Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ConcatTestImpl.hpp" |
| 7 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 8 | #include <QuantizeHelper.hpp> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 9 | #include <ResolveType.hpp> |
| 10 | |
| 11 | #include <armnn/ArmNN.hpp> |
| 12 | |
Matteo Martincigh | e011d20 | 2019-11-28 11:35:47 +0000 | [diff] [blame^] | 13 | #include <armnnUtils/Permute.hpp> |
| 14 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 15 | #include <backendsCommon/test/TensorCopyUtils.hpp> |
| 16 | #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| 17 | |
| 18 | #include <test/TensorHelpers.hpp> |
| 19 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 20 | using namespace armnn; |
| 21 | using namespace armnnUtils; |
| 22 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 23 | // |
| 24 | // Helper functions and templates |
| 25 | // |
| 26 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 27 | OriginsDescriptor CreateDescriptorForConcat( |
| 28 | const std::vector<TensorInfo> & inputTensorInfos, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 29 | unsigned int concatDim) |
| 30 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 31 | std::vector<TensorShape> shapes; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 32 | shapes.reserve(inputTensorInfos.size()); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 33 | for (const TensorInfo& it: inputTensorInfos) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 34 | { |
| 35 | shapes.push_back(it.GetShape()); |
| 36 | } |
| 37 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 38 | return CreateDescriptorForConcatenation(shapes.begin(), shapes.end(), concatDim); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 39 | } |
| 40 | |
| 41 | // |
| 42 | // Concat is only supported for N and C dimensions for NCHW and the inner most dimension |
| 43 | // In case of <4 dimensions we need to make sure that the concat dimensions are at least |
| 44 | // the 3rd slowest iterating one or the inner most dimension. |
| 45 | // |
| 46 | |
| 47 | bool NeedPermuteForConcat( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 48 | const std::vector<TensorInfo> & inputTensorInfos, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 49 | unsigned int concatDim) |
| 50 | { |
| 51 | // See note above. Additionally we expect the input shapes to have the |
| 52 | // same number of dimensions. |
| 53 | unsigned int nDimensions = 0; |
| 54 | |
| 55 | // Determine the number of dimensions as well as sanity check them |
| 56 | // agains test implementation issues. |
| 57 | for (auto && tensorInfo : inputTensorInfos) |
| 58 | { |
| 59 | if (!nDimensions) |
| 60 | { |
| 61 | nDimensions = tensorInfo.GetShape().GetNumDimensions(); |
| 62 | } |
| 63 | else |
| 64 | { |
| 65 | BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(), |
| 66 | "Input shapes must have the same number of dimensions"); |
| 67 | } |
| 68 | } |
| 69 | |
| 70 | return (nDimensions < 3 || (nDimensions == 3 && (nDimensions-concatDim) < 3 && (nDimensions-concatDim) != 1)); |
| 71 | } |
| 72 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 73 | TensorShape ExpandTensorShapeTo3dForPermute(const TensorShape & inputShape) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 74 | { |
| 75 | unsigned int numDims = inputShape.GetNumDimensions(); |
| 76 | if (numDims >= 3) |
| 77 | { |
| 78 | // Nothing to do if the inputShape has at least 3 dimensions. |
| 79 | return inputShape; |
| 80 | } |
| 81 | |
| 82 | std::vector<unsigned int> newDims(size_t(3), 1u); |
| 83 | unsigned int expandedBy = 3 - numDims; |
| 84 | for (unsigned int i=0; i<numDims; ++i) |
| 85 | { |
| 86 | newDims[expandedBy+i] = inputShape[i]; |
| 87 | } |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 88 | return TensorShape(3u, &newDims[0]); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 89 | } |
| 90 | |
| 91 | void Generate3dPermuteVectorForConcat( |
| 92 | unsigned int numDimensions, |
| 93 | unsigned int & concatDim, |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 94 | std::pair<PermutationVector, PermutationVector> & permutations) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 95 | { |
| 96 | BOOST_ASSERT_MSG(numDimensions <= 3, |
| 97 | "Only dimensions 1,2 and 3 are supported by this helper"); |
| 98 | unsigned int expandedBy = 3 - numDimensions; |
| 99 | unsigned int expandedConcatAxis = concatDim + expandedBy; |
| 100 | |
| 101 | if (expandedConcatAxis == 2) |
| 102 | { |
| 103 | concatDim = 0; |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 104 | PermutationVector forwardPermutation({1, 2, 0}); |
| 105 | PermutationVector reversePermutation({2, 0, 1}); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 106 | permutations = std::make_pair(forwardPermutation, reversePermutation); |
| 107 | } |
| 108 | else if (expandedConcatAxis == 1) |
| 109 | { |
| 110 | concatDim = 0; |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 111 | PermutationVector forwardPermutation({2, 0, 1}); |
| 112 | PermutationVector reversePermutation({1, 2, 0}); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 113 | permutations = std::make_pair(forwardPermutation, reversePermutation); |
| 114 | } |
| 115 | else |
| 116 | { |
| 117 | BOOST_ASSERT(expandedConcatAxis == 0); |
| 118 | concatDim = 0; |
| 119 | } |
| 120 | } |
| 121 | |
| 122 | template<typename T> void PermuteTensorData( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 123 | IWorkloadFactory& workloadFactory, |
| 124 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 125 | const PermutationVector& mappings, |
| 126 | TensorInfo & inputTensorInfo, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 127 | const T * inputData, |
| 128 | std::vector<T>& outputData) |
| 129 | { |
| 130 | BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null"); |
| 131 | if (inputData == nullptr) |
| 132 | { |
| 133 | // Nullptr is an error in the test. By returning without doing the concatenation |
| 134 | // I expect the caller to fail the test. It still makes sense to report this as |
| 135 | // an assert for Debug builds. |
| 136 | return; |
| 137 | } |
| 138 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 139 | TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 140 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 141 | std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 142 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 143 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 144 | PermuteQueueDescriptor queueDescriptor; |
| 145 | queueDescriptor.m_Parameters = PermuteDescriptor{mappings}; |
| 146 | WorkloadInfo workloadInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 147 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 148 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 149 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 150 | std::unique_ptr<IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 151 | |
| 152 | inputHandle->Allocate(); |
| 153 | outputHandle->Allocate(); |
| 154 | |
| 155 | CopyDataToITensorHandle(inputHandle.get(), inputData); |
| 156 | |
| 157 | workload->PostAllocationConfigure(); |
| 158 | workload->Execute(); |
| 159 | |
| 160 | outputData.resize(outputTensorInfo.GetNumElements()); |
| 161 | CopyDataFromITensorHandle(&outputData[0], outputHandle.get()); |
| 162 | inputTensorInfo = outputTensorInfo; |
| 163 | } |
| 164 | |
| 165 | // |
| 166 | // Permute the input tensors so we can do a supported concatenation. |
| 167 | // Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions |
| 168 | // at the front. Finally this function tells what the output shape |
| 169 | // of the permuted concatenated tensor is going to be. |
| 170 | // |
| 171 | template<typename T> void PermuteInputsForConcat( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 172 | IWorkloadFactory& workloadFactory, |
| 173 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 174 | std::vector<TensorInfo> & inputTensorInfos, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 175 | std::vector<T *> & inputData, |
| 176 | std::vector<std::vector<T>> & inputDataStorage, |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 177 | PermutationVector & permuteVector, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 178 | unsigned int & concatDim, |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 179 | TensorInfo & outputTensorInfo) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 180 | { |
| 181 | BOOST_ASSERT_MSG(inputTensorInfos.size() > 1, |
| 182 | "Expecting more than one tensor to be concatenated here"); |
| 183 | |
| 184 | unsigned int numDims = 0; |
| 185 | unsigned int nthInput = 0; |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 186 | const PermutationVector identity({0, 1, 2}); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 187 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 188 | std::pair<PermutationVector, PermutationVector> permutations = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 189 | std::make_pair(identity, identity); |
| 190 | |
| 191 | inputDataStorage.resize(inputData.size()); |
| 192 | |
| 193 | for (auto && tensorInfo : inputTensorInfos) |
| 194 | { |
| 195 | if (numDims == 0) |
| 196 | { |
| 197 | numDims = tensorInfo.GetShape().GetNumDimensions(); |
| 198 | Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); |
| 199 | |
| 200 | // Store the reverese permutation. |
| 201 | permuteVector = permutations.second; |
| 202 | BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), |
| 203 | "Test logic error, we don't need permutation, so we shouldn't arrive here"); |
| 204 | } |
| 205 | else |
| 206 | { |
| 207 | BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(), |
| 208 | "All inputs must have the same number of dimensions"); |
| 209 | } |
| 210 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 211 | TensorInfo newTensorInfo = tensorInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 212 | newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); |
| 213 | |
| 214 | PermuteTensorData<T>(workloadFactory, |
| 215 | memoryManager, |
| 216 | permutations.first, |
| 217 | newTensorInfo, |
| 218 | inputData[nthInput], |
| 219 | inputDataStorage[nthInput]); |
| 220 | |
| 221 | inputData[nthInput] = inputDataStorage[nthInput].data(); |
| 222 | inputTensorInfos[nthInput] = newTensorInfo; |
| 223 | |
| 224 | ++nthInput; |
| 225 | } |
| 226 | |
| 227 | outputTensorInfo.SetShape( |
| 228 | armnnUtils::Permuted( |
| 229 | ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()), |
| 230 | permutations.first)); |
| 231 | } |
| 232 | |
| 233 | // |
| 234 | // This is the pair of PermuteInputsForConcat(...) which permutes back |
| 235 | // the output of the concatenation so we can check it against an expected |
| 236 | // output. |
| 237 | // |
| 238 | template <typename T> void PermuteOutputForConcat( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 239 | IWorkloadFactory& workloadFactory, |
| 240 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 241 | const TensorInfo & tensorInfo, |
| 242 | const PermutationVector & permuteVector, |
| 243 | std::unique_ptr<ITensorHandle> && inputDataHandle, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 244 | T * data) |
| 245 | { |
| 246 | BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); |
| 247 | if (data == nullptr) |
| 248 | { |
| 249 | // Nullptr is an error in the test. By returning without doing the permutation |
| 250 | // I expect the caller to fail the test. It still makes sense to report this as |
| 251 | // an assert for Debug builds. |
| 252 | return; |
| 253 | } |
| 254 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 255 | TensorInfo resultTensorInfo = tensorInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 256 | std::vector<T> inputData(tensorInfo.GetNumElements()); |
| 257 | std::vector<T> outputData; |
| 258 | |
| 259 | CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); |
| 260 | |
| 261 | PermuteTensorData<T>(workloadFactory, |
| 262 | memoryManager, |
| 263 | permuteVector, |
| 264 | resultTensorInfo, |
| 265 | &inputData[0], |
| 266 | outputData); |
| 267 | |
| 268 | ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); |
| 269 | } |
| 270 | |
| 271 | template<typename T> void Concatenate( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 272 | IWorkloadFactory& workloadFactory, |
| 273 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 274 | std::initializer_list<const TensorInfo> inputTensorInfosOrig, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 275 | std::initializer_list<T *> inputsOrig, |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 276 | const TensorInfo& outputTensorInfoOrig, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 277 | T * output, |
| 278 | unsigned int concatDim, |
| 279 | bool useSubtensor) |
| 280 | { |
| 281 | BOOST_ASSERT_MSG(output != nullptr, "output must not be null"); |
| 282 | if (output == nullptr) |
| 283 | { |
| 284 | // Nullptr is an error in the test. By returning without doing the permutation |
| 285 | // I expect the caller to fail the test. It still makes sense to report this as |
| 286 | // an assert for Debug builds. |
| 287 | return; |
| 288 | } |
| 289 | |
| 290 | // Saves a copy of the parameters which we might need to change. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 291 | std::vector<TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 292 | std::vector<T *> inputs = inputsOrig; |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 293 | TensorInfo outputTensorInfo = outputTensorInfoOrig; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 294 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 295 | PermutationVector permuteVector{0, 1, 2}; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 296 | |
| 297 | // Holds and automatically releases memory for the reshaped input data. |
| 298 | std::vector<std::vector<T>> tmpInputDataStorage; |
| 299 | |
| 300 | const size_t inputCount = inputTensorInfos.size(); |
| 301 | |
| 302 | bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim); |
| 303 | |
| 304 | if (needPermuteForConcat) |
| 305 | { |
| 306 | // |
| 307 | // We need to permute the inputs, because concatenation along |
| 308 | // the requested axis is not supported. |
| 309 | // |
| 310 | PermuteInputsForConcat<T>(workloadFactory, |
| 311 | memoryManager, |
| 312 | inputTensorInfos, |
| 313 | inputs, |
| 314 | tmpInputDataStorage, |
| 315 | permuteVector, |
| 316 | concatDim, |
| 317 | outputTensorInfo); |
| 318 | } |
| 319 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 320 | WorkloadInfo workloadInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 321 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 322 | std::vector<std::unique_ptr<ITensorHandle>> inputHandles; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 323 | inputHandles.reserve(inputCount); |
| 324 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 325 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 326 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 327 | ConcatQueueDescriptor queueDescriptor; |
| 328 | OriginsDescriptor viewsDescriptor = CreateDescriptorForConcat(inputTensorInfos, concatDim); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 329 | queueDescriptor.m_Parameters = viewsDescriptor; |
| 330 | |
| 331 | if (useSubtensor) |
| 332 | { |
| 333 | queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews()); |
| 334 | for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i) |
| 335 | { |
| 336 | queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i), |
| 337 | viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); |
| 338 | } |
| 339 | |
| 340 | outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 341 | |
| 342 | const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 343 | for (unsigned int i = 0; i < inputCount; ++i) |
| 344 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 345 | const TensorInfo& inputTensorInfo = inputTensorInfos[i]; |
| 346 | std::unique_ptr<ITensorHandle> inputHandle = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 347 | subTensorsSupported ? |
| 348 | workloadFactory.CreateSubTensorHandle(*outputHandle, |
| 349 | inputTensorInfo.GetShape(), |
| 350 | queueDescriptor.m_ViewOrigins[i].m_Origin.data()) : |
| 351 | workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 352 | |
| 353 | inputHandles.emplace_back(std::move(inputHandle)); |
| 354 | } |
| 355 | |
| 356 | } |
| 357 | else |
| 358 | { |
| 359 | for (unsigned int i = 0; i < inputCount; ++i) |
| 360 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 361 | std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfos[i]); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 362 | inputHandles.emplace_back(std::move(inputHandle)); |
| 363 | } |
| 364 | } |
| 365 | |
| 366 | for (unsigned int i = 0; i < inputCount; ++i) |
| 367 | { |
| 368 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get()); |
| 369 | } |
| 370 | |
| 371 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 372 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 373 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(queueDescriptor, workloadInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 374 | |
| 375 | for (auto& inputHandle : inputHandles) |
| 376 | { |
| 377 | inputHandle->Allocate(); |
| 378 | } |
| 379 | |
| 380 | outputHandle->Allocate(); |
| 381 | |
| 382 | unsigned int nextInputId = 0; |
| 383 | for (auto& inputHandle : inputHandles) |
| 384 | { |
| 385 | CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]); |
| 386 | ++nextInputId; |
| 387 | } |
| 388 | |
| 389 | workload->PostAllocationConfigure(); |
| 390 | workload->Execute(); |
| 391 | |
| 392 | if (needPermuteForConcat) |
| 393 | { |
| 394 | PermuteOutputForConcat<T>(workloadFactory, |
| 395 | memoryManager, |
| 396 | outputTensorInfo, |
| 397 | permuteVector, |
| 398 | std::move(outputHandle), |
| 399 | output); |
| 400 | } |
| 401 | else |
| 402 | { |
| 403 | CopyDataFromITensorHandle(output, outputHandle.get()); |
| 404 | } |
| 405 | } |
| 406 | |
| 407 | // |
| 408 | // Implementation templates |
| 409 | // |
| 410 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 411 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 412 | LayerTestResult<T, 1> Concat1dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 413 | IWorkloadFactory& workloadFactory, |
| 414 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 415 | float qScale, |
| 416 | int32_t qOffset) |
| 417 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 418 | TensorInfo inputTensorInfo({ 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 419 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 420 | auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 1.0f, 2.0f, 3.0f }, qScale, qOffset)); |
| 421 | auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 4.0f, 5.0f, 6.0f }, qScale, qOffset)); |
| 422 | auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 7.0f, 8.0f, 9.0f }, qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 423 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 424 | TensorInfo outputTensorInfo({ 9 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 425 | |
| 426 | LayerTestResult<T, 1> result(outputTensorInfo); |
| 427 | |
| 428 | std::vector<T> output; |
| 429 | output.resize(outputTensorInfo.GetNumElements()); |
| 430 | Concatenate<T>(workloadFactory, memoryManager, |
| 431 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 432 | { input0.data(), input1.data(), input2.data() }, |
| 433 | outputTensorInfo, |
| 434 | output.data(), |
| 435 | 0, |
| 436 | true); |
| 437 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 438 | result.output = MakeTensor<T, 1>(outputTensorInfo, output); |
| 439 | result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>( |
| 440 | { |
| 441 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f |
| 442 | }, |
| 443 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 444 | |
| 445 | return result; |
| 446 | } |
| 447 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 448 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 449 | LayerTestResult<T, 2> Concat2dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 450 | IWorkloadFactory& workloadFactory, |
| 451 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 452 | const TensorInfo& outputTensorInfo, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 453 | unsigned int dimension, |
| 454 | const float qScale, |
| 455 | const int32_t qOffset) |
| 456 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 457 | TensorInfo inputTensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 458 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 459 | auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( |
| 460 | { |
| 461 | // Batch 0 |
| 462 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 463 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 464 | // Batch 1 |
| 465 | 10.0f, 11.0f, 12.0f, |
| 466 | }, |
| 467 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 468 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 469 | auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( |
| 470 | { |
| 471 | // Batch 0 |
| 472 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 473 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 474 | // Batch 1 |
| 475 | 13.0f, 14.0f, 15.0f, |
| 476 | }, |
| 477 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 478 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 479 | auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( |
| 480 | { |
| 481 | // Batch 0 |
| 482 | 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 483 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 484 | // Batch 1 |
| 485 | 16.0f, 17.0f, 18.0f, |
| 486 | }, |
| 487 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 488 | |
| 489 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 490 | |
| 491 | std::vector<T> output; |
| 492 | output.resize(outputTensorInfo.GetNumElements()); |
| 493 | Concatenate<T>(workloadFactory, memoryManager, |
| 494 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 495 | { input0.data(), input1.data(), input2.data() }, |
| 496 | outputTensorInfo, |
| 497 | output.data(), |
| 498 | dimension, |
| 499 | true); |
| 500 | |
| 501 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 502 | return result; |
| 503 | } |
| 504 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 505 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 506 | LayerTestResult<T, 2> Concat2dDim0TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 507 | IWorkloadFactory& workloadFactory, |
| 508 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 509 | float qScale, |
| 510 | int32_t qOffset) |
| 511 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 512 | TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 513 | |
| 514 | LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( |
| 515 | workloadFactory, memoryManager, outputTensorInfo, 0, qScale, qOffset); |
| 516 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 517 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 518 | { |
| 519 | // Batch 0 |
| 520 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 521 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 522 | // Batch 1 |
| 523 | 10.0f, 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 524 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 525 | // Batch 2 |
| 526 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 527 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 528 | // Batch 3 |
| 529 | 13.0f, 14.0f, 15.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 530 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 531 | // Batch 4 |
| 532 | 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 533 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 534 | // Batch 5 |
| 535 | 16.0f, 17.0f, 18.0f, |
| 536 | }, |
| 537 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 538 | |
| 539 | return result; |
| 540 | } |
| 541 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 542 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 543 | LayerTestResult<T, 2> Concat2dDim1TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 544 | IWorkloadFactory& workloadFactory, |
| 545 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 546 | float qScale, |
| 547 | int32_t qOffset) |
| 548 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 549 | TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 550 | |
| 551 | LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( |
| 552 | workloadFactory, memoryManager, outputTensorInfo, 1, qScale, qOffset); |
| 553 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 554 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 555 | { |
| 556 | // Batch 0 |
| 557 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 558 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 559 | // Batch 1 |
| 560 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f |
| 561 | }, |
| 562 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 563 | |
| 564 | return result; |
| 565 | } |
| 566 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 567 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 568 | LayerTestResult<T, 2> Concat2dDim0DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 569 | IWorkloadFactory& workloadFactory, |
| 570 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 571 | float qScale, |
| 572 | int32_t qOffset) |
| 573 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 574 | TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
| 575 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>( |
| 576 | { |
| 577 | // Batch 0 |
| 578 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 579 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 580 | // Batch 1 |
| 581 | 10.0f, 11.0f, 12.0f, |
| 582 | }, |
| 583 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 584 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 585 | TensorInfo input1TensorInfo({ 3, 3 }, ArmnnType, qScale, qOffset); |
| 586 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>( |
| 587 | { |
| 588 | // Batch 0 |
| 589 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 590 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 591 | // Batch 1 |
| 592 | 13.0f, 14.0f, 15.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 593 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 594 | // Batch 0 |
| 595 | 7.0f, 8.0f, 9.0f, |
| 596 | }, |
| 597 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 598 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 599 | TensorInfo input2TensorInfo({ 1, 3 }, ArmnnType, qScale, qOffset); |
| 600 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>( |
| 601 | { |
| 602 | // Batch 1 |
| 603 | 16.0f, 17.0f, 18.0f, |
| 604 | }, |
| 605 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 606 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 607 | TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 608 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 609 | |
| 610 | std::vector<T> output; |
| 611 | output.resize(outputTensorInfo.GetNumElements()); |
| 612 | Concatenate<T>(workloadFactory, memoryManager, |
| 613 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 614 | { input0.data(), input1.data(), input2.data() }, |
| 615 | outputTensorInfo, |
| 616 | output.data(), |
| 617 | 0, |
| 618 | true); |
| 619 | |
| 620 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 621 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 622 | { |
| 623 | // Batch 0 |
| 624 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 625 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 626 | // Batch 1 |
| 627 | 10.0f, 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 628 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 629 | // Batch 2 |
| 630 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 631 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 632 | // Batch 3 |
| 633 | 13.0f, 14.0f, 15.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 634 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 635 | // Batch 4 |
| 636 | 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 637 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 638 | // Batch 5 |
| 639 | 16.0f, 17.0f, 18.0f, |
| 640 | }, |
| 641 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 642 | |
| 643 | return result; |
| 644 | } |
| 645 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 646 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 647 | LayerTestResult<T, 2> Concat2dDim1DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 648 | IWorkloadFactory& workloadFactory, |
| 649 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 650 | float qScale, |
| 651 | int32_t qOffset) |
| 652 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 653 | TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
| 654 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>( |
| 655 | { |
| 656 | // Batch 0 |
| 657 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 658 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 659 | // Batch 1 |
| 660 | 10.0f, 11.0f, 12.0f, |
| 661 | }, |
| 662 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 663 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 664 | TensorInfo input1TensorInfo({ 2, 5 }, ArmnnType, qScale, qOffset); |
| 665 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>( |
| 666 | { |
| 667 | // Batch 0 |
| 668 | 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 669 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 670 | // Batch 1 |
| 671 | 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, |
| 672 | }, |
| 673 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 674 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 675 | TensorInfo input2TensorInfo({ 2, 1 }, ArmnnType, qScale, qOffset); |
| 676 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>( |
| 677 | { |
| 678 | // Batch 0 |
| 679 | 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 680 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 681 | // Batch 1 |
| 682 | 18.0f |
| 683 | }, |
| 684 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 685 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 686 | TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 687 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 688 | |
| 689 | std::vector<T> output; |
| 690 | output.resize(outputTensorInfo.GetNumElements()); |
| 691 | Concatenate<T>(workloadFactory, memoryManager, |
| 692 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 693 | { input0.data(), input1.data(), input2.data() }, |
| 694 | outputTensorInfo, |
| 695 | output.data(), |
| 696 | 1, |
| 697 | true); |
| 698 | |
| 699 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 700 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 701 | { |
| 702 | // Batch 0 |
| 703 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 704 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 705 | // Batch 1 |
| 706 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, |
| 707 | }, |
| 708 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 709 | |
| 710 | return result; |
| 711 | } |
| 712 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 713 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 714 | LayerTestResult<T, 3> Concat3dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 715 | IWorkloadFactory& workloadFactory, |
| 716 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 717 | const TensorInfo& outputTensorInfo, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 718 | unsigned int dimension, |
| 719 | bool useSubtensor, |
| 720 | float qScale, |
| 721 | int32_t qOffset) |
| 722 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 723 | TensorInfo inputTensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 724 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 725 | auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( |
| 726 | { |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 727 | // Batch 0, Channel 0 |
| 728 | 1.0f, 2.0f, |
| 729 | |
| 730 | // Batch 0, Channel 1 |
| 731 | 3.0f, 4.0f, |
| 732 | |
| 733 | // Batch 0, Channel 2 |
| 734 | 5.0f, 6.0f, |
| 735 | |
| 736 | // Batch 1, Channel 0 |
| 737 | 19.0f, 20.0f, |
| 738 | |
| 739 | // Batch 1, Channel 1 |
| 740 | 21.0f, 22.0f, |
| 741 | |
| 742 | // Batch 1, Channel 2 |
| 743 | 23.0f, 24.0f |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 744 | }, |
| 745 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 746 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 747 | auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( |
| 748 | { |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 749 | // Batch 0, Channel 0 |
| 750 | 7.0f, 8.0f, |
| 751 | |
| 752 | // Batch 0, Channel 1 |
| 753 | 9.0f, 10.0f, |
| 754 | |
| 755 | // Batch 0, Channel 2 |
| 756 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 757 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 758 | // Batch 1, Channel 0 |
| 759 | 25.0f, 26.0f, |
| 760 | |
| 761 | // Batch 1, Channel 1 |
| 762 | 27.0f, 28.0f, |
| 763 | |
| 764 | // Batch 1, Channel 2 |
| 765 | 29.0f, 30.0f |
| 766 | }, |
| 767 | qScale, qOffset)); |
| 768 | |
| 769 | auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( |
| 770 | { |
| 771 | // Batch 0, Channel 0 |
| 772 | 13.0f, 14.0f, |
| 773 | |
| 774 | // Batch 0, Channel 1 |
| 775 | 15.0f, 16.0f, |
| 776 | |
| 777 | // Batch 0, Channel 2 |
| 778 | 17.0f, 18.0f, |
| 779 | |
| 780 | // Batch 1, Channel 0 |
| 781 | 31.0f, 32.0f, |
| 782 | |
| 783 | // Batch 1, Channel 1 |
| 784 | 33.0f, 34.0f, |
| 785 | |
| 786 | // Batch 1, Channel 2 |
| 787 | 35.0f, 36.0f |
| 788 | }, |
| 789 | qScale, qOffset)); |
| 790 | |
| 791 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 792 | |
| 793 | std::vector<T> output; |
| 794 | output.resize(outputTensorInfo.GetNumElements()); |
| 795 | Concatenate<T>(workloadFactory, memoryManager, |
| 796 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 797 | { input0.data(), input1.data(), input2.data() }, |
| 798 | outputTensorInfo, |
| 799 | output.data(), |
| 800 | dimension, |
| 801 | useSubtensor); |
| 802 | |
| 803 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 804 | return result; |
| 805 | } |
| 806 | |
| 807 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 808 | LayerTestResult<T, 3> Concat3dDim0TestImpl( |
| 809 | IWorkloadFactory& workloadFactory, |
| 810 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 811 | float qScale, |
| 812 | int32_t qOffset) |
| 813 | { |
| 814 | TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType, qScale, qOffset); |
| 815 | |
| 816 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 817 | workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); |
| 818 | |
| 819 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 820 | { |
| 821 | // Batch 0, Channel 0 |
| 822 | 1.0f, 2.0f, |
| 823 | |
| 824 | // Batch 0, Channel 1 |
| 825 | 3.0f, 4.0f, |
| 826 | |
| 827 | // Batch 0, Channel 2 |
| 828 | 5.0f, 6.0f, |
| 829 | |
| 830 | // Batch 1, Channel 0 |
| 831 | 19.0f, 20.0f, |
| 832 | |
| 833 | // Batch 1, Channel 1 |
| 834 | 21.0f, 22.0f, |
| 835 | |
| 836 | // Batch 1, Channel 2 |
| 837 | 23.0f, 24.0f, |
| 838 | |
| 839 | // Batch 2, Channel 0 |
| 840 | 7.0f, 8.0f, |
| 841 | |
| 842 | // Batch 2, Channel 1 |
| 843 | 9.0f, 10.0f, |
| 844 | |
| 845 | // Batch 2, Channel 2 |
| 846 | 11.0f, 12.0f, |
| 847 | |
| 848 | // Batch 3, Channel 0 |
| 849 | 25.0f, 26.0f, |
| 850 | |
| 851 | // Batch 3, Channel 1 |
| 852 | 27.0f, 28.0f, |
| 853 | |
| 854 | // Batch 3, Channel 2 |
| 855 | 29.0f, 30.0f, |
| 856 | |
| 857 | // Batch 4, Channel 0 |
| 858 | 13.0f, 14.0f, |
| 859 | |
| 860 | // Batch 4, Channel 1 |
| 861 | 15.0f, 16.0f, |
| 862 | |
| 863 | // Batch 4, Channel 2 |
| 864 | 17.0f, 18.0f, |
| 865 | |
| 866 | // Batch 5, Channel 0 |
| 867 | 31.0f, 32.0f, |
| 868 | |
| 869 | // Batch 5, Channel 1 |
| 870 | 33.0f, 34.0f, |
| 871 | |
| 872 | // Batch 5, Channel 2 |
| 873 | 35.0f, 36.0f |
| 874 | }, |
| 875 | qScale, qOffset)); |
| 876 | |
| 877 | return result; |
| 878 | } |
| 879 | |
| 880 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 881 | LayerTestResult<T, 3> Concat3dDim1TestImpl( |
| 882 | IWorkloadFactory& workloadFactory, |
| 883 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 884 | float qScale, |
| 885 | int32_t qOffset) |
| 886 | { |
| 887 | TensorInfo outputTensorInfo({ 2, 9, 2 }, ArmnnType, qScale, qOffset); |
| 888 | |
| 889 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 890 | workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); |
| 891 | |
| 892 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 893 | { |
| 894 | // Batch 0, Channel 0 |
| 895 | 1.0f, 2.0f, |
| 896 | |
| 897 | // Batch 0, Channel 1 |
| 898 | 3.0f, 4.0f, |
| 899 | |
| 900 | // Batch 0, Channel 2 |
| 901 | 5.0f, 6.0f, |
| 902 | |
| 903 | // Batch 0, Channel 3 |
| 904 | 7.0f, 8.0f, |
| 905 | |
| 906 | // Batch 0, Channel 4 |
| 907 | 9.0f, 10.0f, |
| 908 | |
| 909 | // Batch 0, Channel 5 |
| 910 | 11.0f, 12.0f, |
| 911 | |
| 912 | // Batch 0, Channel 6 |
| 913 | 13.0f, 14.0f, |
| 914 | |
| 915 | // Batch 0, Channel 7 |
| 916 | 15.0f, 16.0f, |
| 917 | |
| 918 | // Batch 0, Channel 8 |
| 919 | 17.0f, 18.0f, |
| 920 | |
| 921 | // Batch 1, Channel 0 |
| 922 | 19.0f, 20.0f, |
| 923 | |
| 924 | // Batch 1, Channel 1 |
| 925 | 21.0f, 22.0f, |
| 926 | |
| 927 | // Batch 1, Channel 2 |
| 928 | 23.0f, 24.0f, |
| 929 | |
| 930 | // Batch 1, Channel 3 |
| 931 | 25.0f, 26.0f, |
| 932 | |
| 933 | // Batch 1, Channel 4 |
| 934 | 27.0f, 28.0f, |
| 935 | |
| 936 | // Batch 1, Channel 5 |
| 937 | 29.0f, 30.0f, |
| 938 | |
| 939 | // Batch 1, Channel 6 |
| 940 | 31.0f, 32.0f, |
| 941 | |
| 942 | // Batch 1, Channel 7 |
| 943 | 33.0f, 34.0f, |
| 944 | |
| 945 | // Batch 1, Channel 8 |
| 946 | 35.0f, 36.0f |
| 947 | }, |
| 948 | qScale, qOffset)); |
| 949 | |
| 950 | return result; |
| 951 | } |
| 952 | |
| 953 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 954 | LayerTestResult<T, 3> Concat3dDim2TestImpl( |
| 955 | IWorkloadFactory& workloadFactory, |
| 956 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 957 | bool useSubtensor, |
| 958 | float qScale, |
| 959 | int32_t qOffset) |
| 960 | { |
| 961 | TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); |
| 962 | |
| 963 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 964 | workloadFactory, memoryManager, outputTensorInfo, 2, useSubtensor, qScale, qOffset); |
| 965 | |
| 966 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 967 | { |
| 968 | // Batch 0, Channel 0 |
| 969 | 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f, |
| 970 | |
| 971 | // Batch 0, Channel 1 |
| 972 | 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f, |
| 973 | |
| 974 | // Batch 0, Channel 2 |
| 975 | 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f, |
| 976 | |
| 977 | // Batch 1, Channel 0 |
| 978 | 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f, |
| 979 | |
| 980 | // Batch 1, Channel 1 |
| 981 | 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f, |
| 982 | |
| 983 | // Batch 1, Channel 2 |
| 984 | 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, |
| 985 | }, |
| 986 | qScale, qOffset)); |
| 987 | |
| 988 | return result; |
| 989 | } |
| 990 | |
| 991 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 992 | LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( |
| 993 | IWorkloadFactory& workloadFactory, |
| 994 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 995 | float qScale, |
| 996 | int32_t qOffset) |
| 997 | { |
| 998 | TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); |
| 999 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( |
| 1000 | { |
| 1001 | // Batch 0, Channel 0 |
| 1002 | 1.0f, 2.0f, |
| 1003 | |
| 1004 | // Batch 0, Channel 1 |
| 1005 | 3.0f, 4.0f, |
| 1006 | |
| 1007 | // Batch 0, Channel 2 |
| 1008 | 5.0f, 6.0f, |
| 1009 | |
| 1010 | // Batch 1, Channel 0 |
| 1011 | 19.0f, 20.0f, |
| 1012 | |
| 1013 | // Batch 1, Channel 1 |
| 1014 | 21.0f, 22.0f, |
| 1015 | |
| 1016 | // Batch 1, Channel 2 |
| 1017 | 23.0f, 24.0f |
| 1018 | }, |
| 1019 | qScale, qOffset)); |
| 1020 | |
| 1021 | TensorInfo input1TensorInfo({ 1, 3, 2 }, ArmnnType); |
| 1022 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( |
| 1023 | { |
| 1024 | // Batch 0, Channel 0 |
| 1025 | 7.0f, 8.0f, |
| 1026 | |
| 1027 | // Batch 0, Channel 1 |
| 1028 | 9.0f, 10.0f, |
| 1029 | |
| 1030 | // Batch 0, Channel 2 |
| 1031 | 11.0f, 12.0f, |
| 1032 | }, |
| 1033 | qScale, qOffset)); |
| 1034 | |
| 1035 | TensorInfo input2TensorInfo({ 3, 3, 2 }, ArmnnType); |
| 1036 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( |
| 1037 | { |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1038 | // Batch 0, Channel 0 |
| 1039 | 25.0f, 26.0f, |
| 1040 | |
| 1041 | // Batch 0, Channel 1 |
| 1042 | 27.0f, 28.0f, |
| 1043 | |
| 1044 | // Batch 0, Channel 2 |
| 1045 | 29.0f, 30.0f, |
| 1046 | |
| 1047 | // Batch 1, Channel 0 |
| 1048 | 13.0f, 14.0f, |
| 1049 | |
| 1050 | // Batch 1, Channel 1 |
| 1051 | 15.0f, 16.0f, |
| 1052 | |
| 1053 | // Batch 1, Channel 2 |
| 1054 | 17.0f, 18.0f, |
| 1055 | |
| 1056 | // Batch 2, Channel 0 |
| 1057 | 31.0f, 32.0f, |
| 1058 | |
| 1059 | // Batch 2, Channel 1 |
| 1060 | 33.0f, 34.0f, |
| 1061 | |
| 1062 | // Batch 2, Channel 2 |
| 1063 | 35.0f, 36.0f |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1064 | }, |
| 1065 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1066 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1067 | TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1068 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1069 | |
| 1070 | std::vector<T> output; |
| 1071 | output.resize(outputTensorInfo.GetNumElements()); |
| 1072 | Concatenate<T>(workloadFactory, memoryManager, |
| 1073 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1074 | { input0.data(), input1.data(), input2.data() }, |
| 1075 | outputTensorInfo, |
| 1076 | output.data(), |
| 1077 | 0, |
| 1078 | true); |
| 1079 | |
| 1080 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1081 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 1082 | { |
| 1083 | // Batch 0, Channel 0 |
| 1084 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1085 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1086 | // Batch 0, Channel 1 |
| 1087 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1088 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1089 | // Batch 0, Channel 2 |
| 1090 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1091 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1092 | // Batch 1, Channel 0 |
| 1093 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1094 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1095 | // Batch 1, Channel 1 |
| 1096 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1097 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1098 | // Batch 1, Channel 2 |
| 1099 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1100 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1101 | // Batch 2, Channel 0 |
| 1102 | 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1103 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1104 | // Batch 2, Channel 1 |
| 1105 | 9.0f, 10.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1106 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1107 | // Batch 2, Channel 2 |
| 1108 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1109 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1110 | // Batch 3, Channel 0 |
| 1111 | 25.0f, 26.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1112 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1113 | // Batch 3, Channel 1 |
| 1114 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1115 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1116 | // Batch 3, Channel 2 |
| 1117 | 29.0f, 30.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1118 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1119 | // Batch 4, Channel 0 |
| 1120 | 13.0f, 14.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1121 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1122 | // Batch 4, Channel 1 |
| 1123 | 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1124 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1125 | // Batch 4, Channel 2 |
| 1126 | 17.0f, 18.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1127 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1128 | // Batch 5, Channel 0 |
| 1129 | 31.0f, 32.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1130 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1131 | // Batch 5, Channel 1 |
| 1132 | 33.0f, 34.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1133 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1134 | // Batch 5, Channel 2 |
| 1135 | 35.0f, 36.0f |
| 1136 | }, |
| 1137 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1138 | |
| 1139 | return result; |
| 1140 | } |
| 1141 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1142 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1143 | LayerTestResult<T, 3> Concat3dDim1DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1144 | IWorkloadFactory& workloadFactory, |
| 1145 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1146 | float qScale, |
| 1147 | int32_t qOffset) |
| 1148 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1149 | TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1150 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( |
| 1151 | { |
| 1152 | // Batch 0, Channel 0 |
| 1153 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1154 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1155 | // Batch 0, Channel 1 |
| 1156 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1157 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1158 | // Batch 0, Channel 2 |
| 1159 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1160 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1161 | // Batch 1, Channel 0 |
| 1162 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1163 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1164 | // Batch 1, Channel 1 |
| 1165 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1166 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1167 | // Batch 1, Channel 2 |
| 1168 | 23.0f, 24.0f |
| 1169 | }, |
| 1170 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1171 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1172 | TensorInfo input1TensorInfo({ 2, 4, 2 }, ArmnnType, qScale, qOffset); |
| 1173 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( |
| 1174 | { |
| 1175 | // Batch 0, Channel 0 |
| 1176 | 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1177 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1178 | // Batch 0, Channel 1 |
| 1179 | 9.0f, 10.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1180 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1181 | // Batch 0, Channel 2 |
| 1182 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1183 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1184 | // Batch 0, Channel 3 |
| 1185 | 25.0f, 26.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1186 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1187 | // Batch 1, Channel 0 |
| 1188 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1189 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1190 | // Batch 1, Channel 1 |
| 1191 | 29.0f, 30.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1192 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1193 | // Batch 1, Channel 2 |
| 1194 | 13.0f, 14.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1195 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1196 | // Batch 1, Channel 3 |
| 1197 | 15.0f, 16.0f, |
| 1198 | }, |
| 1199 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1200 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1201 | TensorInfo input2TensorInfo({ 2, 1, 2 }, ArmnnType, qScale, qOffset); |
| 1202 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( |
| 1203 | { |
| 1204 | // Batch 0, Channel 0 |
| 1205 | 17.0f, 18.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1206 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1207 | // Batch 1, Channel 0 |
| 1208 | 31.0f, 32.0f, |
| 1209 | }, |
| 1210 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1211 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1212 | TensorInfo outputTensorInfo({ 2, 8, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1213 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1214 | |
| 1215 | std::vector<T> output; |
| 1216 | output.resize(outputTensorInfo.GetNumElements()); |
| 1217 | Concatenate<T>(workloadFactory, memoryManager, |
| 1218 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1219 | { input0.data(), input1.data(), input2.data() }, |
| 1220 | outputTensorInfo, |
| 1221 | output.data(), |
| 1222 | 1, |
| 1223 | true); |
| 1224 | |
| 1225 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1226 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 1227 | { |
| 1228 | // Batch 0, Channel 0 |
| 1229 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1230 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1231 | // Batch 0, Channel 1 |
| 1232 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1233 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1234 | // Batch 0, Channel 2 |
| 1235 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1236 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1237 | // Batch 0, Channel 3 |
| 1238 | 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1239 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1240 | // Batch 0, Channel 4 |
| 1241 | 9.0f, 10.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1242 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1243 | // Batch 0, Channel 5 |
| 1244 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1245 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1246 | // Batch 0, Channel 6 |
| 1247 | 25.0f, 26.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1248 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1249 | // Batch 0, Channel 7 |
| 1250 | 17.0f, 18.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1251 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1252 | // Batch 1, Channel 0 |
| 1253 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1254 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1255 | // Batch 1, Channel 1 |
| 1256 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1257 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1258 | // Batch 1, Channel 2 |
| 1259 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1260 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1261 | // Batch 1, Channel 3 |
| 1262 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1263 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1264 | // Batch 1, Channel 4 |
| 1265 | 29.0f, 30.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1266 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1267 | // Batch 1, Channel 5 |
| 1268 | 13.0f, 14.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1269 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1270 | // Batch 1, Channel 6 |
| 1271 | 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1272 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1273 | // Batch 1, Channel 7 |
| 1274 | 31.0f, 32.0f, |
| 1275 | }, |
| 1276 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1277 | |
| 1278 | return result; |
| 1279 | } |
| 1280 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1281 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1282 | LayerTestResult<T, 3> Concat3dDim2DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1283 | IWorkloadFactory& workloadFactory, |
| 1284 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1285 | bool useSubtensor, |
| 1286 | float qScale, |
| 1287 | int32_t qOffset) |
| 1288 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1289 | TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1290 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( |
| 1291 | { |
| 1292 | // Batch 0, Channel 0 |
| 1293 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1294 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1295 | // Batch 0, Channel 1 |
| 1296 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1297 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1298 | // Batch 0, Channel 2 |
| 1299 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1300 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1301 | // Batch 1, Channel 0 |
| 1302 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1303 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1304 | // Batch 1, Channel 1 |
| 1305 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1306 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1307 | // Batch 1, Channel 2 |
| 1308 | 23.0f, 24.0f |
| 1309 | }, |
| 1310 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1311 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1312 | TensorInfo input1TensorInfo({ 2, 3, 1 }, ArmnnType, qScale, qOffset); |
| 1313 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( |
| 1314 | { |
| 1315 | // Batch 0, Channel 0 |
| 1316 | 7.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1317 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1318 | // Batch 0, Channel 1 |
| 1319 | 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1320 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1321 | // Batch 0, Channel 2 |
| 1322 | 11.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1323 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1324 | // Batch 1, Channel 0 |
| 1325 | 25.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1326 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1327 | // Batch 1, Channel 1 |
| 1328 | 27.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1329 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1330 | // Batch 1, Channel 2 |
| 1331 | 29.0f |
| 1332 | }, |
| 1333 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1334 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1335 | TensorInfo input2TensorInfo({ 2, 3, 3 }, ArmnnType, qScale, qOffset); |
| 1336 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( |
| 1337 | { |
| 1338 | // Batch 0, Channel 0 |
| 1339 | 13.0f, 14.0f, 50.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1340 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1341 | // Batch 0, Channel 1 |
| 1342 | 15.0f, 16.0f, 51.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1343 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1344 | // Batch 0, Channel 2 |
| 1345 | 17.0f, 18.0f, 52.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1346 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1347 | // Batch 1, Channel 0 |
| 1348 | 31.0f, 32.0f, 53.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1349 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1350 | // Batch 1, Channel 1 |
| 1351 | 33.0f, 34.0f, 54.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1352 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1353 | // Batch 1, Channel 2 |
| 1354 | 35.0f, 36.0f, 55.0f, |
| 1355 | }, |
| 1356 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1357 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1358 | TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1359 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1360 | |
| 1361 | std::vector<T> output; |
| 1362 | output.resize(outputTensorInfo.GetNumElements()); |
| 1363 | Concatenate<T>(workloadFactory, memoryManager, |
| 1364 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1365 | { input0.data(), input1.data(), input2.data() }, |
| 1366 | outputTensorInfo, |
| 1367 | output.data(), |
| 1368 | 2, |
| 1369 | useSubtensor); |
| 1370 | |
| 1371 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1372 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 1373 | { |
| 1374 | // Batch 0, Channel 0 |
| 1375 | 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1376 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1377 | // Batch 0, Channel 1 |
| 1378 | 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1379 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1380 | // Batch 0, Channel 2 |
| 1381 | 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1382 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1383 | // Batch 1, Channel 0 |
| 1384 | 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1385 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1386 | // Batch 1, Channel 1 |
| 1387 | 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1388 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1389 | // Batch 1, Channel 2 |
| 1390 | 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, |
| 1391 | }, |
| 1392 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1393 | |
| 1394 | return result; |
| 1395 | } |
| 1396 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1397 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1398 | LayerTestResult<T, 4> Concat4dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1399 | IWorkloadFactory& workloadFactory, |
| 1400 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1401 | const TensorInfo& outputTensorInfo, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1402 | unsigned int dimension, |
| 1403 | bool useSubtensor, |
| 1404 | float qScale, |
| 1405 | int32_t qOffset) |
| 1406 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1407 | TensorInfo inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1408 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1409 | auto input0 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( |
| 1410 | { |
| 1411 | 1.0f, 2.0f, |
| 1412 | 3.0f, 4.0f, |
| 1413 | 5.0f, 6.0f, |
| 1414 | 7.0f, 8.0f, |
| 1415 | 9.0f, 10.0f, |
| 1416 | 11.0f, 12.0f |
| 1417 | }, |
| 1418 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1419 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1420 | auto input1 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( |
| 1421 | { |
| 1422 | 11.0f, 12.0f, |
| 1423 | 13.0f, 14.0f, |
| 1424 | 15.0f, 16.0f, |
| 1425 | 17.0f, 18.0f, |
| 1426 | 19.0f, 20.0f, |
| 1427 | 21.0f, 22.0f |
| 1428 | }, |
| 1429 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1430 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1431 | auto input2 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( |
| 1432 | { |
| 1433 | 21.0f, 22.0f, |
| 1434 | 23.0f, 24.0f, |
| 1435 | 25.0f, 26.0f, |
| 1436 | 27.0f, 28.0f, |
| 1437 | 29.0f, 30.0f, |
| 1438 | 31.0f, 32.0f |
| 1439 | }, |
| 1440 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1441 | |
| 1442 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1443 | |
| 1444 | std::vector<T> output; |
| 1445 | output.resize(outputTensorInfo.GetNumElements()); |
| 1446 | |
| 1447 | Concatenate<T>(workloadFactory, |
| 1448 | memoryManager, |
| 1449 | {inputTensorInfo, inputTensorInfo, inputTensorInfo}, |
| 1450 | {input0.data(), input1.data(), input2.data()}, |
| 1451 | outputTensorInfo, |
| 1452 | output.data(), |
| 1453 | dimension, |
| 1454 | useSubtensor); |
| 1455 | |
| 1456 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1457 | return result; |
| 1458 | } |
| 1459 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1460 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1461 | LayerTestResult<T, 4> Concat4dDim0TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1462 | IWorkloadFactory& workloadFactory, |
| 1463 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1464 | float qScale, |
| 1465 | int32_t qOffset) |
| 1466 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1467 | TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1468 | |
| 1469 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1470 | workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); |
| 1471 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1472 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1473 | { |
| 1474 | 1.0f, 2.0f, |
| 1475 | 3.0f, 4.0f, |
| 1476 | 5.0f, 6.0f, |
| 1477 | 7.0f, 8.0f, |
| 1478 | 9.0f, 10.0f, |
| 1479 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1480 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1481 | 11.0f, 12.0f, |
| 1482 | 13.0f, 14.0f, |
| 1483 | 15.0f, 16.0f, |
| 1484 | 17.0f, 18.0f, |
| 1485 | 19.0f, 20.0f, |
| 1486 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1487 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1488 | 21.0f, 22.0f, |
| 1489 | 23.0f, 24.0f, |
| 1490 | 25.0f, 26.0f, |
| 1491 | 27.0f, 28.0f, |
| 1492 | 29.0f, 30.0f, |
| 1493 | 31.0f, 32.0f |
| 1494 | }, |
| 1495 | qScale, qOffset)); |
| 1496 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1497 | return result; |
| 1498 | } |
| 1499 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1500 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1501 | LayerTestResult<T, 4> Concat4dDim1TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1502 | IWorkloadFactory& workloadFactory, |
| 1503 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1504 | float qScale, |
| 1505 | int32_t qOffset) |
| 1506 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1507 | TensorInfo outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1508 | |
| 1509 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1510 | workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); |
| 1511 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1512 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1513 | { |
| 1514 | 1.0f, 2.0f, |
| 1515 | 3.0f, 4.0f, |
| 1516 | 5.0f, 6.0f, |
| 1517 | 7.0f, 8.0f, |
| 1518 | 9.0f, 10.0f, |
| 1519 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1520 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1521 | 11.0f, 12.0f, |
| 1522 | 13.0f, 14.0f, |
| 1523 | 15.0f, 16.0f, |
| 1524 | 17.0f, 18.0f, |
| 1525 | 19.0f, 20.0f, |
| 1526 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1527 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1528 | 21.0f, 22.0f, |
| 1529 | 23.0f, 24.0f, |
| 1530 | 25.0f, 26.0f, |
| 1531 | 27.0f, 28.0f, |
| 1532 | 29.0f, 30.0f, |
| 1533 | 31.0f, 32.0f |
| 1534 | }, |
| 1535 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1536 | |
| 1537 | return result; |
| 1538 | } |
| 1539 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1540 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1541 | LayerTestResult<T, 4> Concat4dDim2TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1542 | IWorkloadFactory& workloadFactory, |
| 1543 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1544 | float qScale, |
| 1545 | int32_t qOffset) |
| 1546 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1547 | TensorInfo outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1548 | |
| 1549 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1550 | workloadFactory, memoryManager, outputTensorInfo, 2, true, qScale, qOffset); |
| 1551 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1552 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1553 | { |
| 1554 | 1.0f, 2.0f, |
| 1555 | 3.0f, 4.0f, |
| 1556 | 11.0f, 12.0f, |
| 1557 | 13.0f, 14.0f, |
| 1558 | 21.0f, 22.0f, |
| 1559 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1560 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1561 | 5.0f, 6.0f, |
| 1562 | 7.0f, 8.0f, |
| 1563 | 15.0f, 16.0f, |
| 1564 | 17.0f, 18.0f, |
| 1565 | 25.0f, 26.0f, |
| 1566 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1567 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1568 | 9.0f, 10.0f, |
| 1569 | 11.0f, 12.0f, |
| 1570 | 19.0f, 20.0f, |
| 1571 | 21.0f, 22.0f, |
| 1572 | 29.0f, 30.0f, |
| 1573 | 31.0f, 32.0f |
| 1574 | }, |
| 1575 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1576 | |
| 1577 | return result; |
| 1578 | } |
| 1579 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1580 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1581 | LayerTestResult<T, 4> Concat4dDim3TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1582 | IWorkloadFactory& workloadFactory, |
| 1583 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1584 | float qScale, |
| 1585 | int32_t qOffset, |
| 1586 | bool useSubtensor) |
| 1587 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1588 | TensorInfo outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1589 | |
| 1590 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1591 | workloadFactory, memoryManager, outputTensorInfo, 3, useSubtensor, qScale, qOffset); |
| 1592 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1593 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1594 | { |
| 1595 | 1.0f, 2.0f, |
| 1596 | 11.0f, 12.0f, |
| 1597 | 21.0f, 22.0f, |
| 1598 | 3.0f, 4.0f, |
| 1599 | 13.0f, 14.0f, |
| 1600 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1601 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1602 | 5.0f, 6.0f, |
| 1603 | 15.0f, 16.0f, |
| 1604 | 25.0f, 26.0f, |
| 1605 | 7.0f, 8.0f, |
| 1606 | 17.0f, 18.0f, |
| 1607 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1608 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1609 | 9.0f, 10.0f, |
| 1610 | 19.0f, 20.0f, |
| 1611 | 29.0f, 30.0f, |
| 1612 | 11.0f, 12.0f, |
| 1613 | 21.0f, 22.0f, |
| 1614 | 31.0f, 32.0f |
| 1615 | }, |
| 1616 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1617 | |
| 1618 | return result; |
| 1619 | } |
| 1620 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1621 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1622 | LayerTestResult<T, 4> Concat4dDiffShapeDim0TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1623 | IWorkloadFactory& workloadFactory, |
| 1624 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1625 | float qScale, |
| 1626 | int32_t qOffset) |
| 1627 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1628 | constexpr unsigned int dimension = 0u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1629 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1630 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1631 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1632 | { |
| 1633 | 1.0f, 2.0f, |
| 1634 | 3.0f, 4.0f, |
| 1635 | 5.0f, 6.0f, |
| 1636 | 7.0f, 8.0f, |
| 1637 | 9.0f, 10.0f, |
| 1638 | 11.0f, 12.0f |
| 1639 | }, |
| 1640 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1641 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1642 | TensorInfo inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1643 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1644 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1645 | { |
| 1646 | 11.0f, 12.0f, |
| 1647 | 13.0f, 14.0f, |
| 1648 | 15.0f, 16.0f, |
| 1649 | 17.0f, 18.0f, |
| 1650 | 19.0f, 20.0f, |
| 1651 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1652 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1653 | 21.0f, 22.0f, |
| 1654 | 23.0f, 24.0f, |
| 1655 | 25.0f, 26.0f, |
| 1656 | 27.0f, 28.0f, |
| 1657 | 29.0f, 30.0f, |
| 1658 | 31.0f, 32.0f |
| 1659 | }, |
| 1660 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1661 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1662 | TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1663 | |
| 1664 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1665 | |
| 1666 | std::vector<T> output; |
| 1667 | output.resize(outputTensorInfo.GetNumElements()); |
| 1668 | Concatenate<T>(workloadFactory, |
| 1669 | memoryManager, |
| 1670 | {inputTensorInfo0, inputTensorInfo1}, |
| 1671 | {input0.data(), input1.data()}, |
| 1672 | outputTensorInfo, |
| 1673 | output.data(), |
| 1674 | dimension, |
| 1675 | true); |
| 1676 | |
| 1677 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1678 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1679 | { |
| 1680 | 1.0f, 2.0f, |
| 1681 | 3.0f, 4.0f, |
| 1682 | 5.0f, 6.0f, |
| 1683 | 7.0f, 8.0f, |
| 1684 | 9.0f, 10.0f, |
| 1685 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1686 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1687 | 11.0f, 12.0f, |
| 1688 | 13.0f, 14.0f, |
| 1689 | 15.0f, 16.0f, |
| 1690 | 17.0f, 18.0f, |
| 1691 | 19.0f, 20.0f, |
| 1692 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1693 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1694 | 21.0f, 22.0f, |
| 1695 | 23.0f, 24.0f, |
| 1696 | 25.0f, 26.0f, |
| 1697 | 27.0f, 28.0f, |
| 1698 | 29.0f, 30.0f, |
| 1699 | 31.0f, 32.0f |
| 1700 | }, |
| 1701 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1702 | |
| 1703 | return result; |
| 1704 | } |
| 1705 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1706 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1707 | LayerTestResult<T, 4> Concat4dDiffShapeDim1TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1708 | IWorkloadFactory& workloadFactory, |
| 1709 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1710 | float qScale, |
| 1711 | int32_t qOffset) |
| 1712 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1713 | constexpr unsigned int dimension = 1u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1714 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1715 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1716 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1717 | { |
| 1718 | 1.0f, 2.0f, |
| 1719 | 3.0f, 4.0f, |
| 1720 | 5.0f, 6.0f, |
| 1721 | 7.0f, 8.0f, |
| 1722 | 9.0f, 10.0f, |
| 1723 | 11.0f, 12.0f |
| 1724 | }, |
| 1725 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1726 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1727 | TensorInfo inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1728 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1729 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1730 | { |
| 1731 | 11.0f, 12.0f, |
| 1732 | 13.0f, 14.0f, |
| 1733 | 15.0f, 16.0f, |
| 1734 | 17.0f, 18.0f, |
| 1735 | }, |
| 1736 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1737 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1738 | TensorInfo outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1739 | |
| 1740 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1741 | |
| 1742 | std::vector<T> output; |
| 1743 | output.resize(outputTensorInfo.GetNumElements()); |
| 1744 | Concatenate<T>(workloadFactory, |
| 1745 | memoryManager, |
| 1746 | {inputTensorInfo0, inputTensorInfo1}, |
| 1747 | {input0.data(), input1.data()}, |
| 1748 | outputTensorInfo, |
| 1749 | output.data(), |
| 1750 | dimension, |
| 1751 | true); |
| 1752 | |
| 1753 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1754 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1755 | { |
| 1756 | 1.0f, 2.0f, |
| 1757 | 3.0f, 4.0f, |
| 1758 | 5.0f, 6.0f, |
| 1759 | 7.0f, 8.0f, |
| 1760 | 9.0f, 10.0f, |
| 1761 | 11.0f, 12.0f, |
| 1762 | 11.0f, 12.0f, |
| 1763 | 13.0f, 14.0f, |
| 1764 | 15.0f, 16.0f, |
| 1765 | 17.0f, 18.0f |
| 1766 | }, |
| 1767 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1768 | |
| 1769 | return result; |
| 1770 | } |
| 1771 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1772 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1773 | LayerTestResult<T, 4> Concat4dDiffShapeDim2TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1774 | IWorkloadFactory& workloadFactory, |
| 1775 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1776 | float qScale, |
| 1777 | int32_t qOffset) |
| 1778 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1779 | constexpr unsigned int dimension = 2u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1780 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1781 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1782 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1783 | { |
| 1784 | 1.0f, 2.0f, |
| 1785 | 3.0f, 4.0f, |
| 1786 | 5.0f, 6.0f, |
| 1787 | 7.0f, 8.0f, |
| 1788 | 9.0f, 10.0f, |
| 1789 | 11.0f, 12.0f |
| 1790 | }, |
| 1791 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1792 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1793 | TensorInfo inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1794 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1795 | { |
| 1796 | 11.0f, 12.0f, |
| 1797 | 13.0f, 14.0f, |
| 1798 | 15.0f, 16.0f, |
| 1799 | 17.0f, 18.0f, |
| 1800 | 19.0f, 20.0f, |
| 1801 | 21.0f, 22.0f, |
| 1802 | 23.0f, 24.0f, |
| 1803 | 25.0f, 26.0f, |
| 1804 | 27.0f, 28.0f |
| 1805 | }, |
| 1806 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1807 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1808 | TensorInfo outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1809 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1810 | |
| 1811 | std::vector<T> output; |
| 1812 | output.resize(outputTensorInfo.GetNumElements()); |
| 1813 | Concatenate<T>(workloadFactory, |
| 1814 | memoryManager, |
| 1815 | {inputTensorInfo0, inputTensorInfo1}, |
| 1816 | {input0.data(), input1.data()}, |
| 1817 | outputTensorInfo, |
| 1818 | output.data(), |
| 1819 | dimension, |
| 1820 | true); |
| 1821 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1822 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1823 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1824 | { |
| 1825 | 1.0f, 2.0f, |
| 1826 | 3.0f, 4.0f, |
| 1827 | 11.0f, 12.0f, |
| 1828 | 13.0f, 14.0f, |
| 1829 | 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1830 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1831 | 5.0f, 6.0f, |
| 1832 | 7.0f, 8.0f, |
| 1833 | 17.0f, 18.0f, |
| 1834 | 19.0f, 20.0f, |
| 1835 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1836 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1837 | 9.0f, 10.0f, |
| 1838 | 11.0f, 12.0f, |
| 1839 | 23.0f, 24.0f, |
| 1840 | 25.0f, 26.0f, |
| 1841 | 27.0f, 28.0f |
| 1842 | }, |
| 1843 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1844 | |
| 1845 | return result; |
| 1846 | } |
| 1847 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1848 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1849 | LayerTestResult<T, 4> Concat4dDiffShapeDim3TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1850 | IWorkloadFactory& workloadFactory, |
| 1851 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1852 | float qScale, |
| 1853 | int32_t qOffset, |
| 1854 | bool useSubtensor) |
| 1855 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1856 | constexpr unsigned int dimension = 3u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1857 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1858 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1859 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1860 | { |
| 1861 | 1.0f, 2.0f, |
| 1862 | 3.0f, 4.0f, |
| 1863 | 5.0f, 6.0f, |
| 1864 | 7.0f, 8.0f, |
| 1865 | 9.0f, 10.0f, |
| 1866 | 11.0f, 12.0f |
| 1867 | }, |
| 1868 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1869 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1870 | TensorInfo inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType, qScale, qOffset); |
| 1871 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1872 | { |
| 1873 | 11.0f, 12.0f, 13.0f, |
| 1874 | 14.0f, 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1875 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1876 | 17.0f, 18.0f, 19.0f, |
| 1877 | 20.0f, 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1878 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1879 | 23.0f, 24.0f, 25.0f, |
| 1880 | 26.0f, 27.0f, 28.0f |
| 1881 | }, |
| 1882 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1883 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1884 | TensorInfo outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1885 | |
| 1886 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1887 | |
| 1888 | std::vector<T> output; |
| 1889 | output.resize(outputTensorInfo.GetNumElements()); |
| 1890 | Concatenate<T>(workloadFactory, |
| 1891 | memoryManager, |
| 1892 | {inputTensorInfo0, inputTensorInfo1}, |
| 1893 | {input0.data(), input1.data()}, |
| 1894 | outputTensorInfo, |
| 1895 | output.data(), |
| 1896 | dimension, |
| 1897 | useSubtensor); |
| 1898 | |
| 1899 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1900 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1901 | { |
| 1902 | 1.0f, 2.0f, 11.0f, 12.0f, 13.0f, |
| 1903 | 3.0f, 4.0f, 14.0f, 15.0f, 16.0f, |
| 1904 | 5.0f, 6.0f, 17.0f, 18.0f, 19.0f, |
| 1905 | 7.0f, 8.0f, 20.0f, 21.0f, 22.0f, |
| 1906 | 9.0f, 10.0f, 23.0f, 24.0f, 25.0f, |
| 1907 | 11.0f, 12.0f, 26.0f, 27.0f, 28.0f |
| 1908 | }, |
| 1909 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1910 | |
| 1911 | return result; |
| 1912 | } |
| 1913 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1914 | template<DataType ArmnnType, typename T> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1915 | LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1916 | IWorkloadFactory& workloadFactory, |
| 1917 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1918 | bool useSubtensor) |
| 1919 | { |
| 1920 | // Defines the tensor descriptors. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1921 | TensorInfo outputTensorInfo({ 3, 6, 3 }, ArmnnType); |
| 1922 | TensorInfo inputTensorInfo1({ 3, 6, 2 }, ArmnnType); |
| 1923 | TensorInfo inputTensorInfo2({ 3, 6, 1 }, ArmnnType); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1924 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1925 | std::vector<TensorShape> inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()}); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1926 | |
| 1927 | // Quantized input1 tensor. |
| 1928 | const float inputScale1 = 0.5f; |
| 1929 | const int32_t inputOffset1 = 5; |
| 1930 | |
| 1931 | auto input1 = MakeTensor<T, 3>(inputTensorInfo1, std::vector<T>( |
| 1932 | { |
| 1933 | 1, 2, 3, |
| 1934 | 4, 5, 6, |
| 1935 | 7, 8, 9, |
| 1936 | 10, 11, 12, |
| 1937 | 13, 14, 15, |
| 1938 | 16, 17, 18, |
| 1939 | |
| 1940 | 19, 20, 21, |
| 1941 | 22, 23, 24, |
| 1942 | 25, 26, 27, |
| 1943 | 28, 29, 30, |
| 1944 | 31, 32, 33, |
| 1945 | 34, 35, 36 |
| 1946 | })); |
| 1947 | |
| 1948 | // Quatized input2 tensor. |
| 1949 | const float inputScale2 = 0.2f; |
| 1950 | const int32_t inputOffset2 = 10; |
| 1951 | |
| 1952 | auto input2 = MakeTensor<T, 3>(inputTensorInfo2, std::vector<T>( |
| 1953 | { |
| 1954 | 37, 38, 39, |
| 1955 | 40, 41, 42, |
| 1956 | 43, 44, 45, |
| 1957 | 46, 47, 48, |
| 1958 | 49, 50, 51, |
| 1959 | 52, 53, 54 |
| 1960 | })); |
| 1961 | |
| 1962 | // Quantized output tensor. |
| 1963 | const float outputScale = 0.1f; |
| 1964 | const int32_t outputOffset = 20; |
| 1965 | |
| 1966 | LayerTestResult<T, 3> ret(outputTensorInfo); |
| 1967 | |
| 1968 | ret.outputExpected = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>( |
| 1969 | { |
| 1970 | 0, 5, 74, |
| 1971 | 10, 15, 76, |
| 1972 | 20, 25, 78, |
| 1973 | 30, 35, 80, |
| 1974 | 40, 45, 82, |
| 1975 | 50, 55, 84, |
| 1976 | |
| 1977 | 60, 65, 86, |
| 1978 | 70, 75, 88, |
| 1979 | 80, 85, 90, |
| 1980 | 90, 95, 92, |
| 1981 | 100, 105, 94, |
| 1982 | 110, 115, 96, |
| 1983 | |
| 1984 | 120, 125, 98, |
| 1985 | 130, 135, 100, |
| 1986 | 140, 145, 102, |
| 1987 | 150, 155, 104, |
| 1988 | 160, 165, 106, |
| 1989 | 170, 175, 108 |
| 1990 | })); |
| 1991 | |
| 1992 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 1993 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 1994 | inputTensorInfo1.SetQuantizationScale(inputScale1); |
| 1995 | inputTensorInfo1.SetQuantizationOffset(inputOffset1); |
| 1996 | inputTensorInfo2.SetQuantizationScale(inputScale2); |
| 1997 | inputTensorInfo2.SetQuantizationOffset(inputOffset2); |
| 1998 | |
| 1999 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2000 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2001 | |
| 2002 | std::vector<unsigned int> wOrigin2 = { 0, 0, 2 }; //Extent of the window is defined by size of input[1]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2003 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2004 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2005 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2006 | |
| 2007 | bool subTensorsSupported = useSubtensor && workloadFactory.SupportsSubTensors(); |
| 2008 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2009 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2010 | subTensorsSupported ? |
| 2011 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2012 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2013 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2014 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2015 | subTensorsSupported ? |
| 2016 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2017 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2018 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2019 | ConcatQueueDescriptor data; |
| 2020 | OriginsDescriptor desc = CreateDescriptorForConcatenation( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2021 | inputTensorShapes.begin(),inputTensorShapes.end(), 2); |
| 2022 | data.m_Parameters = desc; |
| 2023 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2024 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2025 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2026 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2027 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2028 | |
| 2029 | data.m_ViewOrigins.push_back(window1); |
| 2030 | data.m_ViewOrigins.push_back(window2); |
| 2031 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2032 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2033 | |
| 2034 | inputHandle1->Allocate(); |
| 2035 | inputHandle2->Allocate(); |
| 2036 | outputHandle->Allocate(); |
| 2037 | |
| 2038 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2039 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2040 | |
| 2041 | workload->PostAllocationConfigure(); |
| 2042 | workload->Execute(); |
| 2043 | |
| 2044 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2045 | |
| 2046 | return ret; |
| 2047 | } |
| 2048 | |
| 2049 | // |
| 2050 | // Explicit template specializations |
| 2051 | // |
| 2052 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2053 | template LayerTestResult<ResolveType<DataType::QuantisedAsymm8>, 3> |
| 2054 | ConcatDifferentInputOutputQParamTest<DataType::QuantisedAsymm8>( |
| 2055 | IWorkloadFactory& workloadFactory, |
| 2056 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2057 | bool useSubtensor); |
| 2058 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2059 | template LayerTestResult<ResolveType<DataType::QuantisedSymm16>, 3> |
| 2060 | ConcatDifferentInputOutputQParamTest<DataType::QuantisedSymm16>( |
| 2061 | IWorkloadFactory& workloadFactory, |
| 2062 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2063 | bool useSubtensor); |
| 2064 | |
| 2065 | // |
| 2066 | // Implementation functions |
| 2067 | // |
| 2068 | |
| 2069 | LayerTestResult<float,3> ConcatTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2070 | IWorkloadFactory& workloadFactory, |
| 2071 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2072 | { |
| 2073 | unsigned int outputWidth = 3; |
| 2074 | unsigned int outputHeight = 6; |
| 2075 | unsigned int outputChannels = 3; |
| 2076 | |
| 2077 | unsigned int inputWidth1 = 3; |
| 2078 | unsigned int inputHeight1 = 6; |
| 2079 | unsigned int inputChannels1 = 2; |
| 2080 | |
| 2081 | unsigned int inputWidth2 = 3; |
| 2082 | unsigned int inputHeight2 = 6; |
| 2083 | unsigned int inputChannels2 = 1; |
| 2084 | |
| 2085 | // Define the tensor descriptors. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2086 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::Float32); |
| 2087 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::Float32); |
| 2088 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::Float32); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2089 | |
| 2090 | LayerTestResult<float,3> ret(outputTensorInfo); |
| 2091 | |
| 2092 | ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>( |
| 2093 | { |
| 2094 | 1.0f, 2.0f, 3.0f, |
| 2095 | 4.0f, 5.0f, 6.0f, |
| 2096 | 7.0f, 8.0f, 9.0f, |
| 2097 | 10.0f, 11.0f, 12.0f, |
| 2098 | 13.0f, 14.0f, 15.0f, |
| 2099 | 16.0f, 17.0f, 18.0f, |
| 2100 | |
| 2101 | 19.0f, 20.0f, 21.0f, |
| 2102 | 22.0f, 23.0f, 24.0f, |
| 2103 | 25.0f, 26.0f, 27.0f, |
| 2104 | 28.0f, 29.0f, 30.0f, |
| 2105 | 31.0f, 32.0f, 33.0f, |
| 2106 | 34.0f, 35.0f, 36.0f, |
| 2107 | |
| 2108 | 37.0f, 38.0f, 39.0f, |
| 2109 | 40.0f, 41.0f, 42.0f, |
| 2110 | 43.0f, 44.0f, 45.0f, |
| 2111 | 46.0f, 47.0f, 48.0f, |
| 2112 | 49.0f, 50.0f, 51.0f, |
| 2113 | 52.0f, 53.0f, 54.0f, |
| 2114 | }) |
| 2115 | ); |
| 2116 | |
| 2117 | auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>( |
| 2118 | { |
| 2119 | 1.0f, 2.0f, 3.0f, |
| 2120 | 4.0f, 5.0f, 6.0f, |
| 2121 | 7.0f, 8.0f, 9.0f, |
| 2122 | 10.0f, 11.0f, 12.0f, |
| 2123 | 13.0f, 14.0f, 15.0f, |
| 2124 | 16.0f, 17.0f, 18.0f, |
| 2125 | |
| 2126 | 19.0f, 20.0f, 21.0f, |
| 2127 | 22.0f, 23.0f, 24.0f, |
| 2128 | 25.0f, 26.0f, 27.0f, |
| 2129 | 28.0f, 29.0f, 30.0f, |
| 2130 | 31.0f, 32.0f, 33.0f, |
| 2131 | 34.0f, 35.0f, 36.0f, |
| 2132 | }) |
| 2133 | ); |
| 2134 | |
| 2135 | auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>( |
| 2136 | { |
| 2137 | 37.0f, 38.0f, 39.0f, |
| 2138 | 40.0f, 41.0f, 42.0f, |
| 2139 | 43.0f, 44.0f, 45.0f, |
| 2140 | 46.0f, 47.0f, 48.0f, |
| 2141 | 49.0f, 50.0f, 51.0f, |
| 2142 | 52.0f, 53.0f, 54.0f, |
| 2143 | }) |
| 2144 | ); |
| 2145 | |
| 2146 | std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2147 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2148 | |
| 2149 | std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2150 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2151 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2152 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2153 | |
| 2154 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2155 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2156 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2157 | subTensorsSupported ? |
| 2158 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2159 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2160 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2161 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2162 | subTensorsSupported ? |
| 2163 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2164 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2165 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2166 | ConcatQueueDescriptor data; |
| 2167 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2168 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2169 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2170 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2171 | |
| 2172 | data.m_ViewOrigins.push_back(window1); |
| 2173 | data.m_ViewOrigins.push_back(window2); |
| 2174 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2175 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2176 | |
| 2177 | inputHandle1->Allocate(); |
| 2178 | inputHandle2->Allocate(); |
| 2179 | outputHandle->Allocate(); |
| 2180 | |
| 2181 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2182 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2183 | |
| 2184 | workload->PostAllocationConfigure(); |
| 2185 | workload->Execute(); |
| 2186 | |
| 2187 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2188 | |
| 2189 | return ret; |
| 2190 | } |
| 2191 | |
| 2192 | LayerTestResult<float, 1> Concat1dTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2193 | IWorkloadFactory& workloadFactory, |
| 2194 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2195 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2196 | return Concat1dTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2197 | } |
| 2198 | |
| 2199 | LayerTestResult<float, 2> Concat2dDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2200 | IWorkloadFactory& workloadFactory, |
| 2201 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2202 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2203 | return Concat2dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2204 | } |
| 2205 | |
| 2206 | LayerTestResult<float, 2> Concat2dDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2207 | IWorkloadFactory& workloadFactory, |
| 2208 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2209 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2210 | return Concat2dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2211 | } |
| 2212 | |
| 2213 | LayerTestResult<float, 2> Concat2dDim0DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2214 | IWorkloadFactory& workloadFactory, |
| 2215 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2216 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2217 | return Concat2dDim0DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2218 | } |
| 2219 | |
| 2220 | LayerTestResult<float, 2> Concat2dDim1DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2221 | IWorkloadFactory& workloadFactory, |
| 2222 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2223 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2224 | return Concat2dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2225 | } |
| 2226 | |
| 2227 | LayerTestResult<float, 3> Concat3dDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2228 | IWorkloadFactory& workloadFactory, |
| 2229 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2230 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2231 | return Concat3dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2232 | } |
| 2233 | |
| 2234 | LayerTestResult<float, 3> Concat3dDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2235 | IWorkloadFactory& workloadFactory, |
| 2236 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2237 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2238 | return Concat3dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2239 | } |
| 2240 | |
| 2241 | LayerTestResult<float, 3> Concat3dDim2Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2242 | IWorkloadFactory& workloadFactory, |
| 2243 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2244 | bool useSubtensor) |
| 2245 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2246 | return Concat3dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, useSubtensor, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2247 | } |
| 2248 | |
| 2249 | LayerTestResult<float, 3> Concat3dDim0DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2250 | IWorkloadFactory& workloadFactory, |
| 2251 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2252 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2253 | return Concat3dDim0DiffInputDimsTestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2254 | workloadFactory, memoryManager, 0.0f, 0); |
| 2255 | } |
| 2256 | |
| 2257 | LayerTestResult<float, 3> Concat3dDim1DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2258 | IWorkloadFactory& workloadFactory, |
| 2259 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2260 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2261 | return Concat3dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2262 | } |
| 2263 | |
| 2264 | LayerTestResult<float, 3> Concat3dDim2DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2265 | IWorkloadFactory& workloadFactory, |
| 2266 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2267 | bool useSubtensor) |
| 2268 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2269 | return Concat3dDim2DiffInputDimsTestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2270 | workloadFactory, memoryManager, useSubtensor, 0.0f, 0); |
| 2271 | } |
| 2272 | |
| 2273 | LayerTestResult<float, 4> Concat4dDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2274 | IWorkloadFactory& workloadFactory, |
| 2275 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2276 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2277 | return Concat4dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2278 | } |
| 2279 | |
| 2280 | LayerTestResult<float, 4> Concat4dDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2281 | IWorkloadFactory& workloadFactory, |
| 2282 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2283 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2284 | return Concat4dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2285 | } |
| 2286 | |
| 2287 | LayerTestResult<float, 4> Concat4dDim2Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2288 | IWorkloadFactory& workloadFactory, |
| 2289 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2290 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2291 | return Concat4dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2292 | } |
| 2293 | |
| 2294 | LayerTestResult<float, 4> Concat4dDim3Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2295 | IWorkloadFactory& workloadFactory, |
| 2296 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2297 | bool useSubtensor) |
| 2298 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2299 | return Concat4dDim3TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0, useSubtensor); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2300 | } |
| 2301 | |
| 2302 | LayerTestResult<float, 4> Concat4dDiffShapeDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2303 | IWorkloadFactory& workloadFactory, |
| 2304 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2305 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2306 | return Concat4dDiffShapeDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2307 | } |
| 2308 | |
| 2309 | LayerTestResult<float, 4> Concat4dDiffShapeDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2310 | IWorkloadFactory& workloadFactory, |
| 2311 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2312 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2313 | return Concat4dDiffShapeDim1TestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2314 | workloadFactory, memoryManager, 0.0f, 0); |
| 2315 | } |
| 2316 | |
| 2317 | LayerTestResult<float, 4> Concat4dDiffShapeDim2Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2318 | IWorkloadFactory& workloadFactory, |
| 2319 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2320 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2321 | return Concat4dDiffShapeDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2322 | } |
| 2323 | |
| 2324 | LayerTestResult<float, 4> Concat4dDiffShapeDim3Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2325 | IWorkloadFactory& workloadFactory, |
| 2326 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2327 | bool useSubtensor) |
| 2328 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2329 | return Concat4dDiffShapeDim3TestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2330 | workloadFactory, memoryManager, 0.0f, 0, useSubtensor); |
| 2331 | } |
| 2332 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2333 | LayerTestResult<Half, 3> ConcatFloat16Test( |
| 2334 | IWorkloadFactory& workloadFactory, |
| 2335 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Matthew Jackson | 9bff144 | 2019-09-12 09:08:23 +0100 | [diff] [blame] | 2336 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2337 | return Concat3dDim1TestImpl<DataType::Float16>(workloadFactory, memoryManager, 0.0f, 0); |
Matthew Jackson | 9bff144 | 2019-09-12 09:08:23 +0100 | [diff] [blame] | 2338 | } |
| 2339 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2340 | LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2341 | IWorkloadFactory& workloadFactory, |
| 2342 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2343 | { |
| 2344 | unsigned int outputWidth = 3; |
| 2345 | unsigned int outputHeight = 6; |
| 2346 | unsigned int outputChannels = 3; |
| 2347 | |
| 2348 | unsigned int inputWidth1 = 3; |
| 2349 | unsigned int inputHeight1 = 6; |
| 2350 | unsigned int inputChannels1 = 2; |
| 2351 | |
| 2352 | unsigned int inputWidth2 = 3; |
| 2353 | unsigned int inputHeight2 = 6; |
| 2354 | unsigned int inputChannels2 = 1; |
| 2355 | |
| 2356 | // Defines the tensor descriptors. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2357 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QuantisedAsymm8); |
| 2358 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QuantisedAsymm8); |
| 2359 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QuantisedAsymm8); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2360 | |
| 2361 | // Quantized input1 tensor. Range [-3, 1] |
| 2362 | const float inputScale1 = 0.015686f; |
| 2363 | const int32_t inputOffset1 = 192; |
| 2364 | |
| 2365 | auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( |
| 2366 | { |
| 2367 | 1, 2, 3, |
| 2368 | 4, 5, 6, |
| 2369 | 7, 8, 9, |
| 2370 | 10, 11, 12, |
| 2371 | 13, 14, 15, |
| 2372 | 16, 17, 18, |
| 2373 | |
| 2374 | 19, 20, 21, |
| 2375 | 22, 23, 24, |
| 2376 | 25, 26, 27, |
| 2377 | 28, 29, 30, |
| 2378 | 31, 32, 33, |
| 2379 | 34, 35, 36, |
| 2380 | }) |
| 2381 | ); |
| 2382 | |
| 2383 | // Quatized input2 tensor. Range [-1, 4] |
| 2384 | const float inputScale2 = 0.019608f; |
| 2385 | const int32_t inputOffset2 = 50; |
| 2386 | |
| 2387 | auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( |
| 2388 | { |
| 2389 | 37, 38, 39, |
| 2390 | 40, 41, 42, |
| 2391 | 43, 44, 45, |
| 2392 | 46, 47, 48, |
| 2393 | 49, 50, 51, |
| 2394 | 52, 53, 54, |
| 2395 | }) |
| 2396 | ); |
| 2397 | |
| 2398 | // Output has the same quantization parameters than input1, |
| 2399 | // so that only the requantization of input2 is required |
| 2400 | const float outputScale = 0.015686f; |
| 2401 | const int32_t outputOffset = 192; |
| 2402 | |
| 2403 | LayerTestResult<uint8_t, 3> ret(outputTensorInfo); |
| 2404 | |
| 2405 | ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( |
| 2406 | { |
| 2407 | 1, 2, 3, |
| 2408 | 4, 5, 6, |
| 2409 | 7, 8, 9, |
| 2410 | 10, 11, 12, |
| 2411 | 13, 14, 15, |
| 2412 | 16, 17, 18, |
| 2413 | |
| 2414 | 19, 20, 21, |
| 2415 | 22, 23, 24, |
| 2416 | 25, 26, 27, |
| 2417 | 28, 29, 30, |
| 2418 | 31, 32, 33, |
| 2419 | 34, 35, 36, |
| 2420 | |
| 2421 | 176, 177, 178, |
| 2422 | 179, 181, 182, |
| 2423 | 183, 184, 186, |
| 2424 | 187, 188, 189, |
| 2425 | 191, 192, 193, |
| 2426 | 195, 196, 197, |
| 2427 | }) |
| 2428 | ); |
| 2429 | |
| 2430 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 2431 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 2432 | inputTensorInfo1.SetQuantizationScale(inputScale1); |
| 2433 | inputTensorInfo1.SetQuantizationOffset(inputOffset1); |
| 2434 | inputTensorInfo2.SetQuantizationScale(inputScale2); |
| 2435 | inputTensorInfo2.SetQuantizationOffset(inputOffset2); |
| 2436 | |
| 2437 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2438 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2439 | |
| 2440 | std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2441 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2442 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2443 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2444 | |
| 2445 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2446 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2447 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2448 | subTensorsSupported ? |
| 2449 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2450 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2451 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2452 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2453 | subTensorsSupported ? |
| 2454 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2455 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2456 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2457 | ConcatQueueDescriptor data; |
| 2458 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2459 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2460 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2461 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2462 | |
| 2463 | data.m_ViewOrigins.push_back(window1); |
| 2464 | data.m_ViewOrigins.push_back(window2); |
| 2465 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2466 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2467 | |
| 2468 | inputHandle1->Allocate(); |
| 2469 | inputHandle2->Allocate(); |
| 2470 | outputHandle->Allocate(); |
| 2471 | |
| 2472 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2473 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2474 | |
| 2475 | workload->PostAllocationConfigure(); |
| 2476 | workload->Execute(); |
| 2477 | |
| 2478 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2479 | |
| 2480 | return ret; |
| 2481 | } |
| 2482 | |
| 2483 | LayerTestResult<uint8_t, 3> ConcatUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2484 | IWorkloadFactory& workloadFactory, |
| 2485 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2486 | { |
| 2487 | unsigned int outputWidth = 3; |
| 2488 | unsigned int outputHeight = 6; |
| 2489 | unsigned int outputChannels = 3; |
| 2490 | |
| 2491 | unsigned int inputWidth1 = 3; |
| 2492 | unsigned int inputHeight1 = 6; |
| 2493 | unsigned int inputChannels1 = 2; |
| 2494 | |
| 2495 | unsigned int inputWidth2 = 3; |
| 2496 | unsigned int inputHeight2 = 6; |
| 2497 | unsigned int inputChannels2 = 1; |
| 2498 | |
| 2499 | // Defines the tensor descriptors. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2500 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QuantisedAsymm8); |
| 2501 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QuantisedAsymm8); |
| 2502 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QuantisedAsymm8); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2503 | |
| 2504 | // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. |
| 2505 | const float scale = 0.13497836f; |
| 2506 | const int32_t offset = -7; |
| 2507 | |
| 2508 | outputTensorInfo.SetQuantizationScale(scale); |
| 2509 | outputTensorInfo.SetQuantizationOffset(offset); |
| 2510 | inputTensorInfo1.SetQuantizationScale(scale); |
| 2511 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 2512 | inputTensorInfo2.SetQuantizationScale(scale); |
| 2513 | inputTensorInfo2.SetQuantizationOffset(offset); |
| 2514 | |
| 2515 | LayerTestResult<uint8_t, 3> ret(outputTensorInfo); |
| 2516 | |
| 2517 | ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( |
| 2518 | { |
| 2519 | 1, 2, 3, |
| 2520 | 4, 5, 6, |
| 2521 | 7, 8, 9, |
| 2522 | 10, 11, 12, |
| 2523 | 13, 14, 15, |
| 2524 | 16, 17, 18, |
| 2525 | |
| 2526 | 19, 20, 21, |
| 2527 | 22, 23, 24, |
| 2528 | 25, 26, 27, |
| 2529 | 28, 29, 30, |
| 2530 | 31, 32, 33, |
| 2531 | 34, 35, 36, |
| 2532 | |
| 2533 | 37, 38, 39, |
| 2534 | 40, 41, 42, |
| 2535 | 43, 44, 45, |
| 2536 | 46, 47, 48, |
| 2537 | 49, 50, 51, |
| 2538 | 52, 53, 54, |
| 2539 | }) |
| 2540 | ); |
| 2541 | |
| 2542 | auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( |
| 2543 | { |
| 2544 | 1, 2, 3, |
| 2545 | 4, 5, 6, |
| 2546 | 7, 8, 9, |
| 2547 | 10, 11, 12, |
| 2548 | 13, 14, 15, |
| 2549 | 16, 17, 18, |
| 2550 | |
| 2551 | 19, 20, 21, |
| 2552 | 22, 23, 24, |
| 2553 | 25, 26, 27, |
| 2554 | 28, 29, 30, |
| 2555 | 31, 32, 33, |
| 2556 | 34, 35, 36, |
| 2557 | }) |
| 2558 | ); |
| 2559 | |
| 2560 | auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( |
| 2561 | { |
| 2562 | 37, 38, 39, |
| 2563 | 40, 41, 42, |
| 2564 | 43, 44, 45, |
| 2565 | 46, 47, 48, |
| 2566 | 49, 50, 51, |
| 2567 | 52, 53, 54, |
| 2568 | }) |
| 2569 | ); |
| 2570 | |
| 2571 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2572 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2573 | |
| 2574 | std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2575 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2576 | |
| 2577 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2578 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2579 | |
| 2580 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2581 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2582 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2583 | subTensorsSupported ? |
| 2584 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2585 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2586 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2587 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2588 | subTensorsSupported ? |
| 2589 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2590 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2591 | |
| 2592 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2593 | ConcatQueueDescriptor data; |
| 2594 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2595 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2596 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2597 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2598 | |
| 2599 | data.m_ViewOrigins.push_back(window1); |
| 2600 | data.m_ViewOrigins.push_back(window2); |
| 2601 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2602 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2603 | |
| 2604 | inputHandle1->Allocate(); |
| 2605 | inputHandle2->Allocate(); |
| 2606 | outputHandle->Allocate(); |
| 2607 | |
| 2608 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2609 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2610 | |
| 2611 | workload->PostAllocationConfigure(); |
| 2612 | workload->Execute(); |
| 2613 | |
| 2614 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2615 | |
| 2616 | return ret; |
| 2617 | } |
| 2618 | |
| 2619 | LayerTestResult<uint16_t, 3> ConcatUint16Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2620 | IWorkloadFactory& workloadFactory, |
| 2621 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2622 | { |
| 2623 | unsigned int outputWidth = 3; |
| 2624 | unsigned int outputHeight = 6; |
| 2625 | unsigned int outputChannels = 3; |
| 2626 | |
| 2627 | unsigned int inputWidth1 = 3; |
| 2628 | unsigned int inputHeight1 = 6; |
| 2629 | unsigned int inputChannels1 = 2; |
| 2630 | |
| 2631 | unsigned int inputWidth2 = 3; |
| 2632 | unsigned int inputHeight2 = 6; |
| 2633 | unsigned int inputChannels2 = 1; |
| 2634 | |
| 2635 | // Defines the tensor descriptors. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2636 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QuantisedSymm16); |
| 2637 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QuantisedSymm16); |
| 2638 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QuantisedSymm16); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2639 | |
| 2640 | // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. |
| 2641 | const float scale = 0.13497836f; |
| 2642 | const int32_t offset = -7; |
| 2643 | |
| 2644 | outputTensorInfo.SetQuantizationScale(scale); |
| 2645 | outputTensorInfo.SetQuantizationOffset(offset); |
| 2646 | inputTensorInfo1.SetQuantizationScale(scale); |
| 2647 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 2648 | inputTensorInfo2.SetQuantizationScale(scale); |
| 2649 | inputTensorInfo2.SetQuantizationOffset(offset); |
| 2650 | |
| 2651 | LayerTestResult<uint16_t, 3> ret(outputTensorInfo); |
| 2652 | |
| 2653 | ret.outputExpected = MakeTensor<uint16_t, 3>(outputTensorInfo, std::vector<uint16_t>( |
| 2654 | { |
| 2655 | 1, 2, 3, |
| 2656 | 4, 5, 6, |
| 2657 | 7, 8, 9, |
| 2658 | 10, 11, 12, |
| 2659 | 13, 14, 15, |
| 2660 | 16, 17, 18, |
| 2661 | |
| 2662 | 19, 20, 21, |
| 2663 | 22, 23, 24, |
| 2664 | 25, 26, 27, |
| 2665 | 28, 29, 30, |
| 2666 | 31, 32, 33, |
| 2667 | 34, 35, 36, |
| 2668 | |
| 2669 | 37, 38, 39, |
| 2670 | 40, 41, 42, |
| 2671 | 43, 44, 45, |
| 2672 | 46, 47, 48, |
| 2673 | 49, 50, 51, |
| 2674 | 52, 53, 54, |
| 2675 | })); |
| 2676 | |
| 2677 | auto input1 = MakeTensor<uint16_t, 3>(inputTensorInfo1, std::vector<uint16_t>( |
| 2678 | { |
| 2679 | 1, 2, 3, |
| 2680 | 4, 5, 6, |
| 2681 | 7, 8, 9, |
| 2682 | 10, 11, 12, |
| 2683 | 13, 14, 15, |
| 2684 | 16, 17, 18, |
| 2685 | |
| 2686 | 19, 20, 21, |
| 2687 | 22, 23, 24, |
| 2688 | 25, 26, 27, |
| 2689 | 28, 29, 30, |
| 2690 | 31, 32, 33, |
| 2691 | 34, 35, 36, |
| 2692 | })); |
| 2693 | |
| 2694 | auto input2 = MakeTensor<uint16_t, 3>(inputTensorInfo2, std::vector<uint16_t>( |
| 2695 | { |
| 2696 | 37, 38, 39, |
| 2697 | 40, 41, 42, |
| 2698 | 43, 44, 45, |
| 2699 | 46, 47, 48, |
| 2700 | 49, 50, 51, |
| 2701 | 52, 53, 54, |
| 2702 | })); |
| 2703 | |
| 2704 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2705 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2706 | |
| 2707 | std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2708 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2709 | |
| 2710 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2711 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2712 | |
| 2713 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2714 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2715 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2716 | subTensorsSupported ? |
| 2717 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2718 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2719 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2720 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2721 | subTensorsSupported ? |
| 2722 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2723 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2724 | |
| 2725 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2726 | ConcatQueueDescriptor data; |
| 2727 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2728 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2729 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2730 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2731 | |
| 2732 | data.m_ViewOrigins.push_back(window1); |
| 2733 | data.m_ViewOrigins.push_back(window2); |
| 2734 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2735 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2736 | |
| 2737 | inputHandle1->Allocate(); |
| 2738 | inputHandle2->Allocate(); |
| 2739 | outputHandle->Allocate(); |
| 2740 | |
| 2741 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2742 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2743 | |
| 2744 | workload->PostAllocationConfigure(); |
| 2745 | workload->Execute(); |
| 2746 | |
| 2747 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2748 | |
| 2749 | return ret; |
| 2750 | } |
| 2751 | |
| 2752 | LayerTestResult<uint8_t, 1> Concat1dUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2753 | IWorkloadFactory& workloadFactory, |
| 2754 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2755 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2756 | return Concat1dTestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2757 | } |
| 2758 | |
| 2759 | LayerTestResult<uint8_t, 2> Concat2dDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2760 | IWorkloadFactory& workloadFactory, |
| 2761 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2762 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2763 | return Concat2dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2764 | } |
| 2765 | |
| 2766 | LayerTestResult<uint8_t, 2> Concat2dDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2767 | IWorkloadFactory& workloadFactory, |
| 2768 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2769 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2770 | return Concat2dDim1TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2771 | } |
| 2772 | |
| 2773 | LayerTestResult<uint8_t, 2> Concat2dDim0DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2774 | IWorkloadFactory& workloadFactory, |
| 2775 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2776 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2777 | return Concat2dDim0DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2778 | workloadFactory, memoryManager, 0.5f, -1); |
| 2779 | } |
| 2780 | |
| 2781 | LayerTestResult<uint8_t, 2> Concat2dDim1DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2782 | IWorkloadFactory& workloadFactory, |
| 2783 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2784 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2785 | return Concat2dDim1DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2786 | workloadFactory, memoryManager, 0.5f, -1); |
| 2787 | } |
| 2788 | |
| 2789 | LayerTestResult<uint8_t, 3> Concat3dDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2790 | IWorkloadFactory& workloadFactory, |
| 2791 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2792 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2793 | return Concat3dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2794 | } |
| 2795 | |
| 2796 | LayerTestResult<uint8_t, 3> Concat3dDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2797 | IWorkloadFactory& workloadFactory, |
| 2798 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2799 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2800 | return Concat3dDim1TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2801 | } |
| 2802 | |
| 2803 | LayerTestResult<uint8_t, 3> Concat3dDim2Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2804 | IWorkloadFactory& workloadFactory, |
| 2805 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2806 | bool useSubtensor) |
| 2807 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2808 | return Concat3dDim2TestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2809 | workloadFactory, memoryManager, useSubtensor, 0.5f, -1); |
| 2810 | } |
| 2811 | |
| 2812 | LayerTestResult<uint8_t, 3> Concat3dDim0DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2813 | IWorkloadFactory& workloadFactory, |
| 2814 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2815 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2816 | return Concat3dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2817 | } |
| 2818 | |
| 2819 | LayerTestResult<uint8_t, 3> Concat3dDim1DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2820 | IWorkloadFactory& workloadFactory, |
| 2821 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2822 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2823 | return Concat3dDim1DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2824 | workloadFactory, memoryManager, 0.5f, -1); |
| 2825 | } |
| 2826 | |
| 2827 | LayerTestResult<uint8_t, 3> Concat3dDim2DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2828 | IWorkloadFactory& workloadFactory, |
| 2829 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2830 | bool useSubtensor) |
| 2831 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2832 | return Concat3dDim2DiffInputDimsTestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2833 | workloadFactory, memoryManager, useSubtensor, 0.5f, -1); |
| 2834 | } |
| 2835 | |
| 2836 | LayerTestResult<uint8_t, 4> Concat4dDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2837 | IWorkloadFactory& workloadFactory, |
| 2838 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2839 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2840 | return Concat4dDim0TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2841 | } |
| 2842 | |
| 2843 | LayerTestResult<uint8_t, 4> Concat4dDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2844 | IWorkloadFactory& workloadFactory, |
| 2845 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2846 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2847 | return Concat4dDim1TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2848 | } |
| 2849 | |
| 2850 | LayerTestResult<uint8_t, 4> Concat4dDim2Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2851 | IWorkloadFactory& workloadFactory, |
| 2852 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2853 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2854 | return Concat4dDim2TestImpl<DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2855 | } |
| 2856 | |
| 2857 | LayerTestResult<uint8_t, 4> Concat4dDim3Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2858 | IWorkloadFactory& workloadFactory, |
| 2859 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2860 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2861 | return Concat4dDim3TestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2862 | workloadFactory, memoryManager, 0.5f, -1, useSubtensor); |
| 2863 | } |
| 2864 | |
| 2865 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2866 | IWorkloadFactory& workloadFactory, |
| 2867 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2868 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2869 | return Concat4dDiffShapeDim0TestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2870 | workloadFactory, memoryManager, 0.5f, -1); |
| 2871 | } |
| 2872 | |
| 2873 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2874 | IWorkloadFactory& workloadFactory, |
| 2875 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2876 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2877 | return Concat4dDiffShapeDim1TestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2878 | workloadFactory, memoryManager, 0.5f, -1); |
| 2879 | } |
| 2880 | |
| 2881 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim2Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2882 | IWorkloadFactory& workloadFactory, |
| 2883 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2884 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2885 | return Concat4dDiffShapeDim2TestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2886 | workloadFactory, memoryManager, 0.5f, -1); |
| 2887 | } |
| 2888 | |
| 2889 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim3Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2890 | IWorkloadFactory& workloadFactory, |
| 2891 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2892 | bool useSubtensor) |
| 2893 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2894 | return Concat4dDiffShapeDim3TestImpl<DataType::QuantisedAsymm8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2895 | workloadFactory, memoryManager, 0.5f, -1, useSubtensor); |
| 2896 | } |