IVGCVSW-3119 Rename MergerLayer to ConcatLayer
!android-nn-driver:1210
Change-Id: I940b3b9e421c92bfd55ae996f7bc54ac077f2604
Signed-off-by: Jim Flynn <jim.flynn@arm.com>
diff --git a/src/backends/backendsCommon/test/ConcatTestImpl.hpp b/src/backends/backendsCommon/test/ConcatTestImpl.hpp
new file mode 100644
index 0000000..ded3857
--- /dev/null
+++ b/src/backends/backendsCommon/test/ConcatTestImpl.hpp
@@ -0,0 +1,307 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "CommonTestUtils.hpp"
+
+#include <ResolveType.hpp>
+
+#include <armnn/INetwork.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+#include <vector>
+
+namespace
+{
+
+template<typename armnn::DataType DataType>
+INetworkPtr CreateConcatNetwork(const std::vector<TensorShape>& inputShapes,
+ const TensorShape &outputShape,
+ unsigned int concatAxis,
+ const float qScale = 1.0f,
+ const int32_t qOffset = 0)
+{
+ using namespace armnn;
+ // Builds up the structure of the network.
+ INetworkPtr net(INetwork::Create());
+
+ OriginsDescriptor descriptor;
+
+ descriptor = CreateDescriptorForConcatenation(inputShapes.begin(),
+ inputShapes.end(),
+ concatAxis);
+ IConnectableLayer* concat = net->AddConcatLayer(descriptor, "concat");
+
+ for (unsigned int i = 0; i < inputShapes.size(); ++i)
+ {
+ TensorInfo inputTensorInfo(inputShapes[i], DataType, qScale, qOffset);
+ IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i));
+ Connect(input, concat, inputTensorInfo, 0, i);
+ }
+
+ TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
+ IConnectableLayer* output = net->AddOutputLayer(0, "output");
+ Connect(concat, output, outputTensorInfo, 0, 0);
+
+ return net;
+}
+
+template<armnn::DataType ArmnnType>
+void ConcatDim0EndToEnd(const std::vector<BackendId>& backends)
+{
+ using namespace armnn;
+ using T = ResolveType<ArmnnType>;
+
+ unsigned int concatAxis = 0;
+ const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
+ const TensorShape& outputShape = { 4, 3, 2, 2 };
+
+ // Builds up the structure of the network
+ INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
+
+ BOOST_TEST_CHECKPOINT("create a network");
+
+ // Creates structures for input & output.
+ std::vector<T> inputData{
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12
+ };
+
+ std::vector<T> expectedOutput{
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12
+ };
+
+ std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
+ std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
+}
+
+template<armnn::DataType ArmnnType>
+void ConcatDim1EndToEnd(const std::vector<BackendId>& backends)
+{
+ using namespace armnn;
+ using T = ResolveType<ArmnnType>;
+
+ unsigned int concatAxis = 1;
+ const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
+ const TensorShape& outputShape = { 2, 6, 2, 2 };
+
+ // Builds up the structure of the network
+ INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
+
+ BOOST_TEST_CHECKPOINT("create a network");
+
+ // Creates structures for input & output.
+ std::vector<T> inputData{
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12
+ };
+
+ std::vector<T> expectedOutput{
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12
+ };
+
+ std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
+ std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
+}
+
+template<armnn::DataType ArmnnType>
+void ConcatDim2EndToEnd(const std::vector<BackendId>& backends)
+{
+ using namespace armnn;
+ using T = ResolveType<ArmnnType>;
+
+ unsigned int concatAxis = 2;
+ const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
+ const TensorShape& outputShape = { 2, 3, 4, 2 };
+
+ // Builds up the structure of the network
+ INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
+
+ BOOST_TEST_CHECKPOINT("create a network");
+
+ // Creates structures for input & output.
+ std::vector<T> inputData{
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12
+ };
+
+ std::vector<T> expectedOutput{
+ 1, 2,
+ 3, 4,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 9, 10,
+ 11, 12
+ };
+
+ std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
+ std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+void ConcatDim3EndToEnd(const std::vector<BackendId>& backends)
+{
+ using namespace armnn;
+
+ unsigned int concatAxis = 3;
+ const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
+ const TensorShape& outputShape = { 2, 3, 2, 4 };
+
+ // Builds up the structure of the network
+ INetworkPtr net = CreateConcatNetwork<ArmnnType>(inputShapes, outputShape, concatAxis);
+
+ BOOST_TEST_CHECKPOINT("create a network");
+
+ // Creates structures for input & output.
+ std::vector<T> inputData{
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12,
+ 1, 2,
+ 3, 4,
+ 5, 6,
+ 7, 8,
+ 9, 10,
+ 11, 12
+ };
+
+ std::vector<T> expectedOutput{
+ 1, 2,
+ 1, 2,
+ 3, 4,
+ 3, 4,
+ 5, 6,
+ 5, 6,
+ 7, 8,
+ 7, 8,
+ 9, 10,
+ 9, 10,
+ 11, 12,
+ 11, 12,
+ 1, 2,
+ 1, 2,
+ 3, 4,
+ 3, 4,
+ 5, 6,
+ 5, 6,
+ 7, 8,
+ 7, 8,
+ 9, 10,
+ 9, 10,
+ 11, 12,
+ 11, 12
+ };
+
+ std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
+ std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);
+}
+
+} // anonymous namespace