Nattapat Chaimanowong | 3ea76d5 | 2018-11-09 14:10:38 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include <armnn/ArmNN.hpp> |
| 8 | #include <armnn/Tensor.hpp> |
| 9 | #include <armnn/TypesUtils.hpp> |
| 10 | |
| 11 | #include <backendsCommon/CpuTensorHandle.hpp> |
| 12 | #include <backendsCommon/WorkloadFactory.hpp> |
| 13 | |
| 14 | #include <test/TensorHelpers.hpp> |
| 15 | |
| 16 | template<typename T> |
| 17 | LayerTestResult<T, 4> SpaceToBatchNdTestImpl( |
| 18 | const armnn::IWorkloadFactory& workloadFactory, |
| 19 | armnn::TensorInfo& inputTensorInfo, |
| 20 | armnn::TensorInfo& outputTensorInfo, |
| 21 | std::vector<float>& inputData, |
| 22 | std::vector<float>& outputExpectedData, |
| 23 | armnn::SpaceToBatchNdQueueDescriptor descriptor, |
| 24 | const float qScale = 1.0f, |
| 25 | const int32_t qOffset = 0) |
| 26 | { |
| 27 | const armnn::PermutationVector NCHWToNHWC = {0, 3, 1, 2}; |
| 28 | if (descriptor.m_Parameters.m_DataLayout == armnn::DataLayout::NHWC) |
| 29 | { |
| 30 | inputTensorInfo = armnnUtils::Permuted(inputTensorInfo, NCHWToNHWC); |
| 31 | outputTensorInfo = armnnUtils::Permuted(outputTensorInfo, NCHWToNHWC); |
| 32 | |
| 33 | std::vector<float> inputTmp(inputData.size()); |
| 34 | armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), inputTmp.data()); |
| 35 | inputData = inputTmp; |
| 36 | |
| 37 | std::vector<float> outputTmp(outputExpectedData.size()); |
| 38 | armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputExpectedData.data(), outputTmp.data()); |
| 39 | outputExpectedData = outputTmp; |
| 40 | } |
| 41 | |
| 42 | if(armnn::IsQuantizedType<T>()) |
| 43 | { |
| 44 | inputTensorInfo.SetQuantizationScale(qScale); |
| 45 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 46 | outputTensorInfo.SetQuantizationScale(qScale); |
| 47 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 48 | } |
| 49 | |
| 50 | boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 51 | |
| 52 | LayerTestResult<T, 4> ret(outputTensorInfo); |
| 53 | ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); |
| 54 | |
| 55 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 56 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 57 | |
| 58 | armnn::WorkloadInfo info; |
| 59 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 60 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 61 | |
| 62 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSpaceToBatchNd(descriptor, info); |
| 63 | |
| 64 | inputHandle->Allocate(); |
| 65 | outputHandle->Allocate(); |
| 66 | |
| 67 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 68 | |
| 69 | workload->Execute(); |
| 70 | |
| 71 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 72 | |
| 73 | return ret; |
| 74 | } |
| 75 | |
| 76 | template <typename T> |
| 77 | LayerTestResult<T, 4> SpaceToBatchNdSimpleTest(armnn::IWorkloadFactory& workloadFactory, |
| 78 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW) |
| 79 | { |
| 80 | armnn::TensorInfo inputTensorInfo; |
| 81 | armnn::TensorInfo outputTensorInfo; |
| 82 | |
| 83 | unsigned int inputShape[] = {1, 1, 2, 2}; |
| 84 | unsigned int outputShape[] = {4, 1, 1, 1}; |
| 85 | |
| 86 | armnn::SpaceToBatchNdQueueDescriptor desc; |
| 87 | desc.m_Parameters.m_DataLayout = dataLayout; |
| 88 | desc.m_Parameters.m_BlockShape = {2, 2}; |
| 89 | desc.m_Parameters.m_PadList = {{0, 0}, {0, 0}}; |
| 90 | |
| 91 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 92 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 93 | |
| 94 | std::vector<float> input = std::vector<float>( |
| 95 | { |
| 96 | 1.0f, 2.0f, 3.0f, 4.0f |
| 97 | }); |
| 98 | |
| 99 | std::vector<float> outputExpected = std::vector<float>( |
| 100 | { |
| 101 | 1.0f, 2.0f, 3.0f, 4.0f |
| 102 | }); |
| 103 | |
| 104 | return SpaceToBatchNdTestImpl<T>(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| 105 | } |
| 106 | |
| 107 | template <typename T> |
| 108 | LayerTestResult<T, 4> SpaceToBatchNdMultiChannelsTest(armnn::IWorkloadFactory& workloadFactory, |
| 109 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW) |
| 110 | { |
| 111 | armnn::TensorInfo inputTensorInfo; |
| 112 | armnn::TensorInfo outputTensorInfo; |
| 113 | |
| 114 | unsigned int inputShape[] = {1, 3, 2, 2}; |
| 115 | unsigned int outputShape[] = {4, 3, 1, 1}; |
| 116 | |
| 117 | armnn::SpaceToBatchNdQueueDescriptor desc; |
| 118 | desc.m_Parameters.m_DataLayout = dataLayout; |
| 119 | desc.m_Parameters.m_BlockShape = {2, 2}; |
| 120 | desc.m_Parameters.m_PadList = {{0, 0}, {0, 0}}; |
| 121 | |
| 122 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 123 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 124 | |
| 125 | std::vector<float> input = std::vector<float>( |
| 126 | { |
| 127 | 1.0f, 4.0f, 7.0f, 10.0f, |
| 128 | 2.0f, 5.0, 8.0, 11.0f, |
| 129 | 3.0f, 6.0f, 9.0f, 12.0f |
| 130 | }); |
| 131 | |
| 132 | std::vector<float> outputExpected = std::vector<float>( |
| 133 | { |
| 134 | 1.0f, 2.0f, 3.0f, |
| 135 | 4.0f, 5.0f, 6.0f, |
| 136 | 7.0f, 8.0f, 9.0f, |
| 137 | 10.0f, 11.0f, 12.0f |
| 138 | }); |
| 139 | |
| 140 | return SpaceToBatchNdTestImpl<T>(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| 141 | } |
| 142 | |
| 143 | template <typename T> |
| 144 | LayerTestResult<T, 4> SpaceToBatchNdMultiBlockTest(armnn::IWorkloadFactory& workloadFactory, |
| 145 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW) |
| 146 | { |
| 147 | armnn::TensorInfo inputTensorInfo; |
| 148 | armnn::TensorInfo outputTensorInfo; |
| 149 | |
| 150 | unsigned int inputShape[] = {1, 1, 4, 4}; |
| 151 | unsigned int outputShape[] = {4, 1, 2, 2}; |
| 152 | |
| 153 | armnn::SpaceToBatchNdQueueDescriptor desc; |
| 154 | desc.m_Parameters.m_DataLayout = dataLayout; |
| 155 | desc.m_Parameters.m_BlockShape = {2, 2}; |
| 156 | desc.m_Parameters.m_PadList = {{0, 0}, {0, 0}}; |
| 157 | |
| 158 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 159 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 160 | |
| 161 | std::vector<float> input = std::vector<float>( |
| 162 | { |
| 163 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 164 | 5.0f, 6.0f, 7.0f, 8.0f, |
| 165 | 9.0f, 10.0f, 11.0f, 12.0f, |
| 166 | 13.0f, 14.0f, 15.0f, 16.0f |
| 167 | }); |
| 168 | |
| 169 | std::vector<float> outputExpected = std::vector<float>( |
| 170 | { |
| 171 | 1.0f, 3.0f, 9.0f, 11.0f, |
| 172 | 2.0f, 4.0f, 10.0f, 12.0f, |
| 173 | 5.0f, 7.0f, 13.0f, 15.0f, |
| 174 | 6.0f, 8.0f, 14.0f, 16.0f |
| 175 | }); |
| 176 | |
| 177 | return SpaceToBatchNdTestImpl<T>(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| 178 | } |
| 179 | |
| 180 | template <typename T> |
| 181 | LayerTestResult<T, 4> SpaceToBatchNdPaddingTest(armnn::IWorkloadFactory& workloadFactory, |
| 182 | armnn::DataLayout dataLayout = armnn::DataLayout::NCHW) |
| 183 | { |
| 184 | armnn::TensorInfo inputTensorInfo; |
| 185 | armnn::TensorInfo outputTensorInfo; |
| 186 | |
| 187 | unsigned int inputShape[] = {2, 1, 2, 4}; |
| 188 | unsigned int outputShape[] = {8, 1, 1, 3}; |
| 189 | |
| 190 | armnn::SpaceToBatchNdQueueDescriptor desc; |
| 191 | desc.m_Parameters.m_DataLayout = dataLayout; |
| 192 | desc.m_Parameters.m_BlockShape = {2, 2}; |
| 193 | desc.m_Parameters.m_PadList = {{0, 0}, {2, 0}}; |
| 194 | |
| 195 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); |
| 196 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); |
| 197 | |
| 198 | std::vector<float> input = std::vector<float>( |
| 199 | { |
| 200 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 201 | 5.0f, 6.0f, 7.0f, 8.0f, |
| 202 | 9.0f, 10.0f, 11.0f, 12.0f, |
| 203 | 13.0f, 14.0f, 15.0f, 16.0f |
| 204 | }); |
| 205 | |
| 206 | std::vector<float> outputExpected = std::vector<float>( |
| 207 | { |
| 208 | 0.0f, 1.0f, 3.0f, |
| 209 | 0.0f, 9.0f, 11.0f, |
| 210 | 0.0f, 2.0f, 4.0f, |
| 211 | 0.0f, 10.0f, 12.0f, |
| 212 | 0.0f, 5.0f, 7.0f, |
| 213 | 0.0f, 13.0f, 15.0f, |
| 214 | 0.0f, 6.0f, 8.0f, |
| 215 | 0.0f, 14.0f, 16.0f |
| 216 | }); |
| 217 | |
| 218 | return SpaceToBatchNdTestImpl<T>(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected, desc); |
| 219 | } |
| 220 | |
| 221 | template <typename T> |
| 222 | LayerTestResult<T, 4> SpaceToBatchNdSimpleNHWCTest(armnn::IWorkloadFactory& workloadFactory) |
| 223 | { |
| 224 | return SpaceToBatchNdSimpleTest<T>(workloadFactory, armnn::DataLayout::NHWC); |
| 225 | } |
| 226 | |
| 227 | template <typename T> |
| 228 | LayerTestResult<T, 4> SpaceToBatchNdMultiChannelsNHWCTest(armnn::IWorkloadFactory& workloadFactory) |
| 229 | { |
| 230 | return SpaceToBatchNdMultiChannelsTest<T>(workloadFactory, armnn::DataLayout::NHWC); |
| 231 | } |
| 232 | |
| 233 | template <typename T> |
| 234 | LayerTestResult<T, 4> SpaceToBatchNdMultiBlockNHWCTest(armnn::IWorkloadFactory& workloadFactory) |
| 235 | { |
| 236 | return SpaceToBatchNdMultiBlockTest<T>(workloadFactory, armnn::DataLayout::NHWC); |
| 237 | } |
| 238 | |
| 239 | template <typename T> |
| 240 | LayerTestResult<T, 4> SpaceToBatchNdPaddingNHWCTest(armnn::IWorkloadFactory& workloadFactory) |
| 241 | { |
| 242 | return SpaceToBatchNdPaddingTest<T>(workloadFactory, armnn::DataLayout::NHWC); |
| 243 | } |