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Diffstat (limited to 'src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp')
| -rw-r--r-- | src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp | 132 |
1 files changed, 132 insertions, 0 deletions
diff --git a/src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp b/src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp new file mode 100644 index 0000000000..ec1dd511c9 --- /dev/null +++ b/src/armnn/test/optimizations/PermuteAndBatchToSpaceAsDepthToSpaceTests.cpp @@ -0,0 +1,132 @@ +// +// Copyright © 2019 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "../TestUtils.hpp" + +#include <Network.hpp> +#include <Optimizer.hpp> + +#include <boost/test/unit_test.hpp> + +using namespace armnn; + +BOOST_AUTO_TEST_SUITE(Optimizer) +using namespace armnn::optimizations; + +namespace +{ + +/// Shared function for the below tests, so that we test the same network in both cases. +INetworkPtr CreateTestNetwork() +{ + // Create a network + INetworkPtr network = INetwork::Create(); + + auto input = network->AddInputLayer(0, "input"); + const TensorInfo inputInfo({ 1, 2, 3, 4 }, DataType::Float32); + input->GetOutputSlot(0).SetTensorInfo(inputInfo); + + // Insert Permute which swaps batches and channels dimensions + auto permute = network->AddPermuteLayer(PermuteDescriptor(PermutationVector{ 3, 1, 2, 0 }), "permute"); + const TensorInfo permuteInfo({ 4, 2, 3, 1 }, DataType::Float32); + permute->GetOutputSlot(0).SetTensorInfo(permuteInfo); + input->GetOutputSlot(0).Connect(permute->GetInputSlot(0)); + + // Insert BatchToSpace + BatchToSpaceNdDescriptor batchToSpaceDesc; + batchToSpaceDesc.m_BlockShape = { 2, 2 }; + batchToSpaceDesc.m_DataLayout = DataLayout::NHWC; + auto batchToSpace = network->AddBatchToSpaceNdLayer(batchToSpaceDesc, "batchToSpace"); + const TensorInfo batchToSpaceInfo({ 1, 4, 6, 1 }, DataType::Float32); + batchToSpace->GetOutputSlot(0).SetTensorInfo(batchToSpaceInfo); + permute->GetOutputSlot(0).Connect(batchToSpace->GetInputSlot(0)); + + auto output = network->AddOutputLayer(0, "output"); + batchToSpace->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + return network; +} + +} // namespace + +/// Tests that the optimization performed by PermuteAndBatchToSpaceAsDepthToSpace is as expected. +/// Note this does not ensure the correctness of the optimization - that is done in the below test. +BOOST_AUTO_TEST_CASE(PermuteAndBatchToSpaceAsDepthToSpaceOptimizerTest) +{ + INetworkPtr network = CreateTestNetwork(); + Graph graph = static_cast<Network*>(network.get())->GetGraph(); + + // Confirm initial graph is as we expect + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>, &IsLayerOfType<PermuteLayer>, + &IsLayerOfType<BatchToSpaceNdLayer>, &IsLayerOfType<OutputLayer>)); + + // Perform the optimization which should merge the two layers into a DepthToSpace + armnn::Optimizer::Pass(graph, MakeOptimizations(PermuteAndBatchToSpaceAsDepthToSpace())); + + // Check that the replacement has been made as expected + auto checkDepthToSpace = [](const Layer* const layer) -> bool { + return IsLayerOfType<DepthToSpaceLayer>(layer) && + static_cast<const DepthToSpaceLayer*>(layer)->GetParameters().m_BlockSize == 2 && + static_cast<const DepthToSpaceLayer*>(layer)->GetParameters().m_DataLayout == DataLayout::NHWC && + layer->GetOutputHandler().GetTensorInfo() == TensorInfo({ 1, 4, 6, 1 }, DataType::Float32); + }; + + BOOST_TEST(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>, checkDepthToSpace, + &IsLayerOfType<OutputLayer>)); + + // Check the new layer has the two merged layers listed as related layers + std::list<std::string> testRelatedLayers = { "batchToSpace", "permute" }; + BOOST_TEST(CheckRelatedLayers<DepthToSpaceLayer>(graph, testRelatedLayers)); +} + +/// Tests that a optimization performed by PermuteAndBatchToSpaceAsDepthToSpace does not change the behaviour +/// of the network (i.e. it still produces the correct output). +BOOST_AUTO_TEST_CASE(PermuteAndBatchToSpaceAsDepthToSpaceCorrectnessTest) +{ + INetworkPtr network = CreateTestNetwork(); + + IRuntimePtr runtime = IRuntime::Create(IRuntime::CreationOptions()); + + IOptimizedNetworkPtr optimizedNetwork = Optimize(*network, { Compute::CpuRef }, runtime->GetDeviceSpec()); + + // Confirm that the optimization has actually taken place + const Graph& optGraph = static_cast<OptimizedNetwork*>(optimizedNetwork.get())->GetGraph(); + BOOST_TEST(CheckSequence(optGraph.cbegin(), optGraph.cend(), &IsLayerOfType<InputLayer>, + &IsLayerOfType<DepthToSpaceLayer>, &IsLayerOfType<OutputLayer>)); + + // Load the graph into a runtime so we can check it produces the correct output + NetworkId netId; + runtime->LoadNetwork(netId, std::move(optimizedNetwork)); + + std::vector<float> inputData{ + // Each row here is a row of pixels where each pixel has 4 channels + // clang-format off + 1.0f, 2.0f, 3.0f, 4.0f, 10.0f, 20.0f, 30.0f, 40.0f, 100.0f, 200.0f, 300.0f, 400.0f, + -1.0f, -2.0f, -3.0f, -4.0f, -10.0f, -20.0f, -30.0f, -40.0f, -100.0f, -200.0f, -300.0f, -400.0f, + // clang-format on + }; + ConstTensor input(TensorInfo({ 1, 2, 3, 4 }, DataType::Float32), inputData); + InputTensors inputs = { { 0, input } }; + std::vector<float> outputData(4 * 6); + Tensor output(TensorInfo({ 1, 4, 6, 1 }, DataType::Float32), outputData.data()); + OutputTensors outputs = { { 0, output } }; + runtime->EnqueueWorkload(netId, inputs, outputs); + + // Check the output is as expected. + // Note this output has been generated by running the network *without* the optimization. + std::vector<float> expectedOutput = { + // Rows and columns here match exactly with the tensor, as there is only 1 channel. + // clang-format off + 1.0f, 2.0f, 10.0f, 20.0f, 100.0f, 200.0f, + 3.0f, 4.0f, 30.0f, 40.0f, 300.0f, 400.0f, + + -1.0f, -2.0f, -10.0f, -20.0f, -100.0f, -200.0f, + -3.0f, -4.0f, -30.0f, -40.0f, -300.0f, -400.0f, + // clang-format on + }; + BOOST_TEST(outputData == expectedOutput); +} + +BOOST_AUTO_TEST_SUITE_END()
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