IVGCVSW-2073: Move remaining backend-specific tests from armnn to backends
Change-Id: I45fd5b6dd32c435b78a54dc377a623e60978ce13
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()