Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include "EndToEndTestImpl.hpp" |
Colm Donelan | c42a987 | 2022-02-02 16:35:09 +0000 | [diff] [blame] | 8 | #include <armnnUtils/QuantizeHelper.hpp> |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 9 | |
| 10 | #include <ResolveType.hpp> |
| 11 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 12 | #include <CommonTestUtils.hpp> |
| 13 | #include <armnnTestUtils/DataLayoutUtils.hpp> |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 14 | |
| 15 | #include <map> |
| 16 | #include <vector> |
| 17 | |
| 18 | namespace |
| 19 | { |
| 20 | |
| 21 | armnn::INetworkPtr CreateConvolution3dNetwork(const armnn::Convolution3dDescriptor& descriptor, |
| 22 | const armnn::TensorInfo& inputInfo, |
| 23 | const armnn::TensorInfo& weightsInfo, |
| 24 | const armnn::TensorInfo& biasInfo, |
| 25 | const armnn::TensorInfo& outputInfo, |
| 26 | const armnn::ConstTensor& weights, |
| 27 | const armnn::ConstTensor& biases) |
| 28 | { |
| 29 | using namespace armnn; |
| 30 | |
| 31 | INetworkPtr network(INetwork::Create()); |
| 32 | IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| 33 | armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights"); |
| 34 | armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias"); |
| 35 | IConnectableLayer* convolution3d = network->AddConvolution3dLayer(descriptor, "convolution3d"); |
| 36 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 37 | |
| 38 | Connect(input, convolution3d, inputInfo, 0, 0); |
| 39 | Connect(weightsLayer, convolution3d, weightsInfo, 0, 1); |
| 40 | Connect(biasLayer, convolution3d, biasInfo, 0, 2); |
| 41 | Connect(convolution3d, output, outputInfo, 0, 0); |
| 42 | |
| 43 | return network; |
| 44 | } |
| 45 | |
| 46 | } // anonymous namespace |
| 47 | |
| 48 | template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType> |
| 49 | void Convolution3dEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 50 | armnn::DataLayout dataLayout) |
| 51 | { |
| 52 | using namespace armnn; |
| 53 | using T = ResolveType<ArmnnType>; |
| 54 | using BT = ResolveType<ArmnnBType>; |
| 55 | |
| 56 | const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; |
| 57 | const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; |
| 58 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 59 | TensorInfo inputInfo({ 1, 5, 5, 5, 1 }, ArmnnType, qScale, qOffset, true); |
Matthew Sloyan | 5d7b0a3 | 2021-10-18 13:07:49 +0100 | [diff] [blame] | 60 | TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); |
| 61 | TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, ArmnnType, qScale, qOffset, true); |
| 62 | TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0, true); |
| 63 | |
| 64 | std::vector<float> inputData = |
| 65 | { |
| 66 | 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, |
| 67 | 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| 68 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, |
| 69 | 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, |
| 70 | |
| 71 | 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, |
| 72 | 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, |
| 73 | 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, |
| 74 | 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, |
| 75 | 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, |
| 76 | |
| 77 | 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, |
| 78 | 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, |
| 79 | 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, |
| 80 | 60.0f, 61.0f, 62.0f, 63.0f, 64.0f, |
| 81 | 65.0f, 66.0f, 67.0f, 68.0f, 69.0f, |
| 82 | |
| 83 | 70.0f, 71.0f, 72.0f, 73.0f, 74.0f, |
| 84 | 75.0f, 76.0f, 77.0f, 78.0f, 79.0f, |
| 85 | 80.0f, 81.0f, 82.0f, 83.0f, 84.0f, |
| 86 | 85.0f, 86.0f, 87.0f, 88.0f, 89.0f, |
| 87 | 90.0f, 91.0f, 92.0f, 93.0f, 94.0f, |
| 88 | 95.0f, 96.0f, 97.0f, 98.0f, 99.0f, |
| 89 | |
| 90 | 100.0f, 101.0f, 102.0f, 103.0f, 104.0f, |
| 91 | 105.0f, 106.0f, 107.0f, 108.0f, 109.0f, |
| 92 | 110.0f, 111.0f, 112.0f, 113.0f, 114.0f, |
| 93 | 115.0f, 116.0f, 117.0f, 118.0f, 119.0f, |
| 94 | 120.0f, 121.0f, 122.0f, 123.0f, 124.0f |
| 95 | }; |
| 96 | |
| 97 | std::vector<float> weightsData = |
| 98 | { |
| 99 | 1.0f, 1.0f, 1.0f, |
| 100 | 1.0f, 1.0f, 1.0f, |
| 101 | 1.0f, 1.0f, 1.0f, |
| 102 | |
| 103 | 0.0f, 0.0f, 0.0f, |
| 104 | 0.0f, 0.0f, 0.0f, |
| 105 | 0.0f, 0.0f, 0.0f, |
| 106 | |
| 107 | 1.0f, 1.0f, 1.0f, |
| 108 | 1.0f, 1.0f, 1.0f, |
| 109 | 1.0f, 1.0f, 1.0f, |
| 110 | }; |
| 111 | |
| 112 | std::vector<float> biasesData = { 1.f }; |
| 113 | |
| 114 | std::vector<float> expectedOutputData = |
| 115 | { |
| 116 | 559.0f, 595.0f, |
| 117 | |
| 118 | 739.0f, 775.0f, |
| 119 | |
| 120 | 1459.0f, 1495.0f, |
| 121 | |
| 122 | 1639.0f, 1675.0f, |
| 123 | }; |
| 124 | |
| 125 | Convolution3dDescriptor descriptor; |
| 126 | descriptor.m_PadLeft = 0; |
| 127 | descriptor.m_PadRight = 0; |
| 128 | descriptor.m_PadTop = 0; |
| 129 | descriptor.m_PadBottom = 0; |
| 130 | descriptor.m_PadFront = 0; |
| 131 | descriptor.m_PadBack = 0; |
| 132 | descriptor.m_StrideX = 2; |
| 133 | descriptor.m_StrideY = 2; |
| 134 | descriptor.m_StrideZ = 2; |
| 135 | descriptor.m_BiasEnabled = true; |
| 136 | descriptor.m_DataLayout = dataLayout; |
| 137 | |
| 138 | // Permute input and output if NCDHW. |
| 139 | if (dataLayout == DataLayout::NCDHW) |
| 140 | { |
| 141 | PermuteTensorNdhwcToNcdhw(inputInfo, inputData); |
| 142 | PermuteTensorNdhwcToNcdhw(outputInfo, expectedOutputData); |
| 143 | } |
| 144 | |
| 145 | // Quantize data |
| 146 | std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); |
| 147 | std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset); |
| 148 | std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); |
| 149 | |
| 150 | std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0); |
| 151 | |
| 152 | ConstTensor weights(weightsInfo, qWeightsData); |
| 153 | ConstTensor biases(biasesInfo, qBiasesData); |
| 154 | |
| 155 | INetworkPtr network = CreateConvolution3dNetwork(descriptor, |
| 156 | inputInfo, |
| 157 | weightsInfo, |
| 158 | biasesInfo, |
| 159 | outputInfo, |
| 160 | weights, |
| 161 | biases); |
| 162 | |
| 163 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| 164 | { { 0, qInputData } }, |
| 165 | { { 0, qExpectedOutputData } }, |
| 166 | backends); |
| 167 | } |