| // |
| // 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); |
| } |