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 | |
| 7 | #include <armnn/ArmNN.hpp> |
| 8 | #include <armnn/Tensor.hpp> |
| 9 | #include <armnn/TypesUtils.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 10 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 11 | #include <test/TensorHelpers.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 12 | #include "QuantizeHelper.hpp" |
| 13 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 14 | #include <backends/CpuTensorHandle.hpp> |
| 15 | #include <backends/WorkloadFactory.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 16 | #include "ActivationFixture.hpp" |
| 17 | |
| 18 | #include <algorithm> |
| 19 | |
| 20 | template<typename T> |
| 21 | LayerTestResult<T, 4> BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 22 | float upperBound, float lowerBound, |
| 23 | float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset, |
| 24 | const std::vector<T>& inputData, const std::vector<T>& outputExpectedData, |
| 25 | unsigned int inputWidth, unsigned int inputHeight, |
| 26 | unsigned int inputChannels, unsigned int inputBatchSize) |
| 27 | { |
| 28 | unsigned int outputWidth = inputWidth; |
| 29 | unsigned int outputHeight = inputHeight; |
| 30 | unsigned int outputChannels = inputChannels; |
| 31 | unsigned int outputBatchSize = inputBatchSize; |
| 32 | |
| 33 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 34 | armnn::GetDataType<T>()); |
| 35 | |
| 36 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 37 | armnn::GetDataType<T>()); |
| 38 | |
| 39 | if(armnn::IsQuantizedType<T>()) |
| 40 | { |
| 41 | inputTensorInfo.SetQuantizationScale(inputScale); |
| 42 | inputTensorInfo.SetQuantizationOffset(inputOffset); |
| 43 | |
| 44 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 45 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 46 | } |
| 47 | |
| 48 | LayerTestResult<T, 4> result(inputTensorInfo); |
| 49 | |
| 50 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 51 | |
| 52 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 53 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 54 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 55 | // Setup bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 56 | armnn::ActivationQueueDescriptor descriptor; |
| 57 | armnn::WorkloadInfo workloadInfo; |
| 58 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 59 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 60 | |
| 61 | descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 62 | descriptor.m_Parameters.m_A = upperBound; |
| 63 | descriptor.m_Parameters.m_B = lowerBound; |
| 64 | |
| 65 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 66 | |
| 67 | inputHandle->Allocate(); |
| 68 | outputHandle->Allocate(); |
| 69 | |
| 70 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 71 | |
| 72 | workload->Execute(); |
| 73 | |
| 74 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 75 | |
| 76 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData); |
| 77 | |
| 78 | return result; |
| 79 | } |
| 80 | |
| 81 | LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) |
| 82 | { |
| 83 | unsigned int inputWidth = 4u; |
| 84 | unsigned int inputHeight = 5u; |
| 85 | unsigned int inputChannels = 1u; |
| 86 | unsigned int inputBatchSize = 1; |
| 87 | |
| 88 | std::vector<float> input = std::vector<float>{ |
| 89 | -2.0f, 0.1f, 0.5f, 1.25f, |
| 90 | 0.786f, 0.9875f, -1.5f, 0.384f, |
| 91 | 1.0001f, 3.5f, 7.5f, 0.896f, |
| 92 | 2.126f, 2.0f, 0.3f, 0.15f, |
| 93 | 0.999f, 1.2f, 0.89f, 6.1f, |
| 94 | }; |
| 95 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 96 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 97 | std::vector<float> output = std::vector<float>{ |
| 98 | -1.0f, 0.1f, 0.5f, 1.0f, |
| 99 | 0.786f, 0.9875f, -1.0f, 0.384f, |
| 100 | 1.0f, 1.0f, 1.0f, 0.896f, |
| 101 | 1.0f, 1.0f, 0.3f, 0.15f, |
| 102 | 0.999f, 1.0f, 0.89f, 1.0f, |
| 103 | }; |
| 104 | |
| 105 | return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output, |
| 106 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 107 | } |
| 108 | |
| 109 | LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) |
| 110 | { |
| 111 | unsigned int inputWidth = 4u; |
| 112 | unsigned int inputHeight = 5u; |
| 113 | unsigned int inputChannels = 1u; |
| 114 | unsigned int inputBatchSize = 1; |
| 115 | |
| 116 | std::vector<float> input = std::vector<float>{ |
| 117 | -1.0f, 0.1f, 0.5f, 6.25f, |
| 118 | 0.786f, 5.9875f, -0.5f, 0.384f, |
| 119 | 6.0001f, 3.5f, 7.5f, 0.896f, |
| 120 | 2.126f, 12.0f, 0.3f, 0.15f, |
| 121 | 0.999f, 1.2f, 0.89f, 6.1f, |
| 122 | }; |
| 123 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 124 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 125 | std::vector<float> output = std::vector<float>{ |
| 126 | 0.0f, 0.1f, 0.5f, 6.0f, |
| 127 | 0.786f, 5.9875f, 0.0f, 0.384f, |
| 128 | 6.0f, 3.5f, 6.0f, 0.896f, |
| 129 | 2.126f, 6.0f, 0.3f, 0.15f, |
| 130 | 0.999f, 1.2f, 0.89f, 6.0f, |
| 131 | }; |
| 132 | |
| 133 | return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output, |
| 134 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 135 | } |
| 136 | |
| 137 | LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) |
| 138 | { |
| 139 | unsigned int inputWidth = 3u; |
| 140 | unsigned int inputHeight = 2u; |
| 141 | unsigned int inputChannels = 1u; |
| 142 | unsigned int inputBatchSize = 1; |
| 143 | |
| 144 | std::vector<uint8_t> input = std::vector<uint8_t>{ |
| 145 | 51, 124, 28, |
| 146 | 251, 8, 92 |
| 147 | }; |
| 148 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 149 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 150 | std::vector<uint8_t> output = std::vector<uint8_t>{ |
| 151 | 0, 122, 0, |
| 152 | 255, 0, 58 |
| 153 | }; |
| 154 | |
| 155 | float inputScale = 12.0f / 255.0f; |
| 156 | int32_t inputOffset = 63; |
| 157 | float outputScale = 6.0f / 255.0f; |
| 158 | int32_t outputOffset = 0; |
| 159 | |
| 160 | return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, |
| 161 | inputScale, inputOffset, outputScale, outputOffset, |
| 162 | input, output, |
| 163 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 164 | } |
| 165 | |
| 166 | LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) |
| 167 | { |
| 168 | unsigned int inputWidth = 3u; |
| 169 | unsigned int inputHeight = 2u; |
| 170 | unsigned int inputChannels = 1u; |
| 171 | unsigned int inputBatchSize = 1; |
| 172 | |
| 173 | std::vector<uint8_t> input = std::vector<uint8_t>{ |
| 174 | 51, 230, 28, |
| 175 | 251, 8, 92 |
| 176 | }; |
| 177 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 178 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 179 | std::vector<uint8_t> output = std::vector<uint8_t>{ |
| 180 | 51, 192, 32, |
| 181 | 192, 32, 92 |
| 182 | }; |
| 183 | |
| 184 | int32_t inputOffset = 112; |
| 185 | float inputScale = 0.0125f; |
| 186 | |
| 187 | return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 188 | inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 189 | input, output, |
| 190 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 191 | } |
| 192 | |
| 193 | namespace |
| 194 | { |
| 195 | |
| 196 | struct BoundedReLuRandomInputTestTraits |
| 197 | { |
| 198 | constexpr static unsigned int inputHeight = 31u; |
| 199 | constexpr static unsigned int inputWidth = 19u; |
| 200 | constexpr static unsigned int inputChannels = 4u; |
| 201 | constexpr static unsigned int inputBatchSize = 2; |
| 202 | |
| 203 | constexpr static unsigned int outputHeight = inputHeight; |
| 204 | constexpr static unsigned int outputWidth = inputWidth; |
| 205 | constexpr static unsigned int outputChannels = inputChannels; |
| 206 | constexpr static unsigned int outputBatchSize = inputBatchSize; |
| 207 | |
| 208 | static armnn::TensorInfo GetInputTensorInfo() |
| 209 | { |
| 210 | return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 211 | armnn::DataType::Float32); |
| 212 | } |
| 213 | |
| 214 | static armnn::TensorInfo GetOutputTensorInfo() |
| 215 | { |
| 216 | return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 217 | armnn::DataType::Float32); |
| 218 | } |
| 219 | }; |
| 220 | |
| 221 | boost::multi_array<float, 4> BoundedReLuRandomInputTest(armnn::IWorkloadFactory& workloadFactory, |
| 222 | float lowerBound, |
| 223 | float upperBound, |
| 224 | const armnn::ActivationDescriptor& activationDescriptor) |
| 225 | { |
| 226 | const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo(); |
| 227 | const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo(); |
| 228 | |
| 229 | boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo)); |
| 230 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 231 | // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu |
| 232 | // range [lowerBound, upperBound]. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 233 | auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f); |
| 234 | |
| 235 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 236 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 237 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 238 | // Set up bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 239 | armnn::ActivationQueueDescriptor descriptor; |
| 240 | armnn::WorkloadInfo workloadInfo; |
| 241 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 242 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 243 | descriptor.m_Parameters = activationDescriptor; |
| 244 | |
| 245 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 246 | |
| 247 | inputHandle->Allocate(); |
| 248 | outputHandle->Allocate(); |
| 249 | |
| 250 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 251 | |
| 252 | workload->Execute(); |
| 253 | |
| 254 | CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get()); |
| 255 | |
| 256 | return output; |
| 257 | } |
| 258 | |
| 259 | } // namespace |
| 260 | |
| 261 | LayerTestResult<float, 4> CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory, |
| 262 | armnn::IWorkloadFactory& otherWorkloadFactory, |
| 263 | float upperBound, |
| 264 | float lowerBound) |
| 265 | { |
| 266 | LayerTestResult<float, 4> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo()); |
| 267 | |
| 268 | armnn::ActivationDescriptor activationDescriptor; |
| 269 | activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 270 | activationDescriptor.m_A = upperBound; |
| 271 | activationDescriptor.m_B = lowerBound; |
| 272 | |
| 273 | result.output = BoundedReLuRandomInputTest(workloadFactory, 0.0f, upperBound, activationDescriptor); |
| 274 | result.outputExpected = BoundedReLuRandomInputTest(otherWorkloadFactory, 0.0f, upperBound, activationDescriptor); |
| 275 | |
| 276 | return result; |
| 277 | } |
| 278 | |
| 279 | template<typename T> |
| 280 | LayerTestResult<T,4> ConstantLinearActivationTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 281 | float qScale = 0.0f, |
| 282 | int32_t qOffset = 0) |
| 283 | { |
| 284 | unsigned int inputHeight = 20; |
| 285 | unsigned int inputWidth = 17; |
| 286 | unsigned int inputChannels = 3; |
| 287 | unsigned int batchSize = 5; |
| 288 | |
| 289 | armnn::TensorInfo inputTensorInfo; |
| 290 | armnn::TensorInfo outputTensorInfo; |
| 291 | |
| 292 | unsigned int shape[] = {batchSize, inputChannels, inputHeight, inputWidth}; |
| 293 | |
| 294 | inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 295 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 296 | |
| 297 | // Set quantization parameters if the requested type is a quantized type. |
| 298 | if(armnn::IsQuantizedType<T>()) |
| 299 | { |
| 300 | inputTensorInfo.SetQuantizationScale(qScale); |
| 301 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 302 | outputTensorInfo.SetQuantizationScale(qScale); |
| 303 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 304 | } |
| 305 | |
| 306 | LayerTestResult<T, 4> ret(outputTensorInfo); |
| 307 | |
| 308 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 309 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 310 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 311 | // Do linear activation that should leave the tensor unchanged. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 312 | armnn::ActivationQueueDescriptor data; |
| 313 | armnn::WorkloadInfo info; |
| 314 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 315 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 316 | data.m_Parameters.m_A = 1.0f; |
| 317 | data.m_Parameters.m_B = 0.0f; |
| 318 | data.m_Parameters.m_Function = armnn::ActivationFunction::Linear; |
| 319 | |
| 320 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info); |
| 321 | |
| 322 | inputHandle->Allocate(); |
| 323 | outputHandle->Allocate(); |
| 324 | |
| 325 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561); |
| 326 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 327 | |
| 328 | workload->Execute(); |
| 329 | |
| 330 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 331 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 332 | // Ensure output equals input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 333 | ret.outputExpected = input; |
| 334 | |
| 335 | return ret; |
| 336 | } |
| 337 | |
| 338 | LayerTestResult<float, 4> ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory) |
| 339 | { |
| 340 | return ConstantLinearActivationTestCommon<float>(workloadFactory); |
| 341 | } |
| 342 | |
| 343 | LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 344 | { |
| 345 | return ConstantLinearActivationTestCommon<uint8_t>(workloadFactory, 4.0f, 3); |
| 346 | } |
| 347 | |
| 348 | template<typename T> |
| 349 | LayerTestResult<T, 4> SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory, |
| 350 | armnn::ActivationFunction activationFunction, |
| 351 | float activationParameterA, |
| 352 | float activationParameterB, |
| 353 | float qScale, |
| 354 | int32_t qOffset, |
| 355 | const std::vector<float>& inputData, |
| 356 | const std::vector<float>& outputExpectedData) |
| 357 | { |
| 358 | constexpr static unsigned int inputWidth = 16u; |
| 359 | constexpr static unsigned int inputHeight = 1u; |
| 360 | constexpr static unsigned int inputChannels = 1u; |
| 361 | constexpr static unsigned int inputBatchSize = 1u; |
| 362 | |
| 363 | constexpr static unsigned int outputWidth = inputWidth; |
| 364 | constexpr static unsigned int outputHeight = inputHeight; |
| 365 | constexpr static unsigned int outputChannels = inputChannels; |
| 366 | constexpr static unsigned int outputBatchSize = inputBatchSize; |
| 367 | |
| 368 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 369 | armnn::GetDataType<T>()); |
| 370 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 371 | armnn::GetDataType<T>()); |
| 372 | |
| 373 | // Set quantization parameters if the requested type is a quantized type. |
| 374 | if(armnn::IsQuantizedType<T>()) |
| 375 | { |
| 376 | inputTensorInfo.SetQuantizationScale(qScale); |
| 377 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 378 | outputTensorInfo.SetQuantizationScale(qScale); |
| 379 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 380 | } |
| 381 | |
| 382 | LayerTestResult<T, 4> result(inputTensorInfo); |
| 383 | |
| 384 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 385 | |
| 386 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 387 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 388 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 389 | // Setup bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 390 | armnn::ActivationQueueDescriptor descriptor; |
| 391 | armnn::WorkloadInfo workloadInfo; |
| 392 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 393 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 394 | |
| 395 | descriptor.m_Parameters.m_Function = activationFunction; |
| 396 | descriptor.m_Parameters.m_A = activationParameterA; |
| 397 | descriptor.m_Parameters.m_B = activationParameterB; |
| 398 | |
| 399 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 400 | |
| 401 | inputHandle->Allocate(); |
| 402 | outputHandle->Allocate(); |
| 403 | |
| 404 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 405 | |
| 406 | workload->Execute(); |
| 407 | |
| 408 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 409 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 410 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 411 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); |
| 412 | |
| 413 | return result; |
| 414 | } |
| 415 | |
| 416 | template<typename T> |
| 417 | LayerTestResult<T, 4> SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) |
| 418 | { |
| 419 | std::vector<float> inputData = { |
| 420 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 421 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 422 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 423 | 1.0f, 2.0f, 3.0f, 4.0f |
| 424 | }; |
| 425 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 426 | // Calculate output values for input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 427 | auto f = [](float value) |
| 428 | { |
| 429 | return 1.0f / (1.0f + std::exp(-value)); |
| 430 | }; |
| 431 | std::vector<float> outputExpectedData(inputData.size()); |
| 432 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 433 | |
| 434 | return SimpleActivationTest<T>(workloadFactory, |
| 435 | armnn::ActivationFunction::Sigmoid, |
| 436 | 0.f, |
| 437 | 0.f, |
| 438 | qScale, |
| 439 | qOffset, |
| 440 | inputData, |
| 441 | outputExpectedData); |
| 442 | } |
| 443 | |
| 444 | LayerTestResult<float, 4> SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory) |
| 445 | { |
| 446 | return SimpleSigmoidTestCommon<float>(workloadFactory, 0.0f, 0); |
| 447 | } |
| 448 | |
| 449 | LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 450 | { |
| 451 | return SimpleSigmoidTestCommon<uint8_t>(workloadFactory, 0.1f, 50); |
| 452 | } |
| 453 | |
| 454 | template<typename T> |
| 455 | LayerTestResult<T,4> CompareActivationTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 456 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 457 | armnn::ActivationFunction f, |
| 458 | unsigned int batchSize = 5, |
| 459 | float qScale = 0.0f, |
| 460 | int32_t qOffset = 0) |
| 461 | { |
| 462 | unsigned int width = 17; |
| 463 | unsigned int height = 29; |
| 464 | unsigned int channels = 2; |
| 465 | |
| 466 | float a = 0.234f; |
| 467 | float b = -12.345f; |
| 468 | |
| 469 | armnn::TensorInfo inputTensorInfo; |
| 470 | armnn::TensorInfo outputTensorInfo; |
| 471 | |
| 472 | unsigned int shape[] = {batchSize, channels, height, width}; |
| 473 | |
| 474 | inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 475 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 476 | |
| 477 | // Set quantization parameters if the requested type is a quantized type. |
| 478 | if(armnn::IsQuantizedType<T>()) |
| 479 | { |
| 480 | inputTensorInfo.SetQuantizationScale(qScale); |
| 481 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 482 | outputTensorInfo.SetQuantizationScale(qScale); |
| 483 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 484 | } |
| 485 | |
| 486 | float minVal = -10.f; |
| 487 | if (f == armnn::ActivationFunction::Sqrt) |
| 488 | { |
| 489 | minVal = 0.f; |
| 490 | } |
| 491 | |
| 492 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f); |
| 493 | |
| 494 | |
| 495 | LayerTestResult<T,4> ret(outputTensorInfo); |
| 496 | auto boostArrayExtents = boost::extents |
| 497 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] |
| 498 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] |
| 499 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] |
| 500 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; |
| 501 | ret.output.resize(boostArrayExtents); |
| 502 | ret.outputExpected.resize(boostArrayExtents); |
| 503 | |
| 504 | |
| 505 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 506 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 507 | |
| 508 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 509 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 510 | |
| 511 | armnn::ActivationQueueDescriptor data; |
| 512 | armnn::WorkloadInfo info; |
| 513 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 514 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 515 | data.m_Parameters.m_A = a; |
| 516 | data.m_Parameters.m_B = b; |
| 517 | data.m_Parameters.m_Function = f; |
| 518 | |
| 519 | armnn::ActivationQueueDescriptor refData = data; |
| 520 | armnn::WorkloadInfo refInfo = info; |
| 521 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 522 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 523 | |
| 524 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info); |
| 525 | BOOST_ASSERT(workload != nullptr); |
| 526 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo); |
| 527 | BOOST_ASSERT(workloadRef != nullptr); |
| 528 | |
| 529 | inputHandle->Allocate(); |
| 530 | outputHandle->Allocate(); |
| 531 | inputHandleRef->Allocate(); |
| 532 | outputHandleRef->Allocate(); |
| 533 | |
| 534 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 535 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 536 | |
| 537 | workload->Execute(); |
| 538 | workloadRef->Execute(); |
| 539 | |
| 540 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 541 | CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 542 | |
| 543 | return ret; |
| 544 | } |
| 545 | |
| 546 | LayerTestResult<float,4> CompareActivationTest(armnn::IWorkloadFactory& workloadFactory, |
| 547 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 548 | armnn::ActivationFunction f, |
| 549 | unsigned int batchSize) |
| 550 | { |
| 551 | return CompareActivationTestImpl<float>(workloadFactory, refWorkloadFactory, f, batchSize); |
| 552 | } |
| 553 | |
| 554 | LayerTestResult<uint8_t,4> CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 555 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 556 | armnn::ActivationFunction f) |
| 557 | { |
| 558 | return CompareActivationTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50); |
| 559 | } |