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author | Sadik Armagan <sadik.armagan@arm.com> | 2019-10-14 10:31:43 +0100 |
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committer | Sadik Armagan <sadik.armagan@arm.com> | 2019-10-15 13:42:40 +0000 |
commit | 062e0e95a4332430efa7b0d5af3aab7a5b45a2dc (patch) | |
tree | 72dee2b6ba2c07834331750d8bbe468687cbf321 /src/backends/backendsCommon/test/InstanceNormalizationEndToEndTestImpl.cpp | |
parent | c949ab548d84abb3faf552620571ed941e768a69 (diff) | |
download | armnn-062e0e95a4332430efa7b0d5af3aab7a5b45a2dc.tar.gz |
IVGCVSW-3892 Add EndToEnd Layer test for INSTANCE_NORMALIZATION
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Ia646446d52a7b597c3021f1e235465a96ce2beed
Diffstat (limited to 'src/backends/backendsCommon/test/InstanceNormalizationEndToEndTestImpl.cpp')
-rw-r--r-- | src/backends/backendsCommon/test/InstanceNormalizationEndToEndTestImpl.cpp | 380 |
1 files changed, 380 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/InstanceNormalizationEndToEndTestImpl.cpp b/src/backends/backendsCommon/test/InstanceNormalizationEndToEndTestImpl.cpp new file mode 100644 index 0000000000..0ba2a74895 --- /dev/null +++ b/src/backends/backendsCommon/test/InstanceNormalizationEndToEndTestImpl.cpp @@ -0,0 +1,380 @@ +// +// Copyright © 2019 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "InstanceNormalizationEndToEndTestImpl.hpp" + +#include "DataLayoutIndexed.hpp" +#include "EndToEndTestImpl.hpp" +#include "ResolveType.hpp" + +#include <Permute.hpp> + +#include <armnn/INetwork.hpp> + +#include <backendsCommon/test/DataLayoutUtils.hpp> + +#include <test/TestUtils.hpp> + +#include <boost/test/unit_test.hpp> + +namespace +{ + +template<typename armnn::DataType DataType> +armnn::INetworkPtr CreateInstanceNormalizationNetwork(const armnn::TensorShape& inputShape, + const armnn::TensorShape& outputShape, + const armnn::DataLayout dataLayout, + const float gamma, + const float beta, + const float eps, + const float qScale = 1.0f, + const int32_t qOffset = 0) +{ + using namespace armnn; + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset); + + InstanceNormalizationDescriptor instanceNormalizationDesc; + instanceNormalizationDesc.m_Gamma = gamma; + instanceNormalizationDesc.m_Beta = beta; + instanceNormalizationDesc.m_Eps = eps; + instanceNormalizationDesc.m_DataLayout = dataLayout; + + IConnectableLayer* instanceNormalization = net->AddInstanceNormalizationLayer(instanceNormalizationDesc, + "InstanceNormalization"); + IConnectableLayer* input = net->AddInputLayer(0, "input"); + Connect(input, instanceNormalization, inputTensorInfo, 0, 0); + + TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + Connect(instanceNormalization, output, outputTensorInfo, 0, 0); + + return net; +} + +void InstanceNormalizationEndToEnd(const std::vector<armnn::BackendId>& backends, + const armnn::DataLayout& dataLayout, + armnn::TensorInfo& inputTensorInfo, + armnn::TensorInfo& outputTensorInfo, + std::vector<float>& inputData, + std::vector<float>& expectedOutputData, + const float gamma, + const float beta, + const float eps) +{ + using namespace armnn; + + if (dataLayout == DataLayout::NCHW) + { + PermuteTensorNhwcToNchw<float>(inputTensorInfo, inputData); + PermuteTensorNhwcToNchw<float>(outputTensorInfo, expectedOutputData); + } + + // Builds up the structure of the network + INetworkPtr net = CreateInstanceNormalizationNetwork<DataType::Float32>(inputTensorInfo.GetShape(), + outputTensorInfo.GetShape(), + dataLayout, + gamma, + beta, + eps); + + BOOST_TEST_CHECKPOINT("Create a network"); + + std::map<int, std::vector<float>> inputTensorData = { { 0, inputData } }; + std::map<int, std::vector<float>> expectedOutputTensorData = { { 0, expectedOutputData } }; + + EndToEndLayerTestImpl<DataType::Float32, DataType::Float32>(move(net), + inputTensorData, + expectedOutputTensorData, + backends); +} + +} // anonymous namespace + +void InstanceNormalizationNhwcEndToEndTest1(const std::vector<armnn::BackendId>& defaultBackends) +{ + using namespace armnn; + + const float eps = 0.0001f; + const float beta = 0.0f; + const float gamma = 1.0f; + + TensorShape inputShape{2, 2, 2, 2}; + TensorInfo inputTensorInfo(inputShape, DataType::Float32); + + TensorShape outputShape{2, 2, 2, 2}; + TensorInfo outputTensorInfo(outputShape, DataType::Float32); + + std::vector<float> inputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 0.f, 1.f, + // Batch 0, Height 0, Width 1 x Channel (2) + 0.f, 2.f, + + // Batch 0, Height 1, Width 0 x Channel (2) + 0.f, 2.f, + // Batch 0, Height 1, Width 1 x Channel (2) + 0.f, 4.f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 1.f, -1.f, + // Batch 1, Height 0, Width 1 x Channel (2) + -1.f, 2.f, + + // Batch 1, Height 1, Width 0 x Channel (2) + -1.f, -2.f, + // Batch 1, Height 1, Width 1 x Channel (2) + 1.f, 4.f + }); + + std::vector<float> expectedOutputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 0.f, -1.1470304f, + // Batch 0, Height 0, Width 1 x Channel (2) + 0.f, -0.22940612f, + // Batch 0, Height 1, Width 0 x Channel (2) + 0.f, -0.22940612f, + // Batch 0, Height 1, Width 1 x Channel (2) + 0.f, 1.6058424f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 0.99995005f, -0.7337929f, + // Batch 1, Height 0, Width 1 x Channel (2) + -0.99995005f, 0.52413774f, + + // Batch 1, Height 1, Width 0 x Channel (2) + -0.99995005f, -1.1531031f, + // Batch 1, Height 1, Width 1 x Channel (2) + 0.99995005f, 1.3627582f + }); + + InstanceNormalizationEndToEnd(defaultBackends, + DataLayout::NHWC, + inputTensorInfo, + outputTensorInfo, + inputData, + expectedOutputData, + gamma, + beta, + eps); +} + +void InstanceNormalizationNchwEndToEndTest1(const std::vector<armnn::BackendId>& defaultBackends) +{ + using namespace armnn; + + const float eps = 0.0001f; + const float beta = 0.0f; + const float gamma = 1.0f; + + TensorShape inputShape{2, 2, 2, 2}; + TensorInfo inputTensorInfo(inputShape, DataType::Float32); + + TensorShape outputShape{2, 2, 2, 2}; + TensorInfo outputTensorInfo(outputShape, DataType::Float32); + + std::vector<float> inputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 0.f, 1.f, + // Batch 0, Height 0, Width 1 x Channel (2) + 0.f, 2.f, + + // Batch 0, Height 1, Width 0 x Channel (2) + 0.f, 2.f, + // Batch 0, Height 1, Width 1 x Channel (2) + 0.f, 4.f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 1.f, -1.f, + // Batch 1, Height 0, Width 1 x Channel (2) + -1.f, 2.f, + + // Batch 1, Height 1, Width 0 x Channel (2) + -1.f, -2.f, + // Batch 1, Height 1, Width 1 x Channel (2) + 1.f, 4.f + }); + + std::vector<float> expectedOutputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 0.f, -1.1470304f, + // Batch 0, Height 0, Width 1 x Channel (2) + 0.f, -0.22940612f, + // Batch 0, Height 1, Width 0 x Channel (2) + 0.f, -0.22940612f, + // Batch 0, Height 1, Width 1 x Channel (2) + 0.f, 1.6058424f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 0.99995005f, -0.7337929f, + // Batch 1, Height 0, Width 1 x Channel (2) + -0.99995005f, 0.52413774f, + + // Batch 1, Height 1, Width 0 x Channel (2) + -0.99995005f, -1.1531031f, + // Batch 1, Height 1, Width 1 x Channel (2) + 0.99995005f, 1.3627582f + }); + + + InstanceNormalizationEndToEnd(defaultBackends, + DataLayout::NCHW, + inputTensorInfo, + outputTensorInfo, + inputData, + expectedOutputData, + gamma, + beta, + eps); +} + +void InstanceNormalizationNhwcEndToEndTest2(const std::vector<armnn::BackendId>& defaultBackends) +{ + using namespace armnn; + + const float eps = 0.0001f; + const float beta = 10.0f; + const float gamma = 2.0f; + + TensorShape inputShape{2, 2, 2, 2}; + TensorShape outputShape{2, 2, 2, 2}; + + TensorInfo outputTensorInfo(outputShape, DataType::Float32); + TensorInfo inputTensorInfo(inputShape, DataType::Float32); + + std::vector<float> inputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 0.f, 1.f, + // Batch 0, Height 0, Width 1 x Channel (2) + 0.f, 2.f, + + // Batch 0, Height 1, Width 0 x Channel (2) + 0.f, 2.f, + // Batch 0, Height 1, Width 1 x Channel (2) + 0.f, 4.f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 1.f, -1.f, + // Batch 1, Height 0, Width 1 x Channel (2) + -1.f, 2.f, + + // Batch 1, Height 1, Width 0 x Channel (2) + -1.f, -2.f, + // Batch 1, Height 1, Width 1 x Channel (2) + 1.f, 4.f + }); + + std::vector<float> expectedOutputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 10.f, 7.7059393f, + // Batch 0, Height 0, Width 1 x Channel (2) + 10.f, 9.541187f, + + // Batch 0, Height 1, Width 0 x Channel (2) + 10.f, 9.541187f, + // Batch 0, Height 1, Width 1 x Channel (2) + 10.f, 13.211685f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 11.9999f, 8.532414f, + // Batch 1, Height 0, Width 1 x Channel (2) + 8.0001f, 11.048275f, + + // Batch 1, Height 1, Width 0 x Channel (2) + 8.0001f, 7.693794f, + // Batch 1, Height 1, Width 1 x Channel (2) + 11.9999f, 12.725516f + }); + + InstanceNormalizationEndToEnd(defaultBackends, + DataLayout::NHWC, + inputTensorInfo, + outputTensorInfo, + inputData, + expectedOutputData, + gamma, + beta, + eps); +} + +void InstanceNormalizationNchwEndToEndTest2(const std::vector<armnn::BackendId>& defaultBackends) +{ + using namespace armnn; + + const float eps = 0.0001f; + const float beta = 10.0f; + const float gamma = 2.0f; + + TensorShape inputShape{2, 2, 2, 2}; + TensorShape outputShape{2, 2, 2, 2}; + + TensorInfo outputTensorInfo(outputShape, DataType::Float32); + TensorInfo inputTensorInfo(inputShape, DataType::Float32); + + std::vector<float> inputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 0.f, 1.f, + // Batch 0, Height 0, Width 1 x Channel (2) + 0.f, 2.f, + + // Batch 0, Height 1, Width 0 x Channel (2) + 0.f, 2.f, + // Batch 0, Height 1, Width 1 x Channel (2) + 0.f, 4.f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 1.f, -1.f, + // Batch 1, Height 0, Width 1 x Channel (2) + -1.f, 2.f, + + // Batch 1, Height 1, Width 0 x Channel (2) + -1.f, -2.f, + // Batch 1, Height 1, Width 1 x Channel (2) + 1.f, 4.f + }); + + std::vector<float> expectedOutputData = std::vector<float>( + { + // Batch 0, Height 0, Width 0 x Channel (2) + 10.f, 7.7059393f, + // Batch 0, Height 0, Width 1 x Channel (2) + 10.f, 9.541187f, + + // Batch 0, Height 1, Width 0 x Channel (2) + 10.f, 9.541187f, + // Batch 0, Height 1, Width 1 x Channel (2) + 10.f, 13.211685f, + + // Batch 1, Height 0, Width 0 x Channel (2) + 11.9999f, 8.532414f, + // Batch 1, Height 0, Width 1 x Channel (2) + 8.0001f, 11.048275f, + + // Batch 1, Height 1, Width 0 x Channel (2) + 8.0001f, 7.693794f, + // Batch 1, Height 1, Width 1 x Channel (2) + 11.9999f, 12.725516f + }); + + InstanceNormalizationEndToEnd(defaultBackends, + DataLayout::NCHW, + inputTensorInfo, + outputTensorInfo, + inputData, + expectedOutputData, + gamma, + beta, + eps); +}
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