IVGCVSW-2073: Move remaining backend-specific tests from armnn to backends
Change-Id: I45fd5b6dd32c435b78a54dc377a623e60978ce13
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/backends/test/JsonPrinterTestImpl.hpp b/src/backends/test/JsonPrinterTestImpl.hpp
new file mode 100644
index 0000000..47e0ec7
--- /dev/null
+++ b/src/backends/test/JsonPrinterTestImpl.hpp
@@ -0,0 +1,354 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/Descriptors.hpp>
+#include <armnn/IRuntime.hpp>
+#include <armnn/INetwork.hpp>
+#include <armnn/Profiling.hpp>
+
+#include <boost/test/unit_test.hpp>
+#include <boost/algorithm/string.hpp>
+#include <boost/lexical_cast.hpp>
+
+#include <sstream>
+#include <stack>
+#include <string>
+#include <vector>
+
+inline bool AreMatchingPair(const char opening, const char closing)
+{
+ return (opening == '{' && closing == '}') || (opening == '[' && closing == ']');
+}
+
+inline 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();
+}
+
+inline 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;
+}
+
+inline 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;
+}
+
+inline 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();
+}
+
+inline 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));
+}
+
+inline 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);
+}
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()