diff options
Diffstat (limited to 'src/armnn/test')
| -rw-r--r-- | src/armnn/test/EndToEndTest.cpp | 380 | ||||
| -rw-r--r-- | src/armnn/test/GraphUtils.hpp | 3 | ||||
| -rw-r--r-- | src/armnn/test/JsonPrinterTests.cpp | 378 | ||||
| -rw-r--r-- | src/armnn/test/NetworkTests.cpp | 640 |
4 files changed, 10 insertions, 1391 deletions
diff --git a/src/armnn/test/EndToEndTest.cpp b/src/armnn/test/EndToEndTest.cpp index d34bf69548..4f202f174e 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 @@ BOOST_AUTO_TEST_SUITE(EndToEnd) 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_AUTO_TEST_CASE(QuantizedHelper) 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 4d385de92f..3325405eaf 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/JsonPrinterTests.cpp b/src/armnn/test/JsonPrinterTests.cpp deleted file mode 100644 index 93f32cc540..0000000000 --- a/src/armnn/test/JsonPrinterTests.cpp +++ /dev/null @@ -1,378 +0,0 @@ -// -// 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> - -BOOST_FIXTURE_TEST_SUITE(JsonPrinterTests, ClProfilingContextControlFixture) - -bool AreMatchingPair(const char opening, const char closing) -{ - return (opening == '{' && closing == '}') || (opening == '[' && closing == ']'); -} - -bool AreParenthesesMatching(const std::string& exp) -{ - std::stack<char> expStack; - for (size_t i = 0; i < exp.length(); ++i) - { - if (exp[i] == '{' || exp[i] == '[') - { - expStack.push(exp[i]); - } - else if (exp[i] == '}' || exp[i] == ']') - { - if (expStack.empty() || !AreMatchingPair(expStack.top(), exp[i])) - { - return false; - } - else - { - expStack.pop(); - } - } - } - return expStack.empty(); -} - -std::vector<double> ExtractMeasurements(const std::string& exp) -{ - std::vector<double> numbers; - bool inArray = false; - std::string numberString; - for (size_t i = 0; i < exp.size(); ++i) - { - if (exp[i] == '[') - { - inArray = true; - } - else if (exp[i] == ']' && inArray) - { - try - { - boost::trim_if(numberString, boost::is_any_of("\t,\n")); - numbers.push_back(std::stod(numberString)); - } - catch (std::invalid_argument const& e) - { - BOOST_FAIL("Could not convert measurements to double: " + numberString); - } - - numberString.clear(); - inArray = false; - } - else if (exp[i] == ',' && inArray) - { - try - { - boost::trim_if(numberString, boost::is_any_of("\t,\n")); - numbers.push_back(std::stod(numberString)); - } - catch (std::invalid_argument const& e) - { - BOOST_FAIL("Could not convert measurements to double: " + numberString); - } - numberString.clear(); - } - else if (exp[i] != '[' && inArray && exp[i] != ',' && exp[i] != ' ') - { - numberString += exp[i]; - } - } - return numbers; -} - -std::vector<std::string> ExtractSections(const std::string& exp) -{ - std::vector<std::string> sections; - - std::stack<size_t> s; - for (size_t i = 0; i < exp.size(); i++) - { - if (exp.at(i) == '{') - { - s.push(i); - } - else if (exp.at(i) == '}') - { - size_t from = s.top(); - s.pop(); - sections.push_back(exp.substr(from, i - from + 1)); - } - } - - return sections; -} - -std::string SoftmaxProfilerTestSetupHelper(const std::vector<armnn::BackendId>& backends) -{ - using namespace armnn; - - BOOST_CHECK(!backends.empty()); - - ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); - - // Create runtime in which test will run - IRuntime::CreationOptions options; - options.m_EnableGpuProfiling = backends.front() == armnn::Compute::GpuAcc; - IRuntimePtr runtime(IRuntime::Create(options)); - - // build up the structure of the network - 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)); - - // set 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 / 256.0f); - softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); - - // optimize the network - IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); - if(!optNet) - { - BOOST_FAIL("Error occurred during Optimization, Optimize() returned nullptr."); - } - // load it into the runtime - NetworkId netId; - auto error = runtime->LoadNetwork(netId, std::move(optNet)); - BOOST_TEST(error == Status::Success); - - // create structures for input & output - std::vector<uint8_t> inputData - { - 1, 10, 3, 200, 5 - // one of inputs 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())} - }; - - runtime->GetProfiler(netId)->EnableProfiling(true); - - // do the inferences - runtime->EnqueueWorkload(netId, inputTensors, outputTensors); - runtime->EnqueueWorkload(netId, inputTensors, outputTensors); - runtime->EnqueueWorkload(netId, inputTensors, outputTensors); - - // retrieve the Profiler.Print() output - std::stringstream ss; - profilerManager.GetProfiler()->Print(ss); - - return ss.str(); -} - -void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& testData) -{ - // ensure all measurements are greater than zero - std::vector<double> measurementsVector = ExtractMeasurements(result); - BOOST_CHECK(!measurementsVector.empty()); - - // check sections contain raw and unit tags - // first ensure Parenthesis are balanced - if (AreParenthesesMatching(result)) - { - // remove parent sections that will not have raw or unit tag - std::vector<std::string> sectionVector = ExtractSections(result); - for (size_t i = 0; i < sectionVector.size(); ++i) - { - if (boost::contains(sectionVector[i], "\"ArmNN\":") - || boost::contains(sectionVector[i], "\"inference_measurements\":")) - { - sectionVector.erase(sectionVector.begin() + static_cast<int>(i)); - } - } - BOOST_CHECK(!sectionVector.empty()); - - BOOST_CHECK(std::all_of(sectionVector.begin(), sectionVector.end(), - [](std::string i) { return boost::contains(i, "\"raw\":"); })); - - BOOST_CHECK(std::all_of(sectionVector.begin(), sectionVector.end(), - [](std::string i) { return boost::contains(i, "\"unit\":"); })); - } - - // remove the time measurements as they vary from test to test - result.erase(std::remove_if (result.begin(),result.end(), - [](char c) { return c == '.'; }), result.end()); - result.erase(std::remove_if (result.begin(), result.end(), &isdigit), result.end()); - result.erase(std::remove_if (result.begin(),result.end(), - [](char c) { return c == '\t'; }), result.end()); - - BOOST_CHECK(boost::contains(result, "ArmNN")); - BOOST_CHECK(boost::contains(result, "inference_measurements")); - BOOST_CHECK(boost::contains(result, "layer_measurements")); - BOOST_CHECK_EQUAL(result, testData); - - // ensure no spare parenthesis present in print output - BOOST_CHECK(AreParenthesesMatching(result)); -} - -void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJSONPrinterResult( - const std::vector<armnn::BackendId>& backends) -{ - // setup the test fixture and obtain JSON Printer result - std::string result = SoftmaxProfilerTestSetupHelper(backends); - - std::string backend = "Ref"; - std::string changeLine31 = "\n},\n\"CopyMemGeneric_Execute\": {"; - std::string changeLine39 = "us\""; - std::string changeLine40; - std::string changeLine45; - - if (backends[0] == armnn::Compute::GpuAcc) { - backend = "Cl"; - changeLine31 = ",\n\"OpenClKernelTimer/: softmax_layer_max_shift_exp_sum_quantized_serial GWS[,,]\": {"; - changeLine39 = R"(us" -}, -"OpenClKernelTimer/: softmax_layer_norm_quantized GWS[,,]": { -"raw": [ -, -, - -], -"unit": "us")"; - - changeLine40 = R"( -}, -"CopyMemGeneric_Execute": { -"raw": [ -, -, - -], -"unit": "us")"; - changeLine45 = "}\n"; - } - else if (backends[0] == armnn::Compute::CpuAcc) - { - backend = "Neon"; - changeLine31 = ",\n\"NeonKernelTimer/: NEFillBorderKernel\": {"; - changeLine39 = R"(us" -}, -"NeonKernelTimer/: NELogitsDMaxKernel": { -"raw": [ -, -, - -], -"unit": "us" -}, -"NeonKernelTimer/: NELogitsDSoftmaxKernel": { -"raw": [ -, -, - -], -"unit": "us")"; - changeLine40 = R"( -}, -"CopyMemGeneric_Execute": { -"raw": [ -, -, - -], -"unit": "us")"; - changeLine45 = "}\n"; - } - - std::string testData = R"({ -"ArmNN": { -"inference_measurements": { -"raw": [ -, -, - -], -"unit": "us", -"layer_measurements": { -"raw": [ -, -, - -], -"unit": "us", -"CopyMemGeneric_Execute": { -"raw": [ -, -, - -], -"unit": "us" -}, -")" + backend + R"(SoftmaxUintWorkload_Execute": { -"raw": [ -, -, - -], -"unit": "us")" + changeLine31 + R"( -"raw": [ -, -, - -], -"unit": ")" + changeLine39 + R"( -})" + changeLine40 + R"( -} -} -} -} -)" + changeLine45 + R"()"; - - // 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/armnn/test/NetworkTests.cpp b/src/armnn/test/NetworkTests.cpp index 4f8dd7ea7b..91ff7fa983 100644 --- a/src/armnn/test/NetworkTests.cpp +++ b/src/armnn/test/NetworkTests.cpp @@ -2,16 +2,13 @@ // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // -#include <boost/test/unit_test.hpp> + +#include "GraphUtils.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 <armnn/Network.hpp> -#include "GraphUtils.hpp" +#include <boost/test/unit_test.hpp> namespace { @@ -43,54 +40,6 @@ BOOST_AUTO_TEST_CASE(LayerGuids) 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 @@ BOOST_AUTO_TEST_CASE(NetworkModification_SplitterMultiplication) 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() |