diff options
Diffstat (limited to 'src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp')
| -rw-r--r-- | src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp | 175 |
1 files changed, 87 insertions, 88 deletions
diff --git a/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp b/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp index b42c0a2cfb..df9a0dbc39 100644 --- a/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp +++ b/src/armnn/test/optimizations/ReduceMultipleAxesTests.cpp @@ -8,12 +8,12 @@ #include <armnn/INetwork.hpp> -#include <boost/test/unit_test.hpp> +#include <doctest/doctest.h> using namespace armnn; -BOOST_AUTO_TEST_SUITE(Optimizer) - +TEST_SUITE("Optimizer_ReduceMultipleAxes") +{ INetworkPtr CreateSimpleReduceNetwork(ReduceDescriptor reduceDescriptor, TensorShape& inputShape, TensorShape& outputShape) @@ -22,10 +22,10 @@ INetworkPtr CreateSimpleReduceNetwork(ReduceDescriptor reduceDescriptor, INetworkPtr network = INetwork::Create(); const std::string layerName("reduce_layer"); - const TensorInfo inputInfo (inputShape, DataType::Float32); + const TensorInfo inputInfo(inputShape, DataType::Float32); const TensorInfo outputInfo(outputShape, DataType::Float32); - IConnectableLayer* const inputLayer = network->AddInputLayer(0); + 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); @@ -56,37 +56,36 @@ void ReduceWithMultipleAxesTest(INetworkPtr& network, 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 + CHECK(graph.GetNumLayers() == 5); + CHECK(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>)); + CHECK(graph.GetNumLayers() == 6); + CHECK(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); + CHECK(reduceLayer); auto reduceTensorInfo = reduceLayer->GetOutputSlot().GetTensorInfo(); // Tensorshape and the data type are correct - BOOST_TEST((reduceTensorInfo.GetShape() == outputShape)); - BOOST_TEST((reduceTensorInfo.GetDataType() == DataType::Float32)); + CHECK((reduceTensorInfo.GetShape() == outputShape)); + CHECK((reduceTensorInfo.GetDataType() == DataType::Float32)); // Load network into runtime NetworkId networkIdentifier; @@ -95,45 +94,45 @@ void ReduceWithMultipleAxesTest(INetworkPtr& network, // Create input and output tensors std::vector<float> outputData(expectedOutput.size()); InputTensors inputTensors - { - { 0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data()) } - }; + { + {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()) } - }; + { + {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); + CHECK(outputData == expectedOutput); } void ReduceSumWithTwoAxesKeepDimsTest(Compute backendId) { armnn::ReduceDescriptor reduceDescriptor; - reduceDescriptor.m_vAxis = { 1, 2 }; + 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 }; + 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, + 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, + 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 }); + 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, @@ -146,26 +145,26 @@ void ReduceSumWithTwoAxesKeepDimsTest(Compute backendId) void ReduceSumWithTwoAxesTest(Compute backendId) { armnn::ReduceDescriptor reduceDescriptor; - reduceDescriptor.m_vAxis = { 1, 2 }; + 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 }; + 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, + 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, + 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 }); + 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, @@ -178,29 +177,29 @@ void ReduceSumWithTwoAxesTest(Compute backendId) void ReduceSumWithThreeAxesKeepDimsTest(Compute backendId) { armnn::ReduceDescriptor reduceDescriptor; - reduceDescriptor.m_vAxis = { 0, 2, 3 }; + 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 }; + 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, + const std::vector<float> inputData({1.0f, 2.0f, + 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, - 10.0f, 20.0f, - 30.0f, 40.0f, + 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 }); + 50.0f, 60.0f, + 70.0f, 80.0f}); + const std::vector<float> expectedOutput({110.0f, 286.0f}); ReduceWithMultipleAxesTest(network, outputShape, @@ -213,29 +212,29 @@ void ReduceSumWithThreeAxesKeepDimsTest(Compute backendId) void ReduceSumWithThreeAxesTest(Compute backendId) { armnn::ReduceDescriptor reduceDescriptor; - reduceDescriptor.m_vAxis = { 0, 2, 3 }; + 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 }; + 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, + const std::vector<float> inputData({1.0f, 2.0f, + 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f, + 5.0f, 6.0f, + 7.0f, 8.0f, - 10.0f, 20.0f, - 30.0f, 40.0f, + 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 }); + 50.0f, 60.0f, + 70.0f, 80.0f}); + const std::vector<float> expectedOutput({110.0f, 286.0f}); ReduceWithMultipleAxesTest(network, outputShape, @@ -247,47 +246,47 @@ void ReduceSumWithThreeAxesTest(Compute backendId) using namespace armnn; #if defined(ARMCOMPUTENEON_ENABLED) -BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsCpuAccTest) +TEST_CASE("ReduceSumWithTwoAxesKeepDimsCpuAccTest") { ReduceSumWithTwoAxesKeepDimsTest(Compute::CpuAcc); } -BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesCpuAccTest) +TEST_CASE("ReduceSumWithTwoAxesCpuAccTest") { ReduceSumWithTwoAxesTest(Compute::CpuAcc); } -BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsCpuAccTest) +TEST_CASE("ReduceSumWithThreeAxesKeepDimsCpuAccTest") { ReduceSumWithThreeAxesKeepDimsTest(Compute::CpuAcc); } -BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesCpuAccTest) +TEST_CASE("ReduceSumWithThreeAxesCpuAccTest") { ReduceSumWithThreeAxesTest(Compute::CpuAcc); } #endif #if defined(ARMCOMPUTECL_ENABLED) -BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesKeepDimsGpuAccTest) +TEST_CASE("ReduceSumWithTwoAxesKeepDimsGpuAccTest") { ReduceSumWithTwoAxesKeepDimsTest(Compute::GpuAcc); } -BOOST_AUTO_TEST_CASE(ReduceSumWithTwoAxesGpuAccTest) +TEST_CASE("ReduceSumWithTwoAxesGpuAccTest") { ReduceSumWithTwoAxesTest(Compute::GpuAcc); } -BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesKeepDimsGpuAccTest) +TEST_CASE("ReduceSumWithThreeAxesKeepDimsGpuAccTest") { ReduceSumWithThreeAxesKeepDimsTest(Compute::GpuAcc); } -BOOST_AUTO_TEST_CASE(ReduceSumWithThreeAxesGpuAccTest) +TEST_CASE("ReduceSumWithThreeAxesGpuAccTest") { ReduceSumWithThreeAxesTest(Compute::GpuAcc); } #endif -BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file +}
\ No newline at end of file |