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-rw-r--r--src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp293
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diff --git a/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp b/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp
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+++ b/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp
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+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../GraphUtils.hpp"
+#include "../TestUtils.hpp"
+
+#include <armnn/INetwork.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+using namespace armnn;
+
+BOOST_AUTO_TEST_SUITE(Optimizer)
+
+INetworkPtr CreateSimpleReduceNetwork(ReduceDescriptor reduceDescriptor,
+ TensorShape& inputShape,
+ TensorShape& outputShape)
+{
+ // Create a network
+ INetworkPtr network = INetwork::Create();
+
+ const std::string layerName("reduce_layer");
+ const TensorInfo inputInfo (inputShape, DataType::Float32);
+ const TensorInfo outputInfo(outputShape, DataType::Float32);
+
+ IConnectableLayer* const inputLayer = network->AddInputLayer(0);
+ IConnectableLayer* const reduceLayer = network->AddReduceLayer(reduceDescriptor, layerName.c_str());
+ IConnectableLayer* const outputLayer1 = network->AddOutputLayer(0);
+ IConnectableLayer* const outputLayer2 = network->AddOutputLayer(1);
+
+ inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
+ reduceLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+ inputLayer->GetOutputSlot(0).Connect(reduceLayer->GetInputSlot(0));
+ reduceLayer->GetOutputSlot(0).Connect(outputLayer1->GetInputSlot(0));
+ reduceLayer->GetOutputSlot(0).Connect(outputLayer2->GetInputSlot(0));
+
+ return network;
+}
+
+void ReduceWithMultipleAxesTest(INetworkPtr& network,
+ const TensorShape& outputShape,
+ const std::vector<float>& inputData,
+ const std::vector<float>& expectedOutput,
+ const size_t numOfAxes,
+ Compute backendId)
+{
+ // Create ArmNN runtime
+ IRuntimePtr run = IRuntime::Create(IRuntime::CreationOptions());
+
+ // Optimise ArmNN network
+ IOptimizedNetworkPtr optNet = Optimize(*network, {backendId}, run->GetDeviceSpec());
+
+ Graph& graph = GetGraphForTesting(optNet.get());
+ if (numOfAxes == 2)
+ {
+ BOOST_CHECK(graph.GetNumLayers() == 5);
+ BOOST_TEST(CheckSequence(graph.cbegin(),
+ graph.cend(),
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ReduceLayer>,
+ &IsLayerOfType<ReduceLayer>,
+ &IsLayerOfType<OutputLayer>,
+ &IsLayerOfType<OutputLayer>));
+ }
+ else
+ {
+ BOOST_CHECK(graph.GetNumLayers() == 6);
+ BOOST_TEST(CheckSequence(graph.cbegin(),
+ graph.cend(),
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ReduceLayer>,
+ &IsLayerOfType<ReduceLayer>,
+ &IsLayerOfType<ReduceLayer>,
+ &IsLayerOfType<OutputLayer>,
+ &IsLayerOfType<OutputLayer>));
+ }
+
+ // Get last layer in new chain, layers name follow 0, 1, 2 pattern
+ std::string layerName = "reduce_layer_" + std::to_string(numOfAxes - 1);
+ Layer* const reduceLayer = GetFirstLayerWithName(graph, layerName);
+ BOOST_TEST(reduceLayer);
+ auto reduceTensorInfo = reduceLayer->GetOutputSlot().GetTensorInfo();
+
+ // Tensorshape and the data type are correct
+ BOOST_TEST((reduceTensorInfo.GetShape() == outputShape));
+ BOOST_TEST((reduceTensorInfo.GetDataType() == DataType::Float32));
+
+ // Load network into runtime
+ NetworkId networkIdentifier;
+ run->LoadNetwork(networkIdentifier, std::move(optNet));
+
+ // Create input and output tensors
+ std::vector<float> outputData(expectedOutput.size());
+ InputTensors inputTensors
+ {
+ { 0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data()) }
+ };
+ OutputTensors outputTensors
+ {
+ { 0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data()) },
+ { 1, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 1), outputData.data()) }
+ };
+
+ // Run inference
+ run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+
+ // Checks the results
+ BOOST_TEST(outputData == expectedOutput);
+}
+
+void ReduceSumWithTwoAxesKeepDimsTest(Compute backendId)
+{
+ armnn::ReduceDescriptor reduceDescriptor;
+ reduceDescriptor.m_vAxis = { 1, 2 };
+ reduceDescriptor.m_KeepDims = true;
+ reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
+
+ TensorShape inputShape = { 1, 3, 2, 4 };
+ TensorShape outputShape = { 1, 1, 1, 4 };
+
+ // Construct ArmNN network
+ INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
+
+ // Creates structures for input & output.
+ const std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
+ 5.0f, 6.0f, 7.0f, 8.0f,
+
+ 10.0f, 20.0f, 30.0f, 40.0f,
+ 50.0f, 60.0f, 70.0f, 80.0f,
+
+ 100.0f, 200.0f, 300.0f, 400.0f,
+ 500.0f, 600.0f, 700.0f, 800.0f });
+ const std::vector<float> expectedOutput({ 666.0f, 888.0f, 1110.0f, 1332.0f });
+
+ ReduceWithMultipleAxesTest(network,
+ outputShape,
+ inputData,
+ expectedOutput,
+ reduceDescriptor.m_vAxis.size(),
+ backendId);
+}
+
+void ReduceSumWithTwoAxesTest(Compute backendId)
+{
+ armnn::ReduceDescriptor reduceDescriptor;
+ reduceDescriptor.m_vAxis = { 1, 2 };
+ reduceDescriptor.m_KeepDims = false;
+ reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
+
+ TensorShape inputShape = { 1, 3, 2, 4 };
+ TensorShape outputShape = { 1, 4 };
+
+ // Construct ArmNN network
+ INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
+
+ // Creates structures for input & output.
+ const std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
+ 5.0f, 6.0f, 7.0f, 8.0f,
+
+ 10.0f, 20.0f, 30.0f, 40.0f,
+ 50.0f, 60.0f, 70.0f, 80.0f,
+
+ 100.0f, 200.0f, 300.0f, 400.0f,
+ 500.0f, 600.0f, 700.0f, 800.0f });
+ const std::vector<float> expectedOutput({ 666.0f, 888.0f, 1110.0f, 1332.0f });
+
+ ReduceWithMultipleAxesTest(network,
+ outputShape,
+ inputData,
+ expectedOutput,
+ reduceDescriptor.m_vAxis.size(),
+ backendId);
+}
+
+void ReduceSumWithThreeAxesKeepDimsTest(Compute backendId)
+{
+ armnn::ReduceDescriptor reduceDescriptor;
+ reduceDescriptor.m_vAxis = { 0, 2, 3 };
+ reduceDescriptor.m_KeepDims = true;
+ reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
+
+ TensorShape inputShape = { 2, 2, 2, 2 };
+ TensorShape outputShape = { 1, 2, 1, 1 };
+
+ // Construct ArmNN network
+ INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
+
+ // Creates structures for input & output.
+ const std::vector<float> inputData({ 1.0f, 2.0f,
+ 3.0f, 4.0f,
+
+ 5.0f, 6.0f,
+ 7.0f, 8.0f,
+
+ 10.0f, 20.0f,
+ 30.0f, 40.0f,
+
+ 50.0f, 60.0f,
+ 70.0f, 80.0f });
+ const std::vector<float> expectedOutput({ 110.0f, 286.0f });
+
+ ReduceWithMultipleAxesTest(network,
+ outputShape,
+ inputData,
+ expectedOutput,
+ reduceDescriptor.m_vAxis.size(),
+ backendId);
+}
+
+void ReduceSumWithThreeAxesTest(Compute backendId)
+{
+ armnn::ReduceDescriptor reduceDescriptor;
+ reduceDescriptor.m_vAxis = { 0, 2, 3 };
+ reduceDescriptor.m_KeepDims = false;
+ reduceDescriptor.m_ReduceOperation = armnn::ReduceOperation::Sum;
+
+ TensorShape inputShape = { 2, 2, 2, 2 };
+ TensorShape outputShape = { 2 };
+
+ // Construct ArmNN network
+ INetworkPtr network = CreateSimpleReduceNetwork(reduceDescriptor, inputShape, outputShape);
+
+ // Creates structures for input & output.
+ const std::vector<float> inputData({ 1.0f, 2.0f,
+ 3.0f, 4.0f,
+
+ 5.0f, 6.0f,
+ 7.0f, 8.0f,
+
+ 10.0f, 20.0f,
+ 30.0f, 40.0f,
+
+ 50.0f, 60.0f,
+ 70.0f, 80.0f });
+ const std::vector<float> expectedOutput({ 110.0f, 286.0f });
+
+ ReduceWithMultipleAxesTest(network,
+ outputShape,
+ inputData,
+ expectedOutput,
+ reduceDescriptor.m_vAxis.size(),
+ backendId);
+}
+
+using namespace armnn;
+#if defined(ARMCOMPUTENEON_ENABLED)
+BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsCpuAccTest)
+{
+ ReduceSumWithTwoAxesKeepDimsTest(Compute::CpuAcc);
+}
+
+BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesCpuAccTest)
+{
+ ReduceSumWithTwoAxesTest(Compute::CpuAcc);
+}
+
+BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsCpuAccTest)
+{
+ ReduceSumWithThreeAxesKeepDimsTest(Compute::CpuAcc);
+}
+
+BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesCpuAccTest)
+{
+ ReduceSumWithThreeAxesTest(Compute::CpuAcc);
+}
+#endif
+
+#if defined(ARMCOMPUTECL_ENABLED)
+BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsGpuAccTest)
+{
+ ReduceSumWithTwoAxesKeepDimsTest(Compute::GpuAcc);
+}
+
+BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesGpuAccTest)
+{
+ ReduceSumWithTwoAxesTest(Compute::GpuAcc);
+}
+
+BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsGpuAccTest)
+{
+ ReduceSumWithThreeAxesKeepDimsTest(Compute::GpuAcc);
+}
+
+BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesGpuAccTest)
+{
+ ReduceSumWithThreeAxesTest(Compute::GpuAcc);
+}
+#endif
+
+BOOST_AUTO_TEST_SUITE_END() \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-08 02:40:58 +0000