Cathal Corbett | 0690265 | 2022-04-14 17:55:11 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include "EndToEndTestImpl.hpp" |
| 8 | #include <armnnUtils/QuantizeHelper.hpp> |
| 9 | |
| 10 | #include <ResolveType.hpp> |
| 11 | |
| 12 | #include <CommonTestUtils.hpp> |
| 13 | #include <armnnTestUtils/DataLayoutUtils.hpp> |
| 14 | |
| 15 | #include <map> |
| 16 | #include <vector> |
| 17 | |
| 18 | namespace |
| 19 | { |
| 20 | |
| 21 | armnn::INetworkPtr CreateDepthwiseConvolution2dNetwork(const armnn::DepthwiseConvolution2dDescriptor& 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* convolution2d = network->AddDepthwiseConvolution2dLayer(descriptor, "depthwiseConvolution2d"); |
| 36 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 37 | |
| 38 | Connect(input, convolution2d, inputInfo, 0, 0); |
| 39 | Connect(weightsLayer, convolution2d, weightsInfo, 0, 1); |
| 40 | Connect(biasLayer, convolution2d, biasInfo, 0, 2); |
| 41 | Connect(convolution2d, output, outputInfo, 0, 0); |
| 42 | |
| 43 | return network; |
| 44 | } |
| 45 | |
| 46 | } // anonymous namespace |
| 47 | |
| 48 | template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType> |
| 49 | void DepthwiseConvolution2dEndToEnd(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 | |
| 59 | unsigned int depthMultiplier = 2; |
| 60 | |
| 61 | unsigned int inputHeight = 8; |
| 62 | unsigned int inputWidth = 16; |
| 63 | unsigned int inputChannels = 2; |
| 64 | unsigned int inputBatchSize = 1; |
| 65 | |
| 66 | unsigned int kernelHeight = 5; |
| 67 | unsigned int kernelWidth = 3; |
| 68 | |
| 69 | unsigned int outputHeight = inputHeight - kernelHeight + 1 + 2; |
| 70 | unsigned int outputWidth = (inputWidth - kernelWidth + 1)/2; |
| 71 | unsigned int outputChannels = inputChannels * depthMultiplier; |
| 72 | unsigned int outputBatchSize = inputBatchSize; |
| 73 | |
| 74 | TensorInfo inputInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType, qScale, qOffset, true); |
| 75 | TensorInfo outputInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType, qScale, qOffset); |
| 76 | TensorInfo weightsInfo({1, kernelHeight, kernelWidth, outputChannels}, ArmnnType, qScale, qOffset, true); |
| 77 | TensorInfo biasesInfo({outputChannels}, ArmnnBType, qScale * qScale, 0, true); |
| 78 | |
| 79 | std::vector<float> inputData = |
| 80 | { |
| 81 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 82 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 83 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 84 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 85 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 86 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 87 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 88 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 89 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 90 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 91 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 92 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 93 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 94 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 95 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 96 | 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f |
| 97 | }; |
| 98 | |
| 99 | std::vector<float> weightsData = |
| 100 | { |
| 101 | 1.0f, 1.0f, 1.0f, |
| 102 | 1.0f, -1.0f, 1.0f, |
| 103 | 1.0f, 1.0f, 1.0f, |
| 104 | 1.0f, 1.0f, 1.0f, |
| 105 | 1.0f, 1.0f, 1.0f, |
| 106 | |
| 107 | 2.0f, 2.0f, 2.0f, |
| 108 | 2.0f, 2.0f, 2.0f, |
| 109 | 2.0f, 2.0f, 2.0f, |
| 110 | 2.0f, 2.0f, 2.0f, |
| 111 | 2.0f, 2.0f, 2.0f, |
| 112 | |
| 113 | 0.0f, 0.0f, 0.0f, |
| 114 | 0.0f, -1.0f, 0.0f, |
| 115 | 0.0f, 0.0f, 0.0f, |
| 116 | 0.0f, 0.0f, 0.0f, |
| 117 | 0.0f, 0.0f, 0.0f, |
| 118 | |
| 119 | 0.0f, 0.0f, 0.0f, |
| 120 | 0.0f, 0.0f, 0.0f, |
| 121 | 0.0f, 1.0f, 0.0f, |
| 122 | 0.0f, 0.0f, 0.0f, |
| 123 | 0.0f, 0.0f, 0.0f |
| 124 | }; |
| 125 | |
| 126 | std::vector<float> biasesData = { 0.0f, 2.0f, 1.0f, -1.0f }; |
| 127 | |
| 128 | std::vector<float> expectedOutputData = |
| 129 | { |
| 130 | 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, 3.0f, |
| 131 | 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, |
| 132 | 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.5f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, |
| 133 | 2.5f, 2.5f, 2.5f, 2.5f, 2.5f, 2.5f, 2.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, |
| 134 | 4.5f, 4.5f, 4.5f, 4.5f, 4.5f, 4.5f, 4.5f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, |
| 135 | 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, |
| 136 | 1.0f, 3.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 137 | 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 138 | 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 139 | 2.0f, 4.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 140 | 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 141 | 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f |
| 142 | }; |
| 143 | |
| 144 | DepthwiseConvolution2dDescriptor descriptor; |
| 145 | descriptor.m_PadLeft = 0; |
| 146 | descriptor.m_PadRight = 0; |
| 147 | descriptor.m_PadTop = 1; |
| 148 | descriptor.m_PadBottom = 0; |
| 149 | descriptor.m_StrideX = 2; |
| 150 | descriptor.m_StrideY = 1; |
| 151 | descriptor.m_BiasEnabled = true; |
| 152 | descriptor.m_DataLayout = dataLayout; |
| 153 | |
| 154 | // Permute input and output if NCDHW. |
| 155 | if (dataLayout == DataLayout::NCHW) |
| 156 | { |
| 157 | PermuteTensorNhwcToNchw(inputInfo, inputData); |
| 158 | PermuteTensorNhwcToNchw(outputInfo, expectedOutputData); |
| 159 | } |
| 160 | |
| 161 | // Quantize data |
| 162 | std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); |
| 163 | std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset); |
| 164 | std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); |
| 165 | |
| 166 | std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0); |
| 167 | |
| 168 | ConstTensor weights(weightsInfo, qWeightsData); |
| 169 | ConstTensor biases(biasesInfo, qBiasesData); |
| 170 | |
| 171 | INetworkPtr network = CreateDepthwiseConvolution2dNetwork(descriptor, |
| 172 | inputInfo, |
| 173 | weightsInfo, |
| 174 | biasesInfo, |
| 175 | outputInfo, |
| 176 | weights, |
| 177 | biases); |
| 178 | |
| 179 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| 180 | { { 0, qInputData } }, |
| 181 | { { 0, qExpectedOutputData } }, |
| 182 | backends); |
| 183 | } |