IVGCVSW-5108 Allow Concat to use subtensor on x and y
* Updated ConcatLayer to allow using subtensors on x/y if padding is not required
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I46a8fb9f17b976b76e069bb82614b6628a206717
diff --git a/src/backends/neon/test/NeonTensorHandleTests.cpp b/src/backends/neon/test/NeonTensorHandleTests.cpp
index fe5e8f9..8b3e3fd 100644
--- a/src/backends/neon/test/NeonTensorHandleTests.cpp
+++ b/src/backends/neon/test/NeonTensorHandleTests.cpp
@@ -2,9 +2,17 @@
// 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)
@@ -77,4 +85,79 @@
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()