
+
+ armnn::Network net;
+
+ armnn::NormalizationDescriptor nmDesc;
+ armnn::ActivationDescriptor acDesc;
+
+ // in
+ // |
+ // nm
+ // / |
+ // ac |
+ // \ |
+ // ml
+ // |
+ // sm
+ // |
+ // ot
+ armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
+
+ layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
+ normLayer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ layer = net.AddActivationLayer(acDesc, "ac");
+
+ normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ armnn::IConnectableLayer* prevLayer = layer;
+ layer = net.AddMultiplicationLayer("ml");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ prevLayer = layer;
+ armnn::SoftmaxDescriptor softmaxDescriptor;
+ layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ prevLayer = layer;
+ layer = net.AddOutputLayer(0, "ot");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::Undefined };
+
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
+ BOOST_CHECK(!optNet);
+
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDeviceWithFallback)
+{
+ const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
+
+ armnn::Network net;
+
+ armnn::NormalizationDescriptor nmDesc;
+ armnn::ActivationDescriptor acDesc;
+
+ // in
+ // |
+ // nm
+ // / |
+ // ac |
+ // \ |
+ // ml
+ // |
+ // sm
+ // |
+ // ot
+ armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
+
+ layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
+ normLayer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ layer = net.AddActivationLayer(acDesc, "ac");
+
+ normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ armnn::IConnectableLayer* prevLayer = layer;
+ layer = net.AddMultiplicationLayer("ml");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ prevLayer = layer;
+ armnn::SoftmaxDescriptor softmaxDescriptor;
+ layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+ layer->GetOutputSlot(0).SetTensorInfo(desc);
+
+ prevLayer = layer;
+ layer = net.AddOutputLayer(0, "ot");
+
+ prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::Undefined, armnn::Compute::CpuRef };
+
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
+ BOOST_CHECK(optNet);
+
+ // validate workloads
+ armnn::RefWorkloadFactory fact;
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+ BOOST_CHECK_NO_THROW(
+ layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
+ }
+}
+
+BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback)
+{
+ // build up the structure of the network
+ armnn::INetworkPtr net(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* input = net->AddInputLayer(0);
+
+ // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.
+ armnn::NormalizationDescriptor descriptor;
+ armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
+
+ armnn::IConnectableLayer* output = net->AddOutputLayer(0);
+
+ input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
+ normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+ input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+ normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
+
+ armnn::IRuntime::CreationOptions options;
+ armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
+
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc,
+ armnn::Compute::GpuAcc,
+ armnn::Compute::CpuRef };
+
+ armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
+ BOOST_REQUIRE(optNet);
+
+ for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
+ {
+ // If NEON is enabled, Input and Output layers are supported by CpuAcc,
+ // the other layers are supported by CpuRef.
+ // If only CL is enabled, Input and Output layers are supported by GpuAcc,
+ // the other layers are supported by CpuRef.
+ // If neither NEON, nor CL is enabled, all layers are supported by CpuRef.
+#if ARMCOMPUTENEON_ENABLED
+ if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
+ }
+ else if (layer->GetType() == armnn::LayerType::Normalization)
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+ }
+#elif ARMCOMPUTECL_ENABLED
+ if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc);
+ }
+ else if (layer->GetType() == armnn::LayerType::Normalization)
+ {
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+ }
+#else
+ BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
+#endif
+ }
+}
+
+BOOST_AUTO_TEST_SUITE_END()