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 | #pragma once |
| 6 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 7 | #include "ActivationFixture.hpp" |
| 8 | #include "QuantizeHelper.hpp" |
| 9 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 10 | #include <armnn/ArmNN.hpp> |
| 11 | #include <armnn/Tensor.hpp> |
| 12 | #include <armnn/TypesUtils.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 13 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 14 | #include <backendsCommon/CpuTensorHandle.hpp> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 15 | #include <backendsCommon/IBackendInternal.hpp> |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 16 | #include <backendsCommon/WorkloadFactory.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 17 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 18 | #include <test/TensorHelpers.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 19 | |
| 20 | #include <algorithm> |
| 21 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 22 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 23 | LayerTestResult<T, 4> BoundedReLuTestCommon( |
| 24 | armnn::IWorkloadFactory& workloadFactory, |
| 25 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 26 | float upperBound, |
| 27 | float lowerBound, |
| 28 | float inputScale, |
| 29 | int32_t inputOffset, |
| 30 | float outputScale, |
| 31 | int32_t outputOffset, |
| 32 | const std::vector<T>& inputData, |
| 33 | const std::vector<T>& outputExpectedData, |
| 34 | unsigned int inputWidth, |
| 35 | unsigned int inputHeight, |
| 36 | unsigned int inputChannels, |
| 37 | unsigned int inputBatchSize) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 38 | { |
| 39 | unsigned int outputWidth = inputWidth; |
| 40 | unsigned int outputHeight = inputHeight; |
| 41 | unsigned int outputChannels = inputChannels; |
| 42 | unsigned int outputBatchSize = inputBatchSize; |
| 43 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 44 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 45 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 46 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 47 | |
| 48 | if(armnn::IsQuantizedType<T>()) |
| 49 | { |
| 50 | inputTensorInfo.SetQuantizationScale(inputScale); |
| 51 | inputTensorInfo.SetQuantizationOffset(inputOffset); |
| 52 | |
| 53 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 54 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 55 | } |
| 56 | |
| 57 | LayerTestResult<T, 4> result(inputTensorInfo); |
| 58 | |
| 59 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 60 | |
| 61 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 62 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 63 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 64 | // Setup bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 65 | armnn::ActivationQueueDescriptor descriptor; |
| 66 | armnn::WorkloadInfo workloadInfo; |
| 67 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 68 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 69 | |
| 70 | descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 71 | descriptor.m_Parameters.m_A = upperBound; |
| 72 | descriptor.m_Parameters.m_B = lowerBound; |
| 73 | |
| 74 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 75 | |
| 76 | inputHandle->Allocate(); |
| 77 | outputHandle->Allocate(); |
| 78 | |
| 79 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 80 | |
| 81 | workload->Execute(); |
| 82 | |
| 83 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 84 | |
| 85 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData); |
| 86 | |
| 87 | return result; |
| 88 | } |
| 89 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 90 | LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest( |
| 91 | armnn::IWorkloadFactory& workloadFactory, |
| 92 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 93 | { |
| 94 | unsigned int inputWidth = 4u; |
| 95 | unsigned int inputHeight = 5u; |
| 96 | unsigned int inputChannels = 1u; |
| 97 | unsigned int inputBatchSize = 1; |
| 98 | |
| 99 | std::vector<float> input = std::vector<float>{ |
| 100 | -2.0f, 0.1f, 0.5f, 1.25f, |
| 101 | 0.786f, 0.9875f, -1.5f, 0.384f, |
| 102 | 1.0001f, 3.5f, 7.5f, 0.896f, |
| 103 | 2.126f, 2.0f, 0.3f, 0.15f, |
| 104 | 0.999f, 1.2f, 0.89f, 6.1f, |
| 105 | }; |
| 106 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 107 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 108 | std::vector<float> output = std::vector<float>{ |
| 109 | -1.0f, 0.1f, 0.5f, 1.0f, |
| 110 | 0.786f, 0.9875f, -1.0f, 0.384f, |
| 111 | 1.0f, 1.0f, 1.0f, 0.896f, |
| 112 | 1.0f, 1.0f, 0.3f, 0.15f, |
| 113 | 0.999f, 1.0f, 0.89f, 1.0f, |
| 114 | }; |
| 115 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 116 | return BoundedReLuTestCommon<armnn::DataType::Float32>( |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 117 | workloadFactory, memoryManager, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output, |
| 118 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 119 | } |
| 120 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 121 | LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest( |
| 122 | armnn::IWorkloadFactory& workloadFactory, |
| 123 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 124 | { |
| 125 | unsigned int inputWidth = 4u; |
| 126 | unsigned int inputHeight = 5u; |
| 127 | unsigned int inputChannels = 1u; |
| 128 | unsigned int inputBatchSize = 1; |
| 129 | |
| 130 | std::vector<float> input = std::vector<float>{ |
| 131 | -1.0f, 0.1f, 0.5f, 6.25f, |
| 132 | 0.786f, 5.9875f, -0.5f, 0.384f, |
| 133 | 6.0001f, 3.5f, 7.5f, 0.896f, |
| 134 | 2.126f, 12.0f, 0.3f, 0.15f, |
| 135 | 0.999f, 1.2f, 0.89f, 6.1f, |
| 136 | }; |
| 137 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 138 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 139 | std::vector<float> output = std::vector<float>{ |
| 140 | 0.0f, 0.1f, 0.5f, 6.0f, |
| 141 | 0.786f, 5.9875f, 0.0f, 0.384f, |
| 142 | 6.0f, 3.5f, 6.0f, 0.896f, |
| 143 | 2.126f, 6.0f, 0.3f, 0.15f, |
| 144 | 0.999f, 1.2f, 0.89f, 6.0f, |
| 145 | }; |
| 146 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 147 | return BoundedReLuTestCommon<armnn::DataType::Float32>( |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 148 | workloadFactory, memoryManager, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output, |
| 149 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 150 | } |
| 151 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 152 | LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest( |
| 153 | armnn::IWorkloadFactory& workloadFactory, |
| 154 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 155 | { |
| 156 | unsigned int inputWidth = 3u; |
| 157 | unsigned int inputHeight = 2u; |
| 158 | unsigned int inputChannels = 1u; |
| 159 | unsigned int inputBatchSize = 1; |
| 160 | |
| 161 | std::vector<uint8_t> input = std::vector<uint8_t>{ |
| 162 | 51, 124, 28, |
| 163 | 251, 8, 92 |
| 164 | }; |
| 165 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 166 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 167 | std::vector<uint8_t> output = std::vector<uint8_t>{ |
| 168 | 0, 122, 0, |
| 169 | 255, 0, 58 |
| 170 | }; |
| 171 | |
| 172 | float inputScale = 12.0f / 255.0f; |
| 173 | int32_t inputOffset = 63; |
| 174 | float outputScale = 6.0f / 255.0f; |
| 175 | int32_t outputOffset = 0; |
| 176 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 177 | return BoundedReLuTestCommon<armnn::DataType::QuantisedAsymm8>( |
| 178 | workloadFactory, memoryManager, 6.0f, 0.0f, |
| 179 | inputScale, inputOffset, outputScale, outputOffset, |
| 180 | input, output, inputWidth, inputHeight, inputChannels, inputBatchSize); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 181 | } |
| 182 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 183 | LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest( |
| 184 | armnn::IWorkloadFactory& workloadFactory, |
| 185 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 186 | { |
| 187 | unsigned int inputWidth = 3u; |
| 188 | unsigned int inputHeight = 2u; |
| 189 | unsigned int inputChannels = 1u; |
| 190 | unsigned int inputBatchSize = 1; |
| 191 | |
| 192 | std::vector<uint8_t> input = std::vector<uint8_t>{ |
| 193 | 51, 230, 28, |
| 194 | 251, 8, 92 |
| 195 | }; |
| 196 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 197 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 198 | std::vector<uint8_t> output = std::vector<uint8_t>{ |
| 199 | 51, 192, 32, |
| 200 | 192, 32, 92 |
| 201 | }; |
| 202 | |
| 203 | int32_t inputOffset = 112; |
| 204 | float inputScale = 0.0125f; |
| 205 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 206 | return BoundedReLuTestCommon<armnn::DataType::QuantisedAsymm8>( |
| 207 | workloadFactory, memoryManager, 1.0f, -1.0f, |
| 208 | inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same. |
| 209 | input, output, inputWidth, inputHeight, inputChannels, inputBatchSize); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 210 | } |
| 211 | |
| 212 | namespace |
| 213 | { |
| 214 | |
| 215 | struct BoundedReLuRandomInputTestTraits |
| 216 | { |
| 217 | constexpr static unsigned int inputHeight = 31u; |
| 218 | constexpr static unsigned int inputWidth = 19u; |
| 219 | constexpr static unsigned int inputChannels = 4u; |
| 220 | constexpr static unsigned int inputBatchSize = 2; |
| 221 | |
| 222 | constexpr static unsigned int outputHeight = inputHeight; |
| 223 | constexpr static unsigned int outputWidth = inputWidth; |
| 224 | constexpr static unsigned int outputChannels = inputChannels; |
| 225 | constexpr static unsigned int outputBatchSize = inputBatchSize; |
| 226 | |
| 227 | static armnn::TensorInfo GetInputTensorInfo() |
| 228 | { |
| 229 | return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 230 | armnn::DataType::Float32); |
| 231 | } |
| 232 | |
| 233 | static armnn::TensorInfo GetOutputTensorInfo() |
| 234 | { |
| 235 | return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 236 | armnn::DataType::Float32); |
| 237 | } |
| 238 | }; |
| 239 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 240 | boost::multi_array<float, 4> BoundedReLuRandomInputTest( |
| 241 | armnn::IWorkloadFactory& workloadFactory, |
| 242 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 243 | float lowerBound, |
| 244 | float upperBound, |
| 245 | const armnn::ActivationDescriptor& activationDescriptor) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 246 | { |
| 247 | const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo(); |
| 248 | const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo(); |
| 249 | |
| 250 | boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo)); |
| 251 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 252 | // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu |
| 253 | // range [lowerBound, upperBound]. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 254 | auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f); |
| 255 | |
| 256 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 257 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 258 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 259 | // Set up bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 260 | armnn::ActivationQueueDescriptor descriptor; |
| 261 | armnn::WorkloadInfo workloadInfo; |
| 262 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 263 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 264 | descriptor.m_Parameters = activationDescriptor; |
| 265 | |
| 266 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 267 | |
| 268 | inputHandle->Allocate(); |
| 269 | outputHandle->Allocate(); |
| 270 | |
| 271 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 272 | |
| 273 | workload->Execute(); |
| 274 | |
| 275 | CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get()); |
| 276 | |
| 277 | return output; |
| 278 | } |
| 279 | |
| 280 | } // namespace |
| 281 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 282 | LayerTestResult<float, 4> CompareBoundedReLuTest( |
| 283 | armnn::IWorkloadFactory& workloadFactory, |
| 284 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 285 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 286 | float upperBound, |
| 287 | float lowerBound) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 288 | { |
| 289 | LayerTestResult<float, 4> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo()); |
| 290 | |
| 291 | armnn::ActivationDescriptor activationDescriptor; |
| 292 | activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 293 | activationDescriptor.m_A = upperBound; |
| 294 | activationDescriptor.m_B = lowerBound; |
| 295 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 296 | result.output = BoundedReLuRandomInputTest( |
| 297 | workloadFactory, memoryManager, 0.0f, upperBound, activationDescriptor); |
| 298 | result.outputExpected = BoundedReLuRandomInputTest( |
| 299 | refWorkloadFactory, nullptr, 0.0f, upperBound, activationDescriptor); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 300 | |
| 301 | return result; |
| 302 | } |
| 303 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 304 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 305 | LayerTestResult<T,4> ConstantLinearActivationTestCommon( |
| 306 | armnn::IWorkloadFactory& workloadFactory, |
| 307 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 308 | float qScale = 0.0f, |
| 309 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 310 | { |
| 311 | unsigned int inputHeight = 20; |
| 312 | unsigned int inputWidth = 17; |
| 313 | unsigned int inputChannels = 3; |
| 314 | unsigned int batchSize = 5; |
| 315 | |
| 316 | armnn::TensorInfo inputTensorInfo; |
| 317 | armnn::TensorInfo outputTensorInfo; |
| 318 | |
| 319 | unsigned int shape[] = {batchSize, inputChannels, inputHeight, inputWidth}; |
| 320 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 321 | inputTensorInfo = armnn::TensorInfo(4, shape, ArmnnType); |
| 322 | outputTensorInfo = armnn::TensorInfo(4, shape, ArmnnType); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 323 | |
| 324 | // Set quantization parameters if the requested type is a quantized type. |
| 325 | if(armnn::IsQuantizedType<T>()) |
| 326 | { |
| 327 | inputTensorInfo.SetQuantizationScale(qScale); |
| 328 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 329 | outputTensorInfo.SetQuantizationScale(qScale); |
| 330 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 331 | } |
| 332 | |
| 333 | LayerTestResult<T, 4> ret(outputTensorInfo); |
| 334 | |
| 335 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 336 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 337 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 338 | // Do linear activation that should leave the tensor unchanged. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 339 | armnn::ActivationQueueDescriptor data; |
| 340 | armnn::WorkloadInfo info; |
| 341 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 342 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 343 | data.m_Parameters.m_A = 1.0f; |
| 344 | data.m_Parameters.m_B = 0.0f; |
| 345 | data.m_Parameters.m_Function = armnn::ActivationFunction::Linear; |
| 346 | |
| 347 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info); |
| 348 | |
| 349 | inputHandle->Allocate(); |
| 350 | outputHandle->Allocate(); |
| 351 | |
| 352 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561); |
| 353 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 354 | |
| 355 | workload->Execute(); |
| 356 | |
| 357 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 358 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 359 | // Ensure output equals input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 360 | ret.outputExpected = input; |
| 361 | |
| 362 | return ret; |
| 363 | } |
| 364 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 365 | LayerTestResult<float, 4> ConstantLinearActivationTest( |
| 366 | armnn::IWorkloadFactory& workloadFactory, |
| 367 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 368 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 369 | return ConstantLinearActivationTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 370 | } |
| 371 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 372 | LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test( |
| 373 | armnn::IWorkloadFactory& workloadFactory, |
| 374 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 375 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 376 | return ConstantLinearActivationTestCommon<armnn::DataType::QuantisedAsymm8>( |
| 377 | workloadFactory, memoryManager, 4.0f, 3); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 378 | } |
| 379 | |
Teresa Charlin | 18515e2 | 2019-04-24 10:17:46 +0100 | [diff] [blame] | 380 | LayerTestResult<int16_t, 4> ConstantLinearActivationInt16Test( |
| 381 | armnn::IWorkloadFactory& workloadFactory, |
| 382 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 383 | { |
| 384 | return ConstantLinearActivationTestCommon<armnn::DataType::QuantisedSymm16>( |
| 385 | workloadFactory, memoryManager, 0.1f, 0); |
| 386 | } |
| 387 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 388 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 389 | LayerTestResult<T, 4> SimpleActivationTest( |
| 390 | armnn::IWorkloadFactory& workloadFactory, |
| 391 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 392 | armnn::ActivationFunction activationFunction, |
| 393 | float activationParameterA, |
| 394 | float activationParameterB, |
| 395 | float qScale, |
| 396 | int32_t qOffset, |
| 397 | const std::vector<float>& inputData, |
| 398 | const std::vector<float>& outputExpectedData) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 399 | { |
| 400 | constexpr static unsigned int inputWidth = 16u; |
| 401 | constexpr static unsigned int inputHeight = 1u; |
| 402 | constexpr static unsigned int inputChannels = 1u; |
| 403 | constexpr static unsigned int inputBatchSize = 1u; |
| 404 | |
| 405 | constexpr static unsigned int outputWidth = inputWidth; |
| 406 | constexpr static unsigned int outputHeight = inputHeight; |
| 407 | constexpr static unsigned int outputChannels = inputChannels; |
| 408 | constexpr static unsigned int outputBatchSize = inputBatchSize; |
| 409 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 410 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType); |
| 411 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 412 | |
| 413 | // Set quantization parameters if the requested type is a quantized type. |
| 414 | if(armnn::IsQuantizedType<T>()) |
| 415 | { |
| 416 | inputTensorInfo.SetQuantizationScale(qScale); |
| 417 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 418 | outputTensorInfo.SetQuantizationScale(qScale); |
| 419 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 420 | } |
| 421 | |
| 422 | LayerTestResult<T, 4> result(inputTensorInfo); |
| 423 | |
| 424 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 425 | |
| 426 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 427 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 428 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 429 | // Setup bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 430 | armnn::ActivationQueueDescriptor descriptor; |
| 431 | armnn::WorkloadInfo workloadInfo; |
| 432 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 433 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 434 | |
| 435 | descriptor.m_Parameters.m_Function = activationFunction; |
| 436 | descriptor.m_Parameters.m_A = activationParameterA; |
| 437 | descriptor.m_Parameters.m_B = activationParameterB; |
| 438 | |
| 439 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 440 | |
| 441 | inputHandle->Allocate(); |
| 442 | outputHandle->Allocate(); |
| 443 | |
| 444 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 445 | |
| 446 | workload->Execute(); |
| 447 | |
| 448 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 449 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 450 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 451 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); |
| 452 | |
| 453 | return result; |
| 454 | } |
| 455 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 456 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 457 | LayerTestResult<T, 4> SimpleSigmoidTestCommon( |
| 458 | armnn::IWorkloadFactory& workloadFactory, |
| 459 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 460 | float qScale, |
| 461 | int32_t qOffset) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 462 | { |
| 463 | std::vector<float> inputData = { |
| 464 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 465 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 466 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 467 | 1.0f, 2.0f, 3.0f, 4.0f |
| 468 | }; |
| 469 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 470 | // Calculate output values for input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 471 | auto f = [](float value) |
| 472 | { |
| 473 | return 1.0f / (1.0f + std::exp(-value)); |
| 474 | }; |
| 475 | std::vector<float> outputExpectedData(inputData.size()); |
| 476 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 477 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 478 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 479 | memoryManager, |
| 480 | armnn::ActivationFunction::Sigmoid, |
| 481 | 0.f, |
| 482 | 0.f, |
| 483 | qScale, |
| 484 | qOffset, |
| 485 | inputData, |
| 486 | outputExpectedData); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 487 | } |
| 488 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 489 | LayerTestResult<float, 4> SimpleSigmoidTest( |
| 490 | armnn::IWorkloadFactory& workloadFactory, |
| 491 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 492 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 493 | return SimpleSigmoidTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 494 | } |
| 495 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 496 | LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test( |
| 497 | armnn::IWorkloadFactory& workloadFactory, |
| 498 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 499 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 500 | return SimpleSigmoidTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.1f, 50); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 501 | } |
| 502 | |
Teresa Charlin | 18515e2 | 2019-04-24 10:17:46 +0100 | [diff] [blame] | 503 | LayerTestResult<int16_t, 4> SimpleSigmoidInt16Test( |
| 504 | armnn::IWorkloadFactory& workloadFactory, |
| 505 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 506 | { |
| 507 | return SimpleSigmoidTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 508 | } |
| 509 | |
| 510 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 511 | LayerTestResult<T, 4> ReLuTestCommon( |
| 512 | armnn::IWorkloadFactory& workloadFactory, |
| 513 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 514 | float qScale, |
| 515 | int32_t qOffset) |
| 516 | { |
| 517 | std::vector<float> inputData = { |
| 518 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 519 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 520 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 521 | 1.0f, 2.0f, 3.0f, 4.0f |
| 522 | }; |
| 523 | |
| 524 | // Calculate output values for input. |
| 525 | auto f = [](float value) |
| 526 | { |
| 527 | return std::fmax(0.0f, value); |
| 528 | }; |
| 529 | std::vector<float> outputExpectedData(inputData.size()); |
| 530 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 531 | |
| 532 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 533 | memoryManager, |
| 534 | armnn::ActivationFunction::ReLu, |
| 535 | 0.f, |
| 536 | 0.f, |
| 537 | qScale, |
| 538 | qOffset, |
| 539 | inputData, |
| 540 | outputExpectedData); |
| 541 | } |
| 542 | |
| 543 | LayerTestResult<int16_t, 4> ReLuInt16Test( |
| 544 | armnn::IWorkloadFactory& workloadFactory, |
| 545 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 546 | { |
| 547 | return ReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 548 | } |
| 549 | |
| 550 | |
| 551 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 552 | LayerTestResult<T, 4> BoundedReLuTestCommon( |
| 553 | armnn::IWorkloadFactory& workloadFactory, |
| 554 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 555 | float qScale, |
| 556 | int32_t qOffset) |
| 557 | { |
| 558 | std::vector<float> inputData = { |
| 559 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 560 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 561 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 562 | 1.0f, 2.0f, 3.0f, 4.0f |
| 563 | }; |
| 564 | const float a = 1.0f; |
| 565 | const float b = -1.0f; |
| 566 | // Calculate output values for input. |
| 567 | auto f = [a, b](float value) |
| 568 | { |
| 569 | return std::min(a, std::max(b, value)); |
| 570 | }; |
| 571 | std::vector<float> outputExpectedData(inputData.size()); |
| 572 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 573 | |
| 574 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 575 | memoryManager, |
| 576 | armnn::ActivationFunction::BoundedReLu, |
| 577 | a, |
| 578 | b, |
| 579 | qScale, |
| 580 | qOffset, |
| 581 | inputData, |
| 582 | outputExpectedData); |
| 583 | } |
| 584 | |
| 585 | LayerTestResult<int16_t, 4> BoundedReLuInt16Test( |
| 586 | armnn::IWorkloadFactory& workloadFactory, |
| 587 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 588 | { |
| 589 | return ReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 590 | } |
| 591 | |
| 592 | |
| 593 | |
| 594 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 595 | LayerTestResult<T, 4> SoftReLuTestCommon( |
| 596 | armnn::IWorkloadFactory& workloadFactory, |
| 597 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 598 | float qScale, |
| 599 | int32_t qOffset) |
| 600 | { |
| 601 | std::vector<float> inputData = { |
| 602 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 603 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 604 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 605 | 1.0f, 2.0f, 3.0f, 4.0f |
| 606 | }; |
| 607 | |
| 608 | // Calculate output values for input. |
| 609 | auto f = [](float value) |
| 610 | { |
| 611 | return std::log(1.0f + std::exp(value)); |
| 612 | }; |
| 613 | std::vector<float> outputExpectedData(inputData.size()); |
| 614 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 615 | |
| 616 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 617 | memoryManager, |
| 618 | armnn::ActivationFunction::SoftReLu, |
| 619 | 0.f, |
| 620 | 0.f, |
| 621 | qScale, |
| 622 | qOffset, |
| 623 | inputData, |
| 624 | outputExpectedData); |
| 625 | } |
| 626 | |
| 627 | LayerTestResult<int16_t, 4> SoftReLuInt16Test( |
| 628 | armnn::IWorkloadFactory& workloadFactory, |
| 629 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 630 | { |
| 631 | return SoftReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 632 | } |
| 633 | |
| 634 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 635 | LayerTestResult<T, 4> LeakyReLuTestCommon( |
| 636 | armnn::IWorkloadFactory& workloadFactory, |
| 637 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 638 | float qScale, |
| 639 | int32_t qOffset) |
| 640 | { |
| 641 | std::vector<float> inputData = { |
| 642 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 643 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 644 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 645 | 1.0f, 2.0f, 3.0f, 4.0f |
| 646 | }; |
| 647 | |
| 648 | const float a = 0.01f; |
| 649 | // Calculate output values for input. |
| 650 | auto f = [a](float value) |
| 651 | { |
| 652 | return value > 0.0f ? value : (value * a); |
| 653 | }; |
| 654 | std::vector<float> outputExpectedData(inputData.size()); |
| 655 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 656 | |
| 657 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 658 | memoryManager, |
| 659 | armnn::ActivationFunction::LeakyReLu, |
| 660 | a, |
| 661 | 0.f, |
| 662 | qScale, |
| 663 | qOffset, |
| 664 | inputData, |
| 665 | outputExpectedData); |
| 666 | } |
| 667 | |
| 668 | LayerTestResult<int16_t, 4> LeakyReLuInt16Test( |
| 669 | armnn::IWorkloadFactory& workloadFactory, |
| 670 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 671 | { |
| 672 | return LeakyReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 673 | } |
| 674 | |
| 675 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 676 | LayerTestResult<T, 4> AbsTestCommon( |
| 677 | armnn::IWorkloadFactory& workloadFactory, |
| 678 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 679 | float qScale, |
| 680 | int32_t qOffset) |
| 681 | { |
| 682 | std::vector<float> inputData = { |
| 683 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 684 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 685 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 686 | 1.0f, 2.0f, 3.0f, 4.0f |
| 687 | }; |
| 688 | |
| 689 | // Calculate output values for input. |
| 690 | auto f = [](float value) |
| 691 | { |
| 692 | return std::abs(value); |
| 693 | }; |
| 694 | std::vector<float> outputExpectedData(inputData.size()); |
| 695 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 696 | |
| 697 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 698 | memoryManager, |
| 699 | armnn::ActivationFunction::Abs, |
| 700 | 0.f, |
| 701 | 0.f, |
| 702 | qScale, |
| 703 | qOffset, |
| 704 | inputData, |
| 705 | outputExpectedData); |
| 706 | } |
| 707 | |
| 708 | LayerTestResult<int16_t, 4> AbsInt16Test( |
| 709 | armnn::IWorkloadFactory& workloadFactory, |
| 710 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 711 | { |
| 712 | return AbsTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 713 | } |
| 714 | |
| 715 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 716 | LayerTestResult<T, 4> SqrtTestCommon( |
| 717 | armnn::IWorkloadFactory& workloadFactory, |
| 718 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 719 | float qScale, |
| 720 | int32_t qOffset) |
| 721 | { |
| 722 | std::vector<float> inputData = { |
| 723 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 724 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 725 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 726 | 1.0f, 2.0f, 3.0f, 4.0f |
| 727 | }; |
| 728 | |
| 729 | // Calculate output values for input. |
| 730 | auto f = [](float value) |
| 731 | { |
| 732 | return std::sqrt(value); |
| 733 | }; |
| 734 | std::vector<float> outputExpectedData(inputData.size()); |
| 735 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 736 | |
| 737 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 738 | memoryManager, |
| 739 | armnn::ActivationFunction::Sqrt, |
| 740 | 0.f, |
| 741 | 0.f, |
| 742 | qScale, |
| 743 | qOffset, |
| 744 | inputData, |
| 745 | outputExpectedData); |
| 746 | } |
| 747 | |
| 748 | LayerTestResult<int16_t, 4> SqrtInt16Test( |
| 749 | armnn::IWorkloadFactory& workloadFactory, |
| 750 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 751 | { |
| 752 | return SqrtTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 753 | } |
| 754 | |
| 755 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 756 | LayerTestResult<T, 4> SquareTestCommon( |
| 757 | armnn::IWorkloadFactory& workloadFactory, |
| 758 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 759 | float qScale, |
| 760 | int32_t qOffset) |
| 761 | { |
| 762 | std::vector<float> inputData = { |
| 763 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 764 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 765 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 766 | 1.0f, 2.0f, 3.0f, 4.0f |
| 767 | }; |
| 768 | |
| 769 | // Calculate output values for input. |
| 770 | auto f = [](float value) |
| 771 | { |
| 772 | return std::pow(value,2); |
| 773 | }; |
| 774 | std::vector<float> outputExpectedData(inputData.size()); |
| 775 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 776 | |
| 777 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 778 | memoryManager, |
| 779 | armnn::ActivationFunction::Square, |
| 780 | 0.f, |
| 781 | 0.f, |
| 782 | qScale, |
| 783 | qOffset, |
| 784 | inputData, |
| 785 | outputExpectedData); |
| 786 | } |
| 787 | |
| 788 | LayerTestResult<int16_t, 4> SquareInt16Test( |
| 789 | armnn::IWorkloadFactory& workloadFactory, |
| 790 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 791 | { |
| 792 | return SquareTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 793 | } |
| 794 | |
| 795 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 796 | LayerTestResult<T, 4> TanhTestCommon( |
| 797 | armnn::IWorkloadFactory& workloadFactory, |
| 798 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 799 | float qScale, |
| 800 | int32_t qOffset) |
| 801 | { |
| 802 | std::vector<float> inputData = { |
| 803 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 804 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 805 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 806 | 1.0f, 2.0f, 3.0f, 4.0f |
| 807 | }; |
| 808 | |
| 809 | const float a = 2.0f; |
| 810 | const float b = 3.0f; |
| 811 | // Calculate output values for input. |
| 812 | auto f = [a, b](float value) |
| 813 | { |
| 814 | return a * tanhf(b * value); |
| 815 | }; |
| 816 | std::vector<float> outputExpectedData(inputData.size()); |
| 817 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 818 | |
| 819 | return SimpleActivationTest<ArmnnType>(workloadFactory, |
| 820 | memoryManager, |
| 821 | armnn::ActivationFunction::TanH, |
| 822 | a, |
| 823 | b, |
| 824 | qScale, |
| 825 | qOffset, |
| 826 | inputData, |
| 827 | outputExpectedData); |
| 828 | } |
| 829 | |
| 830 | LayerTestResult<int16_t, 4> TanhInt16Test( |
| 831 | armnn::IWorkloadFactory& workloadFactory, |
| 832 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
| 833 | { |
| 834 | return TanhTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0); |
| 835 | } |
| 836 | |
| 837 | |
| 838 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 839 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 840 | LayerTestResult<T,4> CompareActivationTestImpl( |
| 841 | armnn::IWorkloadFactory& workloadFactory, |
| 842 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 843 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 844 | armnn::ActivationFunction f, |
| 845 | unsigned int batchSize = 5, |
| 846 | float qScale = 0.0f, |
| 847 | int32_t qOffset = 0) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 848 | { |
| 849 | unsigned int width = 17; |
| 850 | unsigned int height = 29; |
| 851 | unsigned int channels = 2; |
| 852 | |
| 853 | float a = 0.234f; |
| 854 | float b = -12.345f; |
| 855 | |
| 856 | armnn::TensorInfo inputTensorInfo; |
| 857 | armnn::TensorInfo outputTensorInfo; |
| 858 | |
| 859 | unsigned int shape[] = {batchSize, channels, height, width}; |
| 860 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 861 | inputTensorInfo = armnn::TensorInfo(4, shape, ArmnnType); |
| 862 | outputTensorInfo = armnn::TensorInfo(4, shape, ArmnnType); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 863 | |
| 864 | // Set quantization parameters if the requested type is a quantized type. |
| 865 | if(armnn::IsQuantizedType<T>()) |
| 866 | { |
| 867 | inputTensorInfo.SetQuantizationScale(qScale); |
| 868 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 869 | outputTensorInfo.SetQuantizationScale(qScale); |
| 870 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 871 | } |
| 872 | |
| 873 | float minVal = -10.f; |
| 874 | if (f == armnn::ActivationFunction::Sqrt) |
| 875 | { |
| 876 | minVal = 0.f; |
| 877 | } |
| 878 | |
| 879 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f); |
| 880 | |
| 881 | |
| 882 | LayerTestResult<T,4> ret(outputTensorInfo); |
| 883 | auto boostArrayExtents = boost::extents |
| 884 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] |
| 885 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] |
| 886 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] |
| 887 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; |
| 888 | ret.output.resize(boostArrayExtents); |
| 889 | ret.outputExpected.resize(boostArrayExtents); |
| 890 | |
| 891 | |
| 892 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 893 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 894 | |
| 895 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 896 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 897 | |
| 898 | armnn::ActivationQueueDescriptor data; |
| 899 | armnn::WorkloadInfo info; |
| 900 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 901 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 902 | data.m_Parameters.m_A = a; |
| 903 | data.m_Parameters.m_B = b; |
| 904 | data.m_Parameters.m_Function = f; |
| 905 | |
| 906 | armnn::ActivationQueueDescriptor refData = data; |
| 907 | armnn::WorkloadInfo refInfo = info; |
| 908 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 909 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 910 | |
| 911 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info); |
| 912 | BOOST_ASSERT(workload != nullptr); |
| 913 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo); |
| 914 | BOOST_ASSERT(workloadRef != nullptr); |
| 915 | |
| 916 | inputHandle->Allocate(); |
| 917 | outputHandle->Allocate(); |
| 918 | inputHandleRef->Allocate(); |
| 919 | outputHandleRef->Allocate(); |
| 920 | |
| 921 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 922 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 923 | |
| 924 | workload->Execute(); |
| 925 | workloadRef->Execute(); |
| 926 | |
| 927 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 928 | CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 929 | |
| 930 | return ret; |
| 931 | } |
| 932 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 933 | LayerTestResult<float,4> CompareActivationTest( |
| 934 | armnn::IWorkloadFactory& workloadFactory, |
| 935 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 936 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 937 | armnn::ActivationFunction f, |
| 938 | unsigned int batchSize) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 939 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 940 | return CompareActivationTestImpl<armnn::DataType::Float32>( |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 941 | workloadFactory, memoryManager, refWorkloadFactory, f, batchSize); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 942 | } |
| 943 | |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 944 | LayerTestResult<uint8_t,4> CompareActivationUint8Test( |
| 945 | armnn::IWorkloadFactory& workloadFactory, |
| 946 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 947 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 948 | armnn::ActivationFunction f) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 949 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 950 | return CompareActivationTestImpl<armnn::DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 5caf907 | 2018-11-14 18:35:18 +0000 | [diff] [blame] | 951 | workloadFactory, memoryManager, refWorkloadFactory, f, 5, 0.1f, 50); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 952 | } |
Teresa Charlin | 18515e2 | 2019-04-24 10:17:46 +0100 | [diff] [blame] | 953 | |
| 954 | LayerTestResult<int16_t,4> CompareActivationInt16Test( |
| 955 | armnn::IWorkloadFactory& workloadFactory, |
| 956 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 957 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 958 | armnn::ActivationFunction f) |
| 959 | { |
| 960 | return CompareActivationTestImpl<armnn::DataType::QuantisedSymm16>( |
| 961 | workloadFactory, memoryManager, refWorkloadFactory, f, 5, 0.1f, 0); |
| 962 | } |