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Diffstat (limited to 'src/armnn/test/optimizations')
-rw-r--r-- | src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp | 288 |
1 files changed, 288 insertions, 0 deletions
diff --git a/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp b/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp new file mode 100644 index 0000000000..fe3cc31838 --- /dev/null +++ b/src/armnn/test/optimizations/AddBroadcastReshapeLayerTests.cpp @@ -0,0 +1,288 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "../GraphUtils.hpp" +#include "../TestUtils.hpp" + +#include <Optimizer.hpp> + +#include <boost/test/unit_test.hpp> + +using namespace armnn; + +BOOST_AUTO_TEST_SUITE(Optimizer) +using namespace optimizations; + +void AddBroadcastReshapeLayerOptimizerTest(const TensorInfo& info0, + const TensorInfo& info1, + const TensorInfo& outputInfo, + const std::string& reshapeLayerName, + const TensorShape& expectedReshapeShape, + const DataType expectedDataType) +{ + Graph graph; + + auto input0 = graph.AddLayer<InputLayer>(0, "input0"); + auto input1 = graph.AddLayer<InputLayer>(1, "input1"); + auto add = graph.AddLayer<AdditionLayer>("add"); + auto output = graph.AddLayer<OutputLayer>(0, "output"); + input0->GetOutputSlot().SetTensorInfo(info0); + input1->GetOutputSlot().SetTensorInfo(info1); + add->GetOutputSlot().SetTensorInfo(outputInfo); + + input0->GetOutputSlot().Connect(add->GetInputSlot(0)); + input1->GetOutputSlot().Connect(add->GetInputSlot(1)); + add->GetOutputSlot().Connect(output->GetInputSlot(0)); + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<AdditionLayer>, + &IsLayerOfType<OutputLayer>)); + + // Run optimizer + armnn::Optimizer::Pass(graph, MakeOptimizations(AddBroadcastReshapeLayer())); + + // Broadcast reshape layer has been added to the graph correctly + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<ReshapeLayer>, + &IsLayerOfType<AdditionLayer>, + &IsLayerOfType<OutputLayer>)); + + Layer* const reshapeLayer = GetFirstLayerWithName(graph, reshapeLayerName); + BOOST_TEST(reshapeLayer); + auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo(); + + // Tensorshape and the data type are correct + BOOST_TEST((addedReshapeTensorInfo.GetShape() == expectedReshapeShape)); + BOOST_TEST((addedReshapeTensorInfo.GetDataType() == expectedDataType)); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayerSimpleTest) +{ + const TensorInfo info0({ 1, 2, 3, 5 }, DataType::Float32); + const TensorInfo info1({ 1 }, DataType::Float32); + AddBroadcastReshapeLayerOptimizerTest(info0, info1, info0, "Reshape_for:add-1", + TensorShape({ 1, 1, 1, 1 }), + DataType::Float32); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayer1DTest) +{ + const TensorInfo info0({ 1, 2, 3, 5 }, DataType::Float32); + const TensorInfo info1({ 5 }, DataType::Float32); + const TensorInfo outputInfo({ 1, 1, 1, 5 }, DataType::Float32); + AddBroadcastReshapeLayerOptimizerTest(info0, info1, outputInfo, "Reshape_for:add-1", + TensorShape({ 1, 1, 1, 5 }), + DataType::Float32); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayer2DTest) +{ + const TensorInfo info0({ 1, 2, 3, 5 }, DataType::Float32); + const TensorInfo info1({ 3, 5 }, DataType::Float32); + const TensorInfo outputInfo({ 1, 2, 3, 5 }, DataType::Float32); + AddBroadcastReshapeLayerOptimizerTest(info0, info1, outputInfo, "Reshape_for:add-1", + TensorShape({ 1, 1, 3, 5 }), + DataType::Float32); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayer3DTest) +{ + const TensorInfo info0({ 2, 1, 1, 1 }, DataType::Float32); + const TensorInfo info1({ 3, 4, 5 }, DataType::Float32); + const TensorInfo outputInfo({ 2, 3, 4, 5 }, DataType::Float32); + AddBroadcastReshapeLayerOptimizerTest(info0, info1, outputInfo, "Reshape_for:add-1", + TensorShape({ 1, 3, 4, 5 }), + DataType::Float32); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayer3DMergedTest) +{ + const TensorInfo info0({ 2, 3, 1, 1 }, DataType::Float32); + const TensorInfo info1({ 3, 4, 5 }, DataType::Float32); + const TensorInfo outputInfo({ 2, 3, 4, 5 }, DataType::Float32); + AddBroadcastReshapeLayerOptimizerTest(info0, info1, outputInfo, "Reshape_for:add-1", + TensorShape({ 1, 3, 4, 5 }), + DataType::Float32); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayerSubtractionTest) +{ + Graph graph; + const TensorInfo info0({ 5 }, DataType::Float32); + const TensorInfo info1({ 1, 2, 3, 5 }, DataType::Float32); + const TensorInfo outputInfo({ 1, 2, 3, 5 }, DataType::Float32); + + auto input0 = graph.AddLayer<InputLayer>(0, "input0"); + auto input1 = graph.AddLayer<InputLayer>(1, "input1"); + auto sub = graph.AddLayer<SubtractionLayer>("sub"); + auto output = graph.AddLayer<OutputLayer>(0, "output"); + input0->GetOutputSlot().SetTensorInfo(info0); + input1->GetOutputSlot().SetTensorInfo(info1); + sub->GetOutputSlot().SetTensorInfo(outputInfo); + + input0->GetOutputSlot().Connect(sub->GetInputSlot(0)); + input1->GetOutputSlot().Connect(sub->GetInputSlot(1)); + sub->GetOutputSlot().Connect(output->GetInputSlot(0)); + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<SubtractionLayer>, + &IsLayerOfType<OutputLayer>)); + + // Run optimizer + armnn::Optimizer::Pass(graph, MakeOptimizations(AddBroadcastReshapeLayer())); + + // Broadcast reshape layer has been added to the graph correctly + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<ReshapeLayer>, + &IsLayerOfType<SubtractionLayer>, + &IsLayerOfType<OutputLayer>)); + + Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:sub-0"); + BOOST_TEST(reshapeLayer); + auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo(); + + // Tensorshape and the data type are correct + BOOST_TEST((addedReshapeTensorInfo.GetShape() == TensorShape({ 1, 1, 1, 5 }))); + BOOST_TEST((addedReshapeTensorInfo.GetDataType() == DataType::Float32)); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayerDivisionTest) +{ + Graph graph; + const TensorInfo info0({ 1, 4, 5 }, DataType::QAsymmS8); + const TensorInfo info1({ 1, 2, 4, 5 }, DataType::QAsymmS8); + const TensorInfo outputInfo({ 1, 2, 4, 5 }, DataType::QAsymmS8); + + auto input0 = graph.AddLayer<InputLayer>(0, "input0"); + auto input1 = graph.AddLayer<InputLayer>(1, "input1"); + auto div = graph.AddLayer<DivisionLayer>("div"); + auto output = graph.AddLayer<OutputLayer>(0, "output"); + input0->GetOutputSlot().SetTensorInfo(info0); + input1->GetOutputSlot().SetTensorInfo(info1); + div->GetOutputSlot().SetTensorInfo(outputInfo); + + input0->GetOutputSlot().Connect(div->GetInputSlot(0)); + input1->GetOutputSlot().Connect(div->GetInputSlot(1)); + div->GetOutputSlot().Connect(output->GetInputSlot(0)); + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<DivisionLayer>, + &IsLayerOfType<OutputLayer>)); + + // Run optimizer + armnn::Optimizer::Pass(graph, MakeOptimizations(AddBroadcastReshapeLayer())); + + // Broadcast reshape layer has been added to the graph correctly + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<ReshapeLayer>, + &IsLayerOfType<DivisionLayer>, + &IsLayerOfType<OutputLayer>)); + + Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:div-0"); + BOOST_TEST(reshapeLayer); + auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo(); + + // Tensorshape and the data type are correct + BOOST_TEST((addedReshapeTensorInfo.GetShape() == TensorShape({ 1, 1, 4, 5 }))); + BOOST_TEST((addedReshapeTensorInfo.GetDataType() == DataType::QAsymmS8)); +} + +BOOST_AUTO_TEST_CASE(AddBroadcastReshapeLayerMultiplicationTest) +{ + Graph graph; + const TensorInfo info0({ 3, 5 }, DataType::QAsymmU8); + const TensorInfo info1({ 1, 2, 3, 5 }, DataType::QAsymmU8); + const TensorInfo outputInfo({ 1, 2, 3, 5 }, DataType::QAsymmU8); + + auto input0 = graph.AddLayer<InputLayer>(0, "input0"); + auto input1 = graph.AddLayer<InputLayer>(1, "input1"); + auto mul = graph.AddLayer<MultiplicationLayer>("mul"); + auto output = graph.AddLayer<OutputLayer>(0, "output"); + input0->GetOutputSlot().SetTensorInfo(info0); + input1->GetOutputSlot().SetTensorInfo(info1); + mul->GetOutputSlot().SetTensorInfo(outputInfo); + + input0->GetOutputSlot().Connect(mul->GetInputSlot(0)); + input1->GetOutputSlot().Connect(mul->GetInputSlot(1)); + mul->GetOutputSlot().Connect(output->GetInputSlot(0)); + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<MultiplicationLayer>, + &IsLayerOfType<OutputLayer>)); + + // Run optimizer + armnn::Optimizer::Pass(graph, MakeOptimizations(AddBroadcastReshapeLayer())); + + // Broadcast reshape layer has been added to the graph correctly + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<ReshapeLayer>, + &IsLayerOfType<MultiplicationLayer>, + &IsLayerOfType<OutputLayer>)); + + Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:mul-0"); + BOOST_TEST(reshapeLayer); + auto addedReshapeTensorInfo = reshapeLayer->GetOutputSlot().GetTensorInfo(); + + // Tensorshape and the data type are correct + BOOST_TEST((addedReshapeTensorInfo.GetShape() == TensorShape({ 1, 1, 3, 5 }))); + BOOST_TEST((addedReshapeTensorInfo.GetDataType() == DataType::QAsymmU8)); +} + +BOOST_AUTO_TEST_CASE(AddNoBroadcastReshapeLayerTest) +{ + Graph graph; + const TensorInfo info0({ 1, 1, 1, 1 }, DataType::QAsymmU8); + const TensorInfo info1({ 1, 2, 3, 5 }, DataType::QAsymmU8); + const TensorInfo outputInfo({ 1, 2, 3, 5 }, DataType::QAsymmU8); + + auto input0 = graph.AddLayer<InputLayer>(0, "input0"); + auto input1 = graph.AddLayer<InputLayer>(1, "input1"); + auto mul = graph.AddLayer<MultiplicationLayer>("mul"); + auto output = graph.AddLayer<OutputLayer>(0, "output"); + input0->GetOutputSlot().SetTensorInfo(info0); + input1->GetOutputSlot().SetTensorInfo(info1); + mul->GetOutputSlot().SetTensorInfo(outputInfo); + + input0->GetOutputSlot().Connect(mul->GetInputSlot(0)); + input1->GetOutputSlot().Connect(mul->GetInputSlot(1)); + mul->GetOutputSlot().Connect(output->GetInputSlot(0)); + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<MultiplicationLayer>, + &IsLayerOfType<OutputLayer>)); + + // Run optimizer + armnn::Optimizer::Pass(graph, MakeOptimizations(AddBroadcastReshapeLayer())); + + // Broadcast reshape layer has not been added to the graph + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), + &IsLayerOfType<InputLayer>, + &IsLayerOfType<InputLayer>, + &IsLayerOfType<MultiplicationLayer>, + &IsLayerOfType<OutputLayer>)); + + Layer* const reshapeLayer = GetFirstLayerWithName(graph, "Reshape_for:mul-0"); + BOOST_TEST(!reshapeLayer); +} + +BOOST_AUTO_TEST_SUITE_END()
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