Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ConcatTestImpl.hpp" |
| 7 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 8 | #include <QuantizeHelper.hpp> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 9 | #include <ResolveType.hpp> |
| 10 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 11 | |
Matteo Martincigh | e011d20 | 2019-11-28 11:35:47 +0000 | [diff] [blame] | 12 | #include <armnnUtils/Permute.hpp> |
| 13 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 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 | { |
Derek Lamberti | c374ff0 | 2019-12-10 21:57:35 +0000 | [diff] [blame] | 129 | boost::ignore_unused(memoryManager); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 130 | BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null"); |
| 131 | if (inputData == nullptr) |
| 132 | { |
| 133 | // Nullptr is an error in the test. By returning without doing the concatenation |
| 134 | // I expect the caller to fail the test. It still makes sense to report this as |
| 135 | // an assert for Debug builds. |
| 136 | return; |
| 137 | } |
| 138 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 139 | TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 140 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 141 | std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 142 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 143 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 144 | PermuteQueueDescriptor queueDescriptor; |
| 145 | queueDescriptor.m_Parameters = PermuteDescriptor{mappings}; |
| 146 | WorkloadInfo workloadInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 147 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 148 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 149 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 150 | std::unique_ptr<IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 151 | |
| 152 | inputHandle->Allocate(); |
| 153 | outputHandle->Allocate(); |
| 154 | |
| 155 | CopyDataToITensorHandle(inputHandle.get(), inputData); |
| 156 | |
| 157 | workload->PostAllocationConfigure(); |
| 158 | workload->Execute(); |
| 159 | |
| 160 | outputData.resize(outputTensorInfo.GetNumElements()); |
| 161 | CopyDataFromITensorHandle(&outputData[0], outputHandle.get()); |
| 162 | inputTensorInfo = outputTensorInfo; |
| 163 | } |
| 164 | |
| 165 | // |
| 166 | // Permute the input tensors so we can do a supported concatenation. |
| 167 | // Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions |
| 168 | // at the front. Finally this function tells what the output shape |
| 169 | // of the permuted concatenated tensor is going to be. |
| 170 | // |
| 171 | template<typename T> void PermuteInputsForConcat( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 172 | IWorkloadFactory& workloadFactory, |
| 173 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 174 | std::vector<TensorInfo> & inputTensorInfos, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 175 | std::vector<T *> & inputData, |
| 176 | std::vector<std::vector<T>> & inputDataStorage, |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 177 | PermutationVector & permuteVector, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 178 | unsigned int & concatDim, |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 179 | TensorInfo & outputTensorInfo) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 180 | { |
Derek Lamberti | c374ff0 | 2019-12-10 21:57:35 +0000 | [diff] [blame] | 181 | boost::ignore_unused(memoryManager); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 182 | BOOST_ASSERT_MSG(inputTensorInfos.size() > 1, |
| 183 | "Expecting more than one tensor to be concatenated here"); |
| 184 | |
| 185 | unsigned int numDims = 0; |
| 186 | unsigned int nthInput = 0; |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 187 | const PermutationVector identity({0, 1, 2}); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 188 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 189 | std::pair<PermutationVector, PermutationVector> permutations = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 190 | std::make_pair(identity, identity); |
| 191 | |
| 192 | inputDataStorage.resize(inputData.size()); |
| 193 | |
| 194 | for (auto && tensorInfo : inputTensorInfos) |
| 195 | { |
| 196 | if (numDims == 0) |
| 197 | { |
| 198 | numDims = tensorInfo.GetShape().GetNumDimensions(); |
| 199 | Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); |
| 200 | |
| 201 | // Store the reverese permutation. |
| 202 | permuteVector = permutations.second; |
| 203 | BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), |
| 204 | "Test logic error, we don't need permutation, so we shouldn't arrive here"); |
| 205 | } |
| 206 | else |
| 207 | { |
| 208 | BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(), |
| 209 | "All inputs must have the same number of dimensions"); |
| 210 | } |
| 211 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 212 | TensorInfo newTensorInfo = tensorInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 213 | newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); |
| 214 | |
| 215 | PermuteTensorData<T>(workloadFactory, |
| 216 | memoryManager, |
| 217 | permutations.first, |
| 218 | newTensorInfo, |
| 219 | inputData[nthInput], |
| 220 | inputDataStorage[nthInput]); |
| 221 | |
| 222 | inputData[nthInput] = inputDataStorage[nthInput].data(); |
| 223 | inputTensorInfos[nthInput] = newTensorInfo; |
| 224 | |
| 225 | ++nthInput; |
| 226 | } |
| 227 | |
| 228 | outputTensorInfo.SetShape( |
| 229 | armnnUtils::Permuted( |
| 230 | ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()), |
| 231 | permutations.first)); |
| 232 | } |
| 233 | |
| 234 | // |
| 235 | // This is the pair of PermuteInputsForConcat(...) which permutes back |
| 236 | // the output of the concatenation so we can check it against an expected |
| 237 | // output. |
| 238 | // |
| 239 | template <typename T> void PermuteOutputForConcat( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 240 | IWorkloadFactory& workloadFactory, |
| 241 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 242 | const TensorInfo & tensorInfo, |
| 243 | const PermutationVector & permuteVector, |
| 244 | std::unique_ptr<ITensorHandle> && inputDataHandle, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 245 | T * data) |
| 246 | { |
| 247 | BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); |
| 248 | if (data == nullptr) |
| 249 | { |
| 250 | // Nullptr is an error in the test. By returning without doing the permutation |
| 251 | // I expect the caller to fail the test. It still makes sense to report this as |
| 252 | // an assert for Debug builds. |
| 253 | return; |
| 254 | } |
| 255 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 256 | TensorInfo resultTensorInfo = tensorInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 257 | std::vector<T> inputData(tensorInfo.GetNumElements()); |
| 258 | std::vector<T> outputData; |
| 259 | |
| 260 | CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); |
| 261 | |
| 262 | PermuteTensorData<T>(workloadFactory, |
| 263 | memoryManager, |
| 264 | permuteVector, |
| 265 | resultTensorInfo, |
| 266 | &inputData[0], |
| 267 | outputData); |
| 268 | |
| 269 | ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); |
| 270 | } |
| 271 | |
| 272 | template<typename T> void Concatenate( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 273 | IWorkloadFactory& workloadFactory, |
| 274 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 275 | std::initializer_list<const TensorInfo> inputTensorInfosOrig, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 276 | std::initializer_list<T *> inputsOrig, |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 277 | const TensorInfo& outputTensorInfoOrig, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 278 | T * output, |
| 279 | unsigned int concatDim, |
| 280 | bool useSubtensor) |
| 281 | { |
| 282 | BOOST_ASSERT_MSG(output != nullptr, "output must not be null"); |
| 283 | if (output == nullptr) |
| 284 | { |
| 285 | // Nullptr is an error in the test. By returning without doing the permutation |
| 286 | // I expect the caller to fail the test. It still makes sense to report this as |
| 287 | // an assert for Debug builds. |
| 288 | return; |
| 289 | } |
| 290 | |
| 291 | // 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] | 292 | std::vector<TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 293 | std::vector<T *> inputs = inputsOrig; |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 294 | TensorInfo outputTensorInfo = outputTensorInfoOrig; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 295 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 296 | PermutationVector permuteVector{0, 1, 2}; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 297 | |
| 298 | // Holds and automatically releases memory for the reshaped input data. |
| 299 | std::vector<std::vector<T>> tmpInputDataStorage; |
| 300 | |
| 301 | const size_t inputCount = inputTensorInfos.size(); |
| 302 | |
| 303 | bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim); |
| 304 | |
| 305 | if (needPermuteForConcat) |
| 306 | { |
| 307 | // |
| 308 | // We need to permute the inputs, because concatenation along |
| 309 | // the requested axis is not supported. |
| 310 | // |
| 311 | PermuteInputsForConcat<T>(workloadFactory, |
| 312 | memoryManager, |
| 313 | inputTensorInfos, |
| 314 | inputs, |
| 315 | tmpInputDataStorage, |
| 316 | permuteVector, |
| 317 | concatDim, |
| 318 | outputTensorInfo); |
| 319 | } |
| 320 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 321 | WorkloadInfo workloadInfo; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 322 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 323 | std::vector<std::unique_ptr<ITensorHandle>> inputHandles; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 324 | inputHandles.reserve(inputCount); |
| 325 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 326 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 327 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 328 | ConcatQueueDescriptor queueDescriptor; |
| 329 | OriginsDescriptor viewsDescriptor = CreateDescriptorForConcat(inputTensorInfos, concatDim); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 330 | queueDescriptor.m_Parameters = viewsDescriptor; |
| 331 | |
| 332 | if (useSubtensor) |
| 333 | { |
| 334 | queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews()); |
| 335 | for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i) |
| 336 | { |
| 337 | queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i), |
| 338 | viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); |
| 339 | } |
| 340 | |
| 341 | outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 342 | |
| 343 | const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 344 | for (unsigned int i = 0; i < inputCount; ++i) |
| 345 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 346 | const TensorInfo& inputTensorInfo = inputTensorInfos[i]; |
| 347 | std::unique_ptr<ITensorHandle> inputHandle = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 348 | subTensorsSupported ? |
| 349 | workloadFactory.CreateSubTensorHandle(*outputHandle, |
| 350 | inputTensorInfo.GetShape(), |
| 351 | queueDescriptor.m_ViewOrigins[i].m_Origin.data()) : |
| 352 | workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 353 | |
| 354 | inputHandles.emplace_back(std::move(inputHandle)); |
| 355 | } |
| 356 | |
| 357 | } |
| 358 | else |
| 359 | { |
| 360 | for (unsigned int i = 0; i < inputCount; ++i) |
| 361 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 362 | std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfos[i]); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 363 | inputHandles.emplace_back(std::move(inputHandle)); |
| 364 | } |
| 365 | } |
| 366 | |
| 367 | for (unsigned int i = 0; i < inputCount; ++i) |
| 368 | { |
| 369 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get()); |
| 370 | } |
| 371 | |
| 372 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 373 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 374 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(queueDescriptor, workloadInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 375 | |
| 376 | for (auto& inputHandle : inputHandles) |
| 377 | { |
| 378 | inputHandle->Allocate(); |
| 379 | } |
| 380 | |
| 381 | outputHandle->Allocate(); |
| 382 | |
| 383 | unsigned int nextInputId = 0; |
| 384 | for (auto& inputHandle : inputHandles) |
| 385 | { |
| 386 | CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]); |
| 387 | ++nextInputId; |
| 388 | } |
| 389 | |
| 390 | workload->PostAllocationConfigure(); |
| 391 | workload->Execute(); |
| 392 | |
| 393 | if (needPermuteForConcat) |
| 394 | { |
| 395 | PermuteOutputForConcat<T>(workloadFactory, |
| 396 | memoryManager, |
| 397 | outputTensorInfo, |
| 398 | permuteVector, |
| 399 | std::move(outputHandle), |
| 400 | output); |
| 401 | } |
| 402 | else |
| 403 | { |
| 404 | CopyDataFromITensorHandle(output, outputHandle.get()); |
| 405 | } |
| 406 | } |
| 407 | |
| 408 | // |
| 409 | // Implementation templates |
| 410 | // |
| 411 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 412 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 413 | LayerTestResult<T, 1> Concat1dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 414 | IWorkloadFactory& workloadFactory, |
| 415 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 416 | float qScale, |
| 417 | int32_t qOffset) |
| 418 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 419 | TensorInfo inputTensorInfo({ 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 420 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 421 | auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 1.0f, 2.0f, 3.0f }, qScale, qOffset)); |
| 422 | auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 4.0f, 5.0f, 6.0f }, qScale, qOffset)); |
| 423 | 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] | 424 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 425 | TensorInfo outputTensorInfo({ 9 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 426 | |
| 427 | LayerTestResult<T, 1> result(outputTensorInfo); |
| 428 | |
| 429 | std::vector<T> output; |
| 430 | output.resize(outputTensorInfo.GetNumElements()); |
| 431 | Concatenate<T>(workloadFactory, memoryManager, |
| 432 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 433 | { input0.data(), input1.data(), input2.data() }, |
| 434 | outputTensorInfo, |
| 435 | output.data(), |
| 436 | 0, |
| 437 | true); |
| 438 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 439 | result.output = MakeTensor<T, 1>(outputTensorInfo, output); |
| 440 | result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>( |
| 441 | { |
| 442 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f |
| 443 | }, |
| 444 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 445 | |
| 446 | return result; |
| 447 | } |
| 448 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 449 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 450 | LayerTestResult<T, 2> Concat2dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 451 | IWorkloadFactory& workloadFactory, |
| 452 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 453 | const TensorInfo& outputTensorInfo, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 454 | unsigned int dimension, |
| 455 | const float qScale, |
| 456 | const int32_t qOffset) |
| 457 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 458 | TensorInfo inputTensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 459 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 460 | auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( |
| 461 | { |
| 462 | // Batch 0 |
| 463 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 464 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 465 | // Batch 1 |
| 466 | 10.0f, 11.0f, 12.0f, |
| 467 | }, |
| 468 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 469 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 470 | auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( |
| 471 | { |
| 472 | // Batch 0 |
| 473 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 474 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 475 | // Batch 1 |
| 476 | 13.0f, 14.0f, 15.0f, |
| 477 | }, |
| 478 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 479 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 480 | auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>( |
| 481 | { |
| 482 | // Batch 0 |
| 483 | 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 484 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 485 | // Batch 1 |
| 486 | 16.0f, 17.0f, 18.0f, |
| 487 | }, |
| 488 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 489 | |
| 490 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 491 | |
| 492 | std::vector<T> output; |
| 493 | output.resize(outputTensorInfo.GetNumElements()); |
| 494 | Concatenate<T>(workloadFactory, memoryManager, |
| 495 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 496 | { input0.data(), input1.data(), input2.data() }, |
| 497 | outputTensorInfo, |
| 498 | output.data(), |
| 499 | dimension, |
| 500 | true); |
| 501 | |
| 502 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 503 | return result; |
| 504 | } |
| 505 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 506 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 507 | LayerTestResult<T, 2> Concat2dDim0TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 508 | IWorkloadFactory& workloadFactory, |
| 509 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 510 | float qScale, |
| 511 | int32_t qOffset) |
| 512 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 513 | TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 514 | |
| 515 | LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( |
| 516 | workloadFactory, memoryManager, outputTensorInfo, 0, qScale, qOffset); |
| 517 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 518 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 519 | { |
| 520 | // Batch 0 |
| 521 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 522 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 523 | // Batch 1 |
| 524 | 10.0f, 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 525 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 526 | // Batch 2 |
| 527 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 528 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 529 | // Batch 3 |
| 530 | 13.0f, 14.0f, 15.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 531 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 532 | // Batch 4 |
| 533 | 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 534 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 535 | // Batch 5 |
| 536 | 16.0f, 17.0f, 18.0f, |
| 537 | }, |
| 538 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 539 | |
| 540 | return result; |
| 541 | } |
| 542 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 543 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 544 | LayerTestResult<T, 2> Concat2dDim1TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 545 | IWorkloadFactory& workloadFactory, |
| 546 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 547 | float qScale, |
| 548 | int32_t qOffset) |
| 549 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 550 | TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 551 | |
| 552 | LayerTestResult<T, 2> result = Concat2dTestImpl<ArmnnType>( |
| 553 | workloadFactory, memoryManager, outputTensorInfo, 1, qScale, qOffset); |
| 554 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 555 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 556 | { |
| 557 | // Batch 0 |
| 558 | 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] | 559 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 560 | // Batch 1 |
| 561 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f |
| 562 | }, |
| 563 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 564 | |
| 565 | return result; |
| 566 | } |
| 567 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 568 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 569 | LayerTestResult<T, 2> Concat2dDim0DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 570 | IWorkloadFactory& workloadFactory, |
| 571 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 572 | float qScale, |
| 573 | int32_t qOffset) |
| 574 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 575 | TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
| 576 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>( |
| 577 | { |
| 578 | // Batch 0 |
| 579 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 580 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 581 | // Batch 1 |
| 582 | 10.0f, 11.0f, 12.0f, |
| 583 | }, |
| 584 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 585 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 586 | TensorInfo input1TensorInfo({ 3, 3 }, ArmnnType, qScale, qOffset); |
| 587 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>( |
| 588 | { |
| 589 | // Batch 0 |
| 590 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 591 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 592 | // Batch 1 |
| 593 | 13.0f, 14.0f, 15.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 594 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 595 | // Batch 0 |
| 596 | 7.0f, 8.0f, 9.0f, |
| 597 | }, |
| 598 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 599 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 600 | TensorInfo input2TensorInfo({ 1, 3 }, ArmnnType, qScale, qOffset); |
| 601 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>( |
| 602 | { |
| 603 | // Batch 1 |
| 604 | 16.0f, 17.0f, 18.0f, |
| 605 | }, |
| 606 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 607 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 608 | TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 609 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 610 | |
| 611 | std::vector<T> output; |
| 612 | output.resize(outputTensorInfo.GetNumElements()); |
| 613 | Concatenate<T>(workloadFactory, memoryManager, |
| 614 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 615 | { input0.data(), input1.data(), input2.data() }, |
| 616 | outputTensorInfo, |
| 617 | output.data(), |
| 618 | 0, |
| 619 | true); |
| 620 | |
| 621 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 622 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 623 | { |
| 624 | // Batch 0 |
| 625 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 626 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 627 | // Batch 1 |
| 628 | 10.0f, 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 629 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 630 | // Batch 2 |
| 631 | 4.0f, 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 632 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 633 | // Batch 3 |
| 634 | 13.0f, 14.0f, 15.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 635 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 636 | // Batch 4 |
| 637 | 7.0f, 8.0f, 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 638 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 639 | // Batch 5 |
| 640 | 16.0f, 17.0f, 18.0f, |
| 641 | }, |
| 642 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 643 | |
| 644 | return result; |
| 645 | } |
| 646 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 647 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 648 | LayerTestResult<T, 2> Concat2dDim1DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 649 | IWorkloadFactory& workloadFactory, |
| 650 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 651 | float qScale, |
| 652 | int32_t qOffset) |
| 653 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 654 | TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset); |
| 655 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>( |
| 656 | { |
| 657 | // Batch 0 |
| 658 | 1.0f, 2.0f, 3.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 659 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 660 | // Batch 1 |
| 661 | 10.0f, 11.0f, 12.0f, |
| 662 | }, |
| 663 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 664 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 665 | TensorInfo input1TensorInfo({ 2, 5 }, ArmnnType, qScale, qOffset); |
| 666 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>( |
| 667 | { |
| 668 | // Batch 0 |
| 669 | 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 670 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 671 | // Batch 1 |
| 672 | 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, |
| 673 | }, |
| 674 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 675 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 676 | TensorInfo input2TensorInfo({ 2, 1 }, ArmnnType, qScale, qOffset); |
| 677 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>( |
| 678 | { |
| 679 | // Batch 0 |
| 680 | 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 681 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 682 | // Batch 1 |
| 683 | 18.0f |
| 684 | }, |
| 685 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 686 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 687 | TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 688 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 689 | |
| 690 | std::vector<T> output; |
| 691 | output.resize(outputTensorInfo.GetNumElements()); |
| 692 | Concatenate<T>(workloadFactory, memoryManager, |
| 693 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 694 | { input0.data(), input1.data(), input2.data() }, |
| 695 | outputTensorInfo, |
| 696 | output.data(), |
| 697 | 1, |
| 698 | true); |
| 699 | |
| 700 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 701 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>( |
| 702 | { |
| 703 | // Batch 0 |
| 704 | 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] | 705 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 706 | // Batch 1 |
| 707 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, |
| 708 | }, |
| 709 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 710 | |
| 711 | return result; |
| 712 | } |
| 713 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 714 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 715 | LayerTestResult<T, 3> Concat3dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 716 | IWorkloadFactory& workloadFactory, |
| 717 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 718 | const TensorInfo& outputTensorInfo, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 719 | unsigned int dimension, |
| 720 | bool useSubtensor, |
| 721 | float qScale, |
| 722 | int32_t qOffset) |
| 723 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 724 | TensorInfo inputTensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 725 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 726 | auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( |
| 727 | { |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 728 | // Batch 0, Channel 0 |
| 729 | 1.0f, 2.0f, |
| 730 | |
| 731 | // Batch 0, Channel 1 |
| 732 | 3.0f, 4.0f, |
| 733 | |
| 734 | // Batch 0, Channel 2 |
| 735 | 5.0f, 6.0f, |
| 736 | |
| 737 | // Batch 1, Channel 0 |
| 738 | 19.0f, 20.0f, |
| 739 | |
| 740 | // Batch 1, Channel 1 |
| 741 | 21.0f, 22.0f, |
| 742 | |
| 743 | // Batch 1, Channel 2 |
| 744 | 23.0f, 24.0f |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 745 | }, |
| 746 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 747 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 748 | auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( |
| 749 | { |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 750 | // Batch 0, Channel 0 |
| 751 | 7.0f, 8.0f, |
| 752 | |
| 753 | // Batch 0, Channel 1 |
| 754 | 9.0f, 10.0f, |
| 755 | |
| 756 | // Batch 0, Channel 2 |
| 757 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 758 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 759 | // Batch 1, Channel 0 |
| 760 | 25.0f, 26.0f, |
| 761 | |
| 762 | // Batch 1, Channel 1 |
| 763 | 27.0f, 28.0f, |
| 764 | |
| 765 | // Batch 1, Channel 2 |
| 766 | 29.0f, 30.0f |
| 767 | }, |
| 768 | qScale, qOffset)); |
| 769 | |
| 770 | auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>( |
| 771 | { |
| 772 | // Batch 0, Channel 0 |
| 773 | 13.0f, 14.0f, |
| 774 | |
| 775 | // Batch 0, Channel 1 |
| 776 | 15.0f, 16.0f, |
| 777 | |
| 778 | // Batch 0, Channel 2 |
| 779 | 17.0f, 18.0f, |
| 780 | |
| 781 | // Batch 1, Channel 0 |
| 782 | 31.0f, 32.0f, |
| 783 | |
| 784 | // Batch 1, Channel 1 |
| 785 | 33.0f, 34.0f, |
| 786 | |
| 787 | // Batch 1, Channel 2 |
| 788 | 35.0f, 36.0f |
| 789 | }, |
| 790 | qScale, qOffset)); |
| 791 | |
| 792 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 793 | |
| 794 | std::vector<T> output; |
| 795 | output.resize(outputTensorInfo.GetNumElements()); |
| 796 | Concatenate<T>(workloadFactory, memoryManager, |
| 797 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 798 | { input0.data(), input1.data(), input2.data() }, |
| 799 | outputTensorInfo, |
| 800 | output.data(), |
| 801 | dimension, |
| 802 | useSubtensor); |
| 803 | |
| 804 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 805 | return result; |
| 806 | } |
| 807 | |
| 808 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 809 | LayerTestResult<T, 3> Concat3dDim0TestImpl( |
| 810 | IWorkloadFactory& workloadFactory, |
| 811 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 812 | float qScale, |
| 813 | int32_t qOffset) |
| 814 | { |
| 815 | TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType, qScale, qOffset); |
| 816 | |
| 817 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 818 | workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); |
| 819 | |
| 820 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 821 | { |
| 822 | // Batch 0, Channel 0 |
| 823 | 1.0f, 2.0f, |
| 824 | |
| 825 | // Batch 0, Channel 1 |
| 826 | 3.0f, 4.0f, |
| 827 | |
| 828 | // Batch 0, Channel 2 |
| 829 | 5.0f, 6.0f, |
| 830 | |
| 831 | // Batch 1, Channel 0 |
| 832 | 19.0f, 20.0f, |
| 833 | |
| 834 | // Batch 1, Channel 1 |
| 835 | 21.0f, 22.0f, |
| 836 | |
| 837 | // Batch 1, Channel 2 |
| 838 | 23.0f, 24.0f, |
| 839 | |
| 840 | // Batch 2, Channel 0 |
| 841 | 7.0f, 8.0f, |
| 842 | |
| 843 | // Batch 2, Channel 1 |
| 844 | 9.0f, 10.0f, |
| 845 | |
| 846 | // Batch 2, Channel 2 |
| 847 | 11.0f, 12.0f, |
| 848 | |
| 849 | // Batch 3, Channel 0 |
| 850 | 25.0f, 26.0f, |
| 851 | |
| 852 | // Batch 3, Channel 1 |
| 853 | 27.0f, 28.0f, |
| 854 | |
| 855 | // Batch 3, Channel 2 |
| 856 | 29.0f, 30.0f, |
| 857 | |
| 858 | // Batch 4, Channel 0 |
| 859 | 13.0f, 14.0f, |
| 860 | |
| 861 | // Batch 4, Channel 1 |
| 862 | 15.0f, 16.0f, |
| 863 | |
| 864 | // Batch 4, Channel 2 |
| 865 | 17.0f, 18.0f, |
| 866 | |
| 867 | // Batch 5, Channel 0 |
| 868 | 31.0f, 32.0f, |
| 869 | |
| 870 | // Batch 5, Channel 1 |
| 871 | 33.0f, 34.0f, |
| 872 | |
| 873 | // Batch 5, Channel 2 |
| 874 | 35.0f, 36.0f |
| 875 | }, |
| 876 | qScale, qOffset)); |
| 877 | |
| 878 | return result; |
| 879 | } |
| 880 | |
| 881 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 882 | LayerTestResult<T, 3> Concat3dDim1TestImpl( |
| 883 | IWorkloadFactory& workloadFactory, |
| 884 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 885 | float qScale, |
| 886 | int32_t qOffset) |
| 887 | { |
| 888 | TensorInfo outputTensorInfo({ 2, 9, 2 }, ArmnnType, qScale, qOffset); |
| 889 | |
| 890 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 891 | workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); |
| 892 | |
| 893 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 894 | { |
| 895 | // Batch 0, Channel 0 |
| 896 | 1.0f, 2.0f, |
| 897 | |
| 898 | // Batch 0, Channel 1 |
| 899 | 3.0f, 4.0f, |
| 900 | |
| 901 | // Batch 0, Channel 2 |
| 902 | 5.0f, 6.0f, |
| 903 | |
| 904 | // Batch 0, Channel 3 |
| 905 | 7.0f, 8.0f, |
| 906 | |
| 907 | // Batch 0, Channel 4 |
| 908 | 9.0f, 10.0f, |
| 909 | |
| 910 | // Batch 0, Channel 5 |
| 911 | 11.0f, 12.0f, |
| 912 | |
| 913 | // Batch 0, Channel 6 |
| 914 | 13.0f, 14.0f, |
| 915 | |
| 916 | // Batch 0, Channel 7 |
| 917 | 15.0f, 16.0f, |
| 918 | |
| 919 | // Batch 0, Channel 8 |
| 920 | 17.0f, 18.0f, |
| 921 | |
| 922 | // Batch 1, Channel 0 |
| 923 | 19.0f, 20.0f, |
| 924 | |
| 925 | // Batch 1, Channel 1 |
| 926 | 21.0f, 22.0f, |
| 927 | |
| 928 | // Batch 1, Channel 2 |
| 929 | 23.0f, 24.0f, |
| 930 | |
| 931 | // Batch 1, Channel 3 |
| 932 | 25.0f, 26.0f, |
| 933 | |
| 934 | // Batch 1, Channel 4 |
| 935 | 27.0f, 28.0f, |
| 936 | |
| 937 | // Batch 1, Channel 5 |
| 938 | 29.0f, 30.0f, |
| 939 | |
| 940 | // Batch 1, Channel 6 |
| 941 | 31.0f, 32.0f, |
| 942 | |
| 943 | // Batch 1, Channel 7 |
| 944 | 33.0f, 34.0f, |
| 945 | |
| 946 | // Batch 1, Channel 8 |
| 947 | 35.0f, 36.0f |
| 948 | }, |
| 949 | qScale, qOffset)); |
| 950 | |
| 951 | return result; |
| 952 | } |
| 953 | |
| 954 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 955 | LayerTestResult<T, 3> Concat3dDim2TestImpl( |
| 956 | IWorkloadFactory& workloadFactory, |
| 957 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 958 | bool useSubtensor, |
| 959 | float qScale, |
| 960 | int32_t qOffset) |
| 961 | { |
| 962 | TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); |
| 963 | |
| 964 | LayerTestResult<T, 3> result = Concat3dTestImpl<ArmnnType>( |
| 965 | workloadFactory, memoryManager, outputTensorInfo, 2, useSubtensor, qScale, qOffset); |
| 966 | |
| 967 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 968 | { |
| 969 | // Batch 0, Channel 0 |
| 970 | 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f, |
| 971 | |
| 972 | // Batch 0, Channel 1 |
| 973 | 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f, |
| 974 | |
| 975 | // Batch 0, Channel 2 |
| 976 | 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f, |
| 977 | |
| 978 | // Batch 1, Channel 0 |
| 979 | 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f, |
| 980 | |
| 981 | // Batch 1, Channel 1 |
| 982 | 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f, |
| 983 | |
| 984 | // Batch 1, Channel 2 |
| 985 | 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, |
| 986 | }, |
| 987 | qScale, qOffset)); |
| 988 | |
| 989 | return result; |
| 990 | } |
| 991 | |
| 992 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
| 993 | LayerTestResult<T, 3> Concat3dDim0DiffInputDimsTestImpl( |
| 994 | IWorkloadFactory& workloadFactory, |
| 995 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 996 | float qScale, |
| 997 | int32_t qOffset) |
| 998 | { |
| 999 | TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); |
| 1000 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( |
| 1001 | { |
| 1002 | // Batch 0, Channel 0 |
| 1003 | 1.0f, 2.0f, |
| 1004 | |
| 1005 | // Batch 0, Channel 1 |
| 1006 | 3.0f, 4.0f, |
| 1007 | |
| 1008 | // Batch 0, Channel 2 |
| 1009 | 5.0f, 6.0f, |
| 1010 | |
| 1011 | // Batch 1, Channel 0 |
| 1012 | 19.0f, 20.0f, |
| 1013 | |
| 1014 | // Batch 1, Channel 1 |
| 1015 | 21.0f, 22.0f, |
| 1016 | |
| 1017 | // Batch 1, Channel 2 |
| 1018 | 23.0f, 24.0f |
| 1019 | }, |
| 1020 | qScale, qOffset)); |
| 1021 | |
| 1022 | TensorInfo input1TensorInfo({ 1, 3, 2 }, ArmnnType); |
| 1023 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( |
| 1024 | { |
| 1025 | // Batch 0, Channel 0 |
| 1026 | 7.0f, 8.0f, |
| 1027 | |
| 1028 | // Batch 0, Channel 1 |
| 1029 | 9.0f, 10.0f, |
| 1030 | |
| 1031 | // Batch 0, Channel 2 |
| 1032 | 11.0f, 12.0f, |
| 1033 | }, |
| 1034 | qScale, qOffset)); |
| 1035 | |
| 1036 | TensorInfo input2TensorInfo({ 3, 3, 2 }, ArmnnType); |
| 1037 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( |
| 1038 | { |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1039 | // Batch 0, Channel 0 |
| 1040 | 25.0f, 26.0f, |
| 1041 | |
| 1042 | // Batch 0, Channel 1 |
| 1043 | 27.0f, 28.0f, |
| 1044 | |
| 1045 | // Batch 0, Channel 2 |
| 1046 | 29.0f, 30.0f, |
| 1047 | |
| 1048 | // Batch 1, Channel 0 |
| 1049 | 13.0f, 14.0f, |
| 1050 | |
| 1051 | // Batch 1, Channel 1 |
| 1052 | 15.0f, 16.0f, |
| 1053 | |
| 1054 | // Batch 1, Channel 2 |
| 1055 | 17.0f, 18.0f, |
| 1056 | |
| 1057 | // Batch 2, Channel 0 |
| 1058 | 31.0f, 32.0f, |
| 1059 | |
| 1060 | // Batch 2, Channel 1 |
| 1061 | 33.0f, 34.0f, |
| 1062 | |
| 1063 | // Batch 2, Channel 2 |
| 1064 | 35.0f, 36.0f |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1065 | }, |
| 1066 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1067 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1068 | TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1069 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1070 | |
| 1071 | std::vector<T> output; |
| 1072 | output.resize(outputTensorInfo.GetNumElements()); |
| 1073 | Concatenate<T>(workloadFactory, memoryManager, |
| 1074 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1075 | { input0.data(), input1.data(), input2.data() }, |
| 1076 | outputTensorInfo, |
| 1077 | output.data(), |
| 1078 | 0, |
| 1079 | true); |
| 1080 | |
| 1081 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1082 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 1083 | { |
| 1084 | // Batch 0, Channel 0 |
| 1085 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1086 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1087 | // Batch 0, Channel 1 |
| 1088 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1089 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1090 | // Batch 0, Channel 2 |
| 1091 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1092 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1093 | // Batch 1, Channel 0 |
| 1094 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1095 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1096 | // Batch 1, Channel 1 |
| 1097 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1098 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1099 | // Batch 1, Channel 2 |
| 1100 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1101 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1102 | // Batch 2, Channel 0 |
| 1103 | 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1104 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1105 | // Batch 2, Channel 1 |
| 1106 | 9.0f, 10.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1107 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1108 | // Batch 2, Channel 2 |
| 1109 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1110 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1111 | // Batch 3, Channel 0 |
| 1112 | 25.0f, 26.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1113 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1114 | // Batch 3, Channel 1 |
| 1115 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1116 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1117 | // Batch 3, Channel 2 |
| 1118 | 29.0f, 30.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1119 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1120 | // Batch 4, Channel 0 |
| 1121 | 13.0f, 14.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1122 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1123 | // Batch 4, Channel 1 |
| 1124 | 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1125 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1126 | // Batch 4, Channel 2 |
| 1127 | 17.0f, 18.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1128 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1129 | // Batch 5, Channel 0 |
| 1130 | 31.0f, 32.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1131 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1132 | // Batch 5, Channel 1 |
| 1133 | 33.0f, 34.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1134 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1135 | // Batch 5, Channel 2 |
| 1136 | 35.0f, 36.0f |
| 1137 | }, |
| 1138 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1139 | |
| 1140 | return result; |
| 1141 | } |
| 1142 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1143 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1144 | LayerTestResult<T, 3> Concat3dDim1DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1145 | IWorkloadFactory& workloadFactory, |
| 1146 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1147 | float qScale, |
| 1148 | int32_t qOffset) |
| 1149 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1150 | TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1151 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( |
| 1152 | { |
| 1153 | // Batch 0, Channel 0 |
| 1154 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1155 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1156 | // Batch 0, Channel 1 |
| 1157 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1158 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1159 | // Batch 0, Channel 2 |
| 1160 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1161 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1162 | // Batch 1, Channel 0 |
| 1163 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1164 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1165 | // Batch 1, Channel 1 |
| 1166 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1167 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1168 | // Batch 1, Channel 2 |
| 1169 | 23.0f, 24.0f |
| 1170 | }, |
| 1171 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1172 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1173 | TensorInfo input1TensorInfo({ 2, 4, 2 }, ArmnnType, qScale, qOffset); |
| 1174 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( |
| 1175 | { |
| 1176 | // Batch 0, Channel 0 |
| 1177 | 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1178 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1179 | // Batch 0, Channel 1 |
| 1180 | 9.0f, 10.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1181 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1182 | // Batch 0, Channel 2 |
| 1183 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1184 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1185 | // Batch 0, Channel 3 |
| 1186 | 25.0f, 26.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1187 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1188 | // Batch 1, Channel 0 |
| 1189 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1190 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1191 | // Batch 1, Channel 1 |
| 1192 | 29.0f, 30.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1193 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1194 | // Batch 1, Channel 2 |
| 1195 | 13.0f, 14.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1196 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1197 | // Batch 1, Channel 3 |
| 1198 | 15.0f, 16.0f, |
| 1199 | }, |
| 1200 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1201 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1202 | TensorInfo input2TensorInfo({ 2, 1, 2 }, ArmnnType, qScale, qOffset); |
| 1203 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( |
| 1204 | { |
| 1205 | // Batch 0, Channel 0 |
| 1206 | 17.0f, 18.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1207 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1208 | // Batch 1, Channel 0 |
| 1209 | 31.0f, 32.0f, |
| 1210 | }, |
| 1211 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1212 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1213 | TensorInfo outputTensorInfo({ 2, 8, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1214 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1215 | |
| 1216 | std::vector<T> output; |
| 1217 | output.resize(outputTensorInfo.GetNumElements()); |
| 1218 | Concatenate<T>(workloadFactory, memoryManager, |
| 1219 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1220 | { input0.data(), input1.data(), input2.data() }, |
| 1221 | outputTensorInfo, |
| 1222 | output.data(), |
| 1223 | 1, |
| 1224 | true); |
| 1225 | |
| 1226 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1227 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 1228 | { |
| 1229 | // Batch 0, Channel 0 |
| 1230 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1231 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1232 | // Batch 0, Channel 1 |
| 1233 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1234 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1235 | // Batch 0, Channel 2 |
| 1236 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1237 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1238 | // Batch 0, Channel 3 |
| 1239 | 7.0f, 8.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1240 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1241 | // Batch 0, Channel 4 |
| 1242 | 9.0f, 10.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1243 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1244 | // Batch 0, Channel 5 |
| 1245 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1246 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1247 | // Batch 0, Channel 6 |
| 1248 | 25.0f, 26.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1249 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1250 | // Batch 0, Channel 7 |
| 1251 | 17.0f, 18.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1252 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1253 | // Batch 1, Channel 0 |
| 1254 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1255 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1256 | // Batch 1, Channel 1 |
| 1257 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1258 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1259 | // Batch 1, Channel 2 |
| 1260 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1261 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1262 | // Batch 1, Channel 3 |
| 1263 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1264 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1265 | // Batch 1, Channel 4 |
| 1266 | 29.0f, 30.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1267 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1268 | // Batch 1, Channel 5 |
| 1269 | 13.0f, 14.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1270 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1271 | // Batch 1, Channel 6 |
| 1272 | 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1273 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1274 | // Batch 1, Channel 7 |
| 1275 | 31.0f, 32.0f, |
| 1276 | }, |
| 1277 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1278 | |
| 1279 | return result; |
| 1280 | } |
| 1281 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1282 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1283 | LayerTestResult<T, 3> Concat3dDim2DiffInputDimsTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1284 | IWorkloadFactory& workloadFactory, |
| 1285 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1286 | bool useSubtensor, |
| 1287 | float qScale, |
| 1288 | int32_t qOffset) |
| 1289 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1290 | TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1291 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>( |
| 1292 | { |
| 1293 | // Batch 0, Channel 0 |
| 1294 | 1.0f, 2.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1295 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1296 | // Batch 0, Channel 1 |
| 1297 | 3.0f, 4.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1298 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1299 | // Batch 0, Channel 2 |
| 1300 | 5.0f, 6.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1301 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1302 | // Batch 1, Channel 0 |
| 1303 | 19.0f, 20.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1304 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1305 | // Batch 1, Channel 1 |
| 1306 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1307 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1308 | // Batch 1, Channel 2 |
| 1309 | 23.0f, 24.0f |
| 1310 | }, |
| 1311 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1312 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1313 | TensorInfo input1TensorInfo({ 2, 3, 1 }, ArmnnType, qScale, qOffset); |
| 1314 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>( |
| 1315 | { |
| 1316 | // Batch 0, Channel 0 |
| 1317 | 7.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1318 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1319 | // Batch 0, Channel 1 |
| 1320 | 9.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1321 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1322 | // Batch 0, Channel 2 |
| 1323 | 11.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1324 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1325 | // Batch 1, Channel 0 |
| 1326 | 25.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1327 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1328 | // Batch 1, Channel 1 |
| 1329 | 27.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1330 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1331 | // Batch 1, Channel 2 |
| 1332 | 29.0f |
| 1333 | }, |
| 1334 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1335 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1336 | TensorInfo input2TensorInfo({ 2, 3, 3 }, ArmnnType, qScale, qOffset); |
| 1337 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>( |
| 1338 | { |
| 1339 | // Batch 0, Channel 0 |
| 1340 | 13.0f, 14.0f, 50.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1341 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1342 | // Batch 0, Channel 1 |
| 1343 | 15.0f, 16.0f, 51.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1344 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1345 | // Batch 0, Channel 2 |
| 1346 | 17.0f, 18.0f, 52.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1347 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1348 | // Batch 1, Channel 0 |
| 1349 | 31.0f, 32.0f, 53.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1350 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1351 | // Batch 1, Channel 1 |
| 1352 | 33.0f, 34.0f, 54.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1353 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1354 | // Batch 1, Channel 2 |
| 1355 | 35.0f, 36.0f, 55.0f, |
| 1356 | }, |
| 1357 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1358 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1359 | TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1360 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 1361 | |
| 1362 | std::vector<T> output; |
| 1363 | output.resize(outputTensorInfo.GetNumElements()); |
| 1364 | Concatenate<T>(workloadFactory, memoryManager, |
| 1365 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 1366 | { input0.data(), input1.data(), input2.data() }, |
| 1367 | outputTensorInfo, |
| 1368 | output.data(), |
| 1369 | 2, |
| 1370 | useSubtensor); |
| 1371 | |
| 1372 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1373 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>( |
| 1374 | { |
| 1375 | // Batch 0, Channel 0 |
| 1376 | 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] | 1377 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1378 | // Batch 0, Channel 1 |
| 1379 | 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] | 1380 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1381 | // Batch 0, Channel 2 |
| 1382 | 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] | 1383 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1384 | // Batch 1, Channel 0 |
| 1385 | 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] | 1386 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1387 | // Batch 1, Channel 1 |
| 1388 | 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] | 1389 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1390 | // Batch 1, Channel 2 |
| 1391 | 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, |
| 1392 | }, |
| 1393 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1394 | |
| 1395 | return result; |
| 1396 | } |
| 1397 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1398 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1399 | LayerTestResult<T, 4> Concat4dTestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1400 | IWorkloadFactory& workloadFactory, |
| 1401 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1402 | const TensorInfo& outputTensorInfo, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1403 | unsigned int dimension, |
| 1404 | bool useSubtensor, |
| 1405 | float qScale, |
| 1406 | int32_t qOffset) |
| 1407 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1408 | TensorInfo inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1409 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1410 | auto input0 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( |
| 1411 | { |
| 1412 | 1.0f, 2.0f, |
| 1413 | 3.0f, 4.0f, |
| 1414 | 5.0f, 6.0f, |
| 1415 | 7.0f, 8.0f, |
| 1416 | 9.0f, 10.0f, |
| 1417 | 11.0f, 12.0f |
| 1418 | }, |
| 1419 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1420 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1421 | auto input1 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( |
| 1422 | { |
| 1423 | 11.0f, 12.0f, |
| 1424 | 13.0f, 14.0f, |
| 1425 | 15.0f, 16.0f, |
| 1426 | 17.0f, 18.0f, |
| 1427 | 19.0f, 20.0f, |
| 1428 | 21.0f, 22.0f |
| 1429 | }, |
| 1430 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1431 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1432 | auto input2 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>( |
| 1433 | { |
| 1434 | 21.0f, 22.0f, |
| 1435 | 23.0f, 24.0f, |
| 1436 | 25.0f, 26.0f, |
| 1437 | 27.0f, 28.0f, |
| 1438 | 29.0f, 30.0f, |
| 1439 | 31.0f, 32.0f |
| 1440 | }, |
| 1441 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1442 | |
| 1443 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1444 | |
| 1445 | std::vector<T> output; |
| 1446 | output.resize(outputTensorInfo.GetNumElements()); |
| 1447 | |
| 1448 | Concatenate<T>(workloadFactory, |
| 1449 | memoryManager, |
| 1450 | {inputTensorInfo, inputTensorInfo, inputTensorInfo}, |
| 1451 | {input0.data(), input1.data(), input2.data()}, |
| 1452 | outputTensorInfo, |
| 1453 | output.data(), |
| 1454 | dimension, |
| 1455 | useSubtensor); |
| 1456 | |
| 1457 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1458 | return result; |
| 1459 | } |
| 1460 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1461 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1462 | LayerTestResult<T, 4> Concat4dDim0TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1463 | IWorkloadFactory& workloadFactory, |
| 1464 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1465 | float qScale, |
| 1466 | int32_t qOffset) |
| 1467 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1468 | TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1469 | |
| 1470 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1471 | workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); |
| 1472 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1473 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1474 | { |
| 1475 | 1.0f, 2.0f, |
| 1476 | 3.0f, 4.0f, |
| 1477 | 5.0f, 6.0f, |
| 1478 | 7.0f, 8.0f, |
| 1479 | 9.0f, 10.0f, |
| 1480 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1481 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1482 | 11.0f, 12.0f, |
| 1483 | 13.0f, 14.0f, |
| 1484 | 15.0f, 16.0f, |
| 1485 | 17.0f, 18.0f, |
| 1486 | 19.0f, 20.0f, |
| 1487 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1488 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1489 | 21.0f, 22.0f, |
| 1490 | 23.0f, 24.0f, |
| 1491 | 25.0f, 26.0f, |
| 1492 | 27.0f, 28.0f, |
| 1493 | 29.0f, 30.0f, |
| 1494 | 31.0f, 32.0f |
| 1495 | }, |
| 1496 | qScale, qOffset)); |
| 1497 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1498 | return result; |
| 1499 | } |
| 1500 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1501 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1502 | LayerTestResult<T, 4> Concat4dDim1TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1503 | IWorkloadFactory& workloadFactory, |
| 1504 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1505 | float qScale, |
| 1506 | int32_t qOffset) |
| 1507 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1508 | TensorInfo outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1509 | |
| 1510 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1511 | workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); |
| 1512 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1513 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1514 | { |
| 1515 | 1.0f, 2.0f, |
| 1516 | 3.0f, 4.0f, |
| 1517 | 5.0f, 6.0f, |
| 1518 | 7.0f, 8.0f, |
| 1519 | 9.0f, 10.0f, |
| 1520 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1521 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1522 | 11.0f, 12.0f, |
| 1523 | 13.0f, 14.0f, |
| 1524 | 15.0f, 16.0f, |
| 1525 | 17.0f, 18.0f, |
| 1526 | 19.0f, 20.0f, |
| 1527 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1528 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1529 | 21.0f, 22.0f, |
| 1530 | 23.0f, 24.0f, |
| 1531 | 25.0f, 26.0f, |
| 1532 | 27.0f, 28.0f, |
| 1533 | 29.0f, 30.0f, |
| 1534 | 31.0f, 32.0f |
| 1535 | }, |
| 1536 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1537 | |
| 1538 | return result; |
| 1539 | } |
| 1540 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1541 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1542 | LayerTestResult<T, 4> Concat4dDim2TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1543 | IWorkloadFactory& workloadFactory, |
| 1544 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1545 | float qScale, |
| 1546 | int32_t qOffset) |
| 1547 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1548 | TensorInfo outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1549 | |
| 1550 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1551 | workloadFactory, memoryManager, outputTensorInfo, 2, true, qScale, qOffset); |
| 1552 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1553 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1554 | { |
| 1555 | 1.0f, 2.0f, |
| 1556 | 3.0f, 4.0f, |
| 1557 | 11.0f, 12.0f, |
| 1558 | 13.0f, 14.0f, |
| 1559 | 21.0f, 22.0f, |
| 1560 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1561 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1562 | 5.0f, 6.0f, |
| 1563 | 7.0f, 8.0f, |
| 1564 | 15.0f, 16.0f, |
| 1565 | 17.0f, 18.0f, |
| 1566 | 25.0f, 26.0f, |
| 1567 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1568 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1569 | 9.0f, 10.0f, |
| 1570 | 11.0f, 12.0f, |
| 1571 | 19.0f, 20.0f, |
| 1572 | 21.0f, 22.0f, |
| 1573 | 29.0f, 30.0f, |
| 1574 | 31.0f, 32.0f |
| 1575 | }, |
| 1576 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1577 | |
| 1578 | return result; |
| 1579 | } |
| 1580 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1581 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1582 | LayerTestResult<T, 4> Concat4dDim3TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1583 | IWorkloadFactory& workloadFactory, |
| 1584 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1585 | float qScale, |
| 1586 | int32_t qOffset, |
| 1587 | bool useSubtensor) |
| 1588 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1589 | TensorInfo outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1590 | |
| 1591 | LayerTestResult<T, 4> result = Concat4dTestImpl<ArmnnType>( |
| 1592 | workloadFactory, memoryManager, outputTensorInfo, 3, useSubtensor, qScale, qOffset); |
| 1593 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1594 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1595 | { |
| 1596 | 1.0f, 2.0f, |
| 1597 | 11.0f, 12.0f, |
| 1598 | 21.0f, 22.0f, |
| 1599 | 3.0f, 4.0f, |
| 1600 | 13.0f, 14.0f, |
| 1601 | 23.0f, 24.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1602 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1603 | 5.0f, 6.0f, |
| 1604 | 15.0f, 16.0f, |
| 1605 | 25.0f, 26.0f, |
| 1606 | 7.0f, 8.0f, |
| 1607 | 17.0f, 18.0f, |
| 1608 | 27.0f, 28.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1609 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1610 | 9.0f, 10.0f, |
| 1611 | 19.0f, 20.0f, |
| 1612 | 29.0f, 30.0f, |
| 1613 | 11.0f, 12.0f, |
| 1614 | 21.0f, 22.0f, |
| 1615 | 31.0f, 32.0f |
| 1616 | }, |
| 1617 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1618 | |
| 1619 | return result; |
| 1620 | } |
| 1621 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1622 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1623 | LayerTestResult<T, 4> Concat4dDiffShapeDim0TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1624 | IWorkloadFactory& workloadFactory, |
| 1625 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1626 | float qScale, |
| 1627 | int32_t qOffset) |
| 1628 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1629 | constexpr unsigned int dimension = 0u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1630 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1631 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1632 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1633 | { |
| 1634 | 1.0f, 2.0f, |
| 1635 | 3.0f, 4.0f, |
| 1636 | 5.0f, 6.0f, |
| 1637 | 7.0f, 8.0f, |
| 1638 | 9.0f, 10.0f, |
| 1639 | 11.0f, 12.0f |
| 1640 | }, |
| 1641 | 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 | TensorInfo inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1644 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1645 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1646 | { |
| 1647 | 11.0f, 12.0f, |
| 1648 | 13.0f, 14.0f, |
| 1649 | 15.0f, 16.0f, |
| 1650 | 17.0f, 18.0f, |
| 1651 | 19.0f, 20.0f, |
| 1652 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1653 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1654 | 21.0f, 22.0f, |
| 1655 | 23.0f, 24.0f, |
| 1656 | 25.0f, 26.0f, |
| 1657 | 27.0f, 28.0f, |
| 1658 | 29.0f, 30.0f, |
| 1659 | 31.0f, 32.0f |
| 1660 | }, |
| 1661 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1662 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1663 | TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1664 | |
| 1665 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1666 | |
| 1667 | std::vector<T> output; |
| 1668 | output.resize(outputTensorInfo.GetNumElements()); |
| 1669 | Concatenate<T>(workloadFactory, |
| 1670 | memoryManager, |
| 1671 | {inputTensorInfo0, inputTensorInfo1}, |
| 1672 | {input0.data(), input1.data()}, |
| 1673 | outputTensorInfo, |
| 1674 | output.data(), |
| 1675 | dimension, |
| 1676 | true); |
| 1677 | |
| 1678 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1679 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1680 | { |
| 1681 | 1.0f, 2.0f, |
| 1682 | 3.0f, 4.0f, |
| 1683 | 5.0f, 6.0f, |
| 1684 | 7.0f, 8.0f, |
| 1685 | 9.0f, 10.0f, |
| 1686 | 11.0f, 12.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1687 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1688 | 11.0f, 12.0f, |
| 1689 | 13.0f, 14.0f, |
| 1690 | 15.0f, 16.0f, |
| 1691 | 17.0f, 18.0f, |
| 1692 | 19.0f, 20.0f, |
| 1693 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1694 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1695 | 21.0f, 22.0f, |
| 1696 | 23.0f, 24.0f, |
| 1697 | 25.0f, 26.0f, |
| 1698 | 27.0f, 28.0f, |
| 1699 | 29.0f, 30.0f, |
| 1700 | 31.0f, 32.0f |
| 1701 | }, |
| 1702 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1703 | |
| 1704 | return result; |
| 1705 | } |
| 1706 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1707 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1708 | LayerTestResult<T, 4> Concat4dDiffShapeDim1TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1709 | IWorkloadFactory& workloadFactory, |
| 1710 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1711 | float qScale, |
| 1712 | int32_t qOffset) |
| 1713 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1714 | constexpr unsigned int dimension = 1u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1715 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1716 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1717 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1718 | { |
| 1719 | 1.0f, 2.0f, |
| 1720 | 3.0f, 4.0f, |
| 1721 | 5.0f, 6.0f, |
| 1722 | 7.0f, 8.0f, |
| 1723 | 9.0f, 10.0f, |
| 1724 | 11.0f, 12.0f |
| 1725 | }, |
| 1726 | 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 | TensorInfo inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1729 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1730 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1731 | { |
| 1732 | 11.0f, 12.0f, |
| 1733 | 13.0f, 14.0f, |
| 1734 | 15.0f, 16.0f, |
| 1735 | 17.0f, 18.0f, |
| 1736 | }, |
| 1737 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1738 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1739 | TensorInfo outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1740 | |
| 1741 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1742 | |
| 1743 | std::vector<T> output; |
| 1744 | output.resize(outputTensorInfo.GetNumElements()); |
| 1745 | Concatenate<T>(workloadFactory, |
| 1746 | memoryManager, |
| 1747 | {inputTensorInfo0, inputTensorInfo1}, |
| 1748 | {input0.data(), input1.data()}, |
| 1749 | outputTensorInfo, |
| 1750 | output.data(), |
| 1751 | dimension, |
| 1752 | true); |
| 1753 | |
| 1754 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1755 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1756 | { |
| 1757 | 1.0f, 2.0f, |
| 1758 | 3.0f, 4.0f, |
| 1759 | 5.0f, 6.0f, |
| 1760 | 7.0f, 8.0f, |
| 1761 | 9.0f, 10.0f, |
| 1762 | 11.0f, 12.0f, |
| 1763 | 11.0f, 12.0f, |
| 1764 | 13.0f, 14.0f, |
| 1765 | 15.0f, 16.0f, |
| 1766 | 17.0f, 18.0f |
| 1767 | }, |
| 1768 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1769 | |
| 1770 | return result; |
| 1771 | } |
| 1772 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1773 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1774 | LayerTestResult<T, 4> Concat4dDiffShapeDim2TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1775 | IWorkloadFactory& workloadFactory, |
| 1776 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1777 | float qScale, |
| 1778 | int32_t qOffset) |
| 1779 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1780 | constexpr unsigned int dimension = 2u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1781 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1782 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1783 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1784 | { |
| 1785 | 1.0f, 2.0f, |
| 1786 | 3.0f, 4.0f, |
| 1787 | 5.0f, 6.0f, |
| 1788 | 7.0f, 8.0f, |
| 1789 | 9.0f, 10.0f, |
| 1790 | 11.0f, 12.0f |
| 1791 | }, |
| 1792 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1793 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1794 | TensorInfo inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType, qScale, qOffset); |
| 1795 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1796 | { |
| 1797 | 11.0f, 12.0f, |
| 1798 | 13.0f, 14.0f, |
| 1799 | 15.0f, 16.0f, |
| 1800 | 17.0f, 18.0f, |
| 1801 | 19.0f, 20.0f, |
| 1802 | 21.0f, 22.0f, |
| 1803 | 23.0f, 24.0f, |
| 1804 | 25.0f, 26.0f, |
| 1805 | 27.0f, 28.0f |
| 1806 | }, |
| 1807 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1808 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1809 | TensorInfo outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1810 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1811 | |
| 1812 | std::vector<T> output; |
| 1813 | output.resize(outputTensorInfo.GetNumElements()); |
| 1814 | Concatenate<T>(workloadFactory, |
| 1815 | memoryManager, |
| 1816 | {inputTensorInfo0, inputTensorInfo1}, |
| 1817 | {input0.data(), input1.data()}, |
| 1818 | outputTensorInfo, |
| 1819 | output.data(), |
| 1820 | dimension, |
| 1821 | true); |
| 1822 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1823 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
| 1824 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1825 | { |
| 1826 | 1.0f, 2.0f, |
| 1827 | 3.0f, 4.0f, |
| 1828 | 11.0f, 12.0f, |
| 1829 | 13.0f, 14.0f, |
| 1830 | 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1831 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1832 | 5.0f, 6.0f, |
| 1833 | 7.0f, 8.0f, |
| 1834 | 17.0f, 18.0f, |
| 1835 | 19.0f, 20.0f, |
| 1836 | 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1837 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1838 | 9.0f, 10.0f, |
| 1839 | 11.0f, 12.0f, |
| 1840 | 23.0f, 24.0f, |
| 1841 | 25.0f, 26.0f, |
| 1842 | 27.0f, 28.0f |
| 1843 | }, |
| 1844 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1845 | |
| 1846 | return result; |
| 1847 | } |
| 1848 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1849 | template<DataType ArmnnType, typename T = ResolveType<ArmnnType>> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1850 | LayerTestResult<T, 4> Concat4dDiffShapeDim3TestImpl( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1851 | IWorkloadFactory& workloadFactory, |
| 1852 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1853 | float qScale, |
| 1854 | int32_t qOffset, |
| 1855 | bool useSubtensor) |
| 1856 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1857 | constexpr unsigned int dimension = 3u; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1858 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1859 | TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset); |
| 1860 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>( |
| 1861 | { |
| 1862 | 1.0f, 2.0f, |
| 1863 | 3.0f, 4.0f, |
| 1864 | 5.0f, 6.0f, |
| 1865 | 7.0f, 8.0f, |
| 1866 | 9.0f, 10.0f, |
| 1867 | 11.0f, 12.0f |
| 1868 | }, |
| 1869 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1870 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1871 | TensorInfo inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType, qScale, qOffset); |
| 1872 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>( |
| 1873 | { |
| 1874 | 11.0f, 12.0f, 13.0f, |
| 1875 | 14.0f, 15.0f, 16.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1876 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1877 | 17.0f, 18.0f, 19.0f, |
| 1878 | 20.0f, 21.0f, 22.0f, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1879 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1880 | 23.0f, 24.0f, 25.0f, |
| 1881 | 26.0f, 27.0f, 28.0f |
| 1882 | }, |
| 1883 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1884 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1885 | TensorInfo outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType, qScale, qOffset); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1886 | |
| 1887 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1888 | |
| 1889 | std::vector<T> output; |
| 1890 | output.resize(outputTensorInfo.GetNumElements()); |
| 1891 | Concatenate<T>(workloadFactory, |
| 1892 | memoryManager, |
| 1893 | {inputTensorInfo0, inputTensorInfo1}, |
| 1894 | {input0.data(), input1.data()}, |
| 1895 | outputTensorInfo, |
| 1896 | output.data(), |
| 1897 | dimension, |
| 1898 | useSubtensor); |
| 1899 | |
| 1900 | result.output = MakeTensor<T, 4>(outputTensorInfo, output); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1901 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>( |
| 1902 | { |
| 1903 | 1.0f, 2.0f, 11.0f, 12.0f, 13.0f, |
| 1904 | 3.0f, 4.0f, 14.0f, 15.0f, 16.0f, |
| 1905 | 5.0f, 6.0f, 17.0f, 18.0f, 19.0f, |
| 1906 | 7.0f, 8.0f, 20.0f, 21.0f, 22.0f, |
| 1907 | 9.0f, 10.0f, 23.0f, 24.0f, 25.0f, |
| 1908 | 11.0f, 12.0f, 26.0f, 27.0f, 28.0f |
| 1909 | }, |
| 1910 | qScale, qOffset)); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1911 | |
| 1912 | return result; |
| 1913 | } |
| 1914 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1915 | template<DataType ArmnnType, typename T> |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1916 | LayerTestResult<T, 3> ConcatDifferentInputOutputQParamTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1917 | IWorkloadFactory& workloadFactory, |
| 1918 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1919 | bool useSubtensor) |
| 1920 | { |
Derek Lamberti | c374ff0 | 2019-12-10 21:57:35 +0000 | [diff] [blame] | 1921 | boost::ignore_unused(memoryManager); |
| 1922 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1923 | // Defines the tensor descriptors. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1924 | TensorInfo outputTensorInfo({ 3, 6, 3 }, ArmnnType); |
| 1925 | TensorInfo inputTensorInfo1({ 3, 6, 2 }, ArmnnType); |
| 1926 | TensorInfo inputTensorInfo2({ 3, 6, 1 }, ArmnnType); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1927 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 1928 | std::vector<TensorShape> inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()}); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 1929 | |
| 1930 | // Quantized input1 tensor. |
| 1931 | const float inputScale1 = 0.5f; |
| 1932 | const int32_t inputOffset1 = 5; |
| 1933 | |
| 1934 | auto input1 = MakeTensor<T, 3>(inputTensorInfo1, std::vector<T>( |
| 1935 | { |
| 1936 | 1, 2, 3, |
| 1937 | 4, 5, 6, |
| 1938 | 7, 8, 9, |
| 1939 | 10, 11, 12, |
| 1940 | 13, 14, 15, |
| 1941 | 16, 17, 18, |
| 1942 | |
| 1943 | 19, 20, 21, |
| 1944 | 22, 23, 24, |
| 1945 | 25, 26, 27, |
| 1946 | 28, 29, 30, |
| 1947 | 31, 32, 33, |
| 1948 | 34, 35, 36 |
| 1949 | })); |
| 1950 | |
| 1951 | // Quatized input2 tensor. |
| 1952 | const float inputScale2 = 0.2f; |
| 1953 | const int32_t inputOffset2 = 10; |
| 1954 | |
| 1955 | auto input2 = MakeTensor<T, 3>(inputTensorInfo2, std::vector<T>( |
| 1956 | { |
| 1957 | 37, 38, 39, |
| 1958 | 40, 41, 42, |
| 1959 | 43, 44, 45, |
| 1960 | 46, 47, 48, |
| 1961 | 49, 50, 51, |
| 1962 | 52, 53, 54 |
| 1963 | })); |
| 1964 | |
| 1965 | // Quantized output tensor. |
| 1966 | const float outputScale = 0.1f; |
| 1967 | const int32_t outputOffset = 20; |
| 1968 | |
| 1969 | LayerTestResult<T, 3> ret(outputTensorInfo); |
| 1970 | |
| 1971 | ret.outputExpected = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>( |
| 1972 | { |
| 1973 | 0, 5, 74, |
| 1974 | 10, 15, 76, |
| 1975 | 20, 25, 78, |
| 1976 | 30, 35, 80, |
| 1977 | 40, 45, 82, |
| 1978 | 50, 55, 84, |
| 1979 | |
| 1980 | 60, 65, 86, |
| 1981 | 70, 75, 88, |
| 1982 | 80, 85, 90, |
| 1983 | 90, 95, 92, |
| 1984 | 100, 105, 94, |
| 1985 | 110, 115, 96, |
| 1986 | |
| 1987 | 120, 125, 98, |
| 1988 | 130, 135, 100, |
| 1989 | 140, 145, 102, |
| 1990 | 150, 155, 104, |
| 1991 | 160, 165, 106, |
| 1992 | 170, 175, 108 |
| 1993 | })); |
| 1994 | |
| 1995 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 1996 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 1997 | inputTensorInfo1.SetQuantizationScale(inputScale1); |
| 1998 | inputTensorInfo1.SetQuantizationOffset(inputOffset1); |
| 1999 | inputTensorInfo2.SetQuantizationScale(inputScale2); |
| 2000 | inputTensorInfo2.SetQuantizationOffset(inputOffset2); |
| 2001 | |
| 2002 | 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] | 2003 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2004 | |
| 2005 | 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] | 2006 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2007 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2008 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2009 | |
| 2010 | bool subTensorsSupported = useSubtensor && workloadFactory.SupportsSubTensors(); |
| 2011 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2012 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2013 | subTensorsSupported ? |
| 2014 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2015 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2016 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2017 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2018 | subTensorsSupported ? |
| 2019 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2020 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2021 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2022 | ConcatQueueDescriptor data; |
| 2023 | OriginsDescriptor desc = CreateDescriptorForConcatenation( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2024 | inputTensorShapes.begin(),inputTensorShapes.end(), 2); |
| 2025 | data.m_Parameters = desc; |
| 2026 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2027 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2028 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2029 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2030 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2031 | |
| 2032 | data.m_ViewOrigins.push_back(window1); |
| 2033 | data.m_ViewOrigins.push_back(window2); |
| 2034 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2035 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2036 | |
| 2037 | inputHandle1->Allocate(); |
| 2038 | inputHandle2->Allocate(); |
| 2039 | outputHandle->Allocate(); |
| 2040 | |
| 2041 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2042 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2043 | |
| 2044 | workload->PostAllocationConfigure(); |
| 2045 | workload->Execute(); |
| 2046 | |
| 2047 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2048 | |
| 2049 | return ret; |
| 2050 | } |
| 2051 | |
| 2052 | // |
| 2053 | // Explicit template specializations |
| 2054 | // |
| 2055 | |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2056 | template LayerTestResult<ResolveType<DataType::QAsymmU8>, 3> |
| 2057 | ConcatDifferentInputOutputQParamTest<DataType::QAsymmU8>( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2058 | IWorkloadFactory& workloadFactory, |
| 2059 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2060 | bool useSubtensor); |
| 2061 | |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2062 | template LayerTestResult<ResolveType<DataType::QSymmS16>, 3> |
| 2063 | ConcatDifferentInputOutputQParamTest<DataType::QSymmS16>( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2064 | IWorkloadFactory& workloadFactory, |
| 2065 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2066 | bool useSubtensor); |
| 2067 | |
| 2068 | // |
| 2069 | // Implementation functions |
| 2070 | // |
| 2071 | |
| 2072 | LayerTestResult<float,3> ConcatTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2073 | IWorkloadFactory& workloadFactory, |
| 2074 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2075 | { |
Derek Lamberti | c374ff0 | 2019-12-10 21:57:35 +0000 | [diff] [blame] | 2076 | boost::ignore_unused(memoryManager); |
| 2077 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2078 | unsigned int outputWidth = 3; |
| 2079 | unsigned int outputHeight = 6; |
| 2080 | unsigned int outputChannels = 3; |
| 2081 | |
| 2082 | unsigned int inputWidth1 = 3; |
| 2083 | unsigned int inputHeight1 = 6; |
| 2084 | unsigned int inputChannels1 = 2; |
| 2085 | |
| 2086 | unsigned int inputWidth2 = 3; |
| 2087 | unsigned int inputHeight2 = 6; |
| 2088 | unsigned int inputChannels2 = 1; |
| 2089 | |
| 2090 | // Define the tensor descriptors. |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2091 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::Float32); |
| 2092 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::Float32); |
| 2093 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::Float32); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2094 | |
| 2095 | LayerTestResult<float,3> ret(outputTensorInfo); |
| 2096 | |
| 2097 | ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>( |
| 2098 | { |
| 2099 | 1.0f, 2.0f, 3.0f, |
| 2100 | 4.0f, 5.0f, 6.0f, |
| 2101 | 7.0f, 8.0f, 9.0f, |
| 2102 | 10.0f, 11.0f, 12.0f, |
| 2103 | 13.0f, 14.0f, 15.0f, |
| 2104 | 16.0f, 17.0f, 18.0f, |
| 2105 | |
| 2106 | 19.0f, 20.0f, 21.0f, |
| 2107 | 22.0f, 23.0f, 24.0f, |
| 2108 | 25.0f, 26.0f, 27.0f, |
| 2109 | 28.0f, 29.0f, 30.0f, |
| 2110 | 31.0f, 32.0f, 33.0f, |
| 2111 | 34.0f, 35.0f, 36.0f, |
| 2112 | |
| 2113 | 37.0f, 38.0f, 39.0f, |
| 2114 | 40.0f, 41.0f, 42.0f, |
| 2115 | 43.0f, 44.0f, 45.0f, |
| 2116 | 46.0f, 47.0f, 48.0f, |
| 2117 | 49.0f, 50.0f, 51.0f, |
| 2118 | 52.0f, 53.0f, 54.0f, |
| 2119 | }) |
| 2120 | ); |
| 2121 | |
| 2122 | auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>( |
| 2123 | { |
| 2124 | 1.0f, 2.0f, 3.0f, |
| 2125 | 4.0f, 5.0f, 6.0f, |
| 2126 | 7.0f, 8.0f, 9.0f, |
| 2127 | 10.0f, 11.0f, 12.0f, |
| 2128 | 13.0f, 14.0f, 15.0f, |
| 2129 | 16.0f, 17.0f, 18.0f, |
| 2130 | |
| 2131 | 19.0f, 20.0f, 21.0f, |
| 2132 | 22.0f, 23.0f, 24.0f, |
| 2133 | 25.0f, 26.0f, 27.0f, |
| 2134 | 28.0f, 29.0f, 30.0f, |
| 2135 | 31.0f, 32.0f, 33.0f, |
| 2136 | 34.0f, 35.0f, 36.0f, |
| 2137 | }) |
| 2138 | ); |
| 2139 | |
| 2140 | auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>( |
| 2141 | { |
| 2142 | 37.0f, 38.0f, 39.0f, |
| 2143 | 40.0f, 41.0f, 42.0f, |
| 2144 | 43.0f, 44.0f, 45.0f, |
| 2145 | 46.0f, 47.0f, 48.0f, |
| 2146 | 49.0f, 50.0f, 51.0f, |
| 2147 | 52.0f, 53.0f, 54.0f, |
| 2148 | }) |
| 2149 | ); |
| 2150 | |
| 2151 | 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] | 2152 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2153 | |
| 2154 | 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] | 2155 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2156 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2157 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2158 | |
| 2159 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2160 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2161 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2162 | subTensorsSupported ? |
| 2163 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2164 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2165 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2166 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2167 | subTensorsSupported ? |
| 2168 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2169 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2170 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2171 | ConcatQueueDescriptor data; |
| 2172 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2173 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2174 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2175 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2176 | |
| 2177 | data.m_ViewOrigins.push_back(window1); |
| 2178 | data.m_ViewOrigins.push_back(window2); |
| 2179 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2180 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2181 | |
| 2182 | inputHandle1->Allocate(); |
| 2183 | inputHandle2->Allocate(); |
| 2184 | outputHandle->Allocate(); |
| 2185 | |
| 2186 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2187 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2188 | |
| 2189 | workload->PostAllocationConfigure(); |
| 2190 | workload->Execute(); |
| 2191 | |
| 2192 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2193 | |
| 2194 | return ret; |
| 2195 | } |
| 2196 | |
| 2197 | LayerTestResult<float, 1> Concat1dTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2198 | IWorkloadFactory& workloadFactory, |
| 2199 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2200 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2201 | return Concat1dTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2202 | } |
| 2203 | |
| 2204 | LayerTestResult<float, 2> Concat2dDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2205 | IWorkloadFactory& workloadFactory, |
| 2206 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2207 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2208 | return Concat2dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2209 | } |
| 2210 | |
| 2211 | LayerTestResult<float, 2> Concat2dDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2212 | IWorkloadFactory& workloadFactory, |
| 2213 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2214 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2215 | return Concat2dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2216 | } |
| 2217 | |
| 2218 | LayerTestResult<float, 2> Concat2dDim0DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2219 | IWorkloadFactory& workloadFactory, |
| 2220 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2221 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2222 | return Concat2dDim0DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2223 | } |
| 2224 | |
| 2225 | LayerTestResult<float, 2> Concat2dDim1DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2226 | IWorkloadFactory& workloadFactory, |
| 2227 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2228 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2229 | return Concat2dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2230 | } |
| 2231 | |
| 2232 | LayerTestResult<float, 3> Concat3dDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2233 | IWorkloadFactory& workloadFactory, |
| 2234 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2235 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2236 | return Concat3dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2237 | } |
| 2238 | |
| 2239 | LayerTestResult<float, 3> Concat3dDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2240 | IWorkloadFactory& workloadFactory, |
| 2241 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2242 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2243 | return Concat3dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2244 | } |
| 2245 | |
| 2246 | LayerTestResult<float, 3> Concat3dDim2Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2247 | IWorkloadFactory& workloadFactory, |
| 2248 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2249 | bool useSubtensor) |
| 2250 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2251 | return Concat3dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, useSubtensor, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2252 | } |
| 2253 | |
| 2254 | LayerTestResult<float, 3> Concat3dDim0DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2255 | IWorkloadFactory& workloadFactory, |
| 2256 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2257 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2258 | return Concat3dDim0DiffInputDimsTestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2259 | workloadFactory, memoryManager, 0.0f, 0); |
| 2260 | } |
| 2261 | |
| 2262 | LayerTestResult<float, 3> Concat3dDim1DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2263 | IWorkloadFactory& workloadFactory, |
| 2264 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2265 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2266 | return Concat3dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2267 | } |
| 2268 | |
| 2269 | LayerTestResult<float, 3> Concat3dDim2DiffInputDimsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2270 | IWorkloadFactory& workloadFactory, |
| 2271 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2272 | bool useSubtensor) |
| 2273 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2274 | return Concat3dDim2DiffInputDimsTestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2275 | workloadFactory, memoryManager, useSubtensor, 0.0f, 0); |
| 2276 | } |
| 2277 | |
| 2278 | LayerTestResult<float, 4> Concat4dDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2279 | IWorkloadFactory& workloadFactory, |
| 2280 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2281 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2282 | return Concat4dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2283 | } |
| 2284 | |
| 2285 | LayerTestResult<float, 4> Concat4dDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2286 | IWorkloadFactory& workloadFactory, |
| 2287 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2288 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2289 | return Concat4dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2290 | } |
| 2291 | |
| 2292 | LayerTestResult<float, 4> Concat4dDim2Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2293 | IWorkloadFactory& workloadFactory, |
| 2294 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2295 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2296 | return Concat4dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2297 | } |
| 2298 | |
| 2299 | LayerTestResult<float, 4> Concat4dDim3Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2300 | IWorkloadFactory& workloadFactory, |
| 2301 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2302 | bool useSubtensor) |
| 2303 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2304 | return Concat4dDim3TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0, useSubtensor); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2305 | } |
| 2306 | |
| 2307 | LayerTestResult<float, 4> Concat4dDiffShapeDim0Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2308 | IWorkloadFactory& workloadFactory, |
| 2309 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2310 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2311 | return Concat4dDiffShapeDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2312 | } |
| 2313 | |
| 2314 | LayerTestResult<float, 4> Concat4dDiffShapeDim1Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2315 | IWorkloadFactory& workloadFactory, |
| 2316 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2317 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2318 | return Concat4dDiffShapeDim1TestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2319 | workloadFactory, memoryManager, 0.0f, 0); |
| 2320 | } |
| 2321 | |
| 2322 | LayerTestResult<float, 4> Concat4dDiffShapeDim2Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2323 | IWorkloadFactory& workloadFactory, |
| 2324 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2325 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2326 | return Concat4dDiffShapeDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2327 | } |
| 2328 | |
| 2329 | LayerTestResult<float, 4> Concat4dDiffShapeDim3Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2330 | IWorkloadFactory& workloadFactory, |
| 2331 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2332 | bool useSubtensor) |
| 2333 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2334 | return Concat4dDiffShapeDim3TestImpl<DataType::Float32>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2335 | workloadFactory, memoryManager, 0.0f, 0, useSubtensor); |
| 2336 | } |
| 2337 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2338 | LayerTestResult<Half, 3> ConcatFloat16Test( |
| 2339 | IWorkloadFactory& workloadFactory, |
| 2340 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Matthew Jackson | 9bff144 | 2019-09-12 09:08:23 +0100 | [diff] [blame] | 2341 | { |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2342 | return Concat3dDim1TestImpl<DataType::Float16>(workloadFactory, memoryManager, 0.0f, 0); |
Matthew Jackson | 9bff144 | 2019-09-12 09:08:23 +0100 | [diff] [blame] | 2343 | } |
| 2344 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2345 | LayerTestResult<uint8_t, 3> ConcatUint8DifferentQParamsTest( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2346 | IWorkloadFactory& workloadFactory, |
| 2347 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2348 | { |
Derek Lamberti | c374ff0 | 2019-12-10 21:57:35 +0000 | [diff] [blame] | 2349 | boost::ignore_unused(memoryManager); |
| 2350 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2351 | unsigned int outputWidth = 3; |
| 2352 | unsigned int outputHeight = 6; |
| 2353 | unsigned int outputChannels = 3; |
| 2354 | |
| 2355 | unsigned int inputWidth1 = 3; |
| 2356 | unsigned int inputHeight1 = 6; |
| 2357 | unsigned int inputChannels1 = 2; |
| 2358 | |
| 2359 | unsigned int inputWidth2 = 3; |
| 2360 | unsigned int inputHeight2 = 6; |
| 2361 | unsigned int inputChannels2 = 1; |
| 2362 | |
| 2363 | // Defines the tensor descriptors. |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2364 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QAsymmU8); |
| 2365 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QAsymmU8); |
| 2366 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QAsymmU8); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2367 | |
| 2368 | // Quantized input1 tensor. Range [-3, 1] |
| 2369 | const float inputScale1 = 0.015686f; |
| 2370 | const int32_t inputOffset1 = 192; |
| 2371 | |
| 2372 | auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( |
| 2373 | { |
| 2374 | 1, 2, 3, |
| 2375 | 4, 5, 6, |
| 2376 | 7, 8, 9, |
| 2377 | 10, 11, 12, |
| 2378 | 13, 14, 15, |
| 2379 | 16, 17, 18, |
| 2380 | |
| 2381 | 19, 20, 21, |
| 2382 | 22, 23, 24, |
| 2383 | 25, 26, 27, |
| 2384 | 28, 29, 30, |
| 2385 | 31, 32, 33, |
| 2386 | 34, 35, 36, |
| 2387 | }) |
| 2388 | ); |
| 2389 | |
| 2390 | // Quatized input2 tensor. Range [-1, 4] |
| 2391 | const float inputScale2 = 0.019608f; |
| 2392 | const int32_t inputOffset2 = 50; |
| 2393 | |
| 2394 | auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( |
| 2395 | { |
| 2396 | 37, 38, 39, |
| 2397 | 40, 41, 42, |
| 2398 | 43, 44, 45, |
| 2399 | 46, 47, 48, |
| 2400 | 49, 50, 51, |
| 2401 | 52, 53, 54, |
| 2402 | }) |
| 2403 | ); |
| 2404 | |
| 2405 | // Output has the same quantization parameters than input1, |
| 2406 | // so that only the requantization of input2 is required |
| 2407 | const float outputScale = 0.015686f; |
| 2408 | const int32_t outputOffset = 192; |
| 2409 | |
| 2410 | LayerTestResult<uint8_t, 3> ret(outputTensorInfo); |
| 2411 | |
| 2412 | ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( |
| 2413 | { |
| 2414 | 1, 2, 3, |
| 2415 | 4, 5, 6, |
| 2416 | 7, 8, 9, |
| 2417 | 10, 11, 12, |
| 2418 | 13, 14, 15, |
| 2419 | 16, 17, 18, |
| 2420 | |
| 2421 | 19, 20, 21, |
| 2422 | 22, 23, 24, |
| 2423 | 25, 26, 27, |
| 2424 | 28, 29, 30, |
| 2425 | 31, 32, 33, |
| 2426 | 34, 35, 36, |
| 2427 | |
| 2428 | 176, 177, 178, |
| 2429 | 179, 181, 182, |
| 2430 | 183, 184, 186, |
| 2431 | 187, 188, 189, |
| 2432 | 191, 192, 193, |
| 2433 | 195, 196, 197, |
| 2434 | }) |
| 2435 | ); |
| 2436 | |
| 2437 | outputTensorInfo.SetQuantizationScale(outputScale); |
| 2438 | outputTensorInfo.SetQuantizationOffset(outputOffset); |
| 2439 | inputTensorInfo1.SetQuantizationScale(inputScale1); |
| 2440 | inputTensorInfo1.SetQuantizationOffset(inputOffset1); |
| 2441 | inputTensorInfo2.SetQuantizationScale(inputScale2); |
| 2442 | inputTensorInfo2.SetQuantizationOffset(inputOffset2); |
| 2443 | |
| 2444 | 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] | 2445 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2446 | |
| 2447 | 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] | 2448 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2449 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2450 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2451 | |
| 2452 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2453 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2454 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2455 | subTensorsSupported ? |
| 2456 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2457 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2458 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2459 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2460 | subTensorsSupported ? |
| 2461 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2462 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2463 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2464 | ConcatQueueDescriptor data; |
| 2465 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2466 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2467 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2468 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2469 | |
| 2470 | data.m_ViewOrigins.push_back(window1); |
| 2471 | data.m_ViewOrigins.push_back(window2); |
| 2472 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2473 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2474 | |
| 2475 | inputHandle1->Allocate(); |
| 2476 | inputHandle2->Allocate(); |
| 2477 | outputHandle->Allocate(); |
| 2478 | |
| 2479 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2480 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2481 | |
| 2482 | workload->PostAllocationConfigure(); |
| 2483 | workload->Execute(); |
| 2484 | |
| 2485 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2486 | |
| 2487 | return ret; |
| 2488 | } |
| 2489 | |
| 2490 | LayerTestResult<uint8_t, 3> ConcatUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2491 | IWorkloadFactory& workloadFactory, |
| 2492 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2493 | { |
Derek Lamberti | c374ff0 | 2019-12-10 21:57:35 +0000 | [diff] [blame] | 2494 | boost::ignore_unused(memoryManager); |
| 2495 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2496 | unsigned int outputWidth = 3; |
| 2497 | unsigned int outputHeight = 6; |
| 2498 | unsigned int outputChannels = 3; |
| 2499 | |
| 2500 | unsigned int inputWidth1 = 3; |
| 2501 | unsigned int inputHeight1 = 6; |
| 2502 | unsigned int inputChannels1 = 2; |
| 2503 | |
| 2504 | unsigned int inputWidth2 = 3; |
| 2505 | unsigned int inputHeight2 = 6; |
| 2506 | unsigned int inputChannels2 = 1; |
| 2507 | |
| 2508 | // Defines the tensor descriptors. |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2509 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QAsymmU8); |
| 2510 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QAsymmU8); |
| 2511 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QAsymmU8); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2512 | |
| 2513 | // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. |
| 2514 | const float scale = 0.13497836f; |
| 2515 | const int32_t offset = -7; |
| 2516 | |
| 2517 | outputTensorInfo.SetQuantizationScale(scale); |
| 2518 | outputTensorInfo.SetQuantizationOffset(offset); |
| 2519 | inputTensorInfo1.SetQuantizationScale(scale); |
| 2520 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 2521 | inputTensorInfo2.SetQuantizationScale(scale); |
| 2522 | inputTensorInfo2.SetQuantizationOffset(offset); |
| 2523 | |
| 2524 | LayerTestResult<uint8_t, 3> ret(outputTensorInfo); |
| 2525 | |
| 2526 | ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( |
| 2527 | { |
| 2528 | 1, 2, 3, |
| 2529 | 4, 5, 6, |
| 2530 | 7, 8, 9, |
| 2531 | 10, 11, 12, |
| 2532 | 13, 14, 15, |
| 2533 | 16, 17, 18, |
| 2534 | |
| 2535 | 19, 20, 21, |
| 2536 | 22, 23, 24, |
| 2537 | 25, 26, 27, |
| 2538 | 28, 29, 30, |
| 2539 | 31, 32, 33, |
| 2540 | 34, 35, 36, |
| 2541 | |
| 2542 | 37, 38, 39, |
| 2543 | 40, 41, 42, |
| 2544 | 43, 44, 45, |
| 2545 | 46, 47, 48, |
| 2546 | 49, 50, 51, |
| 2547 | 52, 53, 54, |
| 2548 | }) |
| 2549 | ); |
| 2550 | |
| 2551 | auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( |
| 2552 | { |
| 2553 | 1, 2, 3, |
| 2554 | 4, 5, 6, |
| 2555 | 7, 8, 9, |
| 2556 | 10, 11, 12, |
| 2557 | 13, 14, 15, |
| 2558 | 16, 17, 18, |
| 2559 | |
| 2560 | 19, 20, 21, |
| 2561 | 22, 23, 24, |
| 2562 | 25, 26, 27, |
| 2563 | 28, 29, 30, |
| 2564 | 31, 32, 33, |
| 2565 | 34, 35, 36, |
| 2566 | }) |
| 2567 | ); |
| 2568 | |
| 2569 | auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( |
| 2570 | { |
| 2571 | 37, 38, 39, |
| 2572 | 40, 41, 42, |
| 2573 | 43, 44, 45, |
| 2574 | 46, 47, 48, |
| 2575 | 49, 50, 51, |
| 2576 | 52, 53, 54, |
| 2577 | }) |
| 2578 | ); |
| 2579 | |
| 2580 | 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] | 2581 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2582 | |
| 2583 | 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] | 2584 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2585 | |
| 2586 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2587 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2588 | |
| 2589 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2590 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2591 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2592 | subTensorsSupported ? |
| 2593 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2594 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2595 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2596 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2597 | subTensorsSupported ? |
| 2598 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2599 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2600 | |
| 2601 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2602 | ConcatQueueDescriptor data; |
| 2603 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2604 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2605 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2606 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2607 | |
| 2608 | data.m_ViewOrigins.push_back(window1); |
| 2609 | data.m_ViewOrigins.push_back(window2); |
| 2610 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2611 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2612 | |
| 2613 | inputHandle1->Allocate(); |
| 2614 | inputHandle2->Allocate(); |
| 2615 | outputHandle->Allocate(); |
| 2616 | |
| 2617 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2618 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2619 | |
| 2620 | workload->PostAllocationConfigure(); |
| 2621 | workload->Execute(); |
| 2622 | |
| 2623 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2624 | |
| 2625 | return ret; |
| 2626 | } |
| 2627 | |
| 2628 | LayerTestResult<uint16_t, 3> ConcatUint16Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2629 | IWorkloadFactory& workloadFactory, |
| 2630 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2631 | { |
Derek Lamberti | c374ff0 | 2019-12-10 21:57:35 +0000 | [diff] [blame] | 2632 | boost::ignore_unused(memoryManager); |
| 2633 | |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2634 | unsigned int outputWidth = 3; |
| 2635 | unsigned int outputHeight = 6; |
| 2636 | unsigned int outputChannels = 3; |
| 2637 | |
| 2638 | unsigned int inputWidth1 = 3; |
| 2639 | unsigned int inputHeight1 = 6; |
| 2640 | unsigned int inputChannels1 = 2; |
| 2641 | |
| 2642 | unsigned int inputWidth2 = 3; |
| 2643 | unsigned int inputHeight2 = 6; |
| 2644 | unsigned int inputChannels2 = 1; |
| 2645 | |
| 2646 | // Defines the tensor descriptors. |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2647 | TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, DataType::QSymmS16); |
| 2648 | TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, DataType::QSymmS16); |
| 2649 | TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, DataType::QSymmS16); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2650 | |
| 2651 | // Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them. |
| 2652 | const float scale = 0.13497836f; |
| 2653 | const int32_t offset = -7; |
| 2654 | |
| 2655 | outputTensorInfo.SetQuantizationScale(scale); |
| 2656 | outputTensorInfo.SetQuantizationOffset(offset); |
| 2657 | inputTensorInfo1.SetQuantizationScale(scale); |
| 2658 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 2659 | inputTensorInfo2.SetQuantizationScale(scale); |
| 2660 | inputTensorInfo2.SetQuantizationOffset(offset); |
| 2661 | |
| 2662 | LayerTestResult<uint16_t, 3> ret(outputTensorInfo); |
| 2663 | |
| 2664 | ret.outputExpected = MakeTensor<uint16_t, 3>(outputTensorInfo, std::vector<uint16_t>( |
| 2665 | { |
| 2666 | 1, 2, 3, |
| 2667 | 4, 5, 6, |
| 2668 | 7, 8, 9, |
| 2669 | 10, 11, 12, |
| 2670 | 13, 14, 15, |
| 2671 | 16, 17, 18, |
| 2672 | |
| 2673 | 19, 20, 21, |
| 2674 | 22, 23, 24, |
| 2675 | 25, 26, 27, |
| 2676 | 28, 29, 30, |
| 2677 | 31, 32, 33, |
| 2678 | 34, 35, 36, |
| 2679 | |
| 2680 | 37, 38, 39, |
| 2681 | 40, 41, 42, |
| 2682 | 43, 44, 45, |
| 2683 | 46, 47, 48, |
| 2684 | 49, 50, 51, |
| 2685 | 52, 53, 54, |
| 2686 | })); |
| 2687 | |
| 2688 | auto input1 = MakeTensor<uint16_t, 3>(inputTensorInfo1, std::vector<uint16_t>( |
| 2689 | { |
| 2690 | 1, 2, 3, |
| 2691 | 4, 5, 6, |
| 2692 | 7, 8, 9, |
| 2693 | 10, 11, 12, |
| 2694 | 13, 14, 15, |
| 2695 | 16, 17, 18, |
| 2696 | |
| 2697 | 19, 20, 21, |
| 2698 | 22, 23, 24, |
| 2699 | 25, 26, 27, |
| 2700 | 28, 29, 30, |
| 2701 | 31, 32, 33, |
| 2702 | 34, 35, 36, |
| 2703 | })); |
| 2704 | |
| 2705 | auto input2 = MakeTensor<uint16_t, 3>(inputTensorInfo2, std::vector<uint16_t>( |
| 2706 | { |
| 2707 | 37, 38, 39, |
| 2708 | 40, 41, 42, |
| 2709 | 43, 44, 45, |
| 2710 | 46, 47, 48, |
| 2711 | 49, 50, 51, |
| 2712 | 52, 53, 54, |
| 2713 | })); |
| 2714 | |
| 2715 | 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] | 2716 | ConcatQueueDescriptor::ViewOrigin window1(wOrigin1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2717 | |
| 2718 | 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] | 2719 | ConcatQueueDescriptor::ViewOrigin window2(wOrigin2); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2720 | |
| 2721 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2722 | std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2723 | |
| 2724 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2725 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2726 | std::unique_ptr<ITensorHandle> inputHandle1 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2727 | subTensorsSupported ? |
| 2728 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 2729 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 2730 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2731 | std::unique_ptr<ITensorHandle> inputHandle2 = |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2732 | subTensorsSupported ? |
| 2733 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 2734 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 2735 | |
| 2736 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2737 | ConcatQueueDescriptor data; |
| 2738 | WorkloadInfo info; |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2739 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 2740 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 2741 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 2742 | |
| 2743 | data.m_ViewOrigins.push_back(window1); |
| 2744 | data.m_ViewOrigins.push_back(window2); |
| 2745 | |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2746 | std::unique_ptr<IWorkload> workload = workloadFactory.CreateConcat(data, info); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2747 | |
| 2748 | inputHandle1->Allocate(); |
| 2749 | inputHandle2->Allocate(); |
| 2750 | outputHandle->Allocate(); |
| 2751 | |
| 2752 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 2753 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
| 2754 | |
| 2755 | workload->PostAllocationConfigure(); |
| 2756 | workload->Execute(); |
| 2757 | |
| 2758 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 2759 | |
| 2760 | return ret; |
| 2761 | } |
| 2762 | |
| 2763 | LayerTestResult<uint8_t, 1> Concat1dUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2764 | IWorkloadFactory& workloadFactory, |
| 2765 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2766 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2767 | return Concat1dTestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2768 | } |
| 2769 | |
| 2770 | LayerTestResult<uint8_t, 2> Concat2dDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2771 | IWorkloadFactory& workloadFactory, |
| 2772 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2773 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2774 | return Concat2dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2775 | } |
| 2776 | |
| 2777 | LayerTestResult<uint8_t, 2> Concat2dDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2778 | IWorkloadFactory& workloadFactory, |
| 2779 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2780 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2781 | return Concat2dDim1TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2782 | } |
| 2783 | |
| 2784 | LayerTestResult<uint8_t, 2> Concat2dDim0DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2785 | IWorkloadFactory& workloadFactory, |
| 2786 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2787 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2788 | return Concat2dDim0DiffInputDimsTestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2789 | workloadFactory, memoryManager, 0.5f, -1); |
| 2790 | } |
| 2791 | |
| 2792 | LayerTestResult<uint8_t, 2> Concat2dDim1DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2793 | IWorkloadFactory& workloadFactory, |
| 2794 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2795 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2796 | return Concat2dDim1DiffInputDimsTestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2797 | workloadFactory, memoryManager, 0.5f, -1); |
| 2798 | } |
| 2799 | |
| 2800 | LayerTestResult<uint8_t, 3> Concat3dDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2801 | IWorkloadFactory& workloadFactory, |
| 2802 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2803 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2804 | return Concat3dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2805 | } |
| 2806 | |
| 2807 | LayerTestResult<uint8_t, 3> Concat3dDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2808 | IWorkloadFactory& workloadFactory, |
| 2809 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2810 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2811 | return Concat3dDim1TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2812 | } |
| 2813 | |
| 2814 | LayerTestResult<uint8_t, 3> Concat3dDim2Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2815 | IWorkloadFactory& workloadFactory, |
| 2816 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2817 | bool useSubtensor) |
| 2818 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2819 | return Concat3dDim2TestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2820 | workloadFactory, memoryManager, useSubtensor, 0.5f, -1); |
| 2821 | } |
| 2822 | |
| 2823 | LayerTestResult<uint8_t, 3> Concat3dDim0DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2824 | IWorkloadFactory& workloadFactory, |
| 2825 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2826 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2827 | return Concat3dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2828 | } |
| 2829 | |
| 2830 | LayerTestResult<uint8_t, 3> Concat3dDim1DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2831 | IWorkloadFactory& workloadFactory, |
| 2832 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2833 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2834 | return Concat3dDim1DiffInputDimsTestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2835 | workloadFactory, memoryManager, 0.5f, -1); |
| 2836 | } |
| 2837 | |
| 2838 | LayerTestResult<uint8_t, 3> Concat3dDim2DiffInputDimsUint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2839 | IWorkloadFactory& workloadFactory, |
| 2840 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2841 | bool useSubtensor) |
| 2842 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2843 | return Concat3dDim2DiffInputDimsTestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2844 | workloadFactory, memoryManager, useSubtensor, 0.5f, -1); |
| 2845 | } |
| 2846 | |
| 2847 | LayerTestResult<uint8_t, 4> Concat4dDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2848 | IWorkloadFactory& workloadFactory, |
| 2849 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2850 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2851 | return Concat4dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2852 | } |
| 2853 | |
| 2854 | LayerTestResult<uint8_t, 4> Concat4dDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2855 | IWorkloadFactory& workloadFactory, |
| 2856 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2857 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2858 | return Concat4dDim1TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2859 | } |
| 2860 | |
| 2861 | LayerTestResult<uint8_t, 4> Concat4dDim2Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2862 | IWorkloadFactory& workloadFactory, |
| 2863 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2864 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2865 | return Concat4dDim2TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, 0.5f, -1); |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2866 | } |
| 2867 | |
| 2868 | LayerTestResult<uint8_t, 4> Concat4dDim3Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2869 | IWorkloadFactory& workloadFactory, |
| 2870 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2871 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2872 | return Concat4dDim3TestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2873 | workloadFactory, memoryManager, 0.5f, -1, useSubtensor); |
| 2874 | } |
| 2875 | |
| 2876 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim0Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2877 | IWorkloadFactory& workloadFactory, |
| 2878 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2879 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2880 | return Concat4dDiffShapeDim0TestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2881 | workloadFactory, memoryManager, 0.5f, -1); |
| 2882 | } |
| 2883 | |
| 2884 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim1Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2885 | IWorkloadFactory& workloadFactory, |
| 2886 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2887 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2888 | return Concat4dDiffShapeDim1TestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2889 | workloadFactory, memoryManager, 0.5f, -1); |
| 2890 | } |
| 2891 | |
| 2892 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim2Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2893 | IWorkloadFactory& workloadFactory, |
| 2894 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2895 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2896 | return Concat4dDiffShapeDim2TestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2897 | workloadFactory, memoryManager, 0.5f, -1); |
| 2898 | } |
| 2899 | |
| 2900 | LayerTestResult<uint8_t, 4> Concat4dDiffShapeDim3Uint8Test( |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 2901 | IWorkloadFactory& workloadFactory, |
| 2902 | const IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2903 | bool useSubtensor) |
| 2904 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 2905 | return Concat4dDiffShapeDim3TestImpl<DataType::QAsymmU8>( |
Aron Virginas-Tar | 00d306e | 2019-08-28 18:08:46 +0100 | [diff] [blame] | 2906 | workloadFactory, memoryManager, 0.5f, -1, useSubtensor); |
| 2907 | } |