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