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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <Graph.hpp>
#include <Network.hpp>
#include <neon/NeonTensorHandle.hpp>
#include <neon/NeonTensorHandleFactory.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>
#include <test/GraphUtils.hpp>
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(NeonTensorHandleTests)
using namespace armnn;
BOOST_AUTO_TEST_CASE(NeonTensorHandleGetCapabilitiesNoPadding)
{
std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>();
NeonTensorHandleFactory handleFactory(memoryManager);
INetworkPtr network(INetwork::Create());
// Add the layers
IConnectableLayer* input = network->AddInputLayer(0);
SoftmaxDescriptor descriptor;
descriptor.m_Beta = 1.0f;
IConnectableLayer* softmax = network->AddSoftmaxLayer(descriptor);
IConnectableLayer* output = network->AddOutputLayer(2);
// Establish connections
input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0));
softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0));
// No padding required for input
std::vector<Capability> capabilities = handleFactory.GetCapabilities(input,
softmax,
CapabilityClass::PaddingRequired);
BOOST_TEST(capabilities.empty());
// No padding required for Softmax
capabilities = handleFactory.GetCapabilities(softmax, output, CapabilityClass::PaddingRequired);
BOOST_TEST(capabilities.empty());
// No padding required for output
capabilities = handleFactory.GetCapabilities(output, nullptr, CapabilityClass::PaddingRequired);
BOOST_TEST(capabilities.empty());
}
BOOST_AUTO_TEST_CASE(NeonTensorHandleGetCapabilitiesPadding)
{
std::shared_ptr<NeonMemoryManager> memoryManager = std::make_shared<NeonMemoryManager>();
NeonTensorHandleFactory handleFactory(memoryManager);
INetworkPtr network(INetwork::Create());
// Add the layers
IConnectableLayer* input = network->AddInputLayer(0);
Pooling2dDescriptor descriptor;
IConnectableLayer* pooling = network->AddPooling2dLayer(descriptor);
IConnectableLayer* output = network->AddOutputLayer(2);
// Establish connections
input->GetOutputSlot(0).Connect(pooling->GetInputSlot(0));
pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0));
// No padding required for input
std::vector<Capability> capabilities = handleFactory.GetCapabilities(input,
pooling,
CapabilityClass::PaddingRequired);
BOOST_TEST(capabilities.empty());
// No padding required for output
capabilities = handleFactory.GetCapabilities(output, nullptr, CapabilityClass::PaddingRequired);
BOOST_TEST(capabilities.empty());
// Padding required for Pooling2d
capabilities = handleFactory.GetCapabilities(pooling, output, CapabilityClass::PaddingRequired);
BOOST_TEST(capabilities.size() == 1);
BOOST_TEST((capabilities[0].m_CapabilityClass == CapabilityClass::PaddingRequired));
BOOST_TEST(capabilities[0].m_Value);
}
BOOST_AUTO_TEST_CASE(ConcatOnXorYSubTensorsNoPaddinRequiredTest)
{
armnn::INetworkPtr net(armnn::INetwork::Create());
// Set up tensor infos
const armnn::TensorInfo inputInfo = armnn::TensorInfo({2, 3, 2, 2}, armnn::DataType::Float32);
const armnn::TensorInfo intermediateInfo = armnn::TensorInfo({2, 3, 2, 2}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo = armnn::TensorInfo({2, 3, 4, 2}, armnn::DataType::Float32);
armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Abs);
// Create the network
armnn::IConnectableLayer* const input0Layer = net->AddInputLayer(0, "input_0");
input0Layer->GetOutputSlot(0).SetTensorInfo(inputInfo);
armnn::IConnectableLayer* elementwiseUnaryLayer0 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary_0");
elementwiseUnaryLayer0->GetOutputSlot(0).SetTensorInfo(intermediateInfo);
input0Layer->GetOutputSlot(0).Connect(elementwiseUnaryLayer0->GetInputSlot(0));
armnn::IConnectableLayer* const input1Layer = net->AddInputLayer(1, "input_1");
input1Layer->GetOutputSlot(0).SetTensorInfo(inputInfo);
armnn::IConnectableLayer* elementwiseUnaryLayer1 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary_1");
elementwiseUnaryLayer1->GetOutputSlot(0).SetTensorInfo(intermediateInfo);
input1Layer->GetOutputSlot(0).Connect(elementwiseUnaryLayer1->GetInputSlot(0));
std::array<armnn::TensorShape, 2> concatInputShapes = { intermediateInfo.GetShape(), intermediateInfo.GetShape() };
armnn::IConnectableLayer* const concatLayer = net->AddConcatLayer(armnn::CreateDescriptorForConcatenation(
concatInputShapes.begin(), concatInputShapes.end(), 2), "concatenation");
concatLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
elementwiseUnaryLayer0->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(0));
elementwiseUnaryLayer1->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(1));
armnn::IConnectableLayer* const outputLayer = net->AddOutputLayer(0, "output");
concatLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
const armnn::Graph& theGraph = static_cast<armnn::OptimizedNetwork*>(optimizedNet.get())->GetGraph();
// Load graph into runtime
armnn::NetworkId networkIdentifier;
runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet));
// now check the concat how many sub-tensors it is using..
auto TraceSubTensorHandleAncestry = [](armnn::ITensorHandle* const subTensorHandle)
{
if (subTensorHandle && subTensorHandle->GetParent())
{
return true;
}
return false;
};
for (auto&& layer : theGraph)
{
if(layer->GetType() == armnn::LayerType::Concat)
{
unsigned int numberOfSubTensors = 0;
for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i)
{
const armnn::OutputSlot* slot = layer->GetInputSlot(i).GetConnectedOutputSlot();
if (TraceSubTensorHandleAncestry(slot->GetOutputHandler().GetData()))
{
++numberOfSubTensors;
}
}
// sub-tensors should be supported in this configuration
BOOST_CHECK(numberOfSubTensors > 0);
}
}
}
BOOST_AUTO_TEST_SUITE_END()
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