telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4 | // |
| 5 | |
| 6 | #include "armnn/Exceptions.hpp" |
| 7 | #include "armnn/LayerSupport.hpp" |
| 8 | |
| 9 | #include "backends/CpuTensorHandle.hpp" |
| 10 | #include "backends/WorkloadFactory.hpp" |
| 11 | |
| 12 | LayerTestResult<float,4> SimpleNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 13 | armnn::NormalizationAlgorithmChannel normChannel, |
| 14 | armnn::NormalizationAlgorithmMethod normMethod) |
| 15 | { |
| 16 | const unsigned int inputHeight = 2; |
| 17 | const unsigned int inputWidth = 2; |
| 18 | const unsigned int inputChannels = 1; |
| 19 | const unsigned int inputNum = 2; |
| 20 | |
| 21 | unsigned int outputHeight = inputHeight; |
| 22 | unsigned int outputWidth = inputWidth; |
| 23 | unsigned int outputChannels = inputChannels; |
| 24 | unsigned int outputNum = inputNum; |
| 25 | |
| 26 | unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; |
| 27 | unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; |
| 28 | |
| 29 | auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 30 | auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 31 | |
| 32 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 33 | |
| 34 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
| 35 | // Batch #0 |
| 36 | 1.0f, 2.0f, |
| 37 | 3.0f, 4.0f, |
| 38 | // Batch #1 |
| 39 | 5.0f, 6.0f, |
| 40 | 7.0f, 8.0f |
| 41 | })); |
| 42 | |
| 43 | float alpha = 1.f; |
| 44 | float beta = 1.f; |
| 45 | float kappa = 1.f; |
| 46 | uint32_t normSize = 3; |
| 47 | |
| 48 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 49 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 50 | |
| 51 | armnn::NormalizationQueueDescriptor data; |
| 52 | armnn::WorkloadInfo info; |
| 53 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 54 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 55 | data.m_Parameters.m_NormChannelType = normChannel; |
| 56 | data.m_Parameters.m_NormMethodType = normMethod; |
| 57 | data.m_Parameters.m_NormSize = normSize; |
| 58 | data.m_Parameters.m_Alpha = alpha; |
| 59 | data.m_Parameters.m_Beta = beta; |
| 60 | data.m_Parameters.m_K = kappa; |
| 61 | |
| 62 | armnn::PassthroughCpuTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]); |
| 63 | armnn::NormalizationQueueDescriptor refData = data; |
| 64 | armnn::WorkloadInfo refInfo = info; |
| 65 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle); |
| 66 | |
| 67 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 68 | |
| 69 | inputHandle->Allocate(); |
| 70 | outputHandle->Allocate(); |
| 71 | |
| 72 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 73 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 74 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 75 | workload->Execute(); |
| 76 | |
| 77 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 78 | |
| 79 | switch (normMethod) |
| 80 | { |
| 81 | case armnn::NormalizationAlgorithmMethod::LocalBrightness: |
| 82 | { |
| 83 | switch (normChannel) |
| 84 | { |
| 85 | case armnn::NormalizationAlgorithmChannel::Within: |
| 86 | { |
| 87 | // When normalising within channels, the 3x3 kernel covers the entire 2x2 input at every index. |
| 88 | // Therefore, all output values should equal the inputs, but divided by: |
| 89 | // pow((kappa + (accumulatedScale * alpha)), beta) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 90 | // ...where accumulatedScale is the sum of every element squared. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 91 | float divisor[inputNum]; |
| 92 | for(int i = 0; i < boost::numeric_cast<int>(inputNum); i++) |
| 93 | { |
| 94 | float accumulatedScale = input[i][0][0][0]*input[i][0][0][0] + |
| 95 | input[i][0][0][1]*input[i][0][0][1] + |
| 96 | input[i][0][1][0]*input[i][0][1][0] + |
| 97 | input[i][0][1][1]*input[i][0][1][1]; |
| 98 | divisor[i] = powf((kappa + accumulatedScale * alpha), beta); |
| 99 | } |
| 100 | ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, |
| 101 | std::vector<float>({input[0][0][0][0]/divisor[0], |
| 102 | input[0][0][0][1]/divisor[0], |
| 103 | input[0][0][1][0]/divisor[0], |
| 104 | input[0][0][1][1]/divisor[0], |
| 105 | input[1][0][0][0]/divisor[1], |
| 106 | input[1][0][0][1]/divisor[1], |
| 107 | input[1][0][1][0]/divisor[1], |
| 108 | input[1][0][1][1]/divisor[1]})); |
| 109 | break; |
| 110 | } |
| 111 | case armnn::NormalizationAlgorithmChannel::Across: |
| 112 | { |
| 113 | // When normalising across channels, all output values should equal the inputs, but multiplied by: |
| 114 | // pow((kappa + (accumulatedScale * alpha)), -beta) |
| 115 | // ...where accumulatedScale is the sum of the inputs for adjacent channels for this element squared |
| 116 | // ...where adjacent channels means within half the normSize for the channel |
| 117 | // The test data has only one channel, so this is simplified below. |
| 118 | std::vector<float> outputVector; |
| 119 | for (int n = 0; n < boost::numeric_cast<int>(inputNum); ++n) |
| 120 | { |
| 121 | for (int h = 0; h < boost::numeric_cast<int>(inputHeight); ++h) |
| 122 | { |
| 123 | for (int w = 0; w < boost::numeric_cast<int>(inputWidth); ++w) |
| 124 | { |
| 125 | float accumulatedScale = input[n][0][h][w]*input[n][0][h][w]; |
| 126 | float scale = powf((kappa + accumulatedScale * alpha), -beta); |
| 127 | outputVector.push_back(input[n][0][h][w] * scale); |
| 128 | } |
| 129 | } |
| 130 | } |
| 131 | ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputVector); |
| 132 | break; |
| 133 | } |
| 134 | default: |
| 135 | { |
| 136 | throw armnn::UnimplementedException("Unsupported normalisation channel type, " |
| 137 | "only Across and Within are supported"); |
| 138 | } |
| 139 | } |
| 140 | break; |
| 141 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 142 | case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 143 | default: |
| 144 | { |
| 145 | throw armnn::UnimplementedException("Unsupported normalisation method type, " |
| 146 | "only LocalBrightness is supported"); |
| 147 | } |
| 148 | } |
| 149 | |
| 150 | return ret; |
| 151 | } |
| 152 | |
| 153 | LayerTestResult<float,4> CompareNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 154 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 155 | armnn::NormalizationAlgorithmChannel normChannel, |
| 156 | armnn::NormalizationAlgorithmMethod normMethod) |
| 157 | { |
| 158 | constexpr unsigned int inputNum = 5; |
| 159 | constexpr unsigned int inputChannels = 3; |
| 160 | constexpr unsigned int inputHeight = 32; |
| 161 | constexpr unsigned int inputWidth = 24; |
| 162 | |
| 163 | constexpr unsigned int outputNum = inputNum; |
| 164 | constexpr unsigned int outputChannels = inputChannels; |
| 165 | constexpr unsigned int outputHeight = inputHeight; |
| 166 | constexpr unsigned int outputWidth = inputWidth; |
| 167 | |
| 168 | armnn::TensorInfo inputTensorInfo; |
| 169 | armnn::TensorInfo outputTensorInfo; |
| 170 | |
| 171 | unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; |
| 172 | unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; |
| 173 | |
| 174 | inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 175 | outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 176 | |
| 177 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 178 | |
| 179 | auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 111234); |
| 180 | |
| 181 | constexpr float alpha = 1.f; |
| 182 | constexpr float beta = 1.f; |
| 183 | constexpr float kappa = 1.f; |
| 184 | constexpr uint32_t normSize = 5; |
| 185 | |
| 186 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 187 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 188 | |
| 189 | armnn::NormalizationQueueDescriptor data; |
| 190 | armnn::WorkloadInfo info; |
| 191 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 192 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 193 | data.m_Parameters.m_NormChannelType = normChannel; |
| 194 | data.m_Parameters.m_NormMethodType = normMethod; |
| 195 | data.m_Parameters.m_NormSize = normSize; |
| 196 | data.m_Parameters.m_Alpha = alpha; |
| 197 | data.m_Parameters.m_Beta = beta; |
| 198 | data.m_Parameters.m_K = kappa; |
| 199 | |
| 200 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 201 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 202 | |
| 203 | armnn::NormalizationQueueDescriptor refData = data; |
| 204 | armnn::WorkloadInfo refInfo = info; |
| 205 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 206 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 207 | |
| 208 | // Don't execute if Normalization is not supported for the method and channel types, as an exception will be raised. |
| 209 | armnn::Compute compute = workloadFactory.GetCompute(); |
| 210 | const size_t reasonIfUnsupportedMaxLen = 255; |
| 211 | char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; |
| 212 | ret.supported = armnn::IsNormalizationSupported(compute, inputTensorInfo, outputTensorInfo, data.m_Parameters, |
| 213 | reasonIfUnsupported, reasonIfUnsupportedMaxLen); |
| 214 | if (!ret.supported) |
| 215 | { |
| 216 | return ret; |
| 217 | } |
| 218 | |
| 219 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info); |
| 220 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateNormalization(refData, refInfo); |
| 221 | |
| 222 | outputHandleRef->Allocate(); |
| 223 | inputHandleRef->Allocate(); |
| 224 | |
| 225 | inputHandle->Allocate(); |
| 226 | outputHandle->Allocate(); |
| 227 | |
| 228 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 229 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 230 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 231 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 232 | workload->Execute(); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 233 | refWorkloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 234 | workloadRef->Execute(); |
| 235 | |
| 236 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 237 | CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 238 | |
| 239 | return ret; |
| 240 | } |
| 241 | |