| // |
| // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include "EndToEndTestImpl.hpp" |
| #include <armnnUtils/QuantizeHelper.hpp> |
| |
| #include <ResolveType.hpp> |
| |
| #include <CommonTestUtils.hpp> |
| #include <armnnTestUtils/DataLayoutUtils.hpp> |
| |
| #include <map> |
| #include <vector> |
| |
| namespace |
| { |
| |
| armnn::INetworkPtr CreateConvolution3dNetwork(const armnn::Convolution3dDescriptor& descriptor, |
| const armnn::TensorInfo& inputInfo, |
| const armnn::TensorInfo& weightsInfo, |
| const armnn::TensorInfo& biasInfo, |
| const armnn::TensorInfo& outputInfo, |
| const armnn::ConstTensor& weights, |
| const armnn::ConstTensor& biases) |
| { |
| using namespace armnn; |
| |
| INetworkPtr network(INetwork::Create()); |
| IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights"); |
| armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias"); |
| IConnectableLayer* convolution3d = network->AddConvolution3dLayer(descriptor, "convolution3d"); |
| IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| |
| Connect(input, convolution3d, inputInfo, 0, 0); |
| Connect(weightsLayer, convolution3d, weightsInfo, 0, 1); |
| Connect(biasLayer, convolution3d, biasInfo, 0, 2); |
| Connect(convolution3d, output, outputInfo, 0, 0); |
| |
| return network; |
| } |
| |
| } // anonymous namespace |
| |
| template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType> |
| void Convolution3dEndToEnd(const std::vector<armnn::BackendId>& backends, |
| armnn::DataLayout dataLayout) |
| { |
| using namespace armnn; |
| using T = ResolveType<ArmnnType>; |
| using BT = ResolveType<ArmnnBType>; |
| |
| const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; |
| const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; |
| |
| TensorInfo inputInfo({ 1, 5, 5, 5, 1 }, ArmnnType, qScale, qOffset, true); |
| TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); |
| TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, ArmnnType, qScale, qOffset, true); |
| TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0, true); |
| |
| std::vector<float> inputData = |
| { |
| 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, |
| 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, |
| 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, |
| |
| 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, |
| 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, |
| 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, |
| 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, |
| 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, |
| |
| 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, |
| 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, |
| 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, |
| 60.0f, 61.0f, 62.0f, 63.0f, 64.0f, |
| 65.0f, 66.0f, 67.0f, 68.0f, 69.0f, |
| |
| 70.0f, 71.0f, 72.0f, 73.0f, 74.0f, |
| 75.0f, 76.0f, 77.0f, 78.0f, 79.0f, |
| 80.0f, 81.0f, 82.0f, 83.0f, 84.0f, |
| 85.0f, 86.0f, 87.0f, 88.0f, 89.0f, |
| 90.0f, 91.0f, 92.0f, 93.0f, 94.0f, |
| 95.0f, 96.0f, 97.0f, 98.0f, 99.0f, |
| |
| 100.0f, 101.0f, 102.0f, 103.0f, 104.0f, |
| 105.0f, 106.0f, 107.0f, 108.0f, 109.0f, |
| 110.0f, 111.0f, 112.0f, 113.0f, 114.0f, |
| 115.0f, 116.0f, 117.0f, 118.0f, 119.0f, |
| 120.0f, 121.0f, 122.0f, 123.0f, 124.0f |
| }; |
| |
| std::vector<float> weightsData = |
| { |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| |
| 0.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, |
| 0.0f, 0.0f, 0.0f, |
| |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| }; |
| |
| std::vector<float> biasesData = { 1.f }; |
| |
| std::vector<float> expectedOutputData = |
| { |
| 559.0f, 595.0f, |
| |
| 739.0f, 775.0f, |
| |
| 1459.0f, 1495.0f, |
| |
| 1639.0f, 1675.0f, |
| }; |
| |
| Convolution3dDescriptor descriptor; |
| descriptor.m_PadLeft = 0; |
| descriptor.m_PadRight = 0; |
| descriptor.m_PadTop = 0; |
| descriptor.m_PadBottom = 0; |
| descriptor.m_PadFront = 0; |
| descriptor.m_PadBack = 0; |
| descriptor.m_StrideX = 2; |
| descriptor.m_StrideY = 2; |
| descriptor.m_StrideZ = 2; |
| descriptor.m_BiasEnabled = true; |
| descriptor.m_DataLayout = dataLayout; |
| |
| // Permute input and output if NCDHW. |
| if (dataLayout == DataLayout::NCDHW) |
| { |
| PermuteTensorNdhwcToNcdhw(inputInfo, inputData); |
| PermuteTensorNdhwcToNcdhw(outputInfo, expectedOutputData); |
| } |
| |
| // Quantize data |
| std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); |
| std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset); |
| std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); |
| |
| std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0); |
| |
| ConstTensor weights(weightsInfo, qWeightsData); |
| ConstTensor biases(biasesInfo, qBiasesData); |
| |
| INetworkPtr network = CreateConvolution3dNetwork(descriptor, |
| inputInfo, |
| weightsInfo, |
| biasesInfo, |
| outputInfo, |
| weights, |
| biases); |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| { { 0, qInputData } }, |
| { { 0, qExpectedOutputData } }, |
| backends); |
| } |