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> |
| 15 | #include <backendsCommon/WorkloadFactory.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 16 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 17 | #include <test/TensorHelpers.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 18 | |
| 19 | #include <algorithm> |
| 20 | |
| 21 | template<typename T> |
| 22 | LayerTestResult<T, 4> BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 23 | float upperBound, float lowerBound, |
| 24 | float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset, |
| 25 | const std::vector<T>& inputData, const std::vector<T>& outputExpectedData, |
| 26 | unsigned int inputWidth, unsigned int inputHeight, |
| 27 | unsigned int inputChannels, unsigned int inputBatchSize) |
| 28 | { |
| 29 | unsigned int outputWidth = inputWidth; |
| 30 | unsigned int outputHeight = inputHeight; |
| 31 | unsigned int outputChannels = inputChannels; |
| 32 | unsigned int outputBatchSize = inputBatchSize; |
| 33 | |
| 34 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 35 | armnn::GetDataType<T>()); |
| 36 | |
| 37 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 38 | armnn::GetDataType<T>()); |
| 39 | |
| 40 | if(armnn::IsQuantizedType<T>()) |
| 41 | { |
| 42 | inputTensorInfo.SetQuantizationScale(inputScale); |
| 43 | inputTensorInfo.SetQuantizationOffset(inputOffset); |
| 44 | |
| 45 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 46 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 47 | } |
| 48 | |
| 49 | LayerTestResult<T, 4> result(inputTensorInfo); |
| 50 | |
| 51 | auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); |
| 52 | |
| 53 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 54 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 55 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 56 | // Setup bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 57 | armnn::ActivationQueueDescriptor descriptor; |
| 58 | armnn::WorkloadInfo workloadInfo; |
| 59 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 60 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 61 | |
| 62 | descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 63 | descriptor.m_Parameters.m_A = upperBound; |
| 64 | descriptor.m_Parameters.m_B = lowerBound; |
| 65 | |
| 66 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 67 | |
| 68 | inputHandle->Allocate(); |
| 69 | outputHandle->Allocate(); |
| 70 | |
| 71 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 72 | |
| 73 | workload->Execute(); |
| 74 | |
| 75 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 76 | |
| 77 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData); |
| 78 | |
| 79 | return result; |
| 80 | } |
| 81 | |
| 82 | LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) |
| 83 | { |
| 84 | unsigned int inputWidth = 4u; |
| 85 | unsigned int inputHeight = 5u; |
| 86 | unsigned int inputChannels = 1u; |
| 87 | unsigned int inputBatchSize = 1; |
| 88 | |
| 89 | std::vector<float> input = std::vector<float>{ |
| 90 | -2.0f, 0.1f, 0.5f, 1.25f, |
| 91 | 0.786f, 0.9875f, -1.5f, 0.384f, |
| 92 | 1.0001f, 3.5f, 7.5f, 0.896f, |
| 93 | 2.126f, 2.0f, 0.3f, 0.15f, |
| 94 | 0.999f, 1.2f, 0.89f, 6.1f, |
| 95 | }; |
| 96 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 97 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 98 | std::vector<float> output = std::vector<float>{ |
| 99 | -1.0f, 0.1f, 0.5f, 1.0f, |
| 100 | 0.786f, 0.9875f, -1.0f, 0.384f, |
| 101 | 1.0f, 1.0f, 1.0f, 0.896f, |
| 102 | 1.0f, 1.0f, 0.3f, 0.15f, |
| 103 | 0.999f, 1.0f, 0.89f, 1.0f, |
| 104 | }; |
| 105 | |
| 106 | return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output, |
| 107 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 108 | } |
| 109 | |
| 110 | LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) |
| 111 | { |
| 112 | unsigned int inputWidth = 4u; |
| 113 | unsigned int inputHeight = 5u; |
| 114 | unsigned int inputChannels = 1u; |
| 115 | unsigned int inputBatchSize = 1; |
| 116 | |
| 117 | std::vector<float> input = std::vector<float>{ |
| 118 | -1.0f, 0.1f, 0.5f, 6.25f, |
| 119 | 0.786f, 5.9875f, -0.5f, 0.384f, |
| 120 | 6.0001f, 3.5f, 7.5f, 0.896f, |
| 121 | 2.126f, 12.0f, 0.3f, 0.15f, |
| 122 | 0.999f, 1.2f, 0.89f, 6.1f, |
| 123 | }; |
| 124 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 125 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 126 | std::vector<float> output = std::vector<float>{ |
| 127 | 0.0f, 0.1f, 0.5f, 6.0f, |
| 128 | 0.786f, 5.9875f, 0.0f, 0.384f, |
| 129 | 6.0f, 3.5f, 6.0f, 0.896f, |
| 130 | 2.126f, 6.0f, 0.3f, 0.15f, |
| 131 | 0.999f, 1.2f, 0.89f, 6.0f, |
| 132 | }; |
| 133 | |
| 134 | return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output, |
| 135 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 136 | } |
| 137 | |
| 138 | LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory) |
| 139 | { |
| 140 | unsigned int inputWidth = 3u; |
| 141 | unsigned int inputHeight = 2u; |
| 142 | unsigned int inputChannels = 1u; |
| 143 | unsigned int inputBatchSize = 1; |
| 144 | |
| 145 | std::vector<uint8_t> input = std::vector<uint8_t>{ |
| 146 | 51, 124, 28, |
| 147 | 251, 8, 92 |
| 148 | }; |
| 149 | |
David Beck | ac42efd | 2018-09-26 17:41:13 +0100 | [diff] [blame] | 150 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 151 | std::vector<uint8_t> output = std::vector<uint8_t>{ |
| 152 | 0, 122, 0, |
| 153 | 255, 0, 58 |
| 154 | }; |
| 155 | |
| 156 | float inputScale = 12.0f / 255.0f; |
| 157 | int32_t inputOffset = 63; |
| 158 | float outputScale = 6.0f / 255.0f; |
| 159 | int32_t outputOffset = 0; |
| 160 | |
| 161 | return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, |
| 162 | inputScale, inputOffset, outputScale, outputOffset, |
| 163 | input, output, |
| 164 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 165 | } |
| 166 | |
| 167 | LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory) |
| 168 | { |
| 169 | unsigned int inputWidth = 3u; |
| 170 | unsigned int inputHeight = 2u; |
| 171 | unsigned int inputChannels = 1u; |
| 172 | unsigned int inputBatchSize = 1; |
| 173 | |
| 174 | std::vector<uint8_t> input = std::vector<uint8_t>{ |
| 175 | 51, 230, 28, |
| 176 | 251, 8, 92 |
| 177 | }; |
| 178 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 179 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 180 | std::vector<uint8_t> output = std::vector<uint8_t>{ |
| 181 | 51, 192, 32, |
| 182 | 192, 32, 92 |
| 183 | }; |
| 184 | |
| 185 | int32_t inputOffset = 112; |
| 186 | float inputScale = 0.0125f; |
| 187 | |
| 188 | return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 189 | inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 190 | input, output, |
| 191 | inputWidth, inputHeight, inputChannels, inputBatchSize); |
| 192 | } |
| 193 | |
| 194 | namespace |
| 195 | { |
| 196 | |
| 197 | struct BoundedReLuRandomInputTestTraits |
| 198 | { |
| 199 | constexpr static unsigned int inputHeight = 31u; |
| 200 | constexpr static unsigned int inputWidth = 19u; |
| 201 | constexpr static unsigned int inputChannels = 4u; |
| 202 | constexpr static unsigned int inputBatchSize = 2; |
| 203 | |
| 204 | constexpr static unsigned int outputHeight = inputHeight; |
| 205 | constexpr static unsigned int outputWidth = inputWidth; |
| 206 | constexpr static unsigned int outputChannels = inputChannels; |
| 207 | constexpr static unsigned int outputBatchSize = inputBatchSize; |
| 208 | |
| 209 | static armnn::TensorInfo GetInputTensorInfo() |
| 210 | { |
| 211 | return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 212 | armnn::DataType::Float32); |
| 213 | } |
| 214 | |
| 215 | static armnn::TensorInfo GetOutputTensorInfo() |
| 216 | { |
| 217 | return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 218 | armnn::DataType::Float32); |
| 219 | } |
| 220 | }; |
| 221 | |
| 222 | boost::multi_array<float, 4> BoundedReLuRandomInputTest(armnn::IWorkloadFactory& workloadFactory, |
| 223 | float lowerBound, |
| 224 | float upperBound, |
| 225 | const armnn::ActivationDescriptor& activationDescriptor) |
| 226 | { |
| 227 | const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo(); |
| 228 | const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo(); |
| 229 | |
| 230 | boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo)); |
| 231 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 232 | // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu |
| 233 | // range [lowerBound, upperBound]. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 234 | auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f); |
| 235 | |
| 236 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 237 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 238 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 239 | // Set up bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 240 | armnn::ActivationQueueDescriptor descriptor; |
| 241 | armnn::WorkloadInfo workloadInfo; |
| 242 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 243 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 244 | descriptor.m_Parameters = activationDescriptor; |
| 245 | |
| 246 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 247 | |
| 248 | inputHandle->Allocate(); |
| 249 | outputHandle->Allocate(); |
| 250 | |
| 251 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 252 | |
| 253 | workload->Execute(); |
| 254 | |
| 255 | CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get()); |
| 256 | |
| 257 | return output; |
| 258 | } |
| 259 | |
| 260 | } // namespace |
| 261 | |
| 262 | LayerTestResult<float, 4> CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory, |
| 263 | armnn::IWorkloadFactory& otherWorkloadFactory, |
| 264 | float upperBound, |
| 265 | float lowerBound) |
| 266 | { |
| 267 | LayerTestResult<float, 4> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo()); |
| 268 | |
| 269 | armnn::ActivationDescriptor activationDescriptor; |
| 270 | activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 271 | activationDescriptor.m_A = upperBound; |
| 272 | activationDescriptor.m_B = lowerBound; |
| 273 | |
| 274 | result.output = BoundedReLuRandomInputTest(workloadFactory, 0.0f, upperBound, activationDescriptor); |
| 275 | result.outputExpected = BoundedReLuRandomInputTest(otherWorkloadFactory, 0.0f, upperBound, activationDescriptor); |
| 276 | |
| 277 | return result; |
| 278 | } |
| 279 | |
| 280 | template<typename T> |
| 281 | LayerTestResult<T,4> ConstantLinearActivationTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 282 | float qScale = 0.0f, |
| 283 | int32_t qOffset = 0) |
| 284 | { |
| 285 | unsigned int inputHeight = 20; |
| 286 | unsigned int inputWidth = 17; |
| 287 | unsigned int inputChannels = 3; |
| 288 | unsigned int batchSize = 5; |
| 289 | |
| 290 | armnn::TensorInfo inputTensorInfo; |
| 291 | armnn::TensorInfo outputTensorInfo; |
| 292 | |
| 293 | unsigned int shape[] = {batchSize, inputChannels, inputHeight, inputWidth}; |
| 294 | |
| 295 | inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 296 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 297 | |
| 298 | // Set quantization parameters if the requested type is a quantized type. |
| 299 | if(armnn::IsQuantizedType<T>()) |
| 300 | { |
| 301 | inputTensorInfo.SetQuantizationScale(qScale); |
| 302 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 303 | outputTensorInfo.SetQuantizationScale(qScale); |
| 304 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 305 | } |
| 306 | |
| 307 | LayerTestResult<T, 4> ret(outputTensorInfo); |
| 308 | |
| 309 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 310 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 311 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 312 | // Do linear activation that should leave the tensor unchanged. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 313 | armnn::ActivationQueueDescriptor data; |
| 314 | armnn::WorkloadInfo info; |
| 315 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 316 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 317 | data.m_Parameters.m_A = 1.0f; |
| 318 | data.m_Parameters.m_B = 0.0f; |
| 319 | data.m_Parameters.m_Function = armnn::ActivationFunction::Linear; |
| 320 | |
| 321 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info); |
| 322 | |
| 323 | inputHandle->Allocate(); |
| 324 | outputHandle->Allocate(); |
| 325 | |
| 326 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561); |
| 327 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 328 | |
| 329 | workload->Execute(); |
| 330 | |
| 331 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 332 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 333 | // Ensure output equals input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 334 | ret.outputExpected = input; |
| 335 | |
| 336 | return ret; |
| 337 | } |
| 338 | |
| 339 | LayerTestResult<float, 4> ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory) |
| 340 | { |
| 341 | return ConstantLinearActivationTestCommon<float>(workloadFactory); |
| 342 | } |
| 343 | |
| 344 | LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 345 | { |
| 346 | return ConstantLinearActivationTestCommon<uint8_t>(workloadFactory, 4.0f, 3); |
| 347 | } |
| 348 | |
| 349 | template<typename T> |
| 350 | LayerTestResult<T, 4> SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory, |
| 351 | armnn::ActivationFunction activationFunction, |
| 352 | float activationParameterA, |
| 353 | float activationParameterB, |
| 354 | float qScale, |
| 355 | int32_t qOffset, |
| 356 | const std::vector<float>& inputData, |
| 357 | const std::vector<float>& outputExpectedData) |
| 358 | { |
| 359 | constexpr static unsigned int inputWidth = 16u; |
| 360 | constexpr static unsigned int inputHeight = 1u; |
| 361 | constexpr static unsigned int inputChannels = 1u; |
| 362 | constexpr static unsigned int inputBatchSize = 1u; |
| 363 | |
| 364 | constexpr static unsigned int outputWidth = inputWidth; |
| 365 | constexpr static unsigned int outputHeight = inputHeight; |
| 366 | constexpr static unsigned int outputChannels = inputChannels; |
| 367 | constexpr static unsigned int outputBatchSize = inputBatchSize; |
| 368 | |
| 369 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 370 | armnn::GetDataType<T>()); |
| 371 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 372 | armnn::GetDataType<T>()); |
| 373 | |
| 374 | // Set quantization parameters if the requested type is a quantized type. |
| 375 | if(armnn::IsQuantizedType<T>()) |
| 376 | { |
| 377 | inputTensorInfo.SetQuantizationScale(qScale); |
| 378 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 379 | outputTensorInfo.SetQuantizationScale(qScale); |
| 380 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 381 | } |
| 382 | |
| 383 | LayerTestResult<T, 4> result(inputTensorInfo); |
| 384 | |
| 385 | auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); |
| 386 | |
| 387 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 388 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 389 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 390 | // Setup bounded ReLu. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 391 | armnn::ActivationQueueDescriptor descriptor; |
| 392 | armnn::WorkloadInfo workloadInfo; |
| 393 | AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 394 | AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 395 | |
| 396 | descriptor.m_Parameters.m_Function = activationFunction; |
| 397 | descriptor.m_Parameters.m_A = activationParameterA; |
| 398 | descriptor.m_Parameters.m_B = activationParameterB; |
| 399 | |
| 400 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo); |
| 401 | |
| 402 | inputHandle->Allocate(); |
| 403 | outputHandle->Allocate(); |
| 404 | |
| 405 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 406 | |
| 407 | workload->Execute(); |
| 408 | |
| 409 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 410 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 411 | // Calculated manually. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 412 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData)); |
| 413 | |
| 414 | return result; |
| 415 | } |
| 416 | |
| 417 | template<typename T> |
| 418 | LayerTestResult<T, 4> SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) |
| 419 | { |
| 420 | std::vector<float> inputData = { |
| 421 | -0.1f, -0.2f, -0.3f, -0.4f, |
| 422 | 0.1f, 0.2f, 0.3f, 0.4f, |
| 423 | -1.0f, -2.0f, -3.0f, -4.0f, |
| 424 | 1.0f, 2.0f, 3.0f, 4.0f |
| 425 | }; |
| 426 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 427 | // Calculate output values for input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 428 | auto f = [](float value) |
| 429 | { |
| 430 | return 1.0f / (1.0f + std::exp(-value)); |
| 431 | }; |
| 432 | std::vector<float> outputExpectedData(inputData.size()); |
| 433 | std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); |
| 434 | |
| 435 | return SimpleActivationTest<T>(workloadFactory, |
| 436 | armnn::ActivationFunction::Sigmoid, |
| 437 | 0.f, |
| 438 | 0.f, |
| 439 | qScale, |
| 440 | qOffset, |
| 441 | inputData, |
| 442 | outputExpectedData); |
| 443 | } |
| 444 | |
| 445 | LayerTestResult<float, 4> SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory) |
| 446 | { |
| 447 | return SimpleSigmoidTestCommon<float>(workloadFactory, 0.0f, 0); |
| 448 | } |
| 449 | |
| 450 | LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 451 | { |
| 452 | return SimpleSigmoidTestCommon<uint8_t>(workloadFactory, 0.1f, 50); |
| 453 | } |
| 454 | |
| 455 | template<typename T> |
| 456 | LayerTestResult<T,4> CompareActivationTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 457 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 458 | armnn::ActivationFunction f, |
| 459 | unsigned int batchSize = 5, |
| 460 | float qScale = 0.0f, |
| 461 | int32_t qOffset = 0) |
| 462 | { |
| 463 | unsigned int width = 17; |
| 464 | unsigned int height = 29; |
| 465 | unsigned int channels = 2; |
| 466 | |
| 467 | float a = 0.234f; |
| 468 | float b = -12.345f; |
| 469 | |
| 470 | armnn::TensorInfo inputTensorInfo; |
| 471 | armnn::TensorInfo outputTensorInfo; |
| 472 | |
| 473 | unsigned int shape[] = {batchSize, channels, height, width}; |
| 474 | |
| 475 | inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 476 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>()); |
| 477 | |
| 478 | // Set quantization parameters if the requested type is a quantized type. |
| 479 | if(armnn::IsQuantizedType<T>()) |
| 480 | { |
| 481 | inputTensorInfo.SetQuantizationScale(qScale); |
| 482 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 483 | outputTensorInfo.SetQuantizationScale(qScale); |
| 484 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 485 | } |
| 486 | |
| 487 | float minVal = -10.f; |
| 488 | if (f == armnn::ActivationFunction::Sqrt) |
| 489 | { |
| 490 | minVal = 0.f; |
| 491 | } |
| 492 | |
| 493 | boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f); |
| 494 | |
| 495 | |
| 496 | LayerTestResult<T,4> ret(outputTensorInfo); |
| 497 | auto boostArrayExtents = boost::extents |
| 498 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] |
| 499 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] |
| 500 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] |
| 501 | [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; |
| 502 | ret.output.resize(boostArrayExtents); |
| 503 | ret.outputExpected.resize(boostArrayExtents); |
| 504 | |
| 505 | |
| 506 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 507 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 508 | |
| 509 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 510 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 511 | |
| 512 | armnn::ActivationQueueDescriptor data; |
| 513 | armnn::WorkloadInfo info; |
| 514 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 515 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 516 | data.m_Parameters.m_A = a; |
| 517 | data.m_Parameters.m_B = b; |
| 518 | data.m_Parameters.m_Function = f; |
| 519 | |
| 520 | armnn::ActivationQueueDescriptor refData = data; |
| 521 | armnn::WorkloadInfo refInfo = info; |
| 522 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 523 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 524 | |
| 525 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info); |
| 526 | BOOST_ASSERT(workload != nullptr); |
| 527 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo); |
| 528 | BOOST_ASSERT(workloadRef != nullptr); |
| 529 | |
| 530 | inputHandle->Allocate(); |
| 531 | outputHandle->Allocate(); |
| 532 | inputHandleRef->Allocate(); |
| 533 | outputHandleRef->Allocate(); |
| 534 | |
| 535 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 536 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 537 | |
| 538 | workload->Execute(); |
| 539 | workloadRef->Execute(); |
| 540 | |
| 541 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 542 | CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 543 | |
| 544 | return ret; |
| 545 | } |
| 546 | |
| 547 | LayerTestResult<float,4> CompareActivationTest(armnn::IWorkloadFactory& workloadFactory, |
| 548 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 549 | armnn::ActivationFunction f, |
| 550 | unsigned int batchSize) |
| 551 | { |
| 552 | return CompareActivationTestImpl<float>(workloadFactory, refWorkloadFactory, f, batchSize); |
| 553 | } |
| 554 | |
| 555 | LayerTestResult<uint8_t,4> CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 556 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 557 | armnn::ActivationFunction f) |
| 558 | { |
| 559 | return CompareActivationTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50); |
| 560 | } |