Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1 | // |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 2 | // Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "Converter.hpp" |
| 7 | #include <half/half.hpp> |
| 8 | #include <armnnUtils/TensorUtils.hpp> |
| 9 | |
| 10 | namespace armnn_driver |
| 11 | { |
| 12 | |
| 13 | using namespace android::nn; |
| 14 | using Half = half_float::half; |
| 15 | |
| 16 | namespace |
| 17 | { |
| 18 | |
| 19 | } // anonymouse namespace |
| 20 | |
| 21 | bool Converter::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data) |
| 22 | { |
| 23 | switch (operation.type) |
| 24 | { |
| 25 | case OperationType::ABS: |
| 26 | return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Abs); |
| 27 | case OperationType::ADD: |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 28 | return ConvertElementwiseBinary(operation, model, data, armnn::BinaryOperation::Add); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 29 | case OperationType::ARGMAX: |
| 30 | return ConvertArgMinMax(operation, model, data, ArgMinMaxFunction::Max); |
| 31 | case OperationType::ARGMIN: |
| 32 | return ConvertArgMinMax(operation, model, data, ArgMinMaxFunction::Min); |
| 33 | case OperationType::AVERAGE_POOL_2D: |
| 34 | return ConvertAveragePool2d(operation, model, data); |
Kevin May | 9636a9b | 2022-09-21 15:41:41 +0100 | [diff] [blame] | 35 | case OperationType::BATCH_MATMUL: |
| 36 | return ConvertBatchMatMul(operation, model, data); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 37 | case OperationType::BATCH_TO_SPACE_ND: |
| 38 | return ConvertBatchToSpaceNd(operation, model, data); |
| 39 | case OperationType::CAST: |
| 40 | return ConvertCast(operation, model, data); |
| 41 | case OperationType::CONCATENATION: |
| 42 | return ConvertConcatenation(operation, model, data); |
| 43 | case OperationType::CONV_2D: |
| 44 | return ConvertConv2d(operation, model, data); |
| 45 | case OperationType::DEPTH_TO_SPACE: |
| 46 | return ConvertDepthToSpace(operation, model, data); |
| 47 | case OperationType::DEPTHWISE_CONV_2D: |
| 48 | return ConvertDepthwiseConv2d(operation, model, data); |
| 49 | case OperationType::DEQUANTIZE: |
| 50 | return ConvertDequantize(operation, model, data); |
| 51 | case OperationType::DIV: |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 52 | return ConvertElementwiseBinary(operation, model, data, armnn::BinaryOperation::Div); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 53 | case OperationType::ELU: |
| 54 | return ConvertElu(operation, model, data); |
| 55 | case OperationType::EQUAL: |
| 56 | return ConvertComparison(operation, model, data, ComparisonOperation::Equal); |
| 57 | case OperationType::EXP: |
| 58 | return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Exp); |
| 59 | case OperationType::EXPAND_DIMS: |
| 60 | return ConvertExpandDims(operation, model, data); |
| 61 | case OperationType::FILL: |
| 62 | return ConvertFill(operation, model, data); |
| 63 | case OperationType::FLOOR: |
| 64 | return ConvertFloor(operation, model, data); |
| 65 | case OperationType::FULLY_CONNECTED: |
| 66 | return ConvertFullyConnected(operation, model, data); |
| 67 | case OperationType::GATHER: |
| 68 | return ConvertGather(operation, model, data); |
| 69 | case OperationType::GREATER: |
| 70 | return ConvertComparison(operation, model, data, ComparisonOperation::Greater); |
| 71 | case OperationType::GREATER_EQUAL: |
| 72 | return ConvertComparison(operation, model, data, ComparisonOperation::GreaterOrEqual); |
| 73 | case OperationType::GROUPED_CONV_2D: |
| 74 | return ConvertGroupedConv2d(operation, model, data); |
| 75 | case OperationType::HARD_SWISH: |
| 76 | return ConvertHardSwish(operation, model, data); |
| 77 | case OperationType::INSTANCE_NORMALIZATION: |
| 78 | return ConvertInstanceNormalization(operation, model, data); |
| 79 | case OperationType::L2_NORMALIZATION: |
| 80 | return ConvertL2Normalization(operation, model, data); |
| 81 | case OperationType::L2_POOL_2D: |
| 82 | return ConvertL2Pool2d(operation, model, data); |
| 83 | case OperationType::LESS: |
| 84 | return ConvertComparison(operation, model, data, ComparisonOperation::Less); |
| 85 | case OperationType::LESS_EQUAL: |
| 86 | return ConvertComparison(operation, model, data, ComparisonOperation::LessOrEqual); |
| 87 | case OperationType::LOCAL_RESPONSE_NORMALIZATION: |
| 88 | return ConvertLocalResponseNormalization(operation, model, data); |
| 89 | case OperationType::LOG: |
| 90 | return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Log); |
| 91 | case OperationType::LOGICAL_AND: |
| 92 | return ConvertLogicalBinary(operation, model, data, LogicalBinaryOperation::LogicalAnd); |
| 93 | case OperationType::LOGICAL_NOT: |
| 94 | return ConvertElementwiseUnary(operation, model, data, UnaryOperation::LogicalNot); |
| 95 | case OperationType::LOGICAL_OR: |
| 96 | return ConvertLogicalBinary(operation, model, data, LogicalBinaryOperation::LogicalOr); |
| 97 | case OperationType::LOGISTIC: |
| 98 | return ConvertLogistic(operation, model, data); |
| 99 | case OperationType::LOG_SOFTMAX: |
| 100 | return ConvertLogSoftmax(operation, model, data); |
| 101 | case OperationType::LSTM: |
| 102 | return ConvertLstm(operation, model, data); |
| 103 | case OperationType::MAX_POOL_2D: |
| 104 | return ConvertMaxPool2d(operation, model, data); |
| 105 | case OperationType::MAXIMUM: |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 106 | return ConvertElementwiseBinary(operation, model, data, armnn::BinaryOperation::Maximum); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 107 | case OperationType::MEAN: |
| 108 | return ConvertMean(operation, model, data); |
| 109 | case OperationType::MINIMUM: |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 110 | return ConvertElementwiseBinary(operation, model, data, armnn::BinaryOperation::Minimum); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 111 | case OperationType::MUL: |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 112 | return ConvertElementwiseBinary(operation, model, data, armnn::BinaryOperation::Mul); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 113 | case OperationType::NEG: |
| 114 | return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Neg); |
| 115 | case OperationType::NOT_EQUAL: |
| 116 | return ConvertComparison(operation, model, data, ComparisonOperation::NotEqual); |
| 117 | case OperationType::PAD: |
| 118 | return ConvertPad(operation, model, data); |
| 119 | case OperationType::PAD_V2: |
| 120 | return ConvertPadV2(operation, model, data); |
| 121 | case OperationType::PRELU: |
| 122 | return ConvertPrelu(operation, model, data); |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 123 | case OperationType::POW: |
| 124 | return ConvertElementwiseBinary(operation, model, data, BinaryOperation::Power); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 125 | case OperationType::QUANTIZE: |
| 126 | return ConvertQuantize(operation, model, data); |
| 127 | case OperationType::QUANTIZED_LSTM: |
| 128 | return ConvertQuantizedLstm(operation, model, data); |
| 129 | case OperationType::QUANTIZED_16BIT_LSTM: |
| 130 | return ConvertQuantized16BitLstm(operation, model, data); |
| 131 | case OperationType::RANK: |
| 132 | return ConvertRank(operation, model, data); |
| 133 | case OperationType::REDUCE_MAX: |
| 134 | return ConvertReduce(operation, model, data, armnn::ReduceOperation::Max); |
| 135 | case OperationType::REDUCE_MIN: |
| 136 | return ConvertReduce(operation, model, data, armnn::ReduceOperation::Min); |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 137 | case OperationType::REDUCE_PROD: |
| 138 | return ConvertReduce(operation, model, data, armnn::ReduceOperation::Prod); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 139 | case OperationType::REDUCE_SUM: |
| 140 | return ConvertReduce(operation, model, data, armnn::ReduceOperation::Sum); |
| 141 | case OperationType::RELU: |
| 142 | return ConvertReLu(operation, model, data); |
| 143 | case OperationType::RELU1: |
| 144 | return ConvertReLu1(operation, model, data); |
| 145 | case OperationType::RELU6: |
| 146 | return ConvertReLu6(operation, model, data); |
| 147 | case OperationType::RESHAPE: |
| 148 | return ConvertReshape(operation, model, data); |
| 149 | case OperationType::RESIZE_BILINEAR: |
| 150 | return ConvertResize(operation, model, data, ResizeMethod::Bilinear); |
| 151 | case OperationType::RESIZE_NEAREST_NEIGHBOR: |
| 152 | return ConvertResize(operation, model, data, ResizeMethod::NearestNeighbor); |
| 153 | case OperationType::RSQRT: |
| 154 | return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Rsqrt); |
| 155 | case OperationType::SIN: |
| 156 | return ConvertElementwiseUnary(operation, model, data, UnaryOperation::Sin); |
| 157 | case OperationType::SOFTMAX: |
| 158 | return ConvertSoftmax(operation, model, data); |
| 159 | case OperationType::SPACE_TO_BATCH_ND : |
| 160 | return ConvertSpaceToBatchNd(operation, model, data); |
| 161 | case OperationType::SPACE_TO_DEPTH: |
| 162 | return ConvertSpaceToDepth(operation, model, data); |
| 163 | case OperationType::SQRT: |
| 164 | return ConvertSqrt(operation, model, data); |
| 165 | case OperationType::SQUEEZE: |
| 166 | return ConvertSqueeze(operation, model, data); |
| 167 | case OperationType::STRIDED_SLICE: |
| 168 | return ConvertStridedSlice(operation, model, data); |
| 169 | case OperationType::SUB: |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 170 | return ConvertElementwiseBinary(operation, model, data, BinaryOperation::Sub); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 171 | case OperationType::TRANSPOSE: |
| 172 | return ConvertTranspose(operation, model, data); |
| 173 | case OperationType::TRANSPOSE_CONV_2D: |
| 174 | return ConvertTransposeConv2d(operation, model, data); |
| 175 | case OperationType::TANH: |
| 176 | return ConvertTanH(operation, model, data); |
| 177 | default: |
| 178 | VLOG(DRIVER) << "Operation type: " << operation.type << "is not supported in ArmnnDriver"; |
| 179 | return false; |
| 180 | } |
| 181 | } |
| 182 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 183 | bool Converter::ConvertArgMinMax(const Operation& operation, |
| 184 | const Model& model, |
| 185 | ConversionData& data, |
| 186 | armnn::ArgMinMaxFunction argMinMaxFunction) |
| 187 | { |
| 188 | VLOG(DRIVER) << "Converter::ConvertArgMinMax()"; |
| 189 | VLOG(DRIVER) << "argMinMaxFunction = " << GetArgMinMaxFunctionAsCString(argMinMaxFunction); |
| 190 | |
| 191 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 192 | |
| 193 | if (!input0.IsValid()) |
| 194 | { |
| 195 | return Fail("%s: Operation has invalid inputs", __func__); |
| 196 | } |
| 197 | |
| 198 | int32_t axis; |
| 199 | if (!GetInputScalar(operation, 1, OperandType::INT32, axis, model, data)) |
| 200 | { |
| 201 | return Fail("%s: Operation has invalid inputs. Failed to read axis.", __func__); |
| 202 | } |
| 203 | |
| 204 | const armnn::TensorInfo& inputInfo = input0.GetTensorInfo(); |
| 205 | int rank = static_cast<int>(inputInfo.GetNumDimensions()); |
| 206 | |
| 207 | if (((axis < -rank) && (axis < 0)) || ((axis >= rank) && (axis > 0))) |
| 208 | { |
| 209 | // Square bracket denotes inclusive n while parenthesis denotes exclusive n |
| 210 | // E.g. Rank 4 tensor can have axis in range [-4, 3) |
| 211 | // -1 == 3, -2 == 2, -3 == 1, -4 == 0 |
| 212 | return Fail("%s: Axis must be in range [-n, n)", __func__); |
| 213 | } |
| 214 | |
| 215 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 216 | if (!output) |
| 217 | { |
| 218 | return Fail("%s: Could not read output 0", __func__); |
| 219 | } |
| 220 | |
| 221 | const armnn::TensorInfo& inputInfo0 = input0.GetTensorInfo(); |
| 222 | |
| 223 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 224 | |
| 225 | armnn::ArgMinMaxDescriptor descriptor; |
| 226 | descriptor.m_Function = argMinMaxFunction; |
| 227 | descriptor.m_Axis = axis; |
| 228 | |
| 229 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 230 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 231 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 232 | { |
| 233 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 234 | IsArgMinMaxSupported, |
| 235 | data.m_Backends, |
| 236 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 237 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 238 | inputInfo0, |
| 239 | outputInfo, |
| 240 | descriptor); |
| 241 | }; |
| 242 | |
| 243 | if(IsDynamicTensor(outputInfo)) |
| 244 | { |
| 245 | isSupported = AreDynamicTensorsSupported(); |
| 246 | } |
| 247 | else |
| 248 | { |
| 249 | validateFunc(outputInfo, isSupported); |
| 250 | } |
| 251 | |
| 252 | if (!isSupported) |
| 253 | { |
| 254 | return false; |
| 255 | } |
| 256 | |
| 257 | armnn::IConnectableLayer* layer = data.m_Network->AddArgMinMaxLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 258 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 259 | assert(layer != nullptr); |
| 260 | |
| 261 | input0.Connect(layer->GetInputSlot(0)); |
| 262 | |
| 263 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 264 | } |
| 265 | |
| 266 | bool Converter::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data) |
| 267 | { |
| 268 | VLOG(DRIVER) << "Converter::ConvertAveragePool2d()"; |
| 269 | return ConvertPooling2d(operation, __func__, PoolingAlgorithm::Average, model, data); |
| 270 | } |
| 271 | |
Kevin May | 9636a9b | 2022-09-21 15:41:41 +0100 | [diff] [blame] | 272 | bool Converter::ConvertBatchMatMul(const Operation& operation, const Model& model, ConversionData& data) |
| 273 | { |
| 274 | VLOG(DRIVER) << "Converter::ConvertBatchMatMul()"; |
| 275 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 276 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 277 | |
| 278 | if (!input0.IsValid() || !input1.IsValid()) |
| 279 | { |
| 280 | return Fail("%s: Operation has invalid inputs", __func__); |
| 281 | } |
| 282 | |
| 283 | const armnn::TensorInfo& inputInfo0 = input0.GetTensorInfo(); |
| 284 | const armnn::TensorInfo& inputInfo1 = input1.GetTensorInfo(); |
| 285 | |
| 286 | unsigned int rankInput0 = inputInfo0.GetNumDimensions(); |
| 287 | if (rankInput0 > 4 || rankInput0 < 2) |
| 288 | { |
| 289 | Fail("%s: Only inputs with rank at least 2 and up to 4 are supported", __func__); |
| 290 | } |
| 291 | |
| 292 | unsigned int rankInput1 = inputInfo1.GetNumDimensions(); |
| 293 | if (rankInput1 > 4 || rankInput1 < 2) |
| 294 | { |
| 295 | Fail("%s: Only inputs with rank at least 2 and up to 4 are supported", __func__); |
| 296 | } |
| 297 | |
| 298 | // Determine data type of input tensor 0 |
| 299 | OperandType input0Type; |
| 300 | if (!GetOperandType(operation, 0, model, input0Type)) |
| 301 | { |
| 302 | return Fail("%s: Operation has invalid inputs", __func__); |
| 303 | } |
| 304 | |
| 305 | // Determine data type of input tensor 0 |
| 306 | OperandType input1Type; |
| 307 | if (!GetOperandType(operation, 0, model, input1Type)) |
| 308 | { |
| 309 | return Fail("%s: Operation has invalid inputs", __func__); |
| 310 | } |
| 311 | |
| 312 | if (input0Type != input1Type) |
| 313 | { |
| 314 | return Fail("%s: Operation has invalid inputs (Inputs must have same OperandCode)", __func__); |
| 315 | } |
| 316 | |
| 317 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 318 | if (!output) |
| 319 | { |
| 320 | return Fail("%s: Could not read output 0", __func__); |
| 321 | } |
| 322 | |
| 323 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 324 | |
| 325 | armnn::BatchMatMulDescriptor batchMatMulDesc; |
| 326 | |
| 327 | // Inputs 2 and 3 are adjoint in Android NeuralNetworks, but they perform transpose. |
| 328 | // This is why we are linking them with transpose parameters in the descriptor |
| 329 | batchMatMulDesc.m_TransposeX = GetOptionalBool(operation, 2, model, data); |
| 330 | batchMatMulDesc.m_TransposeY = GetOptionalBool(operation, 3, model, data); |
| 331 | |
| 332 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 333 | armnn::BackendId setBackend; |
Kevin May | 9636a9b | 2022-09-21 15:41:41 +0100 | [diff] [blame] | 334 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 335 | { |
| 336 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 337 | IsBatchMatMulSupported, |
| 338 | data.m_Backends, |
| 339 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 340 | setBackend, |
Kevin May | 9636a9b | 2022-09-21 15:41:41 +0100 | [diff] [blame] | 341 | inputInfo0, |
| 342 | inputInfo1, |
| 343 | outputInfo, |
| 344 | batchMatMulDesc); |
| 345 | }; |
| 346 | |
| 347 | if(!IsDynamicTensor(outputInfo)) |
| 348 | { |
| 349 | validateFunc(outputInfo, isSupported); |
| 350 | } |
| 351 | else |
| 352 | { |
| 353 | isSupported = AreDynamicTensorsSupported(); |
| 354 | } |
| 355 | |
| 356 | |
| 357 | if (!isSupported) |
| 358 | { |
| 359 | return false; |
| 360 | } |
| 361 | |
| 362 | armnn::IConnectableLayer* const layer = data.m_Network->AddBatchMatMulLayer(batchMatMulDesc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 363 | layer->SetBackendId(setBackend); |
Kevin May | 9636a9b | 2022-09-21 15:41:41 +0100 | [diff] [blame] | 364 | assert(layer != nullptr); |
| 365 | input0.Connect(layer->GetInputSlot(0)); |
| 366 | input1.Connect(layer->GetInputSlot(1)); |
| 367 | |
| 368 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 369 | } |
| 370 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 371 | bool Converter::ConvertBatchToSpaceNd(const Operation& operation, const Model& model, ConversionData& data) |
| 372 | { |
| 373 | VLOG(DRIVER) << "Converter::ConvertBatchToSpaceNd()"; |
| 374 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 375 | if (!input.IsValid()) |
| 376 | { |
| 377 | return Fail("%s: Operation has invalid inputs", __func__); |
| 378 | } |
| 379 | |
| 380 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 381 | if (!output) |
| 382 | { |
| 383 | return Fail("%s: Could not read output 0", __func__); |
| 384 | } |
| 385 | |
| 386 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 387 | |
| 388 | const Operand* blockOperand = GetInputOperand(operation, 1, model); |
| 389 | if (!blockOperand) |
| 390 | { |
| 391 | return Fail("%s: Could not read input 1", __func__); |
| 392 | } |
| 393 | |
| 394 | // Convert the block operand to int32 |
| 395 | std::vector<int32_t> block; |
| 396 | if (!GetTensorInt32Values(*blockOperand, block, model, data)) |
| 397 | { |
| 398 | return Fail("%s: Input 1 has invalid values", __func__); |
| 399 | } |
| 400 | |
| 401 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 402 | |
| 403 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 404 | if (rank != 4) |
| 405 | { |
| 406 | Fail("%s: Only inputs with rank equal to 4 are supported", __func__); |
| 407 | } |
| 408 | |
| 409 | if (std::any_of(block.cbegin(), block.cend(), [](int32_t i){ return i < 1; })) |
| 410 | { |
| 411 | return Fail("%s: Block sizes for each spatial dimension of the input tensor must be" |
| 412 | " greater than or equal to 1", __func__); |
| 413 | } |
| 414 | |
| 415 | armnn::BatchToSpaceNdDescriptor batchToSpaceNdDesc; |
| 416 | batchToSpaceNdDesc.m_BlockShape.assign(block.cbegin(), block.cend()); |
| 417 | batchToSpaceNdDesc.m_DataLayout = armnn::DataLayout::NHWC; |
| 418 | |
| 419 | if (Is12OrLaterOperand(*output)) |
| 420 | { |
| 421 | batchToSpaceNdDesc.m_DataLayout = OptionalDataLayout(operation, 2, model, data); |
| 422 | } |
| 423 | // Setting crops to 0,0 0,0 as it is not supported in Android NN API |
| 424 | batchToSpaceNdDesc.m_Crops = {{0, 0}, {0, 0}}; |
| 425 | |
| 426 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 427 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 428 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 429 | { |
| 430 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 431 | IsBatchToSpaceNdSupported, |
| 432 | data.m_Backends, |
| 433 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 434 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 435 | inputInfo, |
| 436 | outputInfo, |
| 437 | batchToSpaceNdDesc); |
| 438 | }; |
| 439 | |
| 440 | if(!IsDynamicTensor(outputInfo)) |
| 441 | { |
| 442 | validateFunc(outputInfo, isSupported); |
| 443 | } |
| 444 | else |
| 445 | { |
| 446 | isSupported = AreDynamicTensorsSupported(); |
| 447 | } |
| 448 | |
| 449 | |
| 450 | if (!isSupported) |
| 451 | { |
| 452 | return false; |
| 453 | } |
| 454 | |
| 455 | armnn::IConnectableLayer* const layer = data.m_Network->AddBatchToSpaceNdLayer(batchToSpaceNdDesc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 456 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 457 | assert(layer != nullptr); |
| 458 | input.Connect(layer->GetInputSlot(0)); |
| 459 | |
| 460 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 461 | } |
| 462 | |
| 463 | bool Converter::ConvertCast(const Operation& operation, const Model& model, ConversionData& data) |
| 464 | { |
| 465 | VLOG(DRIVER) << "Converter::ConvertCast()"; |
| 466 | |
| 467 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 468 | |
| 469 | if (!input.IsValid()) |
| 470 | { |
| 471 | return Fail("%s: Operation has invalid inputs", __func__); |
| 472 | } |
| 473 | |
| 474 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 475 | if (!output) |
| 476 | { |
| 477 | return Fail("%s: Could not read output 0", __func__); |
| 478 | } |
| 479 | |
| 480 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 481 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 482 | |
| 483 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 484 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 485 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 486 | { |
| 487 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 488 | IsCastSupported, |
| 489 | data.m_Backends, |
| 490 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 491 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 492 | inputInfo, |
| 493 | outputInfo); |
| 494 | }; |
| 495 | |
| 496 | if(!IsDynamicTensor(outputInfo)) |
| 497 | { |
| 498 | validateFunc(outputInfo, isSupported); |
| 499 | } |
| 500 | else |
| 501 | { |
| 502 | isSupported = AreDynamicTensorsSupported(); |
| 503 | } |
| 504 | |
| 505 | if (!isSupported) |
| 506 | { |
| 507 | return false; |
| 508 | } |
| 509 | |
| 510 | IConnectableLayer* layer = data.m_Network->AddCastLayer(); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 511 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 512 | assert(layer != nullptr); |
| 513 | input.Connect(layer->GetInputSlot(0)); |
| 514 | |
| 515 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 516 | } |
| 517 | |
| 518 | bool Converter::ConvertComparison(const Operation& operation, |
| 519 | const Model& model, |
| 520 | ConversionData& data, |
| 521 | ComparisonOperation comparisonOperation) |
| 522 | { |
| 523 | VLOG(DRIVER) << "Converter::ConvertComparison()"; |
| 524 | VLOG(DRIVER) << "comparisonOperation = " << GetComparisonOperationAsCString(comparisonOperation); |
| 525 | |
| 526 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 527 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 528 | |
| 529 | if (!(input0.IsValid() && input1.IsValid())) |
| 530 | { |
| 531 | return Fail("%s: Operation has invalid inputs", __func__); |
| 532 | } |
| 533 | |
| 534 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 535 | if (!output) |
| 536 | { |
| 537 | return Fail("%s: Could not read output 0", __func__); |
| 538 | } |
| 539 | |
| 540 | const TensorInfo& inputInfo0 = input0.GetTensorInfo(); |
| 541 | const TensorInfo& inputInfo1 = input1.GetTensorInfo(); |
| 542 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 543 | |
| 544 | ComparisonDescriptor descriptor(comparisonOperation); |
| 545 | |
| 546 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 547 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 548 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 549 | { |
| 550 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 551 | IsComparisonSupported, |
| 552 | data.m_Backends, |
| 553 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 554 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 555 | inputInfo0, |
| 556 | inputInfo1, |
| 557 | outputInfo, |
| 558 | descriptor); |
| 559 | }; |
| 560 | |
| 561 | if(!IsDynamicTensor(outputInfo)) |
| 562 | { |
| 563 | validateFunc(outputInfo, isSupported); |
| 564 | } |
| 565 | else |
| 566 | { |
| 567 | isSupported = AreDynamicTensorsSupported(); |
| 568 | } |
| 569 | |
| 570 | if (!isSupported) |
| 571 | { |
| 572 | return false; |
| 573 | } |
| 574 | |
| 575 | IConnectableLayer* layer = data.m_Network->AddComparisonLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 576 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 577 | assert(layer != nullptr); |
| 578 | |
| 579 | bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data); |
| 580 | if (!isReshapeSupported) |
| 581 | { |
| 582 | return false; |
| 583 | } |
| 584 | |
| 585 | if(IsDynamicTensor(outputInfo)) |
| 586 | { |
| 587 | input0.Connect(layer->GetInputSlot(0)); |
| 588 | input1.Connect(layer->GetInputSlot(1)); |
| 589 | } |
| 590 | |
| 591 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 592 | } |
| 593 | |
| 594 | |
| 595 | bool Converter::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data) |
| 596 | { |
| 597 | VLOG(DRIVER) << "Converter::ConvertConcatenation()"; |
| 598 | |
| 599 | // The first N (0..N-1) inputs are tensors. The Nth input is the concatenation axis. |
| 600 | if (operation.inputs.size() <= 1) |
| 601 | { |
| 602 | return Fail("%s: Operation has insufficient arguments", __func__); |
| 603 | } |
| 604 | |
| 605 | // Get inputs and outputs |
| 606 | const std::size_t numInputTensors = operation.inputs.size() - 1; |
| 607 | |
| 608 | int32_t concatDim; |
| 609 | if (!GetInputScalar(operation, numInputTensors, OperandType::INT32, concatDim, model, data)) |
| 610 | { |
| 611 | return Fail("%s: Operation has invalid inputs", __func__); |
| 612 | } |
| 613 | |
| 614 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 615 | if (!outputOperand) |
| 616 | { |
| 617 | return Fail("%s: Operation has no outputs", __func__); |
| 618 | } |
| 619 | |
| 620 | armnn::TensorInfo outputInfo = GetTensorInfoForOperand(*outputOperand); |
| 621 | armnn::TensorShape outputShape = outputInfo.GetShape(); |
| 622 | const bool isDynamicTensor = IsDynamicTensor(outputInfo); |
| 623 | // |
| 624 | // handle negative concat dims along the lines of tensorflow as described here: |
| 625 | // https://www.tensorflow.org/api_docs/python/tf/concat |
| 626 | // "negative axis refers to axis + rank(values)-th dimension" |
| 627 | // |
| 628 | if (concatDim < 0) |
| 629 | { |
| 630 | concatDim += outputShape.GetNumDimensions(); |
| 631 | } |
| 632 | |
| 633 | if (concatDim >= static_cast<int32_t>(outputShape.GetNumDimensions()) || concatDim < 0) |
| 634 | { |
| 635 | return Fail("%s: Operation has invalid concat axis: %d", __func__, concatDim); |
| 636 | } |
| 637 | |
| 638 | std::vector<LayerInputHandle> inputHandles; |
| 639 | std::vector<armnn::TensorShape> inputShapes; |
| 640 | |
| 641 | inputHandles.reserve(numInputTensors); |
| 642 | inputShapes.reserve(numInputTensors); |
| 643 | |
| 644 | bool inputsHaveBeenReshaped = false; |
| 645 | unsigned int tensorDimensionsAdded = 0; |
| 646 | for (uint32_t i = 0; i < numInputTensors; ++i) |
| 647 | { |
| 648 | const Operand* operand = GetInputOperand(operation, i, model); |
| 649 | if (!operand) |
| 650 | { |
| 651 | return Fail("%s: Operation has invalid inputs", __func__); |
| 652 | } |
| 653 | |
| 654 | LayerInputHandle operandInputHandle = ConvertToLayerInputHandle(operation, i, model, data); |
| 655 | if (!operandInputHandle.IsValid()) |
| 656 | { |
| 657 | return Fail("%s: Operation has invalid inputs", __func__); |
| 658 | } |
| 659 | |
| 660 | armnn::TensorShape operandShape = GetTensorShapeForOperand(*operand); |
| 661 | if (operandShape.GetNumDimensions() == 0) |
| 662 | { |
| 663 | return Fail("%s: Operands with rank 0 are not supported", __func__); |
| 664 | } |
| 665 | |
| 666 | if (RequiresReshape(operandShape)) |
| 667 | { |
| 668 | inputsHaveBeenReshaped = true; |
| 669 | |
| 670 | armnn::TensorInfo reshapeInfo = operandInputHandle.GetTensorInfo(); |
| 671 | |
| 672 | // Expand the tensor to three dimensions |
| 673 | if (operandShape.GetNumDimensions() == 2) |
| 674 | { |
| 675 | reshapeInfo.SetShape(armnn::TensorShape({1, operandShape[0], operandShape[1]})); |
| 676 | tensorDimensionsAdded = 1; |
| 677 | } |
| 678 | else |
| 679 | { |
| 680 | reshapeInfo.SetShape(armnn::TensorShape({1, 1, operandShape[0]})); |
| 681 | tensorDimensionsAdded = 2; |
| 682 | } |
| 683 | |
| 684 | armnn::ReshapeDescriptor reshapeDescriptor; |
| 685 | reshapeDescriptor.m_TargetShape = reshapeInfo.GetShape(); |
| 686 | |
| 687 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 688 | armnn::BackendId setBackendReshape; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 689 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 690 | IsReshapeSupported, |
| 691 | data.m_Backends, |
| 692 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 693 | setBackendReshape, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 694 | operandInputHandle.GetTensorInfo(), |
| 695 | reshapeInfo, |
| 696 | reshapeDescriptor); |
| 697 | |
| 698 | if (!isSupported) |
| 699 | { |
| 700 | return false; |
| 701 | } |
| 702 | armnn::IConnectableLayer& newReshape = AddReshapeLayer(*data.m_Network, operandInputHandle, reshapeInfo); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 703 | newReshape.SetBackendId(setBackendReshape); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 704 | |
| 705 | // Point to the reshape operation rather then the input operation |
| 706 | operandShape = reshapeInfo.GetShape(); |
| 707 | operandInputHandle = LayerInputHandle(true, &newReshape.GetOutputSlot(0), reshapeInfo); |
| 708 | } |
| 709 | |
| 710 | inputShapes.emplace_back(operandShape); |
| 711 | inputHandles.emplace_back(operandInputHandle); |
| 712 | |
| 713 | if (!inputHandles.back().IsValid()) |
| 714 | { |
| 715 | return Fail("%s: Operation has invalid inputs", __func__); |
| 716 | } |
| 717 | } |
| 718 | |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 719 | if (inputShapes.size() != inputHandles.size()) |
| 720 | { |
| 721 | return Fail("%s: invalid model input shapes size doesn't match input handles size: %i != %i", __func__, |
| 722 | inputShapes.size(), inputHandles.size()); |
| 723 | } |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 724 | |
| 725 | if (inputsHaveBeenReshaped) |
| 726 | { |
| 727 | // Adjust the concatenation dimension by the amount of dimensions added (if any) |
| 728 | concatDim += tensorDimensionsAdded; |
| 729 | |
| 730 | // Add extra dimensions to the output shape to reflect the addition of the reshape layers |
| 731 | if (tensorDimensionsAdded == 1) |
| 732 | { |
| 733 | if (IsDynamicTensor(outputInfo)) |
| 734 | { |
| 735 | outputShape = armnn::TensorShape({1, 0, 0}, {true, false, false}); |
| 736 | } |
| 737 | else |
| 738 | { |
| 739 | outputShape = armnn::TensorShape({1, outputShape[0], outputShape[1]}); |
| 740 | } |
| 741 | } |
| 742 | else if (tensorDimensionsAdded == 2) |
| 743 | { |
| 744 | if (IsDynamicTensor(outputInfo)) |
| 745 | { |
| 746 | outputShape = armnn::TensorShape({1, 1, 0}, {true, true, false}); |
| 747 | } |
| 748 | else |
| 749 | { |
| 750 | outputShape = armnn::TensorShape({1, 1, outputShape[0]}); |
| 751 | } |
| 752 | } |
| 753 | } |
| 754 | |
| 755 | // Check if permutations is required and get the pair of permutations required for the concatenation. |
| 756 | // Permutation is required when the concat dimension is 2 for a 4D tensor or 1 for a 3D tensor. |
| 757 | std::pair<armnn::PermutationVector, armnn::PermutationVector> permutationPair = |
| 758 | std::make_pair(IdentityPermutation4D, IdentityPermutation4D); |
| 759 | bool needPermute = CreateConcatPermutationParameters(inputShapes[0].GetNumDimensions(), |
| 760 | concatDim, |
| 761 | permutationPair); |
| 762 | |
| 763 | // Only relevant to static tensors as dynamic output tensors will be transposed as a result of inferring from input |
| 764 | if (!isDynamicTensor) |
| 765 | { |
| 766 | if (needPermute) |
| 767 | { |
| 768 | outputShape = armnnUtils::TransposeTensorShape(outputShape, permutationPair.first); |
| 769 | } |
| 770 | |
| 771 | outputInfo.SetShape(outputShape); |
| 772 | } |
| 773 | // this is no-op for identity swizzles, otherwise it replaces both |
| 774 | // the handles and shapes with the swizzled layer output handles and shapes |
| 775 | if (!TransposeInputTensors(data, inputHandles, inputShapes, permutationPair.first)) |
| 776 | { |
| 777 | return false; |
| 778 | } |
| 779 | |
| 780 | // Create an armnn concat layer descriptor - this will also perform validation on the input shapes |
| 781 | armnn::OriginsDescriptor concatDescriptor; |
| 782 | |
| 783 | try |
| 784 | { |
| 785 | // The concat descriptor is always created across the only supported concat dimension |
| 786 | // which is 0, 1 or 3 for a 4-D tensor, or 0 or 2 for a 3-D tensor. |
| 787 | concatDescriptor = armnn::CreateDescriptorForConcatenation(inputShapes.begin(), |
| 788 | inputShapes.end(), |
| 789 | concatDim); |
| 790 | } catch (std::exception& error) |
| 791 | { |
| 792 | return Fail("%s: Error preparing concat descriptor. %s", __func__, error.what()); |
| 793 | } |
| 794 | |
| 795 | // Validate the output shape is correct given the input shapes based on the |
| 796 | // only valid concat dimension which is 0, 1 or 3 for a 4-D tensor, or 0 or 2 for a 3-D tensor. |
| 797 | if (!isDynamicTensor) |
| 798 | { |
| 799 | if (!ValidateConcatOutputShape(inputShapes, outputShape, concatDim)) |
| 800 | { |
| 801 | return Fail("%s: Error validating the output shape for concat", __func__); |
| 802 | } |
| 803 | } |
| 804 | |
| 805 | std::vector<const armnn::TensorInfo*> inputTensorInfos; |
| 806 | std::transform(inputHandles.begin(), inputHandles.end(), std::back_inserter(inputTensorInfos), |
| 807 | [](const LayerInputHandle& h)->const armnn::TensorInfo*{ return &h.GetTensorInfo(); }); |
| 808 | |
| 809 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 810 | armnn::BackendId setBackendConcat; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 811 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported){ |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 812 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 813 | IsConcatSupported, |
| 814 | data.m_Backends, |
| 815 | isSupported, |
| 816 | setBackendConcat, |
| 817 | inputTensorInfos, |
| 818 | outputInfo, |
| 819 | concatDescriptor); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 820 | }; |
| 821 | |
| 822 | if (!isDynamicTensor) |
| 823 | { |
| 824 | validateFunc(outputInfo, isSupported); |
| 825 | } |
| 826 | else |
| 827 | { |
| 828 | isSupported = AreDynamicTensorsSupported(); |
| 829 | } |
| 830 | |
| 831 | if (!isSupported) |
| 832 | { |
| 833 | return false; |
| 834 | } |
| 835 | |
| 836 | armnn::IConnectableLayer* layer = data.m_Network->AddConcatLayer(concatDescriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 837 | layer->SetBackendId(setBackendConcat); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 838 | assert(layer != nullptr); |
| 839 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 840 | // Connect inputs to the layer |
| 841 | const int numInputSlots = layer->GetNumInputSlots(); |
| 842 | assert(static_cast<std::size_t>(numInputSlots) == inputHandles.size()); |
| 843 | for (int i = 0; i < numInputSlots; ++i) |
| 844 | { |
| 845 | // connect the input directly to the merge (concat) layer |
| 846 | inputHandles[static_cast<unsigned int>(i)].Connect(layer->GetInputSlot(i)); |
| 847 | } |
| 848 | |
| 849 | // Transpose the output shape |
| 850 | auto transposeOutputShape = [&](){ |
| 851 | armnn::TransposeDescriptor transposeDesc; |
| 852 | transposeDesc.m_DimMappings = permutationPair.second; |
| 853 | armnn::TensorInfo inputTransposeInfo = layer->GetOutputSlot(0).GetTensorInfo(); |
| 854 | armnn::TensorInfo outputTransposeInfo = armnnUtils::TransposeTensorShape(inputTransposeInfo, |
| 855 | permutationPair.second); |
| 856 | isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 857 | armnn::BackendId setBackendTranspose; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 858 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 859 | IsTransposeSupported, |
| 860 | data.m_Backends, |
| 861 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 862 | setBackendTranspose, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 863 | inputTransposeInfo, |
| 864 | outputTransposeInfo, |
| 865 | transposeDesc); |
| 866 | if (!isSupported) |
| 867 | { |
| 868 | return false; |
| 869 | } |
| 870 | // Add permutation layer and connect the output to it, the permutation becomes the output layer |
| 871 | armnn::IConnectableLayer& deswizzleLayer = AddTransposeLayer(*data.m_Network, layer->GetOutputSlot(0), |
| 872 | permutationPair.second); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 873 | deswizzleLayer.SetBackendId(setBackendTranspose); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 874 | layer = &deswizzleLayer; |
| 875 | |
| 876 | return true; |
| 877 | }; |
| 878 | |
| 879 | if (needPermute && !isDynamicTensor) |
| 880 | { |
| 881 | transposeOutputShape(); |
| 882 | } |
| 883 | |
| 884 | if (inputsHaveBeenReshaped) |
| 885 | { |
| 886 | if (isDynamicTensor) |
| 887 | { |
| 888 | // Infer the output shapes of concat if outputs are type 1 dynamic |
| 889 | ARMNN_ASSERT(layer->GetOutputSlot(0).IsTensorInfoSet()); |
| 890 | if (!ValidateConcatOutputShape(inputShapes, |
| 891 | layer->GetOutputSlot(0).GetTensorInfo().GetShape(), |
| 892 | concatDim)) |
| 893 | { |
| 894 | return Fail("%s: Error validating the output shape for concat", __func__); |
| 895 | } |
| 896 | transposeOutputShape(); |
| 897 | } |
| 898 | |
| 899 | armnn::TensorInfo afterConcatInfo = layer->GetOutputSlot(0).GetTensorInfo(); |
| 900 | // Undo the reshape knowing the amount of dimensions added |
| 901 | if (tensorDimensionsAdded == 1) |
| 902 | { |
| 903 | afterConcatInfo.SetShape( |
| 904 | armnn::TensorShape({afterConcatInfo.GetShape()[1], afterConcatInfo.GetShape()[2]})); |
| 905 | } |
| 906 | else if (tensorDimensionsAdded == 2) |
| 907 | { |
| 908 | afterConcatInfo.SetShape(armnn::TensorShape({afterConcatInfo.GetShape()[2]})); |
| 909 | } |
| 910 | |
| 911 | armnn::ReshapeDescriptor reshapeDescriptor; |
| 912 | reshapeDescriptor.m_TargetShape = afterConcatInfo.GetShape(); |
| 913 | armnn::TensorInfo concatInfo = layer->GetOutputSlot(0).GetTensorInfo(); |
| 914 | |
| 915 | isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 916 | armnn::BackendId setBackendReshape2; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 917 | auto validateReshapeFunc = [&](const armnn::TensorInfo& afterConcatInfo, bool& isSupported){ |
| 918 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 919 | IsReshapeSupported, |
| 920 | data.m_Backends, |
| 921 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 922 | setBackendReshape2, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 923 | concatInfo, |
| 924 | afterConcatInfo, |
| 925 | reshapeDescriptor); |
| 926 | }; |
| 927 | |
| 928 | if (!IsDynamicTensor(afterConcatInfo)) |
| 929 | { |
| 930 | validateReshapeFunc(afterConcatInfo, isSupported); |
| 931 | } |
| 932 | else |
| 933 | { |
| 934 | isSupported = AreDynamicTensorsSupported(); |
| 935 | } |
| 936 | |
| 937 | if (!isSupported) |
| 938 | { |
| 939 | return false; |
| 940 | } |
| 941 | layer = &AddReshapeLayer(*data.m_Network, layer->GetOutputSlot(0), afterConcatInfo); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 942 | layer->SetBackendId(setBackendReshape2); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 943 | return SetupAndTrackLayerOutputSlot(operation, |
| 944 | 0, |
| 945 | *layer, |
| 946 | model, |
| 947 | data, |
| 948 | nullptr, |
| 949 | validateReshapeFunc); |
| 950 | } |
| 951 | |
| 952 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 953 | } |
| 954 | |
| 955 | bool Converter::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data) |
| 956 | { |
| 957 | VLOG(DRIVER) << "Converter::ConvertConv2d()"; |
| 958 | |
| 959 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 960 | if (!input.IsValid()) |
| 961 | { |
| 962 | return Fail("%s: Operation has invalid inputs", __func__); |
| 963 | } |
| 964 | |
| 965 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 966 | if (!output) |
| 967 | { |
| 968 | return Fail("%s: Could not read output 0", __func__); |
| 969 | } |
| 970 | |
| 971 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 972 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 973 | |
| 974 | Convolution2dDescriptor desc; |
| 975 | desc.m_DataLayout = DataLayout::NHWC; |
| 976 | |
| 977 | // Determine whether padding is implicit or explicit |
| 978 | bool implicitPadding = operation.inputs.size() == 7 |
| 979 | || (operation.inputs.size() >= 8 |
| 980 | && GetInputOperand(operation, 7, model)->type == OperandType::BOOL); |
| 981 | |
| 982 | if (implicitPadding) |
| 983 | { |
| 984 | desc.m_DataLayout = OptionalDataLayout(operation, 7, model, data); |
| 985 | } |
| 986 | else if (operation.inputs.size() >= 10) |
| 987 | { |
| 988 | desc.m_DataLayout = OptionalDataLayout(operation, 10, model, data); |
| 989 | } |
| 990 | |
| 991 | const PermutationVector OHWIToOIHW = {0, 2, 3, 1}; |
| 992 | |
| 993 | // ArmNN does not currently support non-fixed weights or bias |
| 994 | // The NNAPI filter is always OHWI [depth_out, filter_height, filter_width, depth_in] but ArmNN expects the |
| 995 | // filter's height and width indices to match the input's height and width indices so we permute it to OIHW if |
| 996 | // the DataLayout is NCHW |
| 997 | |
| 998 | if (!IsWeightsValid(operation, 1, model) && desc.m_DataLayout == DataLayout::NCHW) |
| 999 | { |
| 1000 | return Fail("%s: Operation has unsupported weights OperandLifeTime", __func__); |
| 1001 | } |
| 1002 | |
| 1003 | LayerInputHandle weightsInput = (desc.m_DataLayout == DataLayout::NCHW) |
Sadik Armagan | 1e276f3 | 2022-07-19 12:37:20 +0100 | [diff] [blame] | 1004 | ? ConvertToLayerInputHandle(operation, 1, model, data, OHWIToOIHW, &input) |
| 1005 | : ConvertToLayerInputHandle(operation, 1, model, data, g_DontPermute, &input); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1006 | |
| 1007 | if (!weightsInput.IsValid()) |
| 1008 | { |
| 1009 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1010 | } |
| 1011 | |
Sadik Armagan | 1e276f3 | 2022-07-19 12:37:20 +0100 | [diff] [blame] | 1012 | LayerInputHandle biasInput = ConvertToLayerInputHandle(operation, 2, model, data, g_DontPermute, &input); // 1D |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1013 | if (!biasInput.IsValid()) |
| 1014 | { |
| 1015 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1016 | } |
| 1017 | |
| 1018 | biasInput.SanitizeQuantizationScale(weightsInput, input); |
| 1019 | armnn::TensorInfo weightsInfo = weightsInput.GetTensorInfo(); |
| 1020 | armnn::TensorInfo biasInfo = biasInput.GetTensorInfo(); |
| 1021 | |
| 1022 | ActivationFn activation; |
| 1023 | if (implicitPadding) |
| 1024 | { |
| 1025 | ::android::nn::PaddingScheme paddingScheme; |
| 1026 | if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) |
| 1027 | || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) |
| 1028 | || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) |
| 1029 | || !GetInputActivationFunction(operation, 6, activation, model, data) |
| 1030 | || !GetOptionalConvolutionDilationParams(operation, 8, desc, model, data)) |
| 1031 | { |
| 1032 | return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); |
| 1033 | } |
| 1034 | |
| 1035 | armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout); |
| 1036 | unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); |
| 1037 | unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); |
| 1038 | const uint32_t kernelX = weightsInfo.GetShape()[widthIndex]; |
| 1039 | const uint32_t kernelY = weightsInfo.GetShape()[heightIndex]; |
| 1040 | const uint32_t inputX = inputInfo.GetShape()[widthIndex]; |
| 1041 | const uint32_t inputY = inputInfo.GetShape()[heightIndex]; |
| 1042 | |
| 1043 | CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); |
| 1044 | CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); |
| 1045 | |
| 1046 | } |
| 1047 | else if (operation.inputs.size() >= 10) |
| 1048 | { |
| 1049 | // explicit padding |
| 1050 | if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) |
| 1051 | || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) |
| 1052 | || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) |
| 1053 | || !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) |
| 1054 | || !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) |
| 1055 | || !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) |
| 1056 | || !GetInputActivationFunction(operation, 9, activation, model, data) |
| 1057 | || !GetOptionalConvolutionDilationParams(operation, 11, desc, model, data)) |
| 1058 | { |
| 1059 | return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); |
| 1060 | } |
| 1061 | } |
| 1062 | else |
| 1063 | { |
| 1064 | return Fail("%s: Unsupported number of operation inputs", __func__); |
| 1065 | } |
| 1066 | |
| 1067 | desc.m_BiasEnabled = true; |
| 1068 | Optional<TensorInfo> biases(biasInfo); |
| 1069 | |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1070 | bool requiresValidation = true; |
| 1071 | const Operand* weightsOperand = GetInputOperand(operation, 1, model); |
| 1072 | const Operand* biasOperand = GetInputOperand(operation, 2, model); |
| 1073 | if (IsConnectedToDequantize(weightsInput.GetOutputSlot()) |
| 1074 | || IsConnectedToDequantize(biasInput.GetOutputSlot())) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1075 | { |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1076 | // Do not require validation for now. There will be an optimization step |
| 1077 | // [ConvertConstDequantisationLayersToConstLayers] will convert layers to Constant layers |
| 1078 | // then at the end of the optimization there will be layer supported validation. |
| 1079 | requiresValidation = false; |
| 1080 | VLOG(DRIVER) << "Converter::ConvertConv2d(): Weights and Biases are as INPUTS."; |
| 1081 | } |
| 1082 | |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1083 | armnn::BackendId setBackend; |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1084 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) { |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1085 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1086 | IsConvolution2dSupported, |
| 1087 | data.m_Backends, |
| 1088 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1089 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1090 | inputInfo, |
| 1091 | outputInfo, |
| 1092 | desc, |
| 1093 | weightsInfo, |
| 1094 | biases); |
| 1095 | }; |
| 1096 | |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1097 | if (requiresValidation) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1098 | { |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1099 | VLOG(DRIVER) << "Converter::ConvertConv2d(): Requires Validation!"; |
| 1100 | bool isSupported = false; |
| 1101 | if (!IsDynamicTensor(outputInfo)) |
| 1102 | { |
| 1103 | validateFunc(outputInfo, isSupported); |
| 1104 | } |
| 1105 | else |
| 1106 | { |
| 1107 | isSupported = AreDynamicTensorsSupported(); |
| 1108 | } |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1109 | |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1110 | if (!isSupported) |
| 1111 | { |
| 1112 | return false; |
| 1113 | } |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1114 | } |
| 1115 | |
| 1116 | armnn::IConnectableLayer* startLayer = data.m_Network->AddConvolution2dLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1117 | startLayer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1118 | |
| 1119 | if (!startLayer) |
| 1120 | { |
| 1121 | return Fail("%s: AddConvolution2dLayer failed", __func__); |
| 1122 | } |
| 1123 | |
| 1124 | input.Connect(startLayer->GetInputSlot(0)); |
| 1125 | weightsInput.Connect(startLayer->GetInputSlot(1)); |
| 1126 | biasInput.Connect(startLayer->GetInputSlot(2)); |
| 1127 | |
| 1128 | return SetupAndTrackLayerOutputSlot(operation, 0, *startLayer, model, data, nullptr, validateFunc, activation); |
| 1129 | } |
| 1130 | |
| 1131 | bool Converter::ConvertDepthToSpace(const Operation& operation, const Model& model, ConversionData& data) |
| 1132 | { |
| 1133 | VLOG(DRIVER) << "Converter::ConvertDepthToSpace()"; |
| 1134 | |
| 1135 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1136 | if (!input.IsValid() ) |
| 1137 | { |
| 1138 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1139 | } |
| 1140 | |
| 1141 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 1142 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 1143 | if (rank != 4) |
| 1144 | { |
| 1145 | return Fail("%s: Only inputs with rank 4 are supported", __func__); |
| 1146 | } |
| 1147 | |
| 1148 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 1149 | if (!output) |
| 1150 | { |
| 1151 | return Fail("%s: Could not read output 0", __func__); |
| 1152 | } |
| 1153 | |
| 1154 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 1155 | |
| 1156 | armnn::DepthToSpaceDescriptor descriptor; |
| 1157 | |
| 1158 | GetInputScalar(operation, 1, OperandType::INT32, descriptor.m_BlockSize, model, data); |
| 1159 | if (descriptor.m_BlockSize <= 1) |
| 1160 | { |
| 1161 | return Fail("%s: Block size must be at least 1 in all dimensions"); |
| 1162 | } |
| 1163 | |
| 1164 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 1165 | if (Is12OrLaterOperand(*output)) |
| 1166 | { |
| 1167 | descriptor.m_DataLayout = OptionalDataLayout(operation, 2, model, data); |
| 1168 | } |
| 1169 | |
| 1170 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1171 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1172 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 1173 | { |
| 1174 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1175 | IsDepthToSpaceSupported, |
| 1176 | data.m_Backends, |
| 1177 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1178 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1179 | inputInfo, |
| 1180 | outputInfo, |
| 1181 | descriptor); |
| 1182 | }; |
| 1183 | |
| 1184 | if(!IsDynamicTensor(outputInfo)) |
| 1185 | { |
| 1186 | validateFunc(outputInfo, isSupported); |
| 1187 | } |
| 1188 | else |
| 1189 | { |
| 1190 | isSupported = AreDynamicTensorsSupported(); |
| 1191 | } |
| 1192 | |
| 1193 | if (!isSupported) |
| 1194 | { |
| 1195 | return false; |
| 1196 | } |
| 1197 | |
| 1198 | armnn::IConnectableLayer* const layer = data.m_Network->AddDepthToSpaceLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1199 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1200 | assert(layer != nullptr); |
| 1201 | input.Connect(layer->GetInputSlot(0)); |
| 1202 | |
| 1203 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 1204 | } |
| 1205 | |
| 1206 | bool Converter::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data) |
| 1207 | { |
| 1208 | VLOG(DRIVER) << "Converter::ConvertDepthwiseConv2d()"; |
| 1209 | |
| 1210 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1211 | |
| 1212 | if (!input.IsValid()) |
| 1213 | { |
| 1214 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1215 | } |
| 1216 | |
| 1217 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 1218 | |
| 1219 | if (!output) |
| 1220 | { |
| 1221 | return Fail("%s: Could not read output 0", __func__); |
| 1222 | } |
| 1223 | |
| 1224 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 1225 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 1226 | |
| 1227 | // ArmNN does not currently support non-fixed weights or bias |
| 1228 | // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ] |
| 1229 | const Operand* weightsOperand = GetInputOperand(operation, 1, model); |
| 1230 | |
| 1231 | if (!weightsOperand) |
| 1232 | { |
| 1233 | return Fail("%s: Could not read weights", __func__); |
| 1234 | } |
| 1235 | // Basic sanity check on the weights shape. |
| 1236 | // ANEURALNETWORKS_DEPTHWISE_CONV_2D specifies a 4-D tensor, of shape |
| 1237 | // [1, filter_height, filter_width, depth_out] |
| 1238 | if (weightsOperand->dimensions[0] != 1) |
| 1239 | { |
| 1240 | return Fail("%s: Filter operand dimension 0 is invalid, should be 1", __func__); |
| 1241 | } |
| 1242 | |
| 1243 | armnn::DepthwiseConvolution2dDescriptor desc; |
| 1244 | desc.m_DataLayout = armnn::DataLayout::NHWC; |
| 1245 | |
| 1246 | // Determine whether padding is implicit or explicit |
| 1247 | bool implicitPadding = operation.inputs.size() == 8 |
| 1248 | || (operation.inputs.size() >= 9 |
| 1249 | && GetInputOperand(operation, 8, model)->type == OperandType::BOOL); |
| 1250 | |
| 1251 | // Look ahead to find the optional DataLayout, if present |
| 1252 | const uint32_t dataLayoutFlagIndex = implicitPadding ? 8 : 11; |
| 1253 | desc.m_DataLayout = OptionalDataLayout(operation, dataLayoutFlagIndex, model, data); |
| 1254 | |
| 1255 | armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout); |
| 1256 | unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); |
| 1257 | unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); |
| 1258 | |
Sadik Armagan | 1e276f3 | 2022-07-19 12:37:20 +0100 | [diff] [blame] | 1259 | LayerInputHandle weightsInput = ConvertToLayerInputHandle(operation, 1, model, data, g_DontPermute, &input); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1260 | if (!weightsInput.IsValid()) |
| 1261 | { |
| 1262 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1263 | } |
| 1264 | |
| 1265 | const Operand* biasOperand = GetInputOperand(operation, 2, model); |
| 1266 | if (!biasOperand) |
| 1267 | { |
| 1268 | return Fail("%s: Could not read bias", __func__); |
| 1269 | } |
| 1270 | |
Sadik Armagan | 1e276f3 | 2022-07-19 12:37:20 +0100 | [diff] [blame] | 1271 | LayerInputHandle biasInput = ConvertToLayerInputHandle(operation, 2, model, data, g_DontPermute, &input); // 1D |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1272 | if (!biasInput.IsValid()) |
| 1273 | { |
| 1274 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1275 | } |
| 1276 | |
| 1277 | biasInput.SanitizeQuantizationScale(weightsInput, input); |
| 1278 | armnn::TensorInfo weightsInfo = weightsInput.GetTensorInfo(); |
| 1279 | armnn::TensorInfo biasInfo = biasInput.GetTensorInfo(); |
| 1280 | |
| 1281 | ActivationFn activation; |
| 1282 | if (implicitPadding) |
| 1283 | { |
| 1284 | ::android::nn::PaddingScheme paddingScheme; |
| 1285 | if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) |
| 1286 | || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) |
| 1287 | || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) |
| 1288 | || !GetInputActivationFunction(operation, 7, activation, model, data) |
| 1289 | || !GetOptionalConvolutionDilationParams(operation, 9, desc, model, data)) |
| 1290 | { |
| 1291 | return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); |
| 1292 | } |
| 1293 | |
| 1294 | const uint32_t kernelX = weightsInfo.GetShape()[2]; |
| 1295 | const uint32_t kernelY = weightsInfo.GetShape()[1]; |
| 1296 | const uint32_t inputX = inputInfo.GetShape()[widthIndex]; |
| 1297 | const uint32_t inputY = inputInfo.GetShape()[heightIndex]; |
| 1298 | |
| 1299 | CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); |
| 1300 | CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); |
| 1301 | } |
| 1302 | else if (operation.inputs.size() >= 11) |
| 1303 | { |
| 1304 | // explicit padding |
| 1305 | if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) |
| 1306 | || !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) |
| 1307 | || !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) |
| 1308 | || !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) |
| 1309 | || !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) |
| 1310 | || !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) |
| 1311 | || !GetInputActivationFunction(operation, 10, activation, model, data) |
| 1312 | || !GetOptionalConvolutionDilationParams(operation, 12, desc, model, data)) |
| 1313 | { |
| 1314 | return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); |
| 1315 | } |
| 1316 | } |
| 1317 | else |
| 1318 | { |
| 1319 | return Fail("%s: Unsupported number of operation inputs", __func__); |
| 1320 | } |
| 1321 | |
| 1322 | desc.m_BiasEnabled = true; |
| 1323 | Optional<TensorInfo> biases(biasInfo); |
| 1324 | |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1325 | bool requiresValidation = true; |
| 1326 | if (IsConnectedToDequantize(weightsInput.GetOutputSlot()) || IsConnectedToDequantize(biasInput.GetOutputSlot())) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1327 | { |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1328 | // Do not require validation for now. There will be an optimization step |
| 1329 | // [ConvertConstDequantisationLayersToConstLayers] will convert layers to Constant layers |
| 1330 | // then at the end of the optimization there will be layer supported validation. |
| 1331 | requiresValidation = false; |
| 1332 | VLOG(DRIVER) << "Converter::ConvertDepthwiseConv2d(): Weights and Biases are as INPUTS."; |
| 1333 | } |
| 1334 | |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1335 | armnn::BackendId setBackend; |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1336 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) { |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1337 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1338 | IsDepthwiseConvolutionSupported, |
| 1339 | data.m_Backends, |
| 1340 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1341 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1342 | inputInfo, |
| 1343 | outputInfo, |
| 1344 | desc, |
| 1345 | weightsInfo, |
| 1346 | biases); |
| 1347 | }; |
| 1348 | |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1349 | if (requiresValidation) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1350 | { |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1351 | VLOG(DRIVER) << "Converter::ConvertDepthwiseConv2d(): Requires Validation!"; |
| 1352 | bool isSupported = false; |
| 1353 | if (!IsDynamicTensor(outputInfo)) |
| 1354 | { |
| 1355 | validateFunc(outputInfo, isSupported); |
| 1356 | } |
| 1357 | else |
| 1358 | { |
| 1359 | isSupported = AreDynamicTensorsSupported(); |
| 1360 | } |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1361 | |
Sadik Armagan | b016157 | 2022-08-03 11:27:05 +0100 | [diff] [blame] | 1362 | if (!isSupported) |
| 1363 | { |
| 1364 | return false; |
| 1365 | } |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1366 | } |
| 1367 | |
| 1368 | armnn::IConnectableLayer* startLayer = data.m_Network->AddDepthwiseConvolution2dLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1369 | startLayer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1370 | |
| 1371 | if (!startLayer) |
| 1372 | { |
| 1373 | return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__); |
| 1374 | } |
| 1375 | |
| 1376 | input.Connect(startLayer->GetInputSlot(0)); |
| 1377 | |
| 1378 | // Connect weights and bias inputs |
| 1379 | weightsInput.Connect(startLayer->GetInputSlot(1)); |
| 1380 | biasInput.Connect(startLayer->GetInputSlot(2)); |
| 1381 | |
| 1382 | return SetupAndTrackLayerOutputSlot(operation, 0, *startLayer, model, data, nullptr, validateFunc, activation); |
| 1383 | } |
| 1384 | |
| 1385 | bool Converter::ConvertDequantize(const Operation& operation, const Model& model, ConversionData& data) |
| 1386 | { |
| 1387 | VLOG(DRIVER) << "Converter::ConvertDequantize()"; |
| 1388 | |
| 1389 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1390 | if (!input.IsValid()) |
| 1391 | { |
| 1392 | return Fail("%s: Operation has invalid input", __func__); |
| 1393 | } |
| 1394 | |
| 1395 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 1396 | const armnn::Optional<unsigned int>& quantizationDim = inputInfo.GetQuantizationDim(); |
| 1397 | if (quantizationDim.has_value() && quantizationDim.value() != 0) |
| 1398 | { |
| 1399 | return Fail("%s: Operation has quantization dimension different than 0", __func__); |
| 1400 | } |
| 1401 | |
| 1402 | const Operand* const outputOperand = GetOutputOperand(operation, 0, model); |
| 1403 | if (!outputOperand) |
| 1404 | { |
| 1405 | return Fail("%s: Operation has invalid outputs", __func__); |
| 1406 | } |
| 1407 | |
| 1408 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand); |
| 1409 | |
| 1410 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1411 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1412 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 1413 | { |
| 1414 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1415 | IsDequantizeSupported, |
| 1416 | data.m_Backends, |
| 1417 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1418 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1419 | inputInfo, |
| 1420 | outputInfo); |
| 1421 | }; |
| 1422 | |
| 1423 | if(IsDynamicTensor(outputInfo)) |
| 1424 | { |
| 1425 | isSupported = AreDynamicTensorsSupported(); |
| 1426 | } |
| 1427 | else |
| 1428 | { |
| 1429 | validateFunc(outputInfo, isSupported); |
| 1430 | } |
| 1431 | |
| 1432 | if (!isSupported) |
| 1433 | { |
| 1434 | return false; |
| 1435 | } |
| 1436 | |
| 1437 | armnn::IConnectableLayer* const layer = data.m_Network->AddDequantizeLayer(); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1438 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1439 | assert(layer != nullptr); |
| 1440 | input.Connect(layer->GetInputSlot(0)); |
| 1441 | |
| 1442 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 1443 | } |
| 1444 | |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1445 | bool Converter::ConvertElementwiseBinary(const Operation& operation, |
| 1446 | const Model& model, |
| 1447 | ConversionData& data, |
| 1448 | armnn::BinaryOperation binaryOperation) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1449 | { |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1450 | VLOG(DRIVER) << "Converter::ConvertElementwiseBinary()"; |
| 1451 | VLOG(DRIVER) << "binaryOperation = " << GetBinaryOperationAsCString(binaryOperation); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1452 | |
| 1453 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1454 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 1455 | |
| 1456 | if (!input0.IsValid() || !input1.IsValid()) |
| 1457 | { |
| 1458 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1459 | } |
| 1460 | |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1461 | // The FuseActivation parameter is always the input index 2, and it should be optional |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1462 | ActivationFn activationFunction; |
| 1463 | if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data)) |
| 1464 | { |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1465 | return Fail("%s: Operation has invalid optional input: activation function", __func__); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1466 | } |
| 1467 | |
| 1468 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 1469 | if (!output) |
| 1470 | { |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1471 | return Fail("%s: Could not read output", __func__); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1472 | } |
| 1473 | |
| 1474 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 1475 | |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1476 | armnn::ElementwiseBinaryDescriptor descriptor(binaryOperation); |
| 1477 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1478 | bool isSupported = false; |
| 1479 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 1480 | { |
| 1481 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1482 | IsElementwiseBinarySupported, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1483 | data.m_Backends, |
| 1484 | isSupported, |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1485 | armnn::BackendId(), |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1486 | input0.GetTensorInfo(), |
| 1487 | input1.GetTensorInfo(), |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1488 | outputInfo, |
| 1489 | binaryOperation); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1490 | }; |
| 1491 | |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1492 | if (!IsDynamicTensor(outputInfo)) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1493 | { |
| 1494 | validateFunc(outputInfo, isSupported); |
| 1495 | } |
| 1496 | else |
| 1497 | { |
| 1498 | isSupported = AreDynamicTensorsSupported(); |
| 1499 | } |
| 1500 | |
| 1501 | if (!isSupported) |
| 1502 | { |
| 1503 | return false; |
| 1504 | } |
| 1505 | |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1506 | armnn::IConnectableLayer* layer = data.m_Network->AddElementwiseBinaryLayer(descriptor); |
| 1507 | if (!layer) |
| 1508 | { |
| 1509 | return Fail("%s: Could not add the ElementwiseBinaryLayer", __func__); |
| 1510 | } |
| 1511 | bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1512 | if (!isReshapeSupported) |
| 1513 | { |
| 1514 | return false; |
| 1515 | } |
| 1516 | |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 1517 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1518 | data, nullptr, validateFunc, activationFunction); |
| 1519 | } |
| 1520 | |
| 1521 | bool Converter::ConvertElementwiseUnary(const Operation& operation, |
| 1522 | const Model& model, |
| 1523 | ConversionData& data, |
| 1524 | UnaryOperation unaryOperation) |
| 1525 | { |
| 1526 | VLOG(DRIVER) << "Converter::ConvertElementwiseUnary()"; |
| 1527 | VLOG(DRIVER) << "unaryOperation = " << GetUnaryOperationAsCString(unaryOperation); |
| 1528 | |
| 1529 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1530 | |
| 1531 | if (!input.IsValid()) |
| 1532 | { |
| 1533 | return Fail("%s: Operation has invalid input", __func__); |
| 1534 | } |
| 1535 | |
| 1536 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 1537 | if (!output) |
| 1538 | { |
| 1539 | return Fail("%s: Could not read output 0", __func__); |
| 1540 | } |
| 1541 | |
| 1542 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 1543 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 1544 | |
| 1545 | ElementwiseUnaryDescriptor descriptor(unaryOperation); |
| 1546 | |
| 1547 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1548 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1549 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 1550 | { |
| 1551 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1552 | IsElementwiseUnarySupported, |
| 1553 | data.m_Backends, |
| 1554 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1555 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1556 | inputInfo, |
| 1557 | outputInfo, |
| 1558 | descriptor); |
| 1559 | }; |
| 1560 | |
| 1561 | if(!IsDynamicTensor(outputInfo)) |
| 1562 | { |
| 1563 | validateFunc(outputInfo, isSupported); |
| 1564 | } |
| 1565 | else |
| 1566 | { |
| 1567 | isSupported = AreDynamicTensorsSupported(); |
| 1568 | } |
| 1569 | |
| 1570 | if (!isSupported) |
| 1571 | { |
| 1572 | return false; |
| 1573 | } |
| 1574 | |
| 1575 | IConnectableLayer* layer = data.m_Network->AddElementwiseUnaryLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1576 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1577 | assert(layer != nullptr); |
| 1578 | input.Connect(layer->GetInputSlot(0)); |
| 1579 | |
| 1580 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 1581 | } |
| 1582 | |
| 1583 | bool Converter::ConvertElu(const Operation& operation, const Model& model, ConversionData& data) |
| 1584 | { |
| 1585 | VLOG(DRIVER) << "Converter::ConvertElu()"; |
| 1586 | |
| 1587 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1588 | if (!input0.IsValid()) |
| 1589 | { |
| 1590 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1591 | } |
| 1592 | |
| 1593 | // Determine data type of input tensor |
| 1594 | OperandType inputType; |
| 1595 | if (!GetOperandType(operation, 0, model, inputType)) |
| 1596 | { |
| 1597 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1598 | } |
| 1599 | |
| 1600 | ActivationDescriptor desc; |
| 1601 | desc.m_Function = ActivationFunction::Elu; |
| 1602 | |
| 1603 | // Read alpha |
| 1604 | if (inputType == OperandType::TENSOR_FLOAT16) |
| 1605 | { |
| 1606 | Half alpha; |
| 1607 | |
| 1608 | if (!GetInputScalar(operation, 1, OperandType::FLOAT16, alpha, model, data)) |
| 1609 | { |
| 1610 | return Fail("%s: Operation has invalid inputs (FLOAT16)", __func__); |
| 1611 | } |
| 1612 | |
| 1613 | desc.m_A = static_cast<float>(alpha); |
| 1614 | } |
| 1615 | else if (inputType == OperandType::TENSOR_FLOAT32) |
| 1616 | { |
| 1617 | if (!GetInputScalar(operation, 1, OperandType::FLOAT32, desc.m_A, model, data)) |
| 1618 | { |
| 1619 | return Fail("%s: Operation has invalid inputs (FLOAT32)", __func__); |
| 1620 | } |
| 1621 | } |
| 1622 | else |
| 1623 | { |
| 1624 | return Fail("%s: Unsupported input tensor type: %d", __func__, inputType); |
| 1625 | } |
| 1626 | |
| 1627 | return ::ConvertToActivation(operation, __func__, desc, model, data); |
| 1628 | } |
| 1629 | |
| 1630 | bool Converter::ConvertExpandDims(const Operation& operation, const Model& model, ConversionData& data) |
| 1631 | { |
| 1632 | VLOG(DRIVER) << "Converter::ConvertExpandDims()"; |
| 1633 | |
| 1634 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1635 | |
| 1636 | if (!input.IsValid()) |
| 1637 | { |
| 1638 | return Fail("%s: Operation has invalid input", __func__); |
| 1639 | } |
| 1640 | |
| 1641 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 1642 | if (!output) |
| 1643 | { |
| 1644 | return Fail("%s: Operation has invalid output", __func__); |
| 1645 | } |
| 1646 | |
| 1647 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 1648 | |
| 1649 | int32_t axis; |
| 1650 | if (!GetInputScalar(operation, 1, OperandType::INT32, axis, model, data)) |
| 1651 | { |
| 1652 | return Fail("%s: failed to get axis input value", __func__); |
| 1653 | } |
| 1654 | |
| 1655 | TensorShape targetShape; |
| 1656 | |
| 1657 | try |
| 1658 | { |
| 1659 | targetShape = armnnUtils::ExpandDims(input.GetTensorInfo().GetShape(), axis); |
| 1660 | } |
| 1661 | catch (const std::exception& e) |
| 1662 | { |
| 1663 | return Fail("%s: %s", __func__, e.what()); |
| 1664 | } |
| 1665 | |
| 1666 | ReshapeDescriptor reshapeDescriptor; |
| 1667 | reshapeDescriptor.m_TargetShape = targetShape; |
| 1668 | |
| 1669 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1670 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1671 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 1672 | { |
| 1673 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1674 | IsReshapeSupported, |
| 1675 | data.m_Backends, |
| 1676 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1677 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1678 | input.GetTensorInfo(), |
| 1679 | outputInfo, |
| 1680 | reshapeDescriptor); |
| 1681 | }; |
| 1682 | |
| 1683 | if(!IsDynamicTensor(outputInfo)) |
| 1684 | { |
| 1685 | if (targetShape != outputInfo.GetShape()) |
| 1686 | { |
| 1687 | return Fail("%s: Shape of the output operand does not match the resolved expanded shape", __func__); |
| 1688 | } |
| 1689 | validateFunc(outputInfo, isSupported); |
| 1690 | } |
| 1691 | else |
| 1692 | { |
| 1693 | isSupported = AreDynamicTensorsSupported(); |
| 1694 | } |
| 1695 | |
| 1696 | if (!isSupported) |
| 1697 | { |
| 1698 | return false; |
| 1699 | } |
| 1700 | |
| 1701 | IConnectableLayer* layer = data.m_Network->AddReshapeLayer(reshapeDescriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1702 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1703 | assert(layer != nullptr); |
| 1704 | input.Connect(layer->GetInputSlot(0)); |
| 1705 | |
| 1706 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 1707 | } |
| 1708 | |
| 1709 | bool Converter::ConvertFill(const Operation& operation, const Model& model, ConversionData& data) |
| 1710 | { |
| 1711 | VLOG(DRIVER) << "Converter::ConvertFill()"; |
| 1712 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1713 | if (!input.IsValid()) |
| 1714 | { |
| 1715 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1716 | } |
| 1717 | |
| 1718 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 1719 | if (!output) |
| 1720 | { |
| 1721 | return Fail("%s: Could not read output", __func__); |
| 1722 | } |
| 1723 | |
| 1724 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 1725 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 1726 | if (IsDynamicTensor(outputInfo)) |
| 1727 | { |
| 1728 | return Fail("%s: Dynamic output tensors are not supported", __func__); |
| 1729 | } |
| 1730 | |
| 1731 | // Determine data type of output tensor |
| 1732 | OperandType outputType = output->type; |
| 1733 | FillDescriptor descriptor; |
| 1734 | // Read the scalar fill value |
| 1735 | if (outputType == OperandType::TENSOR_FLOAT16) |
| 1736 | { |
| 1737 | Half value; |
| 1738 | |
| 1739 | if (!GetInputScalar(operation, 1, OperandType::FLOAT16, value, model, data)) |
| 1740 | { |
| 1741 | return Fail("%s: Operation has invalid inputs %d", __func__, outputType); |
| 1742 | } |
| 1743 | |
| 1744 | descriptor.m_Value = static_cast<float>(value); |
| 1745 | } |
| 1746 | else if (outputType == OperandType::TENSOR_FLOAT32) |
| 1747 | { |
| 1748 | if (!GetInputScalar(operation, 1, OperandType::FLOAT32, descriptor.m_Value, model, data)) |
| 1749 | { |
| 1750 | return Fail("%s: Operation has invalid inputs %d", __func__, outputType); |
| 1751 | } |
| 1752 | } |
| 1753 | else if (outputType == OperandType::TENSOR_INT32) |
| 1754 | { |
| 1755 | int32_t value; |
| 1756 | |
| 1757 | if (!GetInputScalar(operation, 1, OperandType::INT32, value, model, data)) |
| 1758 | { |
| 1759 | return Fail("%s: Operation has invalid inputs %d", __func__, outputType); |
| 1760 | } |
| 1761 | |
| 1762 | descriptor.m_Value = static_cast<float>(value); |
| 1763 | } |
| 1764 | else |
| 1765 | { |
| 1766 | return Fail("%s: Unsupported input tensor type: %d", __func__, outputType); |
| 1767 | } |
| 1768 | |
| 1769 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1770 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1771 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1772 | IsFillSupported, |
| 1773 | data.m_Backends, |
| 1774 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1775 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1776 | inputInfo, |
| 1777 | outputInfo, |
| 1778 | descriptor); |
| 1779 | if (!isSupported) |
| 1780 | { |
| 1781 | return false; |
| 1782 | } |
| 1783 | |
| 1784 | IConnectableLayer* const layer = data.m_Network->AddFillLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1785 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1786 | assert(layer != nullptr); |
| 1787 | input.Connect(layer->GetInputSlot(0)); |
| 1788 | |
| 1789 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 1790 | } |
| 1791 | |
| 1792 | bool Converter::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data) |
| 1793 | { |
| 1794 | VLOG(DRIVER) << "Converter::ConvertFloor()"; |
| 1795 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1796 | if (!input.IsValid()) |
| 1797 | { |
| 1798 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1799 | } |
| 1800 | |
| 1801 | const Operand* const outputOperand = GetOutputOperand(operation, 0, model); |
| 1802 | if (!outputOperand) |
| 1803 | { |
| 1804 | return Fail("%s: Operation has invalid outputs", __func__); |
| 1805 | } |
| 1806 | |
| 1807 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand); |
| 1808 | |
| 1809 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1810 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1811 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 1812 | { |
| 1813 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1814 | IsFloorSupported, |
| 1815 | data.m_Backends, |
| 1816 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1817 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1818 | input.GetTensorInfo(), |
| 1819 | outputInfo); |
| 1820 | }; |
| 1821 | |
| 1822 | if(!IsDynamicTensor(outputInfo)) |
| 1823 | { |
| 1824 | validateFunc(outputInfo, isSupported); |
| 1825 | } |
| 1826 | else |
| 1827 | { |
| 1828 | isSupported = AreDynamicTensorsSupported(); |
| 1829 | } |
| 1830 | |
| 1831 | if (!isSupported) |
| 1832 | { |
| 1833 | return false; |
| 1834 | } |
| 1835 | |
| 1836 | armnn::IConnectableLayer* layer = data.m_Network->AddFloorLayer(); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1837 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1838 | assert(layer != nullptr); |
| 1839 | input.Connect(layer->GetInputSlot(0)); |
| 1840 | |
| 1841 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 1842 | } |
| 1843 | |
| 1844 | bool Converter::ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data) |
| 1845 | { |
| 1846 | VLOG(DRIVER) << "Converter::ConvertFullyConnected()"; |
| 1847 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1848 | if (!input.IsValid()) |
| 1849 | { |
| 1850 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1851 | } |
| 1852 | |
| 1853 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 1854 | if (!output) |
| 1855 | { |
| 1856 | return Fail("%s: Could not read output 0", __func__); |
| 1857 | } |
| 1858 | |
| 1859 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 1860 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 1861 | |
| 1862 | LayerInputHandle weightsInput = LayerInputHandle(); |
| 1863 | const Operand* weightsOperand = GetInputOperand(operation, 1, model); |
| 1864 | if (!weightsOperand) |
| 1865 | { |
| 1866 | return Fail("%s: Could not read weights", __func__); |
| 1867 | } |
| 1868 | |
| 1869 | // If weights are constant a separate constant layer will be created to store data. |
| 1870 | // Otherwise handle non const weights as inputs. |
| 1871 | weightsInput = ConvertToLayerInputHandle(operation, 1, model, data); |
| 1872 | if (!weightsInput.IsValid()) |
| 1873 | { |
| 1874 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1875 | } |
| 1876 | |
| 1877 | LayerInputHandle biasInput = LayerInputHandle(); |
| 1878 | const Operand* biasOperand = GetInputOperand(operation, 2, model); |
| 1879 | if (!biasOperand) |
| 1880 | { |
| 1881 | return Fail("%s: Could not read bias", __func__); |
| 1882 | } |
| 1883 | |
| 1884 | // If bias are constant a separate constant layer will be created to store data. |
| 1885 | // Otherwise handle non const bias as inputs. |
| 1886 | biasInput = ConvertToLayerInputHandle(operation, 2, model, data); // 1D |
| 1887 | if (!biasInput.IsValid()) |
| 1888 | { |
| 1889 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1890 | } |
| 1891 | |
| 1892 | armnn::TensorInfo weightsInfo = weightsInput.GetTensorInfo(); |
| 1893 | armnn::TensorInfo reshapedInfo = inputInfo; |
| 1894 | try |
| 1895 | { |
| 1896 | reshapedInfo.SetShape(FlattenFullyConnectedInput(inputInfo.GetShape(), weightsInfo.GetShape())); |
| 1897 | } |
| 1898 | catch (const std::exception& e) |
| 1899 | { |
| 1900 | return Fail("%s: %s", __func__, e.what()); |
| 1901 | } |
| 1902 | |
| 1903 | // Ensuring that the bias value is within 1% of the weights input (small float differences can exist) |
| 1904 | armnn::TensorInfo biasInfo = biasInput.GetTensorInfo(); |
| 1905 | SanitizeBiasQuantizationScale(biasInfo, weightsInfo, reshapedInfo); |
| 1906 | |
| 1907 | ActivationFn activationFunction; |
| 1908 | if (!GetInputActivationFunction(operation, 3, activationFunction, model, data)) |
| 1909 | { |
| 1910 | return Fail("%s: Operation has invalid inputs", __func__); |
| 1911 | } |
| 1912 | |
| 1913 | armnn::FullyConnectedDescriptor desc; |
| 1914 | desc.m_TransposeWeightMatrix = true; |
| 1915 | desc.m_BiasEnabled = true; |
| 1916 | desc.m_ConstantWeights = IsOperandConstant(*weightsOperand); |
| 1917 | |
| 1918 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1919 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1920 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 1921 | { |
| 1922 | if (!VerifyFullyConnectedShapes(reshapedInfo.GetShape(), |
| 1923 | weightsInfo.GetShape(), |
| 1924 | outputInfo.GetShape(), |
| 1925 | desc.m_TransposeWeightMatrix)) |
| 1926 | { |
| 1927 | isSupported = false; |
| 1928 | Fail("%s: Expected outputShape does not match actual outputShape", __func__); |
| 1929 | return; |
| 1930 | } |
| 1931 | |
| 1932 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 1933 | IsFullyConnectedSupported, |
| 1934 | data.m_Backends, |
| 1935 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1936 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1937 | reshapedInfo, |
| 1938 | outputInfo, |
| 1939 | weightsInfo, |
| 1940 | biasInfo, |
| 1941 | desc); |
| 1942 | }; |
| 1943 | |
| 1944 | if(!IsDynamicTensor(outputInfo)) |
| 1945 | { |
| 1946 | validateFunc(outputInfo, isSupported); |
| 1947 | } |
| 1948 | else |
| 1949 | { |
| 1950 | isSupported = AreDynamicTensorsSupported(); |
| 1951 | } |
| 1952 | |
| 1953 | if (!isSupported) |
| 1954 | { |
| 1955 | return false; |
| 1956 | } |
| 1957 | |
| 1958 | // Add FullyConnected layer. Weights and bias will be connected as constant layers or non const inputs. |
| 1959 | armnn::IConnectableLayer* startLayer = data.m_Network->AddFullyConnectedLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 1960 | startLayer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 1961 | |
| 1962 | if (inputInfo.GetNumDimensions() > 2U) |
| 1963 | { |
| 1964 | armnn::ReshapeDescriptor reshapeDescriptor; |
| 1965 | reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape(); |
| 1966 | |
| 1967 | armnn::IConnectableLayer* reshapeLayer = data.m_Network->AddReshapeLayer(reshapeDescriptor); |
| 1968 | assert(reshapeLayer != nullptr); |
| 1969 | input.Connect(reshapeLayer->GetInputSlot(0)); |
| 1970 | reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo); |
| 1971 | reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0)); |
| 1972 | } |
| 1973 | else |
| 1974 | { |
| 1975 | input.Connect(startLayer->GetInputSlot(0)); |
| 1976 | } |
| 1977 | |
| 1978 | // Connect weights and bias inputs |
| 1979 | weightsInput.Connect(startLayer->GetInputSlot(1)); |
| 1980 | biasInput.Connect(startLayer->GetInputSlot(2)); |
| 1981 | |
| 1982 | return SetupAndTrackLayerOutputSlot(operation, 0, *startLayer, model, |
| 1983 | data, nullptr, validateFunc, activationFunction); |
| 1984 | } |
| 1985 | |
| 1986 | bool Converter::ConvertGather(const Operation& operation, const Model& model, ConversionData& data) |
| 1987 | { |
| 1988 | VLOG(DRIVER) << "Converter::ConvertGather()"; |
| 1989 | |
| 1990 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 1991 | if (!input.IsValid()) |
| 1992 | { |
| 1993 | return Fail("%s: Operation has invalid input", __func__); |
| 1994 | } |
| 1995 | auto inputDimensions = input.GetTensorInfo().GetNumDimensions(); |
| 1996 | |
| 1997 | LayerInputHandle indices = ConvertToLayerInputHandle(operation, 2, model, data); |
| 1998 | if (!indices.IsValid()) |
| 1999 | { |
| 2000 | return Fail("%s: Operation has invalid indices", __func__); |
| 2001 | } |
| 2002 | auto indicesDimensions = indices.GetTensorInfo().GetNumDimensions(); |
| 2003 | |
| 2004 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 2005 | if (!output) |
| 2006 | { |
| 2007 | return Fail("%s: Operation has invalid output", __func__); |
| 2008 | } |
| 2009 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 2010 | auto outputDimensions = outputInfo.GetNumDimensions(); |
| 2011 | if (outputDimensions != inputDimensions + indicesDimensions - 1) |
| 2012 | { |
| 2013 | return Fail("%s: Operation has invalid output dimensions: %d. Output must be an (%d + %d - 1)-D tensor", |
| 2014 | __func__, outputDimensions, inputDimensions, indicesDimensions); |
| 2015 | } |
| 2016 | |
| 2017 | int32_t axis; |
| 2018 | if (!GetInputScalar(operation, 1, OperandType::INT32, axis, model, data)) |
| 2019 | { |
| 2020 | return Fail("%s: Operation has invalid or unsupported axis operand", __func__); |
| 2021 | } |
Kevin May | 28c3d0f | 2023-07-04 09:07:30 +0100 | [diff] [blame^] | 2022 | int32_t inputDimensions_int = static_cast<int32_t>(inputDimensions); |
| 2023 | if ((axis < -inputDimensions_int) || (inputDimensions_int <= axis)) |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2024 | { |
| 2025 | return Fail("%s: Operation has invalid axis: %d. It is out of bounds [-%d, %d))", __func__, axis, |
| 2026 | inputDimensions, inputDimensions); |
| 2027 | } |
| 2028 | |
| 2029 | GatherDescriptor desc; |
| 2030 | desc.m_Axis = axis; |
| 2031 | |
| 2032 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2033 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2034 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 2035 | { |
| 2036 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2037 | IsGatherSupported, |
| 2038 | data.m_Backends, |
| 2039 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2040 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2041 | input.GetTensorInfo(), |
| 2042 | indices.GetTensorInfo(), |
| 2043 | outputInfo, |
| 2044 | desc); |
| 2045 | }; |
| 2046 | |
| 2047 | if(!IsDynamicTensor(outputInfo)) |
| 2048 | { |
| 2049 | validateFunc(outputInfo, isSupported); |
| 2050 | } |
| 2051 | else |
| 2052 | { |
| 2053 | isSupported = AreDynamicTensorsSupported(); |
| 2054 | } |
| 2055 | |
| 2056 | if (!isSupported) |
| 2057 | { |
| 2058 | return false; |
| 2059 | } |
| 2060 | |
| 2061 | IConnectableLayer* layer = data.m_Network->AddGatherLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2062 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2063 | assert(layer != nullptr); |
| 2064 | input.Connect(layer->GetInputSlot(0)); |
| 2065 | indices.Connect(layer->GetInputSlot(1)); |
| 2066 | |
| 2067 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 2068 | } |
| 2069 | |
| 2070 | bool Converter::ConvertGroupedConv2d(const Operation& operation, const Model& model, ConversionData& data) |
| 2071 | { |
| 2072 | VLOG(DRIVER) << "Converter::ConvertGroupedConv2d()"; |
| 2073 | // |
| 2074 | // Parse data |
| 2075 | // |
| 2076 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 2077 | if (!input.IsValid()) |
| 2078 | { |
| 2079 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2080 | } |
| 2081 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 2082 | |
| 2083 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 2084 | if (!output) |
| 2085 | { |
| 2086 | return Fail("%s: Could not read output 0", __func__); |
| 2087 | } |
| 2088 | TensorInfo outputInfo = GetTensorInfoForOperand(*output); |
| 2089 | |
| 2090 | // Look ahead to determine data layout |
| 2091 | DataLayout dataLayout = DataLayout::NHWC; |
| 2092 | if (operation.inputs.size() == 12) |
| 2093 | { |
| 2094 | dataLayout = OptionalDataLayout(operation, 11, model, data); |
| 2095 | } |
| 2096 | else |
| 2097 | { |
| 2098 | dataLayout = OptionalDataLayout(operation, 8, model, data); |
| 2099 | } |
| 2100 | |
| 2101 | // NOTE: |
| 2102 | // NNAPI weights are always OHWI, i.e. [depth_out, filter_height, filter_width, depth_group], |
| 2103 | // but Arm NN expects the filter's height and width indices to match the input's height and |
| 2104 | // width indices so when the DataLayout is NCHW, we need to permute the weights to OIHW |
| 2105 | const PermutationVector ohwiToOihw = { 0u, 2u, 3u, 1u }; |
| 2106 | const ConstTensorPin weightsPin = (dataLayout == DataLayout::NCHW) ? |
| 2107 | ConvertOperationInputToConstTensorPin(operation, 1, |
| 2108 | model, data, ohwiToOihw) : |
| 2109 | ConvertOperationInputToConstTensorPin(operation, 1, model, data); |
| 2110 | const ConstTensorPin biasesPin = |
| 2111 | ConvertOperationInputToConstTensorPin(operation, 2, model, data); |
| 2112 | if (!weightsPin.IsValid() || !biasesPin.IsValid()) |
| 2113 | { |
| 2114 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2115 | } |
| 2116 | |
| 2117 | ConstTensor weights = weightsPin.GetConstTensor(); |
| 2118 | ConstTensor biases = biasesPin.GetConstTensor(); |
| 2119 | SanitizeBiasQuantizationScale(biases.GetInfo(), weights.GetInfo(), inputInfo); |
| 2120 | |
| 2121 | const TensorShape& inputShape = inputInfo.GetShape(); |
| 2122 | const TensorShape& outputShape = outputInfo.GetShape(); |
| 2123 | const TensorShape& weightsShape = weights.GetShape(); |
| 2124 | const TensorShape& biasesShape = biases.GetShape(); |
| 2125 | |
| 2126 | armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout); |
| 2127 | const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex(); |
| 2128 | const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); |
| 2129 | const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); |
| 2130 | |
| 2131 | Convolution2dDescriptor desc; |
| 2132 | desc.m_DataLayout = dataLayout; |
| 2133 | desc.m_BiasEnabled = true; |
| 2134 | |
| 2135 | int numGroups; |
| 2136 | ActivationFn activation; |
| 2137 | |
| 2138 | if (operation.inputs.size() == 12) |
| 2139 | { |
| 2140 | if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || |
| 2141 | !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || |
| 2142 | !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || |
| 2143 | !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || |
| 2144 | !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || |
| 2145 | !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || |
| 2146 | !GetInputScalar(operation, 9, OperandType::INT32, numGroups, model, data) || |
| 2147 | !GetInputActivationFunction(operation, 10, activation, model, data)) |
| 2148 | { |
| 2149 | return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); |
| 2150 | } |
| 2151 | |
| 2152 | } |
| 2153 | else if (operation.inputs.size() == 9) |
| 2154 | { |
| 2155 | ::android::nn::PaddingScheme paddingScheme; |
| 2156 | if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) || |
| 2157 | !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) || |
| 2158 | !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) || |
| 2159 | !GetInputScalar(operation, 6, OperandType::INT32, numGroups, model, data) || |
| 2160 | !GetInputActivationFunction(operation, 7, activation, model, data)) |
| 2161 | { |
| 2162 | return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); |
| 2163 | } |
| 2164 | |
| 2165 | const uint32_t inputX = inputInfo.GetShape()[widthIndex]; |
| 2166 | const uint32_t inputY = inputInfo.GetShape()[heightIndex]; |
| 2167 | |
| 2168 | const uint32_t kernelX = weightsShape[widthIndex]; |
| 2169 | const uint32_t kernelY = weightsShape[heightIndex]; |
| 2170 | |
| 2171 | CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); |
| 2172 | CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); |
| 2173 | } |
| 2174 | else |
| 2175 | { |
| 2176 | return Fail("%s: Unsupported number of operation inputs", __func__); |
| 2177 | } |
| 2178 | |
| 2179 | // Equivalent to outputShape[channelsIndex], but we can't know the outputShape in the case of dynamic tensors |
| 2180 | const unsigned int outputChannels = weightsShape[0]; |
| 2181 | |
| 2182 | const unsigned int channelsPerGroup = weightsShape[channelsIndex]; |
| 2183 | const unsigned int channelMultiplier = outputChannels / numGroups; |
| 2184 | |
| 2185 | // |
| 2186 | // Validate all relevant inputs |
| 2187 | // |
| 2188 | if (numGroups <= 0) |
| 2189 | { |
| 2190 | return Fail("%s: Number of groups must be greater than 0. Got: %d", __func__, numGroups); |
| 2191 | } |
| 2192 | |
| 2193 | if (outputChannels % numGroups != 0u) |
| 2194 | { |
| 2195 | return Fail("%s: Output channels must be divisible by the number of groups", __func__); |
| 2196 | } |
| 2197 | |
| 2198 | // |
| 2199 | // Set up Splitter layer |
| 2200 | // |
| 2201 | unsigned int splitterDimSizes[4] = { inputShape[0], inputShape[1], inputShape[2], inputShape[3] }; |
| 2202 | splitterDimSizes[channelsIndex] /= numGroups; // split in depth |
| 2203 | |
| 2204 | TensorInfo splitterOutputInfo(4, |
| 2205 | splitterDimSizes, |
| 2206 | inputInfo.GetDataType(), |
| 2207 | inputInfo.GetQuantizationScale(), |
| 2208 | inputInfo.GetQuantizationOffset()); |
| 2209 | |
| 2210 | std::vector<std::reference_wrapper<TensorInfo>> splitterOutputInfos(numGroups, std::ref(splitterOutputInfo)); |
| 2211 | |
| 2212 | ViewsDescriptor splitterDesc(numGroups); |
| 2213 | for (unsigned int group = 0u; group < numGroups; ++group) |
| 2214 | { |
| 2215 | splitterDesc.SetViewOriginCoord(group, channelsIndex, splitterDimSizes[channelsIndex] * group); |
| 2216 | for (unsigned int dimIdx = 0u; dimIdx < 4u; dimIdx++) |
| 2217 | { |
| 2218 | splitterDesc.SetViewSize(group, dimIdx, splitterDimSizes[dimIdx]); |
| 2219 | } |
| 2220 | } |
| 2221 | |
| 2222 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2223 | armnn::BackendId setBackendSplit; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2224 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2225 | IsSplitterSupported, |
| 2226 | data.m_Backends, |
| 2227 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2228 | setBackendSplit, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2229 | inputInfo, |
| 2230 | splitterOutputInfos, |
| 2231 | splitterDesc); |
| 2232 | if (!isSupported) |
| 2233 | { |
| 2234 | return false; |
| 2235 | } |
| 2236 | |
| 2237 | IConnectableLayer* splitterLayer = data.m_Network->AddSplitterLayer(splitterDesc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2238 | splitterLayer->SetBackendId(setBackendSplit); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2239 | if (!splitterLayer) |
| 2240 | { |
| 2241 | return Fail("%s: Failed to add SplitterLayer", __func__); |
| 2242 | } |
| 2243 | |
| 2244 | input.Connect(splitterLayer->GetInputSlot(0)); |
| 2245 | for (unsigned int group = 0u; group < splitterLayer->GetNumOutputSlots(); ++group) |
| 2246 | { |
| 2247 | splitterLayer->GetOutputSlot(group).SetTensorInfo(splitterOutputInfo); |
| 2248 | } |
| 2249 | |
| 2250 | // |
| 2251 | // Set up Convolution2d layers for each group |
| 2252 | // |
| 2253 | |
| 2254 | // Set up group tensor shapes |
| 2255 | TensorShape groupInputShape(inputShape); |
| 2256 | groupInputShape[channelsIndex] = channelsPerGroup; |
| 2257 | |
| 2258 | TensorShape groupWeightsShape(weightsShape); |
| 2259 | groupWeightsShape[0] /= channelMultiplier * numGroups; |
| 2260 | |
| 2261 | TensorShape groupBiasesShape({ 1 }); |
| 2262 | |
| 2263 | // Set up group tensor infos |
| 2264 | TensorInfo groupInputInfo(inputInfo); |
| 2265 | groupInputInfo.SetShape(groupInputShape); |
| 2266 | |
| 2267 | const TensorInfo& weightsInfo = weights.GetInfo(); |
| 2268 | TensorInfo groupWeightsInfo(weightsInfo); |
| 2269 | groupWeightsInfo.SetShape(groupWeightsShape); |
| 2270 | |
| 2271 | const TensorInfo& biasesInfo = biases.GetInfo(); |
| 2272 | TensorInfo groupBiasesInfo(biasesInfo); |
| 2273 | groupBiasesInfo.SetShape(groupBiasesShape); |
| 2274 | |
| 2275 | TensorInfo groupOutputInfo(outputInfo); |
| 2276 | |
| 2277 | TensorShape groupOutputShape(outputShape); |
| 2278 | const bool isDynamic = IsDynamicTensor(outputInfo); |
| 2279 | if (!isDynamic) |
| 2280 | { |
| 2281 | groupOutputShape[channelsIndex] = 1; |
| 2282 | } |
| 2283 | groupOutputInfo.SetShape(groupOutputShape); |
| 2284 | |
| 2285 | const unsigned int weightsDataTypeSize = GetDataTypeSize(groupWeightsInfo.GetDataType()); |
| 2286 | const unsigned int biasesDataTypeSize = GetDataTypeSize(groupBiasesInfo.GetDataType()); |
| 2287 | |
| 2288 | std::vector<IConnectableLayer*> convLayers(numGroups * channelMultiplier, nullptr); |
| 2289 | for (unsigned int group = 0u; group < numGroups; ++group) |
| 2290 | { |
| 2291 | for (unsigned int m = 0u; m < channelMultiplier; ++m) |
| 2292 | { |
| 2293 | auto index = group * channelMultiplier + m; |
| 2294 | |
| 2295 | const unsigned int weightsDataOffset = groupWeightsShape.GetNumElements() * index * weightsDataTypeSize; |
| 2296 | const unsigned int biasesDataOffset = groupBiasesShape.GetNumElements() * index * biasesDataTypeSize; |
| 2297 | |
| 2298 | if (weightsInfo.HasPerAxisQuantization()) |
| 2299 | { |
| 2300 | // Extract per-axis quantization scales for group weights |
| 2301 | const std::vector<float>& weightsQuantScales = weightsInfo.GetQuantizationScales(); |
| 2302 | groupWeightsInfo.SetQuantizationScales( |
| 2303 | std::vector<float>(weightsQuantScales.begin() + index, |
| 2304 | weightsQuantScales.begin() + index + groupWeightsShape[0])); |
| 2305 | |
| 2306 | // Extract per-axis quantization scales for group biases |
| 2307 | const std::vector<float>& biasesQuantScales = biasesInfo.GetQuantizationScales(); |
| 2308 | groupBiasesInfo.SetQuantizationScales( |
| 2309 | std::vector<float>(biasesQuantScales.begin() + index, |
| 2310 | biasesQuantScales.begin() + index + groupWeightsShape[0])); |
| 2311 | } |
| 2312 | |
| 2313 | // Extract weights and biases data for current group convolution |
| 2314 | ConstTensor groupWeights(groupWeightsInfo, |
| 2315 | static_cast<const void *>(reinterpret_cast<const char *>(weights.GetMemoryArea()) + |
| 2316 | weightsDataOffset)); |
| 2317 | ConstTensor groupBiases(groupBiasesInfo, |
| 2318 | static_cast<const void *>(reinterpret_cast<const char *>(biases.GetMemoryArea()) + |
| 2319 | biasesDataOffset)); |
| 2320 | |
| 2321 | isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2322 | armnn::BackendId setBackendConv; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2323 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 2324 | { |
| 2325 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2326 | IsConvolution2dSupported, |
| 2327 | data.m_Backends, |
| 2328 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2329 | setBackendConv, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2330 | groupInputInfo, |
| 2331 | outputInfo, |
| 2332 | desc, |
| 2333 | groupWeightsInfo, |
| 2334 | Optional<TensorInfo>(groupBiasesInfo)); |
| 2335 | }; |
| 2336 | |
| 2337 | if(!isDynamic) |
| 2338 | { |
| 2339 | validateFunc(groupOutputInfo, isSupported); |
| 2340 | } |
| 2341 | else |
| 2342 | { |
| 2343 | isSupported = AreDynamicTensorsSupported(); |
| 2344 | } |
| 2345 | |
| 2346 | if (!isSupported) |
| 2347 | { |
| 2348 | return false; |
| 2349 | } |
Teresa Charlin | d936033 | 2022-08-30 14:27:10 +0100 | [diff] [blame] | 2350 | |
| 2351 | IConnectableLayer* weightsLayer = data.m_Network->AddConstantLayer(groupWeights); |
| 2352 | IConnectableLayer* biasLayer = data.m_Network->AddConstantLayer(groupBiases); |
| 2353 | IConnectableLayer* convLayer = data.m_Network->AddConvolution2dLayer(desc); |
| 2354 | |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2355 | convLayer->SetBackendId(setBackendConv); |
| 2356 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2357 | if (!convLayer) |
| 2358 | { |
| 2359 | return Fail("%s: AddConvolution2dLayer failed", __func__); |
| 2360 | } |
| 2361 | |
| 2362 | splitterLayer->GetOutputSlot(group).Connect(convLayer->GetInputSlot(0)); |
Teresa Charlin | d936033 | 2022-08-30 14:27:10 +0100 | [diff] [blame] | 2363 | weightsLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(1)); |
| 2364 | biasLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(2)); |
| 2365 | |
| 2366 | weightsLayer->GetOutputSlot(0).SetTensorInfo(groupWeightsInfo); |
| 2367 | biasLayer->GetOutputSlot(0).SetTensorInfo(groupBiasesInfo); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2368 | convLayer->GetOutputSlot(0).SetTensorInfo(groupOutputInfo); |
| 2369 | |
| 2370 | if(isDynamic) |
| 2371 | { |
| 2372 | convLayer->GetOutputSlot(0).IsTensorInfoSet(); |
| 2373 | |
| 2374 | validateFunc(convLayer->GetOutputSlot(0).GetTensorInfo(), isSupported); |
| 2375 | |
| 2376 | outputInfo = convLayer->GetOutputSlot(0).GetTensorInfo(); |
| 2377 | |
| 2378 | if (!isSupported) |
| 2379 | { |
| 2380 | return false; |
| 2381 | } |
| 2382 | } |
| 2383 | |
| 2384 | convLayers[index] = convLayer; |
| 2385 | } |
| 2386 | } |
| 2387 | |
| 2388 | // |
| 2389 | // Set up Concat layer |
| 2390 | // |
| 2391 | ConcatDescriptor concatDescriptor; |
| 2392 | // Equivalent to outputShape[channelsIndex], but we can't know the outputShape in the case of dynamic tensors |
| 2393 | concatDescriptor = ConcatDescriptor(weightsShape[0]); |
| 2394 | for (unsigned int group = 0u; group < numGroups; ++group) |
| 2395 | { |
| 2396 | for (unsigned int m = 0u; m < channelMultiplier; ++m) |
| 2397 | { |
| 2398 | auto index = group * channelMultiplier + m; |
| 2399 | concatDescriptor.SetViewOriginCoord(index, channelsIndex, index); |
| 2400 | concatDescriptor.SetConcatAxis(channelsIndex); |
| 2401 | } |
| 2402 | } |
| 2403 | |
| 2404 | isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2405 | armnn::BackendId setBackendConcat; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2406 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2407 | IsConcatSupported, |
| 2408 | data.m_Backends, |
| 2409 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2410 | setBackendConcat, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2411 | std::vector<const TensorInfo*>(numGroups * channelMultiplier, &groupOutputInfo), |
| 2412 | outputInfo, |
| 2413 | concatDescriptor); |
| 2414 | |
| 2415 | if (!isSupported) |
| 2416 | { |
| 2417 | return false; |
| 2418 | } |
| 2419 | |
| 2420 | IConnectableLayer* concatLayer = data.m_Network->AddConcatLayer(concatDescriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2421 | concatLayer->SetBackendId(setBackendConcat); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2422 | if (!concatLayer) |
| 2423 | { |
| 2424 | return Fail("%s: AddConcatLayer failed", __func__); |
| 2425 | } |
| 2426 | |
| 2427 | for (unsigned int group = 0u; group < numGroups; ++group) |
| 2428 | { |
| 2429 | for (unsigned int m = 0u; m < channelMultiplier; ++m) |
| 2430 | { |
| 2431 | auto index = group * channelMultiplier + m; |
| 2432 | convLayers[index]->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(index)); |
| 2433 | } |
| 2434 | } |
| 2435 | concatLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 2436 | |
| 2437 | return SetupAndTrackLayerOutputSlot(operation, 0, *concatLayer, model, |
| 2438 | data, nullptr, nullptr, activation); |
| 2439 | } |
| 2440 | |
| 2441 | bool Converter::ConvertHardSwish(const Operation& operation, const Model& model, ConversionData& data) |
| 2442 | { |
| 2443 | VLOG(DRIVER) << "Converter::ConvertHardSwish()"; |
| 2444 | ActivationDescriptor desc; |
| 2445 | desc.m_Function = ActivationFunction::HardSwish; |
| 2446 | |
| 2447 | return ::ConvertToActivation(operation, __func__, desc, model, data); |
| 2448 | } |
| 2449 | |
| 2450 | bool Converter::ConvertInstanceNormalization(const Operation& operation, const Model& model, ConversionData& data) |
| 2451 | { |
| 2452 | VLOG(DRIVER) << "Converter::ConvertInstanceNormalization()"; |
| 2453 | |
| 2454 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 2455 | if (!input.IsValid()) |
| 2456 | { |
| 2457 | return Fail("%s: Operation has an invalid input 0", __func__); |
| 2458 | } |
| 2459 | |
| 2460 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 2461 | if (!output) |
| 2462 | { |
| 2463 | return Fail("%s: Operation has an invalid output", __func__); |
| 2464 | } |
| 2465 | |
| 2466 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 2467 | |
| 2468 | // Determine data type of input tensor |
| 2469 | OperandType inputType; |
| 2470 | if (!GetOperandType(operation, 0, model, inputType)) |
| 2471 | { |
| 2472 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2473 | } |
| 2474 | |
| 2475 | InstanceNormalizationDescriptor desc; |
| 2476 | |
| 2477 | // Read gamma, beta & epsilon |
| 2478 | if (inputType == OperandType::TENSOR_FLOAT16) |
| 2479 | { |
| 2480 | Half fp16Gamma; |
| 2481 | Half fp16Beta; |
| 2482 | Half fp16Epsilon; |
| 2483 | |
| 2484 | if (!GetInputScalar(operation, 1, OperandType::FLOAT16, fp16Gamma, model, data) || |
| 2485 | !GetInputScalar(operation, 2, OperandType::FLOAT16, fp16Beta, model, data) || |
| 2486 | !GetInputScalar(operation, 3, OperandType::FLOAT16, fp16Epsilon, model, data)) |
| 2487 | { |
| 2488 | return Fail("%s: Operation has invalid inputs (FLOAT16)", __func__); |
| 2489 | } |
| 2490 | |
| 2491 | desc.m_Gamma = static_cast<float>(fp16Gamma); |
| 2492 | desc.m_Beta = static_cast<float>(fp16Beta); |
| 2493 | desc.m_Eps = static_cast<float>(fp16Epsilon); |
| 2494 | } |
| 2495 | else if (inputType == OperandType::TENSOR_FLOAT32) |
| 2496 | { |
| 2497 | if (!GetInputScalar(operation, 1, OperandType::FLOAT32, desc.m_Gamma, model, data) || |
| 2498 | !GetInputScalar(operation, 2, OperandType::FLOAT32, desc.m_Beta, model, data) || |
| 2499 | !GetInputScalar(operation, 3, OperandType::FLOAT32, desc.m_Eps, model, data)) |
| 2500 | { |
| 2501 | return Fail("%s: Operation has invalid inputs (FLOAT32)", __func__); |
| 2502 | } |
| 2503 | } |
| 2504 | else |
| 2505 | { |
| 2506 | return Fail("%s: Unsupported input tensor type: %d", __func__, inputType); |
| 2507 | } |
| 2508 | |
| 2509 | desc.m_DataLayout = OptionalDataLayout(operation, 4, model, data); |
| 2510 | |
| 2511 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2512 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2513 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 2514 | { |
| 2515 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2516 | IsInstanceNormalizationSupported, |
| 2517 | data.m_Backends, |
| 2518 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2519 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2520 | input.GetTensorInfo(), |
| 2521 | outputInfo, |
| 2522 | desc); |
| 2523 | }; |
| 2524 | |
| 2525 | if(IsDynamicTensor(outputInfo)) |
| 2526 | { |
| 2527 | isSupported = AreDynamicTensorsSupported(); |
| 2528 | } |
| 2529 | else |
| 2530 | { |
| 2531 | validateFunc(outputInfo, isSupported); |
| 2532 | } |
| 2533 | |
| 2534 | if (!isSupported) |
| 2535 | { |
| 2536 | return false; |
| 2537 | } |
| 2538 | |
| 2539 | IConnectableLayer* layer = data.m_Network->AddInstanceNormalizationLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2540 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2541 | input.Connect(layer->GetInputSlot(0)); |
| 2542 | |
| 2543 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 2544 | } |
| 2545 | |
| 2546 | bool Converter::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data) |
| 2547 | { |
| 2548 | VLOG(DRIVER) << "Converter::ConvertL2Normalization()"; |
| 2549 | |
| 2550 | if (operation.inputs.size() != 1) |
| 2551 | { |
| 2552 | return Fail("%s: Optional inputs are not supported", __func__); |
| 2553 | } |
| 2554 | |
| 2555 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 2556 | if (!input.IsValid()) |
| 2557 | { |
| 2558 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2559 | } |
| 2560 | |
| 2561 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 2562 | if (!output) |
| 2563 | { |
| 2564 | return Fail("%s: Could not read output 0", __func__); |
| 2565 | } |
| 2566 | |
| 2567 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 2568 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 2569 | |
| 2570 | if (outputInfo.GetNumDimensions() != 4u) |
| 2571 | { |
| 2572 | return Fail("%s: Tensor Rank other than 4 is not supported", __func__); |
| 2573 | } |
| 2574 | |
| 2575 | armnn::L2NormalizationDescriptor desc; |
| 2576 | desc.m_DataLayout = armnn::DataLayout::NHWC; |
| 2577 | |
| 2578 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2579 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2580 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 2581 | { |
| 2582 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2583 | IsL2NormalizationSupported, |
| 2584 | data.m_Backends, |
| 2585 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2586 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2587 | inputInfo, |
| 2588 | outputInfo, |
| 2589 | desc); |
| 2590 | }; |
| 2591 | |
| 2592 | if(!IsDynamicTensor(outputInfo)) |
| 2593 | { |
| 2594 | validateFunc(outputInfo, isSupported); |
| 2595 | } |
| 2596 | else |
| 2597 | { |
| 2598 | isSupported = AreDynamicTensorsSupported(); |
| 2599 | } |
| 2600 | |
| 2601 | if (!isSupported) |
| 2602 | { |
| 2603 | return false; |
| 2604 | } |
| 2605 | |
| 2606 | armnn::IConnectableLayer* layer = data.m_Network->AddL2NormalizationLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2607 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2608 | assert(layer != nullptr); |
| 2609 | input.Connect(layer->GetInputSlot(0)); |
| 2610 | |
| 2611 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 2612 | } |
| 2613 | |
| 2614 | bool Converter::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data) |
| 2615 | { |
| 2616 | VLOG(DRIVER) << "Converter::ConvertL2Pool2d()"; |
| 2617 | return ConvertPooling2d(operation, __func__, PoolingAlgorithm::L2, model, data); |
| 2618 | } |
| 2619 | |
| 2620 | bool Converter::ConvertLocalResponseNormalization(const Operation& operation, |
| 2621 | const Model& model, |
| 2622 | ConversionData& data) |
| 2623 | { |
| 2624 | VLOG(DRIVER) << "Converter::ConvertLocalResponseNormalization()"; |
| 2625 | |
| 2626 | if (operation.inputs.size() != 5) |
| 2627 | { |
| 2628 | return Fail("%s: Optional inputs are not supported", __func__); |
| 2629 | } |
| 2630 | |
| 2631 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 2632 | if (!input.IsValid()) |
| 2633 | { |
| 2634 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2635 | } |
| 2636 | |
| 2637 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 2638 | if (!output) |
| 2639 | { |
| 2640 | return Fail("%s: Could not read output 0", __func__); |
| 2641 | } |
| 2642 | |
| 2643 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 2644 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 2645 | |
| 2646 | if (outputInfo.GetNumDimensions() != 4u) |
| 2647 | { |
| 2648 | return Fail("%s: Tensor Rank other than 4 is not supported", __func__); |
| 2649 | } |
| 2650 | |
| 2651 | armnn::NormalizationDescriptor descriptor; |
| 2652 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 2653 | descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across; |
| 2654 | descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 2655 | |
| 2656 | if (!input.IsValid() || |
| 2657 | !GetInputScalar(operation, 1, OperandType::INT32, descriptor.m_NormSize, model, data) || |
| 2658 | !GetInputFloat32(operation, 2, descriptor.m_K, model, data) || |
| 2659 | !GetInputFloat32(operation, 3, descriptor.m_Alpha, model, data) || |
| 2660 | !GetInputFloat32(operation, 4, descriptor.m_Beta, model, data)) |
| 2661 | { |
| 2662 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2663 | } |
| 2664 | |
| 2665 | // ArmNN expects normSize to be the full size of the normalization |
| 2666 | // window rather than the radius as in AndroidNN. |
| 2667 | descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize); |
| 2668 | |
| 2669 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2670 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2671 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 2672 | { |
| 2673 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2674 | IsNormalizationSupported, |
| 2675 | data.m_Backends, |
| 2676 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2677 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2678 | inputInfo, |
| 2679 | outputInfo, |
| 2680 | descriptor); |
| 2681 | }; |
| 2682 | |
| 2683 | if(!IsDynamicTensor(outputInfo)) |
| 2684 | { |
| 2685 | validateFunc(outputInfo, isSupported); |
| 2686 | } |
| 2687 | else |
| 2688 | { |
| 2689 | isSupported = AreDynamicTensorsSupported(); |
| 2690 | } |
| 2691 | |
| 2692 | if (!isSupported) |
| 2693 | { |
| 2694 | return false; |
| 2695 | } |
| 2696 | |
| 2697 | |
| 2698 | armnn::IConnectableLayer* layer = data.m_Network->AddNormalizationLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2699 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2700 | assert(layer != nullptr); |
| 2701 | input.Connect(layer->GetInputSlot(0)); |
| 2702 | |
| 2703 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 2704 | } |
| 2705 | |
| 2706 | bool Converter::ConvertLogicalBinary(const Operation& operation, |
| 2707 | const Model& model, |
| 2708 | ConversionData& data, |
| 2709 | armnn::LogicalBinaryOperation logicalOperation) |
| 2710 | { |
| 2711 | VLOG(DRIVER) << "Converter::ConvertLogicalBinary()"; |
| 2712 | VLOG(DRIVER) << "ConvertLogicalBinary()"; |
| 2713 | VLOG(DRIVER) << "logicalOperation = " << GetLogicalBinaryOperationAsCString(logicalOperation); |
| 2714 | |
| 2715 | LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data); |
| 2716 | LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data); |
| 2717 | |
| 2718 | if (!(input0.IsValid() && input1.IsValid())) |
| 2719 | { |
| 2720 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2721 | } |
| 2722 | |
| 2723 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 2724 | if (!output) |
| 2725 | { |
| 2726 | return Fail("%s: Could not read output 0", __func__); |
| 2727 | } |
| 2728 | |
| 2729 | const TensorInfo& inputInfo0 = input0.GetTensorInfo(); |
| 2730 | const TensorInfo& inputInfo1 = input1.GetTensorInfo(); |
| 2731 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 2732 | |
| 2733 | LogicalBinaryDescriptor descriptor(logicalOperation); |
| 2734 | |
| 2735 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2736 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2737 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 2738 | { |
| 2739 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2740 | IsLogicalBinarySupported, |
| 2741 | data.m_Backends, |
| 2742 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2743 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2744 | inputInfo0, |
| 2745 | inputInfo1, |
| 2746 | outputInfo, |
| 2747 | descriptor); |
| 2748 | }; |
| 2749 | |
| 2750 | if(!IsDynamicTensor(outputInfo)) |
| 2751 | { |
| 2752 | validateFunc(outputInfo, isSupported); |
| 2753 | } |
| 2754 | else |
| 2755 | { |
| 2756 | isSupported = AreDynamicTensorsSupported(); |
| 2757 | } |
| 2758 | |
| 2759 | if (!isSupported) |
| 2760 | { |
| 2761 | return false; |
| 2762 | } |
| 2763 | |
| 2764 | IConnectableLayer* layer = data.m_Network->AddLogicalBinaryLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2765 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2766 | assert(layer != nullptr); |
| 2767 | |
| 2768 | bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data); |
| 2769 | if (!isReshapeSupported) |
| 2770 | { |
| 2771 | return false; |
| 2772 | } |
| 2773 | |
| 2774 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 2775 | } |
| 2776 | |
| 2777 | bool Converter::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data) |
| 2778 | { |
| 2779 | VLOG(DRIVER) << "Converter::ConvertLogistic()"; |
| 2780 | armnn::ActivationDescriptor desc; |
| 2781 | desc.m_Function = armnn::ActivationFunction::Sigmoid; |
| 2782 | |
| 2783 | return ConvertToActivation(operation, __func__, desc, model, data); |
| 2784 | } |
| 2785 | |
| 2786 | bool Converter::ConvertLogSoftmax(const Operation& operation, const Model& model, ConversionData& data) |
| 2787 | { |
| 2788 | VLOG(DRIVER) << "Converter::ConvertLogSoftmax()"; |
| 2789 | |
| 2790 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 2791 | if (!input.IsValid()) |
| 2792 | { |
| 2793 | return Fail("%s: Failed to read input 0", __func__); |
| 2794 | } |
| 2795 | |
| 2796 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 2797 | if (!output) |
| 2798 | { |
| 2799 | return Fail("%s: Failed to read output", __func__); |
| 2800 | } |
| 2801 | |
| 2802 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 2803 | |
| 2804 | // Determine data type of input tensor |
| 2805 | OperandType inputType; |
| 2806 | if (!GetOperandType(operation, 0, model, inputType)) |
| 2807 | { |
| 2808 | return Fail("%s: Operation has invalid inputs", __func__); |
| 2809 | } |
| 2810 | |
| 2811 | LogSoftmaxDescriptor descriptor; |
| 2812 | |
| 2813 | // Read beta |
| 2814 | if (inputType == OperandType::TENSOR_FLOAT16) |
| 2815 | { |
| 2816 | Half fp16Beta; |
| 2817 | if (!GetInputScalar(operation, 1, OperandType::FLOAT16, fp16Beta, model, data)) |
| 2818 | { |
| 2819 | return Fail("%s: Failed to read input 1 (FLOAT16)", __func__); |
| 2820 | } |
| 2821 | |
| 2822 | descriptor.m_Beta = static_cast<float>(fp16Beta); |
| 2823 | } |
| 2824 | else if (inputType == OperandType::TENSOR_FLOAT32) |
| 2825 | { |
| 2826 | if (!GetInputScalar(operation, 1, OperandType::FLOAT32, descriptor.m_Beta, model, data)) |
| 2827 | { |
| 2828 | return Fail("%s: Failed to read input 1 (FLOAT32)", __func__); |
| 2829 | } |
| 2830 | } |
| 2831 | else |
| 2832 | { |
| 2833 | return Fail("%s: Unsupported input tensor type: %d", __func__, inputType); |
| 2834 | } |
| 2835 | |
| 2836 | // Read axis |
| 2837 | if (!GetInputInt32(operation, 2, descriptor.m_Axis, model, data)) |
| 2838 | { |
| 2839 | return Fail("%s: Failed to read input 2", __func__); |
| 2840 | } |
| 2841 | |
| 2842 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2843 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2844 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 2845 | { |
| 2846 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 2847 | IsLogSoftmaxSupported, |
| 2848 | data.m_Backends, |
| 2849 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2850 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2851 | input.GetTensorInfo(), |
| 2852 | outputInfo, |
| 2853 | descriptor); |
| 2854 | }; |
| 2855 | |
| 2856 | if(IsDynamicTensor(outputInfo)) |
| 2857 | { |
| 2858 | isSupported = AreDynamicTensorsSupported(); |
| 2859 | } |
| 2860 | else |
| 2861 | { |
| 2862 | validateFunc(outputInfo, isSupported); |
| 2863 | } |
| 2864 | |
| 2865 | if (!isSupported) |
| 2866 | { |
| 2867 | return false; |
| 2868 | } |
| 2869 | |
| 2870 | IConnectableLayer* layer = data.m_Network->AddLogSoftmaxLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 2871 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 2872 | if (!layer) |
| 2873 | { |
| 2874 | return Fail("%s: AddLogSoftmaxLayer() returned nullptr", __func__); |
| 2875 | } |
| 2876 | |
| 2877 | input.Connect(layer->GetInputSlot(0)); |
| 2878 | |
| 2879 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 2880 | } |
| 2881 | |
| 2882 | bool Converter::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data) |
| 2883 | { |
| 2884 | VLOG(DRIVER) << "Converter::ConvertLstm()"; |
| 2885 | |
| 2886 | // Inputs: |
| 2887 | // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where |
| 2888 | // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| 2889 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 2890 | if (!input.IsValid()) |
| 2891 | { |
| 2892 | return Fail("%s: Could not read input 0: input", __func__); |
| 2893 | } |
| 2894 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 2895 | LayerInputHandle outputStateIn = ConvertToLayerInputHandle(operation, 18, model, data); |
| 2896 | if (!outputStateIn.IsValid()) |
| 2897 | { |
| 2898 | return Fail("%s: Could not read input 18: outputStateIn", __func__); |
| 2899 | } |
| 2900 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 2901 | LayerInputHandle cellStateIn = ConvertToLayerInputHandle(operation, 19, model, data); |
| 2902 | if (!cellStateIn.IsValid()) |
| 2903 | { |
| 2904 | return Fail("%s: Could not read input 19: cellStateIn", __func__); |
| 2905 | } |
| 2906 | |
| 2907 | // Get the mandatory input tensors: |
| 2908 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2909 | // [num_units, input_size]. |
| 2910 | const ConstTensorPin inputToForgetWeightsPin = |
| 2911 | (DequantizeAndMakeConstTensorPin(operation, model, data, 2)); |
| 2912 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2913 | // [num_units, input_size]. |
| 2914 | const ConstTensorPin inputToCellWeightsPin = |
| 2915 | (DequantizeAndMakeConstTensorPin(operation, model, data, 3)); |
| 2916 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2917 | // [num_units, input_size]. |
| 2918 | const ConstTensorPin inputToOutputWeightsPin = |
| 2919 | (DequantizeAndMakeConstTensorPin(operation, model, data, 4)); |
| 2920 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2921 | // [num_units, output_size]. |
| 2922 | const ConstTensorPin recurrentToForgetWeightsPin = |
| 2923 | (DequantizeAndMakeConstTensorPin(operation, model, data, 6)); |
| 2924 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2925 | // [num_units, output_size]. |
| 2926 | const ConstTensorPin recurrentToCellWeightsPin = |
| 2927 | (DequantizeAndMakeConstTensorPin(operation, model, data, 7)); |
| 2928 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2929 | // [num_units, output_size]. |
| 2930 | const ConstTensorPin recurrentToOutputWeightsPin = |
| 2931 | (DequantizeAndMakeConstTensorPin(operation, model, data, 8)); |
| 2932 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 2933 | const ConstTensorPin forgetGateBiasPin = |
| 2934 | ConvertOperationInputToConstTensorPin(operation, 13, model, data); |
| 2935 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 2936 | const ConstTensorPin cellBiasPin = |
| 2937 | ConvertOperationInputToConstTensorPin(operation, 14, model, data); |
| 2938 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 2939 | const ConstTensorPin outputGateBiasPin = |
| 2940 | ConvertOperationInputToConstTensorPin(operation, 15, model, data); |
| 2941 | |
| 2942 | if (!inputToForgetWeightsPin.IsValid() || |
| 2943 | !inputToCellWeightsPin.IsValid() || |
| 2944 | !inputToOutputWeightsPin.IsValid() || |
| 2945 | !recurrentToForgetWeightsPin.IsValid() || |
| 2946 | !recurrentToCellWeightsPin.IsValid() || |
| 2947 | !recurrentToOutputWeightsPin.IsValid() || |
| 2948 | !forgetGateBiasPin.IsValid() || |
| 2949 | !cellBiasPin.IsValid() || |
| 2950 | !outputGateBiasPin.IsValid()) |
| 2951 | { |
| 2952 | return Fail("%s: Operation has invalid tensor inputs", __func__); |
| 2953 | } |
| 2954 | |
| 2955 | // Get the optional input tensors: |
| 2956 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2957 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 2958 | const ConstTensorPin inputToInputWeightsPin = |
| 2959 | (DequantizeAndMakeConstTensorPin(operation, model, data, 1, true)); |
| 2960 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2961 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 2962 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 2963 | const ConstTensorPin recurrentToInputWeightsPin = |
| 2964 | (DequantizeAndMakeConstTensorPin(operation, model, data, 5, true)); |
| 2965 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 2966 | const ConstTensorPin cellToInputWeightsPin = |
| 2967 | (DequantizeAndMakeConstTensorPin(operation, model, data, 9, true)); |
| 2968 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 2969 | const ConstTensorPin cellToForgetWeightsPin = |
| 2970 | (DequantizeAndMakeConstTensorPin(operation, model, data, 10, true)); |
| 2971 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 2972 | const ConstTensorPin cellToOutputWeightsPin = |
| 2973 | (DequantizeAndMakeConstTensorPin(operation, model, data, 11, true)); |
| 2974 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 2975 | const ConstTensorPin inputGateBiasPin = |
| 2976 | ConvertOperationInputToConstTensorPin(operation, |
| 2977 | 12, |
| 2978 | model, |
| 2979 | data, |
| 2980 | g_DontPermute, |
| 2981 | nullptr, |
| 2982 | true); |
| 2983 | |
| 2984 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 2985 | // [output_size, num_units]. |
| 2986 | const ConstTensorPin projectionWeightsPin = |
| 2987 | (DequantizeAndMakeConstTensorPin(operation, model, data, 16, true)); |
| 2988 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| 2989 | const ConstTensorPin projectionBiasPin = |
| 2990 | ConvertOperationInputToConstTensorPin(operation, |
| 2991 | 17, |
| 2992 | model, |
| 2993 | data, |
| 2994 | g_DontPermute, |
| 2995 | nullptr, |
| 2996 | true); |
| 2997 | |
| 2998 | if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) || |
| 2999 | (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) || |
| 3000 | (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) || |
| 3001 | (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) || |
| 3002 | (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) || |
| 3003 | (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) || |
| 3004 | (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) || |
| 3005 | (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional())) |
| 3006 | { |
| 3007 | return Fail("%s: Operation has invalid tensor inputs", __func__); |
| 3008 | } |
| 3009 | |
| 3010 | // Get the mandatory input scalars (actually 1-D tensors of size 1): |
| 3011 | // 20: The activation function: A value indicating the activation function: |
| 3012 | // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| 3013 | // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| 3014 | // If set to 0.0 then clipping is disabled. |
| 3015 | // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| 3016 | // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| 3017 | ActivationFn activation = ActivationFn::kActivationNone; |
| 3018 | float cellClip; |
| 3019 | float projClip; |
| 3020 | if (!GetInputActivationFunctionFromTensor(operation, 20, activation, model, data) || |
| 3021 | !GetInputScalar(operation, 21, OperandType::FLOAT32, cellClip, model, data) || |
| 3022 | !GetInputScalar(operation, 22, OperandType::FLOAT32, projClip, model, data)) |
| 3023 | { |
| 3024 | return Fail("%s: Operation has invalid scalar inputs", __func__); |
| 3025 | } |
| 3026 | |
| 3027 | // Get the normalization tensors |
| 3028 | // 23: The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| 3029 | // Used to rescale normalized inputs to activation at input gate. |
| 3030 | const ConstTensorPin inputLayerNormWeightsPin |
| 3031 | (DequantizeAndMakeConstTensorPin(operation, model, data, 23, true)); |
| 3032 | |
| 3033 | // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| 3034 | // Used to rescale normalized inputs to activation at forget gate. |
| 3035 | const ConstTensorPin forgetLayerNormWeightsPin = |
| 3036 | ConvertOperationInputToConstTensorPin(operation, |
| 3037 | 24, |
| 3038 | model, |
| 3039 | data, |
| 3040 | g_DontPermute, |
| 3041 | nullptr, |
| 3042 | true); |
| 3043 | |
| 3044 | // 25: The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| 3045 | // Used to rescale normalized inputs to activation at cell gate. |
| 3046 | const ConstTensorPin cellLayerNormWeightsPin = |
| 3047 | ConvertOperationInputToConstTensorPin(operation, |
| 3048 | 25, |
| 3049 | model, |
| 3050 | data, |
| 3051 | g_DontPermute, |
| 3052 | nullptr, |
| 3053 | true); |
| 3054 | |
| 3055 | // 26: The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 3056 | // Used to rescale normalized inputs to activation at output gate. |
| 3057 | const ConstTensorPin outputLayerNormWeightsPin = |
| 3058 | ConvertOperationInputToConstTensorPin(operation, |
| 3059 | 26, |
| 3060 | model, |
| 3061 | data, |
| 3062 | g_DontPermute, |
| 3063 | nullptr, |
| 3064 | true); |
| 3065 | |
| 3066 | // Outputs: |
| 3067 | // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] |
| 3068 | // with CIFG, or [batch_size, num_units * 3] without CIFG. |
| 3069 | const Operand* scratchBuffer = GetOutputOperand(operation, 0, model); |
| 3070 | if (!scratchBuffer) |
| 3071 | { |
| 3072 | return Fail("%s: Could not read output 0: scratchBuffer", __func__); |
| 3073 | } |
| 3074 | // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 3075 | const Operand* outputStateOut = GetOutputOperand(operation, 1, model); |
| 3076 | if (!outputStateOut) |
| 3077 | { |
| 3078 | return Fail("%s: Could not read output 1: outputStateOut", __func__); |
| 3079 | } |
| 3080 | // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 3081 | const Operand* cellStateOut = GetOutputOperand(operation, 2, model); |
| 3082 | if (!cellStateOut) |
| 3083 | { |
| 3084 | return Fail("%s: Could not read output 2: cellStateOut", __func__); |
| 3085 | } |
| 3086 | // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is |
| 3087 | // effectively the same as the current “output state (out)” value. |
| 3088 | const Operand* output = GetOutputOperand(operation, 3, model); |
| 3089 | if (!output) |
| 3090 | { |
| 3091 | return Fail("%s: Could not read output 3: output", __func__); |
| 3092 | } |
| 3093 | |
| 3094 | // set the params structure for the AddLstmLayer call |
| 3095 | LstmInputParams params; |
| 3096 | params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr(); |
| 3097 | params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr(); |
| 3098 | params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr(); |
| 3099 | params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr(); |
| 3100 | params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr(); |
| 3101 | params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr(); |
| 3102 | params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr(); |
| 3103 | params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr(); |
| 3104 | params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr(); |
| 3105 | params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr(); |
| 3106 | params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr(); |
| 3107 | params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr(); |
| 3108 | params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr(); |
| 3109 | params.m_CellBias = cellBiasPin.GetConstTensorPtr(); |
| 3110 | params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr(); |
| 3111 | params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr(); |
| 3112 | params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr(); |
| 3113 | params.m_InputLayerNormWeights = inputLayerNormWeightsPin.GetConstTensorPtr(); |
| 3114 | params.m_ForgetLayerNormWeights = forgetLayerNormWeightsPin.GetConstTensorPtr(); |
| 3115 | params.m_CellLayerNormWeights = cellLayerNormWeightsPin.GetConstTensorPtr(); |
| 3116 | params.m_OutputLayerNormWeights = outputLayerNormWeightsPin.GetConstTensorPtr(); |
| 3117 | |
| 3118 | // set the layer descriptor |
| 3119 | LstmDescriptor desc; |
| 3120 | desc.m_ActivationFunc = activation; |
| 3121 | desc.m_ClippingThresCell = cellClip; |
| 3122 | desc.m_ClippingThresProj = projClip; |
| 3123 | desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr || |
| 3124 | params.m_RecurrentToInputWeights == nullptr || |
| 3125 | params.m_InputGateBias == nullptr); |
| 3126 | desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr || |
| 3127 | params.m_CellToOutputWeights != nullptr); |
| 3128 | desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr); |
| 3129 | desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr || |
| 3130 | params.m_ForgetLayerNormWeights != nullptr || |
| 3131 | params.m_CellLayerNormWeights != nullptr || |
| 3132 | params.m_OutputLayerNormWeights != nullptr); |
| 3133 | |
| 3134 | // validate the optional input groups |
| 3135 | if (desc.m_CifgEnabled && |
| 3136 | (params.m_InputToInputWeights != nullptr || |
| 3137 | params.m_RecurrentToInputWeights != nullptr || |
| 3138 | params.m_InputGateBias != nullptr)) |
| 3139 | { |
| 3140 | return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights," |
| 3141 | " and input gate bias must be provided", __func__); |
| 3142 | } |
| 3143 | |
| 3144 | if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr) |
| 3145 | { |
| 3146 | return Fail("%s: projection bias should not be provided without projection weights", __func__); |
| 3147 | } |
| 3148 | |
| 3149 | if (desc.m_PeepholeEnabled && |
| 3150 | (params.m_CellToForgetWeights == nullptr || |
| 3151 | params.m_CellToOutputWeights == nullptr || |
| 3152 | (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr))) |
| 3153 | { |
| 3154 | return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided" |
| 3155 | " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__); |
| 3156 | } |
| 3157 | |
| 3158 | if (desc.m_LayerNormEnabled && |
| 3159 | (params.m_ForgetLayerNormWeights == nullptr || |
| 3160 | params.m_CellLayerNormWeights == nullptr || |
| 3161 | params.m_OutputLayerNormWeights == nullptr || |
| 3162 | (!desc.m_CifgEnabled && params.m_InputLayerNormWeights == nullptr))) |
| 3163 | { |
| 3164 | return Fail("%s: All, or none, of forget-norm weights, cell-norm weights and output-norm weights must be" |
| 3165 | " provided and, if CIFG is not enabled, input-norm weights must also be provided", __func__); |
| 3166 | } |
| 3167 | |
| 3168 | // Check if the layer is supported |
| 3169 | // Inputs |
| 3170 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 3171 | const TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo(); |
| 3172 | const TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo(); |
| 3173 | |
| 3174 | // Outputs |
| 3175 | const TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer); |
| 3176 | const TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut); |
| 3177 | const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut); |
| 3178 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 3179 | |
| 3180 | // Basic parameters |
| 3181 | LstmInputParamsInfo paramsInfo; |
| 3182 | paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); |
| 3183 | paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); |
| 3184 | paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); |
| 3185 | paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); |
| 3186 | paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); |
| 3187 | paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); |
| 3188 | paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); |
| 3189 | paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); |
| 3190 | paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); |
| 3191 | |
| 3192 | // Optional parameters |
| 3193 | if (!desc.m_CifgEnabled) |
| 3194 | { |
| 3195 | paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); |
| 3196 | paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); |
| 3197 | if (params.m_CellToInputWeights != nullptr) |
| 3198 | { |
| 3199 | paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); |
| 3200 | } |
| 3201 | paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); |
| 3202 | } |
| 3203 | |
| 3204 | if (desc.m_ProjectionEnabled) |
| 3205 | { |
| 3206 | paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); |
| 3207 | if (params.m_ProjectionBias != nullptr) |
| 3208 | { |
| 3209 | paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); |
| 3210 | } |
| 3211 | } |
| 3212 | |
| 3213 | if (desc.m_PeepholeEnabled) |
| 3214 | { |
| 3215 | paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); |
| 3216 | paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); |
| 3217 | } |
| 3218 | |
| 3219 | if (desc.m_LayerNormEnabled) |
| 3220 | { |
| 3221 | if(!desc.m_CifgEnabled) |
| 3222 | { |
| 3223 | paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo()); |
| 3224 | } |
| 3225 | paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo()); |
| 3226 | paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo()); |
| 3227 | paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo()); |
| 3228 | } |
| 3229 | |
| 3230 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3231 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3232 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 3233 | { |
| 3234 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 3235 | IsLstmSupported, |
| 3236 | data.m_Backends, |
| 3237 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3238 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3239 | inputInfo, |
| 3240 | outputStateInInfo, |
| 3241 | cellStateInInfo, |
| 3242 | scratchBufferInfo, |
| 3243 | outputStateOutInfo, |
| 3244 | cellStateOutInfo, |
| 3245 | outputInfo, |
| 3246 | desc, |
| 3247 | paramsInfo); |
| 3248 | }; |
| 3249 | |
| 3250 | bool isDynamic = false; |
| 3251 | if (!IsDynamicTensor(outputStateOutInfo) && |
| 3252 | !IsDynamicTensor(scratchBufferInfo) && |
| 3253 | !IsDynamicTensor(cellStateOutInfo) && |
| 3254 | !IsDynamicTensor(outputInfo)) |
| 3255 | { |
| 3256 | validateFunc(outputInfo, isSupported); |
| 3257 | } |
| 3258 | else |
| 3259 | { |
| 3260 | isDynamic = true; |
| 3261 | isSupported = AreDynamicTensorsSupported(); |
| 3262 | } |
| 3263 | |
| 3264 | if (!isSupported) |
| 3265 | { |
| 3266 | return false; |
| 3267 | } |
| 3268 | |
| 3269 | // Add the layer |
| 3270 | IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm"); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3271 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3272 | |
| 3273 | input.Connect(layer->GetInputSlot(0)); |
| 3274 | outputStateIn.Connect(layer->GetInputSlot(1)); |
| 3275 | cellStateIn.Connect(layer->GetInputSlot(2)); |
| 3276 | |
| 3277 | if (!isDynamic) |
| 3278 | { |
| 3279 | return ( |
| 3280 | SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) && |
| 3281 | SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data) && |
| 3282 | SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data) && |
| 3283 | SetupAndTrackLayerOutputSlot(operation, 3, *layer, 3, model, data)); |
| 3284 | } |
| 3285 | else |
| 3286 | { |
| 3287 | return ( |
| 3288 | SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) && |
| 3289 | SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data) && |
| 3290 | SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data) && |
| 3291 | SetupAndTrackLayerOutputSlot( |
| 3292 | operation, 3, *layer, 3, model, data, nullptr, validateFunc, ActivationFn::kActivationNone, true)); |
| 3293 | } |
| 3294 | |
| 3295 | } |
| 3296 | |
| 3297 | bool Converter::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data) |
| 3298 | { |
| 3299 | VLOG(DRIVER) << "Converter::ConvertMaxPool2d()"; |
| 3300 | return ConvertPooling2d(operation, __func__, PoolingAlgorithm::Max, model, data); |
| 3301 | } |
| 3302 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3303 | bool Converter::ConvertMean(const Operation& operation, const Model& model, ConversionData& data) |
| 3304 | { |
| 3305 | VLOG(DRIVER) << "Converter::ConvertMean()"; |
| 3306 | |
| 3307 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 3308 | if (!input.IsValid()) |
| 3309 | { |
| 3310 | return Fail("%s: Operation has invalid inputs", __func__); |
| 3311 | } |
| 3312 | |
| 3313 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 3314 | if (!output) |
| 3315 | { |
| 3316 | return Fail("%s: Could not read output 0", __func__); |
| 3317 | } |
| 3318 | |
| 3319 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 3320 | |
| 3321 | const Operand* axisOperand = GetInputOperand(operation, 1, model); |
| 3322 | if (!axisOperand) |
| 3323 | { |
| 3324 | return Fail("%s: Could not read input 1", __func__); |
| 3325 | } |
| 3326 | |
| 3327 | std::vector<int32_t> axis; |
| 3328 | if (!GetTensorInt32Values(*axisOperand, axis, model, data)) |
| 3329 | { |
| 3330 | return Fail("%s: Input 1 has invalid values", __func__); |
| 3331 | } |
| 3332 | |
| 3333 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 3334 | |
| 3335 | // Convert the axis to unsigned int and remove duplicates. |
| 3336 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 3337 | std::set<unsigned int> uniqueAxis; |
| 3338 | std::transform(axis.begin(), axis.end(), |
| 3339 | std::inserter(uniqueAxis, uniqueAxis.begin()), |
| 3340 | [rank](int i) -> unsigned int { return (i + rank) % rank; }); |
| 3341 | |
| 3342 | // Get the "keep dims" flag. |
| 3343 | int32_t keepDims = 0; |
| 3344 | if (!GetInputInt32(operation, 2, keepDims, model, data)) |
| 3345 | { |
| 3346 | return Fail("%s: Could not read input 2", __func__); |
| 3347 | } |
| 3348 | |
| 3349 | armnn::MeanDescriptor descriptor; |
| 3350 | descriptor.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end()); |
| 3351 | descriptor.m_KeepDims = keepDims > 0; |
| 3352 | |
| 3353 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3354 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3355 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 3356 | { |
| 3357 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 3358 | IsMeanSupported, |
| 3359 | data.m_Backends, |
| 3360 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3361 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3362 | inputInfo, |
| 3363 | outputInfo, |
| 3364 | descriptor); |
| 3365 | }; |
| 3366 | |
| 3367 | if(!IsDynamicTensor(outputInfo)) |
| 3368 | { |
| 3369 | validateFunc(outputInfo, isSupported); |
| 3370 | } |
| 3371 | else |
| 3372 | { |
| 3373 | isSupported = AreDynamicTensorsSupported(); |
| 3374 | } |
| 3375 | |
| 3376 | if (!isSupported) |
| 3377 | { |
| 3378 | return false; |
| 3379 | } |
| 3380 | |
| 3381 | armnn::IConnectableLayer* const layer = data.m_Network->AddMeanLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3382 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3383 | assert(layer != nullptr); |
| 3384 | input.Connect(layer->GetInputSlot(0)); |
| 3385 | |
| 3386 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 3387 | } |
| 3388 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3389 | bool Converter::ConvertPad(const Operation& operation, const Model& model, ConversionData& data) |
| 3390 | { |
| 3391 | VLOG(DRIVER) << "Converter::ConvertPad()"; |
| 3392 | |
| 3393 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 3394 | if (!input.IsValid()) |
| 3395 | { |
| 3396 | return Fail("%s: Operation has invalid inputs", __func__); |
| 3397 | } |
| 3398 | |
| 3399 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 3400 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 3401 | |
| 3402 | armnn::PadDescriptor descriptor; |
| 3403 | if (!ConvertPaddings(operation, model, data, rank, descriptor)) |
| 3404 | { |
| 3405 | return Fail("%s: Could not convert paddings", __func__); |
| 3406 | } |
| 3407 | |
| 3408 | // For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, |
| 3409 | // the scale and zeroPoint must be the same as input0 |
| 3410 | // Before Android Q, the pad value for ANEURALNETWORKS_TENSOR_QUANT8_ASYMM was undefined. Since Android Q the pad |
| 3411 | // value must be "logical zero" we set it to be equal to the QuantizationOffset so effectively it ends up as |
| 3412 | // (QuantizationOffset - QuantizationOffset) * scale = 0. |
| 3413 | if (inputInfo.GetDataType() == armnn::DataType::QAsymmU8 || inputInfo.GetDataType() == armnn::DataType::QAsymmS8) |
| 3414 | { |
| 3415 | descriptor.m_PadValue = inputInfo.GetQuantizationOffset(); |
| 3416 | } |
| 3417 | |
| 3418 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 3419 | if (!output) |
| 3420 | { |
| 3421 | return Fail("%s: Could not read output", __func__); |
| 3422 | } |
| 3423 | |
| 3424 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 3425 | |
| 3426 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3427 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3428 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 3429 | { |
| 3430 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 3431 | IsPadSupported, |
| 3432 | data.m_Backends, |
| 3433 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3434 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3435 | inputInfo, |
| 3436 | outputInfo, |
| 3437 | descriptor); |
| 3438 | }; |
| 3439 | |
| 3440 | if(!IsDynamicTensor(outputInfo)) |
| 3441 | { |
| 3442 | validateFunc(outputInfo, isSupported); |
| 3443 | } |
| 3444 | else |
| 3445 | { |
| 3446 | isSupported = AreDynamicTensorsSupported(); |
| 3447 | } |
| 3448 | |
| 3449 | if (!isSupported) |
| 3450 | { |
| 3451 | return false; |
| 3452 | } |
| 3453 | |
| 3454 | armnn::IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3455 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3456 | assert(layer != nullptr); |
| 3457 | input.Connect(layer->GetInputSlot(0)); |
| 3458 | |
| 3459 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 3460 | } |
| 3461 | |
| 3462 | bool Converter::ConvertPadV2(const Operation& operation, const Model& model, ConversionData& data) |
| 3463 | { |
| 3464 | VLOG(DRIVER) << "Converter::ConvertPadV2()"; |
| 3465 | |
| 3466 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 3467 | if (!input.IsValid()) |
| 3468 | { |
| 3469 | return Fail("%s: Could not read input 0", __func__); |
| 3470 | } |
| 3471 | |
| 3472 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 3473 | if (!output) |
| 3474 | { |
| 3475 | return Fail("%s: Could not read output", __func__); |
| 3476 | } |
| 3477 | |
| 3478 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 3479 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 3480 | |
| 3481 | PadDescriptor descriptor; |
| 3482 | if (!ConvertPaddings(operation, model, data, rank, descriptor)) |
| 3483 | { |
| 3484 | return Fail("%s: Could not convert paddings", __func__); |
| 3485 | } |
| 3486 | |
| 3487 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 3488 | |
| 3489 | // Determine type of padding value |
| 3490 | OperandType operandType0; |
| 3491 | OperandType operandType2; |
| 3492 | |
| 3493 | if (!GetOperandType(operation, 0, model, operandType0) || |
| 3494 | !GetOperandType(operation, 2, model, operandType2)) |
| 3495 | { |
| 3496 | return Fail("%s: Operation has invalid inputs", __func__); |
| 3497 | } |
| 3498 | |
| 3499 | // Read value to use for padding |
| 3500 | if (operandType0 == OperandType::TENSOR_FLOAT16 && operandType2 == OperandType::FLOAT16) |
| 3501 | { |
| 3502 | Half f16PadValue; |
| 3503 | if (!GetInputScalar(operation, 2, operandType2, f16PadValue, model, data)) |
| 3504 | { |
| 3505 | return Fail("%s: Could not read input 2 (FLOAT16)", __func__); |
| 3506 | } |
| 3507 | |
| 3508 | descriptor.m_PadValue = f16PadValue; |
| 3509 | } |
| 3510 | else if (operandType0 == OperandType::TENSOR_FLOAT32 && operandType2 == OperandType::FLOAT32) |
| 3511 | { |
| 3512 | if (!GetInputFloat32(operation, 2, descriptor.m_PadValue, model, data)) |
| 3513 | { |
| 3514 | return Fail("%s: Could not read input 2 (FLOAT32)", __func__); |
| 3515 | } |
| 3516 | } |
| 3517 | else if (isQuantizedOperand(operandType0) && operandType2 == OperandType::INT32) |
| 3518 | { |
| 3519 | int32_t intPadValue = 0; |
| 3520 | if (!GetInputInt32(operation, 2, intPadValue, model, data)) |
| 3521 | { |
| 3522 | return Fail("%s: Could not read input 2 (INT32)", __func__); |
| 3523 | } |
| 3524 | descriptor.m_PadValue = intPadValue; |
| 3525 | } |
| 3526 | else |
| 3527 | { |
| 3528 | return Fail("%s: Operation has invalid inputs: type mismatch", __func__); |
| 3529 | } |
| 3530 | |
| 3531 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3532 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3533 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 3534 | { |
| 3535 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 3536 | IsPadSupported, |
| 3537 | data.m_Backends, |
| 3538 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3539 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3540 | inputInfo, |
| 3541 | outputInfo, |
| 3542 | descriptor); |
| 3543 | }; |
| 3544 | |
| 3545 | if(IsDynamicTensor(outputInfo)) |
| 3546 | { |
| 3547 | isSupported = AreDynamicTensorsSupported(); |
| 3548 | } |
| 3549 | else |
| 3550 | { |
| 3551 | validateFunc(outputInfo, isSupported); |
| 3552 | } |
| 3553 | |
| 3554 | if (!isSupported) |
| 3555 | { |
| 3556 | return false; |
| 3557 | } |
| 3558 | |
| 3559 | IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3560 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3561 | assert(layer != nullptr); |
| 3562 | input.Connect(layer->GetInputSlot(0)); |
| 3563 | |
| 3564 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 3565 | } |
| 3566 | |
| 3567 | bool Converter::ConvertPrelu(const Operation& operation, const Model& model, ConversionData& data) |
| 3568 | { |
| 3569 | VLOG(DRIVER) << "Converter::ConvertPrelu()"; |
| 3570 | |
| 3571 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 3572 | LayerInputHandle alpha = ConvertToLayerInputHandle(operation, 1, model, data); |
| 3573 | |
| 3574 | if (!input.IsValid() || !alpha.IsValid()) |
| 3575 | { |
| 3576 | return Fail("%s: Operation has invalid inputs", __func__); |
| 3577 | } |
| 3578 | |
| 3579 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 3580 | |
| 3581 | if (!output) |
| 3582 | { |
| 3583 | return Fail("%s: Could not read output", __func__); |
| 3584 | } |
| 3585 | |
| 3586 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 3587 | const TensorInfo& alphaInfo = alpha.GetTensorInfo(); |
| 3588 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 3589 | |
| 3590 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3591 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3592 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 3593 | { |
| 3594 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 3595 | IsPreluSupported, |
| 3596 | data.m_Backends, |
| 3597 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3598 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3599 | inputInfo, |
| 3600 | alphaInfo, |
| 3601 | outputInfo); |
| 3602 | }; |
| 3603 | |
| 3604 | if(IsDynamicTensor(outputInfo)) |
| 3605 | { |
| 3606 | isSupported = AreDynamicTensorsSupported(); |
| 3607 | } |
| 3608 | else |
| 3609 | { |
| 3610 | validateFunc(outputInfo, isSupported); |
| 3611 | } |
| 3612 | |
| 3613 | if (!isSupported) |
| 3614 | { |
| 3615 | return false; |
| 3616 | } |
| 3617 | |
| 3618 | IConnectableLayer* const layer = data.m_Network->AddPreluLayer(); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3619 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3620 | |
| 3621 | if (!layer) |
| 3622 | { |
| 3623 | return Fail("%s: AddPreluLayer failed", __func__); |
| 3624 | } |
| 3625 | |
| 3626 | bool isReshapeSupported = BroadcastTensor(input, alpha, layer, data); |
| 3627 | if (!isReshapeSupported) |
| 3628 | { |
| 3629 | return false; |
| 3630 | } |
| 3631 | |
| 3632 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 3633 | } |
| 3634 | |
| 3635 | bool Converter::ConvertQuantize(const Operation& operation, const Model& model, ConversionData& data) |
| 3636 | { |
| 3637 | VLOG(DRIVER) << "Converter::ConvertQuantize()"; |
| 3638 | |
| 3639 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 3640 | if (!input.IsValid()) |
| 3641 | { |
| 3642 | return Fail("%s: Operation has invalid input", __func__); |
| 3643 | } |
| 3644 | |
| 3645 | const Operand* const outputOperand = GetOutputOperand(operation, 0, model); |
| 3646 | if (!outputOperand) |
| 3647 | { |
| 3648 | return Fail("%s: Operation has invalid outputs", __func__); |
| 3649 | } |
| 3650 | |
| 3651 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand); |
| 3652 | |
| 3653 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3654 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3655 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 3656 | { |
| 3657 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 3658 | IsQuantizeSupported, |
| 3659 | data.m_Backends, |
| 3660 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3661 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3662 | input.GetTensorInfo(), |
| 3663 | outputInfo); |
| 3664 | }; |
| 3665 | |
| 3666 | if(IsDynamicTensor(outputInfo)) |
| 3667 | { |
| 3668 | isSupported = AreDynamicTensorsSupported(); |
| 3669 | } |
| 3670 | else |
| 3671 | { |
| 3672 | validateFunc(outputInfo, isSupported); |
| 3673 | } |
| 3674 | |
| 3675 | if (!isSupported) |
| 3676 | { |
| 3677 | return false; |
| 3678 | } |
| 3679 | |
| 3680 | IConnectableLayer* const layer = data.m_Network->AddQuantizeLayer(); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 3681 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 3682 | assert(layer != nullptr); |
| 3683 | input.Connect(layer->GetInputSlot(0)); |
| 3684 | |
| 3685 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 3686 | } |
| 3687 | |
| 3688 | bool Converter::ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data) |
| 3689 | { |
| 3690 | VLOG(DRIVER) << "Converter::ConvertQuantizedLstm()"; |
| 3691 | |
| 3692 | VLOG(DRIVER) << "ConvertQuantizedLstm()"; |
| 3693 | |
| 3694 | //Inputs: |
| 3695 | // 0: The input: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize] |
| 3696 | // specifying the input to the LSTM cell. Tensor is quantized with a fixed quantization range of -1, 127/128. |
| 3697 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 3698 | if (!input.IsValid()) |
| 3699 | { |
| 3700 | return Fail("%s: Could not read input 0: input", __func__); |
| 3701 | } |
| 3702 | |
| 3703 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, of shape [batch_size, output_size]. |
| 3704 | LayerInputHandle outputStatePrevTimeStep = ConvertToLayerInputHandle(operation, 18, model, data); |
| 3705 | if (!outputStatePrevTimeStep.IsValid()) |
| 3706 | { |
| 3707 | return Fail("%s: Could not read input 18: outputStatePrevTimeStep", __func__); |
| 3708 | } |
| 3709 | |
| 3710 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape [batch_size, num_units]. |
| 3711 | LayerInputHandle cellStatePrevTimeStep = ConvertToLayerInputHandle(operation, 19, model, data); |
| 3712 | if (!cellStatePrevTimeStep.IsValid()) |
| 3713 | { |
| 3714 | return Fail("%s: Could not read input 19: cellStatePrevTimeStep", __func__); |
| 3715 | } |
| 3716 | |
| 3717 | // Get the mandatory input tensors: |
| 3718 | |
| 3719 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3720 | // [num_units, input_size]. |
| 3721 | const ConstTensorPin inputToForgetWeightsPin = |
| 3722 | ConvertOperationInputToConstTensorPin(operation, 2, model, data); |
| 3723 | |
| 3724 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3725 | // [num_units, input_size]. |
| 3726 | const ConstTensorPin inputToCellWeightsPin = |
| 3727 | ConvertOperationInputToConstTensorPin(operation, 3, model, data); |
| 3728 | |
| 3729 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3730 | // [num_units, input_size]. |
| 3731 | const ConstTensorPin inputToOutputWeightsPin = |
| 3732 | ConvertOperationInputToConstTensorPin(operation, 4, model, data); |
| 3733 | |
| 3734 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3735 | // [num_units, output_size]. |
| 3736 | const ConstTensorPin recurrentToForgetWeightsPin = |
| 3737 | ConvertOperationInputToConstTensorPin(operation, 6, model, data); |
| 3738 | |
| 3739 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3740 | // [num_units, output_size]. |
| 3741 | const ConstTensorPin recurrentToCellWeightsPin = |
| 3742 | ConvertOperationInputToConstTensorPin(operation, 7, model, data); |
| 3743 | |
| 3744 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3745 | // [num_units, output_size]. |
| 3746 | const ConstTensorPin recurrentToOutputWeightsPin = |
| 3747 | ConvertOperationInputToConstTensorPin(operation, 8, model, data); |
| 3748 | |
| 3749 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units]. |
| 3750 | const ConstTensorPin forgetGateBiasPin = |
| 3751 | ConvertOperationInputToConstTensorPin(operation, 13, model, data); |
| 3752 | |
| 3753 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units]. |
| 3754 | const ConstTensorPin cellBiasPin = |
| 3755 | ConvertOperationInputToConstTensorPin(operation, 14, model, data); |
| 3756 | |
| 3757 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units]. |
| 3758 | const ConstTensorPin outputGateBiasPin = |
| 3759 | ConvertOperationInputToConstTensorPin(operation, 15, model, data); |
| 3760 | |
| 3761 | if (!inputToForgetWeightsPin.IsValid() || |
| 3762 | !inputToCellWeightsPin.IsValid() || |
| 3763 | !inputToOutputWeightsPin.IsValid() || |
| 3764 | !recurrentToForgetWeightsPin.IsValid() || |
| 3765 | !recurrentToCellWeightsPin.IsValid() || |
| 3766 | !recurrentToOutputWeightsPin.IsValid() || |
| 3767 | !forgetGateBiasPin.IsValid() || |
| 3768 | !cellBiasPin.IsValid() || |
| 3769 | !outputGateBiasPin.IsValid()) |
| 3770 | { |
| 3771 | return Fail("%s: Operation has invalid tensor inputs", __func__); |
| 3772 | } |
| 3773 | |
| 3774 | // Get the optional input tensors: |
| 3775 | |
| 3776 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3777 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 3778 | const ConstTensorPin inputToInputWeightsPin = |
| 3779 | ConvertOperationInputToConstTensorPin(operation, |
| 3780 | 1, |
| 3781 | model, |
| 3782 | data, |
| 3783 | g_DontPermute, |
| 3784 | nullptr, |
| 3785 | true); |
| 3786 | |
| 3787 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3788 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 3789 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 3790 | const ConstTensorPin recurrentToInputWeightsPin = |
| 3791 | ConvertOperationInputToConstTensorPin(operation, |
| 3792 | 5, |
| 3793 | model, |
| 3794 | data, |
| 3795 | g_DontPermute, |
| 3796 | nullptr, |
| 3797 | true); |
| 3798 | |
| 3799 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape |
| 3800 | // [num_units]. |
| 3801 | const ConstTensorPin cellToInputWeightsPin = |
| 3802 | ConvertOperationInputToConstTensorPin(operation, |
| 3803 | 9, |
| 3804 | model, |
| 3805 | data, |
| 3806 | g_DontPermute, |
| 3807 | nullptr, |
| 3808 | true); |
| 3809 | |
| 3810 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape |
| 3811 | // [num_units]. |
| 3812 | const ConstTensorPin cellToForgetWeightsPin = |
| 3813 | ConvertOperationInputToConstTensorPin(operation, |
| 3814 | 10, |
| 3815 | model, |
| 3816 | data, |
| 3817 | g_DontPermute, |
| 3818 | nullptr, |
| 3819 | true); |
| 3820 | |
| 3821 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape |
| 3822 | // [num_units]. |
| 3823 | const ConstTensorPin cellToOutputWeightsPin = |
| 3824 | ConvertOperationInputToConstTensorPin(operation, |
| 3825 | 11, |
| 3826 | model, |
| 3827 | data, |
| 3828 | g_DontPermute, |
| 3829 | nullptr, |
| 3830 | true); |
| 3831 | |
| 3832 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units]. |
| 3833 | const ConstTensorPin inputGateBiasPin = |
| 3834 | ConvertOperationInputToConstTensorPin(operation, |
| 3835 | 12, |
| 3836 | model, |
| 3837 | data, |
| 3838 | g_DontPermute, |
| 3839 | nullptr, |
| 3840 | true); |
| 3841 | |
| 3842 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape |
| 3843 | // [output_size, num_units]. |
| 3844 | const ConstTensorPin projectionWeightsPin = |
| 3845 | ConvertOperationInputToConstTensorPin(operation, |
| 3846 | 16, |
| 3847 | model, |
| 3848 | data, |
| 3849 | g_DontPermute, |
| 3850 | nullptr, |
| 3851 | true); |
| 3852 | |
| 3853 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [output_size]. |
| 3854 | const ConstTensorPin projectionBiasPin = |
| 3855 | ConvertOperationInputToConstTensorPin(operation, |
| 3856 | 17, |
| 3857 | model, |
| 3858 | data, |
| 3859 | g_DontPermute, |
| 3860 | nullptr, |
| 3861 | true); |
| 3862 | |
| 3863 | if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) |
| 3864 | || (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) |
| 3865 | || (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) |
| 3866 | || (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) |
| 3867 | || (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) |
| 3868 | || (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) |
| 3869 | || (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) |
| 3870 | || (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional())) |
| 3871 | { |
| 3872 | return Fail("%s: Operation has invalid tensor inputs", __func__); |
| 3873 | } |
| 3874 | |
| 3875 | |
| 3876 | // Get the optional normalization tensors |
| 3877 | |
| 3878 | // 20: The input layer normalization weights. A 1-D tensor of shape [num_units] ANEURALNETWORKS_TENSOR_QUANT16_SYMM. |
| 3879 | // Used to rescale normalized inputs to activation at input gate. |
| 3880 | const ConstTensorPin inputLayerNormWeightsPin = |
| 3881 | ConvertOperationInputToConstTensorPin(operation, |
| 3882 | 20, |
| 3883 | model, |
| 3884 | data, |
| 3885 | g_DontPermute, |
| 3886 | nullptr, |
| 3887 | true); |
| 3888 | |
| 3889 | // 21: The forget layer normalization weights. A 1-D tensor of shape [num_units] ANEURALNETWORKS_TENSOR_QUANT16_SYMM |
| 3890 | // Used to rescale normalized inputs to activation at forget gate. |
| 3891 | const ConstTensorPin forgetLayerNormWeightsPin = |
| 3892 | ConvertOperationInputToConstTensorPin(operation, |
| 3893 | 21, |
| 3894 | model, |
| 3895 | data, |
| 3896 | g_DontPermute, |
| 3897 | nullptr, |
| 3898 | true); |
| 3899 | |
| 3900 | // 22: The cell layer normalization weights. A 1-D tensor of shape [num_units] ANEURALNETWORKS_TENSOR_QUANT16_SYMM. |
| 3901 | // Used to rescale normalized inputs to activation at cell gate. |
| 3902 | const ConstTensorPin cellLayerNormWeightsPin = |
| 3903 | ConvertOperationInputToConstTensorPin(operation, |
| 3904 | 22, |
| 3905 | model, |
| 3906 | data, |
| 3907 | g_DontPermute, |
| 3908 | nullptr, |
| 3909 | true); |
| 3910 | |
| 3911 | // 23: The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 3912 | // Used to rescale normalized inputs to activation at output gate. |
| 3913 | const ConstTensorPin outputLayerNormWeightsPin = |
| 3914 | ConvertOperationInputToConstTensorPin(operation, |
| 3915 | 23, |
| 3916 | model, |
| 3917 | data, |
| 3918 | g_DontPermute, |
| 3919 | nullptr, |
| 3920 | true); |
| 3921 | |
| 3922 | if ((!inputLayerNormWeightsPin.IsValid() && !inputLayerNormWeightsPin.IsOptional()) |
| 3923 | || (!forgetLayerNormWeightsPin.IsValid() && !forgetLayerNormWeightsPin.IsOptional()) |
| 3924 | || (!cellLayerNormWeightsPin.IsValid() && !cellLayerNormWeightsPin.IsOptional()) |
| 3925 | || (!outputLayerNormWeightsPin.IsValid() && !outputLayerNormWeightsPin.IsOptional())) |
| 3926 | { |
| 3927 | return Fail("%s: Operation has invalid tensor inputs", __func__); |
| 3928 | } |
| 3929 | |
| 3930 | // Get the optional input scalars: |
| 3931 | // 24: The cell clip: If provided the cell state is clipped by this value prior to the cell output activation. |
| 3932 | // 25: The projection clip: If provided and projection is enabled, this is used for clipping the projected values. |
| 3933 | |
| 3934 | // Get the mandatory input scalars: |
| 3935 | // 26: The scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate. |
| 3936 | // 27: The scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate. |
| 3937 | // 28: The scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate. |
| 3938 | // 29: The scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate. |
| 3939 | // 30: The zero point of the hidden state, i.e. input to projection. |
| 3940 | // 31: The scale of the hidden state, i.e. input to projection. |
| 3941 | float cellClip, projClip, matMulInputGate, matMulForgetGate, matMulCellGate, matMulOutputGate, projInputScale; |
| 3942 | int projInputZeroPoint; |
| 3943 | |
| 3944 | if (!GetInputScalar(operation, 24, OperandType::FLOAT32, cellClip, model, data, true) || |
| 3945 | !GetInputScalar(operation, 25, OperandType::FLOAT32, projClip, model, data, true) || |
| 3946 | !GetInputScalar(operation, 26, OperandType::FLOAT32, matMulInputGate, model, data) || |
| 3947 | !GetInputScalar(operation, 27, OperandType::FLOAT32, matMulForgetGate, model, data) || |
| 3948 | !GetInputScalar(operation, 28, OperandType::FLOAT32, matMulCellGate, model, data) || |
| 3949 | !GetInputScalar(operation, 29, OperandType::FLOAT32, matMulOutputGate, model, data) || |
| 3950 | !GetInputScalar(operation, 30, OperandType::INT32, projInputZeroPoint, model, data) || |
| 3951 | !GetInputScalar(operation, 31, OperandType::FLOAT32, projInputScale, model, data)) |
| 3952 | { |
| 3953 | return Fail("%s: Operation has invalid scalar inputs", __func__); |
| 3954 | } |
| 3955 | |
| 3956 | // Outputs: |
| 3957 | // 0: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, of shape [batch_size, |
| 3958 | // output_size]. |
| 3959 | const Operand* outputStateOut = GetOutputOperand(operation, 0, model); |
| 3960 | if (!outputStateOut) |
| 3961 | { |
| 3962 | return Fail("%s: Could not read output 0: outputStateOut", __func__); |
| 3963 | } |
| 3964 | |
| 3965 | // 1: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape [batch_size, num_units]. |
| 3966 | const Operand* cellStateOut = GetOutputOperand(operation, 1, model); |
| 3967 | if (!cellStateOut) |
| 3968 | { |
| 3969 | return Fail("%s: Could not read output 1: cellStateOut", __func__); |
| 3970 | } |
| 3971 | |
| 3972 | // 2: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, of shape [batch_size, output_size]. |
| 3973 | // This is effectively the same as the current “output state (out)” value. |
| 3974 | const Operand* output = GetOutputOperand(operation, 2, model); |
| 3975 | if (!output) |
| 3976 | { |
| 3977 | return Fail("%s: Could not read output 2: output", __func__); |
| 3978 | } |
| 3979 | |
| 3980 | // set the params structure for the AddLstmLayer call |
| 3981 | LstmInputParams params; |
| 3982 | params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr(); |
| 3983 | params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr(); |
| 3984 | params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr(); |
| 3985 | params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr(); |
| 3986 | params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr(); |
| 3987 | params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr(); |
| 3988 | params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr(); |
| 3989 | params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr(); |
| 3990 | params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr(); |
| 3991 | params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr(); |
| 3992 | params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr(); |
| 3993 | params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr(); |
| 3994 | params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr(); |
| 3995 | params.m_CellBias = cellBiasPin.GetConstTensorPtr(); |
| 3996 | params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr(); |
| 3997 | params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr(); |
| 3998 | params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr(); |
| 3999 | params.m_InputLayerNormWeights = inputLayerNormWeightsPin.GetConstTensorPtr(); |
| 4000 | params.m_ForgetLayerNormWeights = forgetLayerNormWeightsPin.GetConstTensorPtr(); |
| 4001 | params.m_CellLayerNormWeights = cellLayerNormWeightsPin.GetConstTensorPtr(); |
| 4002 | params.m_OutputLayerNormWeights = outputLayerNormWeightsPin.GetConstTensorPtr(); |
| 4003 | |
| 4004 | // set the layer descriptor |
| 4005 | QLstmDescriptor desc; |
| 4006 | desc.m_CellClip = cellClip; |
| 4007 | desc.m_ProjectionClip = projClip; |
| 4008 | desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr || |
| 4009 | params.m_RecurrentToInputWeights == nullptr || |
| 4010 | params.m_InputGateBias == nullptr); |
| 4011 | desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr || |
| 4012 | params.m_CellToOutputWeights != nullptr); |
| 4013 | desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr); |
| 4014 | desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr || |
| 4015 | params.m_ForgetLayerNormWeights != nullptr || |
| 4016 | params.m_CellLayerNormWeights != nullptr || |
| 4017 | params.m_OutputLayerNormWeights != nullptr); |
| 4018 | desc.m_InputIntermediateScale = matMulInputGate; |
| 4019 | desc.m_ForgetIntermediateScale = matMulForgetGate; |
| 4020 | desc.m_CellIntermediateScale = matMulCellGate; |
| 4021 | desc.m_OutputIntermediateScale = matMulOutputGate; |
| 4022 | desc.m_HiddenStateScale = projInputScale; |
| 4023 | desc.m_HiddenStateZeroPoint = projInputZeroPoint; |
| 4024 | |
| 4025 | // validate the optional input groups |
| 4026 | if (desc.m_CifgEnabled && |
| 4027 | (params.m_InputToInputWeights != nullptr || |
| 4028 | params.m_RecurrentToInputWeights != nullptr || |
| 4029 | params.m_InputGateBias != nullptr)) |
| 4030 | { |
| 4031 | return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights," |
| 4032 | " and input gate bias must be provided", __func__); |
| 4033 | } |
| 4034 | |
| 4035 | if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr) |
| 4036 | { |
| 4037 | return Fail("%s: projection bias should not be provided without projection weights", __func__); |
| 4038 | } |
| 4039 | |
| 4040 | if (desc.m_PeepholeEnabled && |
| 4041 | (params.m_CellToForgetWeights == nullptr || |
| 4042 | params.m_CellToOutputWeights == nullptr || |
| 4043 | (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr))) |
| 4044 | { |
| 4045 | return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided" |
| 4046 | " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__); |
| 4047 | } |
| 4048 | |
| 4049 | if (desc.m_LayerNormEnabled && |
| 4050 | (params.m_ForgetLayerNormWeights == nullptr || |
| 4051 | params.m_CellLayerNormWeights == nullptr || |
| 4052 | params.m_OutputLayerNormWeights == nullptr || |
| 4053 | (!desc.m_CifgEnabled && params.m_InputLayerNormWeights == nullptr))) |
| 4054 | { |
| 4055 | return Fail("%s: All, or none, of forget-norm weights, cell-norm weights and output-norm weights must be" |
| 4056 | " provided and, if CIFG is not enabled, input-norm weights must also be provided", __func__); |
| 4057 | } |
| 4058 | |
| 4059 | // Basic parameters |
| 4060 | LstmInputParamsInfo paramsInfo; |
| 4061 | paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); |
| 4062 | paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); |
| 4063 | paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); |
| 4064 | paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); |
| 4065 | paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); |
| 4066 | paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); |
| 4067 | paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); |
| 4068 | paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); |
| 4069 | paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); |
| 4070 | |
| 4071 | // Inputs |
| 4072 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 4073 | const TensorInfo& outputStatePrevTimeStepInfo = outputStatePrevTimeStep.GetTensorInfo(); |
| 4074 | const TensorInfo& cellStatePrevTimeStepInfo = cellStatePrevTimeStep.GetTensorInfo(); |
| 4075 | |
| 4076 | // Outputs |
| 4077 | TensorInfo outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut); |
| 4078 | TensorInfo outputInfo = GetTensorInfoForOperand(*output); |
| 4079 | const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut); |
| 4080 | |
| 4081 | // Optional parameters |
| 4082 | if (!desc.m_CifgEnabled) |
| 4083 | { |
| 4084 | paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); |
| 4085 | paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); |
| 4086 | if (desc.m_PeepholeEnabled) |
| 4087 | { |
| 4088 | paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); |
| 4089 | } |
| 4090 | paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); |
| 4091 | } |
| 4092 | |
| 4093 | |
| 4094 | if (desc.m_ProjectionEnabled) |
| 4095 | { |
| 4096 | paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); |
| 4097 | if (params.m_ProjectionBias != nullptr) |
| 4098 | { |
| 4099 | paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); |
| 4100 | } |
| 4101 | } |
| 4102 | else |
| 4103 | { |
| 4104 | // If Projection is disabled, override non-const outputs to change the quant info with hidden params, then |
| 4105 | // create a new const TensorInfo based on this |
| 4106 | outputStateOutInfo.SetQuantizationScale(projInputScale); |
| 4107 | outputStateOutInfo.SetQuantizationOffset(projInputZeroPoint); |
| 4108 | outputInfo.SetQuantizationScale(projInputScale); |
| 4109 | outputInfo.SetQuantizationOffset(projInputZeroPoint); |
| 4110 | } |
| 4111 | |
| 4112 | const TensorInfo constOutputStateOutInfo(outputStateOutInfo); |
| 4113 | const TensorInfo constOutputInfo(outputInfo); |
| 4114 | |
| 4115 | if (desc.m_PeepholeEnabled) |
| 4116 | { |
| 4117 | paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); |
| 4118 | paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); |
| 4119 | } |
| 4120 | |
| 4121 | if (desc.m_LayerNormEnabled) |
| 4122 | { |
| 4123 | if(!desc.m_CifgEnabled) |
| 4124 | { |
| 4125 | paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo()); |
| 4126 | } |
| 4127 | paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo()); |
| 4128 | paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo()); |
| 4129 | paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo()); |
| 4130 | } |
| 4131 | |
| 4132 | // Check if the layer is supported |
| 4133 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4134 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4135 | auto validateFunc = [&](const armnn::TensorInfo& cellStateOutInfo, bool& isSupported) |
| 4136 | { |
| 4137 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4138 | IsQLstmSupported, |
| 4139 | data.m_Backends, |
| 4140 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4141 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4142 | inputInfo, |
| 4143 | outputStatePrevTimeStepInfo, |
| 4144 | cellStatePrevTimeStepInfo, |
| 4145 | constOutputStateOutInfo, |
| 4146 | cellStateOutInfo, |
| 4147 | constOutputInfo, |
| 4148 | desc, |
| 4149 | paramsInfo); |
| 4150 | }; |
| 4151 | |
| 4152 | bool isDynamic = false; |
| 4153 | if (!IsDynamicTensor(constOutputStateOutInfo) && |
| 4154 | !IsDynamicTensor(cellStateOutInfo) && |
| 4155 | !IsDynamicTensor(constOutputInfo)) |
| 4156 | { |
| 4157 | validateFunc(outputInfo, isSupported); |
| 4158 | } |
| 4159 | else |
| 4160 | { |
| 4161 | isDynamic = true; |
| 4162 | isSupported = AreDynamicTensorsSupported(); |
| 4163 | } |
| 4164 | |
| 4165 | if (!isSupported) |
| 4166 | { |
| 4167 | return false; |
| 4168 | } |
| 4169 | |
| 4170 | // Add the layer |
| 4171 | IConnectableLayer* layer = data.m_Network->AddQLstmLayer(desc, params, "QLstm"); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4172 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4173 | |
| 4174 | input.Connect(layer->GetInputSlot(0)); |
| 4175 | outputStatePrevTimeStep.Connect(layer->GetInputSlot(1)); |
| 4176 | cellStatePrevTimeStep.Connect(layer->GetInputSlot(2)); |
| 4177 | |
| 4178 | if (!isDynamic) |
| 4179 | { |
| 4180 | return ( SetupAndTrackLayerOutputSlot( |
| 4181 | operation, 0, *layer, 0, model, data, &constOutputStateOutInfo) && |
| 4182 | SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data) && |
| 4183 | SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data, &constOutputInfo)); |
| 4184 | } |
| 4185 | else |
| 4186 | { |
| 4187 | return ( SetupAndTrackLayerOutputSlot( |
| 4188 | operation, 0, *layer, 0, model, data, &constOutputStateOutInfo) && |
| 4189 | SetupAndTrackLayerOutputSlot( |
| 4190 | operation, 1, *layer, 1, model, data, nullptr, validateFunc, |
| 4191 | ActivationFn::kActivationNone, true) && |
| 4192 | SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data, &constOutputInfo)); |
| 4193 | } |
| 4194 | } |
| 4195 | |
| 4196 | bool Converter::ConvertQuantized16BitLstm(const Operation& operation, const Model& model, ConversionData& data) |
| 4197 | { |
| 4198 | VLOG(DRIVER) << "Converter::ConvertQuantized16BitLstm()"; |
| 4199 | VLOG(DRIVER) << "Policy::ConvertQuantized16BitLstm()"; |
| 4200 | |
| 4201 | //Inputs: |
| 4202 | // 0: The input: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize] |
| 4203 | // specifying the input to the LSTM cell. Tensor is quantized with a fixed quantization range of -1, 127/128. |
| 4204 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4205 | if (!input.IsValid()) |
| 4206 | { |
| 4207 | return Fail("%s: Could not read input 0: input", __func__); |
| 4208 | } |
| 4209 | |
| 4210 | //13: The previous cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape |
| 4211 | // [numBatches, outputSize] specifying the cell state from the previous time step of the LSTM cell. |
| 4212 | // It is quantized using a quantization range of -2^4, 2^4 * 32767/32768. |
| 4213 | LayerInputHandle previousCellStateIn = ConvertToLayerInputHandle(operation, 13, model, data); |
| 4214 | if (!previousCellStateIn.IsValid()) |
| 4215 | { |
| 4216 | return Fail("%s: Could not read input 13: previousCellStateIn", __func__); |
| 4217 | } |
| 4218 | |
| 4219 | // 14: The previous output state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4220 | // [numBathes, outputSize] specifying the output of the LSTM cell from previous time-step. Tensor |
| 4221 | // is quantized with a fixed quantization range of -1, 127/128. |
| 4222 | LayerInputHandle previousOutputIn = ConvertToLayerInputHandle(operation, 14, model, data); |
| 4223 | if (!previousOutputIn.IsValid()) |
| 4224 | { |
| 4225 | return Fail("%s: Could not read input 14: previousOutputIn", __func__); |
| 4226 | } |
| 4227 | |
| 4228 | // Get the input tensors: |
| 4229 | // 1: The input-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4230 | // [outputSize, inputSize] specifying input-to-input part of weights for fully-connected layer inside the |
| 4231 | // LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4232 | const ConstTensorPin inputToInputWeightsPin = |
| 4233 | ConvertOperationInputToConstTensorPin(operation, 1, model, data); |
| 4234 | |
| 4235 | // 2: The input-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4236 | // [outputSize, inputSize] specifying input-to-forget part of weights for fully-connected layer inside the |
| 4237 | // LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4238 | const ConstTensorPin inputToForgetWeightsPin = |
| 4239 | ConvertOperationInputToConstTensorPin(operation, 2, model, data); |
| 4240 | |
| 4241 | // 3: The input-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4242 | // [outputSize, inputSize] specifying input-to-cell part of weights for fully-connected layer inside the |
| 4243 | // LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4244 | const ConstTensorPin inputToCellWeightsPin = |
| 4245 | ConvertOperationInputToConstTensorPin(operation, 3, model, data); |
| 4246 | |
| 4247 | // 4: The input-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4248 | // [outputSize, inputSize] specifying input-to-output part of weights for fully-connected layer inside the |
| 4249 | // LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4250 | const ConstTensorPin inputToOutputWeightsPin = |
| 4251 | ConvertOperationInputToConstTensorPin(operation, 4, model, data); |
| 4252 | |
| 4253 | // 5: The recurrent-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4254 | // [outputSize, outputSize] specifying recurrent-to-input part of weights for fully-connected layer inside |
| 4255 | // the LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4256 | const ConstTensorPin recurrentToInputWeightsPin = |
| 4257 | ConvertOperationInputToConstTensorPin(operation, 5, model, data); |
| 4258 | |
| 4259 | // 6: The recurrent-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4260 | // [outputSize, outputSize] specifying recurrent-to-forget part of weights for fully-connected layer inside |
| 4261 | // the LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4262 | const ConstTensorPin recurrentToForgetWeightsPin = |
| 4263 | ConvertOperationInputToConstTensorPin(operation, 6, model, data); |
| 4264 | |
| 4265 | // 7: The recurrent-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4266 | // [outputSize, outputSize] specifying recurrent-to-cell part of weights for fully-connected layer inside |
| 4267 | // the LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4268 | const ConstTensorPin recurrentToCellWeightsPin = |
| 4269 | ConvertOperationInputToConstTensorPin(operation, 7, model, data); |
| 4270 | |
| 4271 | // 8: The recurrent-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape |
| 4272 | // [outputSize, outputSize] specifying recurrent-to-output part of weights for fully-connected layer inside |
| 4273 | // the LSTM cell. Quantization zero point and scale must be the same across all the weights. |
| 4274 | const ConstTensorPin recurrentToOutputWeightsPin = |
| 4275 | ConvertOperationInputToConstTensorPin(operation, 8, model, data); |
| 4276 | |
| 4277 | // 9: The input gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the |
| 4278 | // bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product |
| 4279 | // of input and weights scales and zeroPoint equal to 0. |
| 4280 | const ConstTensorPin inputGateBiasPin = |
| 4281 | ConvertOperationInputToConstTensorPin(operation, 9, model, data); |
| 4282 | |
| 4283 | // 10: The forget gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying |
| 4284 | // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product |
| 4285 | // of input and weights scales and zeroPoint equal to 0. |
| 4286 | const ConstTensorPin forgetGateBiasPin = |
| 4287 | ConvertOperationInputToConstTensorPin(operation, 10, model, data); |
| 4288 | |
| 4289 | // 11:The cell bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias |
| 4290 | // for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input |
| 4291 | // and weights scales and zeroPoint equal to 0. |
| 4292 | const ConstTensorPin cellBiasPin = |
| 4293 | ConvertOperationInputToConstTensorPin(operation, 11, model, data); |
| 4294 | |
| 4295 | // 12:The output gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying |
| 4296 | // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product |
| 4297 | // of input and weights scales and zeroPoint equal to 0. |
| 4298 | const ConstTensorPin outputGateBiasPin = |
| 4299 | ConvertOperationInputToConstTensorPin(operation, 12, model, data); |
| 4300 | |
| 4301 | if (!inputToInputWeightsPin.IsValid() || |
| 4302 | !inputToForgetWeightsPin.IsValid() || |
| 4303 | !inputToCellWeightsPin.IsValid() || |
| 4304 | !inputToOutputWeightsPin.IsValid() || |
| 4305 | !recurrentToInputWeightsPin.IsValid() || |
| 4306 | !recurrentToForgetWeightsPin.IsValid() || |
| 4307 | !recurrentToCellWeightsPin.IsValid() || |
| 4308 | !recurrentToOutputWeightsPin.IsValid() || |
| 4309 | !inputGateBiasPin.IsValid() || |
| 4310 | !forgetGateBiasPin.IsValid() || |
| 4311 | !cellBiasPin.IsValid() || |
| 4312 | !outputGateBiasPin.IsValid()) |
| 4313 | { |
| 4314 | return Fail("%s: Operation has invalid tensor inputs", __func__); |
| 4315 | } |
| 4316 | |
| 4317 | // Outputs: |
| 4318 | // 0: The cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape [numBatches, outputSize] |
| 4319 | // which contains a cell state from the current time step. Tensor is quantized using a quantization range |
| 4320 | // of -2^4, 2^4 * 32767/32768. |
| 4321 | const Operand* cellStateOut = GetOutputOperand(operation, 0, model); |
| 4322 | if (!cellStateOut) |
| 4323 | { |
| 4324 | return Fail("%s: Could not read output 0: cellStateOut", __func__); |
| 4325 | } |
| 4326 | |
| 4327 | // 1: The output: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBathes, outputSize] which |
| 4328 | // contains the output value. Tensor is quantized with a fixed quantization range of -1, 127/128. |
| 4329 | const Operand* output = GetOutputOperand(operation, 1, model); |
| 4330 | if (!output) |
| 4331 | { |
| 4332 | return Fail("%s: Could not read output 1: output", __func__); |
| 4333 | } |
| 4334 | |
| 4335 | // Inputs |
| 4336 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 4337 | const TensorInfo& previousCellStateInInfo = previousCellStateIn.GetTensorInfo(); |
| 4338 | const TensorInfo& previousOutputInInfo = previousOutputIn.GetTensorInfo(); |
| 4339 | |
| 4340 | // Outputs |
| 4341 | const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut); |
| 4342 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 4343 | |
| 4344 | // Dynamic tensors currently not supported |
| 4345 | if (IsDynamicTensor(cellStateOutInfo) || IsDynamicTensor(outputInfo)) |
| 4346 | { |
| 4347 | return Fail("%s: Dynamic output tensors are not supported", __func__); |
| 4348 | } |
| 4349 | |
| 4350 | QuantizedLstmInputParams params; |
| 4351 | |
| 4352 | params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr(); |
| 4353 | params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr(); |
| 4354 | params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr(); |
| 4355 | params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr(); |
| 4356 | params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr(); |
| 4357 | params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr(); |
| 4358 | params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr(); |
| 4359 | params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr(); |
| 4360 | params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr(); |
| 4361 | params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr(); |
| 4362 | params.m_CellBias = cellBiasPin.GetConstTensorPtr(); |
| 4363 | params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr(); |
| 4364 | |
| 4365 | QuantizedLstmInputParamsInfo paramsInfo; |
| 4366 | paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); |
| 4367 | paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); |
| 4368 | paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); |
| 4369 | paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); |
| 4370 | paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); |
| 4371 | paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); |
| 4372 | paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); |
| 4373 | paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); |
| 4374 | paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); |
| 4375 | paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); |
| 4376 | paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); |
| 4377 | paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); |
| 4378 | |
| 4379 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4380 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4381 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 4382 | { |
| 4383 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4384 | IsQuantizedLstmSupported, |
| 4385 | data.m_Backends, |
| 4386 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4387 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4388 | inputInfo, |
| 4389 | previousCellStateInInfo, |
| 4390 | previousOutputInInfo, |
| 4391 | cellStateOutInfo, |
| 4392 | outputInfo, |
| 4393 | paramsInfo); |
| 4394 | }; |
| 4395 | |
| 4396 | bool isDynamic = false; |
| 4397 | if (!IsDynamicTensor(cellStateOutInfo) && |
| 4398 | !IsDynamicTensor(outputInfo)) |
| 4399 | { |
| 4400 | validateFunc(outputInfo, isSupported); |
| 4401 | } |
| 4402 | else |
| 4403 | { |
| 4404 | isDynamic = true; |
| 4405 | isSupported = AreDynamicTensorsSupported(); |
| 4406 | } |
| 4407 | |
| 4408 | if (!isSupported) |
| 4409 | { |
| 4410 | return false; |
| 4411 | } |
| 4412 | |
| 4413 | IConnectableLayer* const layer = data.m_Network->AddQuantizedLstmLayer(params, "QuantizedLstm"); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4414 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4415 | input.Connect(layer->GetInputSlot(0)); |
| 4416 | previousCellStateIn.Connect(layer->GetInputSlot(1)); |
| 4417 | previousOutputIn.Connect(layer->GetInputSlot(2)); |
| 4418 | |
| 4419 | if (!isDynamic) |
| 4420 | { |
| 4421 | return (SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) && |
| 4422 | SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data)); |
| 4423 | } |
| 4424 | else |
| 4425 | { |
| 4426 | return (SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) && |
| 4427 | SetupAndTrackLayerOutputSlot( |
| 4428 | operation, 1, *layer, 1, model, data, nullptr, validateFunc, ActivationFn::kActivationNone, true)); |
| 4429 | } |
| 4430 | |
| 4431 | } |
| 4432 | |
| 4433 | bool Converter::ConvertRank(const Operation& operation, const Model& model, ConversionData& data) |
| 4434 | { |
| 4435 | VLOG(DRIVER) << "Converter::ConvertRank()"; |
| 4436 | |
| 4437 | const Operand* inputOperand = GetInputOperand(operation, 0, model); |
| 4438 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 4439 | |
| 4440 | if (inputOperand == nullptr || outputOperand == nullptr) |
| 4441 | { |
| 4442 | return Fail("%s: Operation has invalid inputs", __func__); |
| 4443 | } |
| 4444 | |
| 4445 | const Shape inputOperandShape = GetOperandShape(*inputOperand); |
| 4446 | const Shape outputOperandShape = GetOperandShape(*outputOperand); |
| 4447 | |
| 4448 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4449 | if (!input.IsValid()) |
| 4450 | { |
| 4451 | return Fail("%s: Could not read input 0", __func__); |
| 4452 | } |
| 4453 | |
| 4454 | armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand); |
| 4455 | if (IsDynamicTensor(outInfo)) |
| 4456 | { |
| 4457 | return Fail("%s: Dynamic output tensors are not supported", __func__); |
| 4458 | } |
| 4459 | |
| 4460 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4461 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4462 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4463 | IsRankSupported, |
| 4464 | data.m_Backends, |
| 4465 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4466 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4467 | input.GetTensorInfo(), |
| 4468 | outInfo); |
| 4469 | if (!isSupported) |
| 4470 | { |
| 4471 | return false; |
| 4472 | } |
| 4473 | |
| 4474 | armnn::IConnectableLayer* layer = data.m_Network->AddRankLayer(); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4475 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4476 | assert(layer != nullptr); |
| 4477 | input.Connect(layer->GetInputSlot(0)); |
| 4478 | |
| 4479 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, &outInfo); |
| 4480 | } |
| 4481 | |
| 4482 | bool Converter::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data) |
| 4483 | { |
| 4484 | VLOG(DRIVER) << "Converter::ConvertReLu()"; |
| 4485 | armnn::ActivationDescriptor desc; |
| 4486 | desc.m_Function = armnn::ActivationFunction::ReLu; |
| 4487 | |
| 4488 | |
| 4489 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4490 | if (!input.IsValid()) |
| 4491 | { |
| 4492 | return Fail("%s: Input 0 is invalid", "operationName"); |
| 4493 | } |
| 4494 | |
| 4495 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 4496 | if (!outputOperand) |
| 4497 | { |
| 4498 | return false; |
| 4499 | } |
| 4500 | |
| 4501 | const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); |
| 4502 | |
| 4503 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4504 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4505 | auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) |
| 4506 | { |
| 4507 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4508 | IsActivationSupported, |
| 4509 | data.m_Backends, |
| 4510 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4511 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4512 | input.GetTensorInfo(), |
| 4513 | outInfo, |
| 4514 | desc); |
| 4515 | }; |
| 4516 | |
| 4517 | if(IsDynamicTensor(outInfo)) |
| 4518 | { |
| 4519 | isSupported = AreDynamicTensorsSupported(); |
| 4520 | } |
| 4521 | else |
| 4522 | { |
| 4523 | validateFunc(outInfo, isSupported); |
| 4524 | } |
| 4525 | |
| 4526 | if (!isSupported) |
| 4527 | { |
| 4528 | return false; |
| 4529 | } |
| 4530 | |
| 4531 | armnn::IConnectableLayer* layer = data.m_Network->AddActivationLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4532 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4533 | ARMNN_ASSERT(layer != nullptr); |
| 4534 | input.Connect(layer->GetInputSlot(0)); |
| 4535 | |
| 4536 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 4537 | } |
| 4538 | |
| 4539 | bool Converter::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data) |
| 4540 | { |
| 4541 | VLOG(DRIVER) << "Converter::ConvertReLu1()"; |
| 4542 | armnn::ActivationDescriptor desc; |
| 4543 | desc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 4544 | desc.m_A = 1.0f; |
| 4545 | desc.m_B = -1.0f; |
| 4546 | |
| 4547 | return ConvertToActivation(operation, __func__, desc, model, data); |
| 4548 | } |
| 4549 | |
| 4550 | bool Converter::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data) |
| 4551 | { |
| 4552 | VLOG(DRIVER) << "Converter::ConvertReLu6()"; |
| 4553 | armnn::ActivationDescriptor desc; |
| 4554 | desc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 4555 | desc.m_A = 6.0f; |
| 4556 | |
| 4557 | return ConvertToActivation(operation, __func__, desc, model, data); |
| 4558 | } |
| 4559 | |
| 4560 | bool Converter::ConvertReshape(const Operation& operation, const Model& model, ConversionData& data) |
| 4561 | { |
| 4562 | VLOG(DRIVER) << "Converter::ConvertReshape()"; |
| 4563 | |
| 4564 | const Operand* inputOperand = GetInputOperand(operation, 0, model); |
| 4565 | const Operand* requestedShapeOperand = GetInputOperand(operation, 1, model); |
| 4566 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 4567 | |
| 4568 | if (inputOperand == nullptr |
| 4569 | || requestedShapeOperand == nullptr |
| 4570 | || outputOperand == nullptr) |
| 4571 | { |
| 4572 | return Fail("%s: Operation has invalid inputs", __func__); |
| 4573 | } |
| 4574 | |
| 4575 | if (requestedShapeOperand->dimensions.size() != 1) |
| 4576 | { |
| 4577 | return Fail("%s: Input 1 expected to be one-dimensional (found %i dimensions)", |
| 4578 | __func__, requestedShapeOperand->dimensions.size()); |
| 4579 | } |
| 4580 | |
| 4581 | std::vector<int32_t> targetDimensions; |
| 4582 | if (!GetTensorInt32Values(*requestedShapeOperand, targetDimensions, model, data)) |
| 4583 | { |
| 4584 | return Fail("%s: Could not read values of input 1", __func__); |
| 4585 | } |
| 4586 | |
| 4587 | const Shape inputOperandShape = GetOperandShape(*inputOperand); |
| 4588 | |
| 4589 | Shape requestedShape; |
| 4590 | // targetDimensions may contain special values (e.g. -1). reshapePrepare() is an AndroidNN provided utility |
| 4591 | // function that resolves these values into a fully specified tensor shape. |
| 4592 | if (!reshapePrepare(inputOperandShape, targetDimensions.data(), targetDimensions.size(), &requestedShape)) |
| 4593 | { |
| 4594 | return Fail("%s: Failed to resolve the requested shape", __func__); |
| 4595 | } |
| 4596 | |
| 4597 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4598 | if (!input.IsValid()) |
| 4599 | { |
| 4600 | return Fail("%s: Could not read input 0", __func__); |
| 4601 | } |
| 4602 | |
| 4603 | armnn::ReshapeDescriptor reshapeDescriptor; |
| 4604 | reshapeDescriptor.m_TargetShape = armnn::TensorShape(requestedShape.dimensions.size(), |
| 4605 | requestedShape.dimensions.data()); |
| 4606 | |
| 4607 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand); |
| 4608 | |
| 4609 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4610 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4611 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 4612 | { |
| 4613 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4614 | IsReshapeSupported, |
| 4615 | data.m_Backends, |
| 4616 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4617 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4618 | input.GetTensorInfo(), |
| 4619 | outputInfo, |
| 4620 | reshapeDescriptor); |
| 4621 | }; |
| 4622 | |
| 4623 | if(!IsDynamicTensor(outputInfo)) |
| 4624 | { |
| 4625 | validateFunc(outputInfo, isSupported); |
| 4626 | } |
| 4627 | else |
| 4628 | { |
| 4629 | isSupported = AreDynamicTensorsSupported(); |
| 4630 | } |
| 4631 | |
| 4632 | if (!isSupported) |
| 4633 | { |
| 4634 | return false; |
| 4635 | } |
| 4636 | |
| 4637 | armnn::IConnectableLayer* layer = data.m_Network->AddReshapeLayer(reshapeDescriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4638 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4639 | assert(layer != nullptr); |
| 4640 | input.Connect(layer->GetInputSlot(0)); |
| 4641 | |
| 4642 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 4643 | } |
| 4644 | |
| 4645 | bool Converter::ConvertResize(const Operation& operation, |
| 4646 | const Model& model, |
| 4647 | ConversionData& data, |
| 4648 | ResizeMethod resizeMethod) |
| 4649 | { |
| 4650 | VLOG(DRIVER) << "Converter::ConvertResize()"; |
| 4651 | VLOG(DRIVER) << "resizeMethod = " << GetResizeMethodAsCString(resizeMethod); |
| 4652 | |
| 4653 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4654 | if (!input.IsValid()) |
| 4655 | { |
| 4656 | return Fail("%s: Could not read input 0", __func__); |
| 4657 | } |
| 4658 | |
| 4659 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 4660 | if (!output) |
| 4661 | { |
| 4662 | return Fail("%s: Could not read output 0", __func__); |
| 4663 | } |
| 4664 | |
| 4665 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 4666 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 4667 | |
| 4668 | ResizeDescriptor descriptor; |
| 4669 | descriptor.m_Method = resizeMethod; |
| 4670 | descriptor.m_DataLayout = OptionalDataLayout(operation, 3, model, data); |
| 4671 | |
| 4672 | OperandType operandType1; |
| 4673 | OperandType operandType2; |
| 4674 | |
| 4675 | if (!GetOperandType(operation, 1, model, operandType1) || |
| 4676 | !GetOperandType(operation, 2, model, operandType2)) |
| 4677 | { |
| 4678 | return Fail("%s: Operation has invalid inputs", __func__); |
| 4679 | } |
| 4680 | |
| 4681 | if (operandType1 != operandType2) |
| 4682 | { |
| 4683 | return Fail("%s: Operation has invalid inputs. Type of input 1 and 2 should be the same", __func__); |
| 4684 | } |
| 4685 | |
| 4686 | if (operandType1 == OperandType::INT32) |
| 4687 | { |
| 4688 | // Case 1: resizing by shape |
| 4689 | int32_t targetWidth = 0; |
| 4690 | int32_t targetHeight = 0; |
| 4691 | |
| 4692 | if (!GetInputInt32(operation, 1, targetWidth, model, data) || |
| 4693 | !GetInputInt32(operation, 2, targetHeight, model, data)) |
| 4694 | { |
| 4695 | return Fail("%s: Operation has invalid inputs for resizing by shape", __func__); |
| 4696 | } |
| 4697 | |
| 4698 | if (targetWidth < 0 || targetHeight < 0) |
| 4699 | { |
| 4700 | return Fail("%s: Operation has invalid inputs for resizing by shape. " |
| 4701 | "Target width/height cannot be < 0", __func__); |
| 4702 | } |
| 4703 | |
| 4704 | descriptor.m_TargetWidth = static_cast<uint32_t>(targetWidth); |
| 4705 | descriptor.m_TargetHeight = static_cast<uint32_t>(targetHeight); |
| 4706 | } |
| 4707 | else if (operandType1 == OperandType::FLOAT32) |
| 4708 | { |
| 4709 | // Case 2: resizing by scale |
| 4710 | float widthScale = 1.0f; |
| 4711 | float heightScale = 1.0f; |
| 4712 | |
| 4713 | if (!GetInputFloat32(operation, 1, widthScale, model, data) || |
| 4714 | !GetInputFloat32(operation, 2, heightScale, model, data)) |
| 4715 | { |
| 4716 | return Fail("%s: Operation has invalid inputs for resizing by scale", __func__); |
| 4717 | } |
| 4718 | |
| 4719 | const TensorShape& inputShape = inputInfo.GetShape(); |
| 4720 | armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout); |
| 4721 | |
| 4722 | float width = inputShape[dataLayoutIndexed.GetWidthIndex()]; |
| 4723 | float height = inputShape[dataLayoutIndexed.GetHeightIndex()]; |
| 4724 | |
| 4725 | descriptor.m_TargetWidth = std::floor(width * widthScale); |
| 4726 | descriptor.m_TargetHeight = std::floor(height * heightScale); |
| 4727 | } |
| 4728 | else if (operandType1 == OperandType::FLOAT16) |
| 4729 | { |
| 4730 | Half widthScale; |
| 4731 | Half heightScale; |
| 4732 | |
| 4733 | if (!GetInputScalar(operation, 1, OperandType::FLOAT16, widthScale, model, data) || |
| 4734 | !GetInputScalar(operation, 2, OperandType::FLOAT16, heightScale, model, data)) |
| 4735 | { |
| 4736 | return Fail("%s: Operation has invalid inputs for resizing by scale", __func__); |
| 4737 | } |
| 4738 | |
| 4739 | const TensorShape& inputShape = inputInfo.GetShape(); |
| 4740 | armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout); |
| 4741 | |
| 4742 | Half width = static_cast<Half>(inputShape[dataLayoutIndexed.GetWidthIndex()]); |
| 4743 | Half height = static_cast<Half>(inputShape[dataLayoutIndexed.GetHeightIndex()]); |
| 4744 | |
| 4745 | descriptor.m_TargetWidth = std::floor(width * widthScale); |
| 4746 | descriptor.m_TargetHeight = std::floor(height * heightScale); |
| 4747 | } |
| 4748 | else |
| 4749 | { |
| 4750 | return Fail("%s: Operand has invalid data type for resizing by scale", __func__); |
| 4751 | } |
| 4752 | |
| 4753 | descriptor.m_AlignCorners = GetOptionalBool(operation, 4, model, data); |
| 4754 | descriptor.m_HalfPixelCenters = GetOptionalBool(operation, 5, model, data); |
| 4755 | |
| 4756 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4757 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4758 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 4759 | { |
| 4760 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4761 | IsResizeSupported, |
| 4762 | data.m_Backends, |
| 4763 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4764 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4765 | inputInfo, |
| 4766 | outputInfo, |
| 4767 | descriptor); |
| 4768 | }; |
| 4769 | |
| 4770 | if(IsDynamicTensor(outputInfo)) |
| 4771 | { |
| 4772 | isSupported = AreDynamicTensorsSupported(); |
| 4773 | } |
| 4774 | else |
| 4775 | { |
| 4776 | validateFunc(outputInfo, isSupported); |
| 4777 | } |
| 4778 | |
| 4779 | if (!isSupported) |
| 4780 | { |
| 4781 | return false; |
| 4782 | } |
| 4783 | |
| 4784 | IConnectableLayer* layer = data.m_Network->AddResizeLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4785 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4786 | assert(layer != nullptr); |
| 4787 | input.Connect(layer->GetInputSlot(0)); |
| 4788 | |
| 4789 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 4790 | } |
| 4791 | |
| 4792 | bool Converter::ConvertSpaceToBatchNd(const Operation& operation, const Model& model, ConversionData& data) |
| 4793 | { |
| 4794 | VLOG(DRIVER) << "Converter::ConvertSpaceToBatchNd()"; |
| 4795 | |
| 4796 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4797 | if(!input.IsValid()) |
| 4798 | { |
| 4799 | return Fail("%s: Operation has invalid inputs", __func__); |
| 4800 | } |
| 4801 | |
| 4802 | const armnn::TensorInfo &inputInfo = input.GetTensorInfo(); |
| 4803 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 4804 | unsigned int spatialDim = rank - 2; |
| 4805 | |
| 4806 | if(rank != 4) |
| 4807 | { |
| 4808 | Fail("%s: Only inputs with rank 4 are supported", __func__); |
| 4809 | } |
| 4810 | |
| 4811 | const Operand *output = GetOutputOperand(operation, 0, model); |
| 4812 | if(!output) |
| 4813 | { |
| 4814 | return Fail("%s: Could not read output 0", __func__); |
| 4815 | } |
| 4816 | |
| 4817 | const armnn::TensorInfo &outputInfo = GetTensorInfoForOperand(*output); |
| 4818 | |
| 4819 | const Operand *blockShapeOperand = GetInputOperand(operation, 1, model); |
| 4820 | const Operand *paddingsOperand = GetInputOperand(operation, 2, model); |
| 4821 | |
| 4822 | armnn::TensorShape blockShapeOperandShape = GetTensorShapeForOperand(*blockShapeOperand); |
| 4823 | if(blockShapeOperandShape.GetNumDimensions() != 1 || blockShapeOperandShape.GetNumElements() != spatialDim) |
| 4824 | { |
| 4825 | return Fail("%s: Operation has invalid block shape operand: expected shape [%d]", __func__, spatialDim); |
| 4826 | } |
| 4827 | |
| 4828 | std::vector<int32_t> blockShape; |
| 4829 | if(!GetTensorInt32Values(*blockShapeOperand, blockShape, model, data)) |
| 4830 | { |
| 4831 | return Fail("%s: Operation has an invalid or unsupported block size operand", __func__); |
| 4832 | } |
| 4833 | if(std::any_of(blockShape.cbegin(), blockShape.cend(), [](int32_t i) |
| 4834 | { return i < 1; })) |
| 4835 | { |
| 4836 | return Fail("%s: Block shape must be at least 1 in all dimensions.", __func__); |
| 4837 | } |
| 4838 | |
| 4839 | armnn::TensorShape paddingsOperandShape = GetTensorShapeForOperand(*paddingsOperand); |
| 4840 | if(paddingsOperandShape.GetNumDimensions() != 2 || paddingsOperandShape.GetNumElements() != 2 * spatialDim) |
| 4841 | { |
| 4842 | return Fail("%s: Operation has invalid paddings operand: expected shape [%d, 2]", __func__, spatialDim); |
| 4843 | } |
| 4844 | |
| 4845 | std::vector<std::pair<unsigned int, unsigned int>> paddingList; |
| 4846 | std::vector<int32_t> paddings; |
| 4847 | if(!GetTensorInt32Values(*paddingsOperand, paddings, model, data)) |
| 4848 | { |
| 4849 | return Fail("%s: Operation has an invalid or unsupported paddings operand", __func__); |
| 4850 | } |
| 4851 | for (unsigned int i = 0; i < paddings.size() - 1; i += 2) |
| 4852 | { |
| 4853 | int paddingBeforeInput = paddings[i]; |
| 4854 | int paddingAfterInput = paddings[i + 1]; |
| 4855 | if(paddingBeforeInput < 0 || paddingAfterInput < 0) |
| 4856 | { |
| 4857 | return Fail("%s: Operation has invalid paddings operand, invalid padding values.", __func__); |
| 4858 | } |
| 4859 | |
| 4860 | paddingList.emplace_back((unsigned int) paddingBeforeInput, (unsigned int) paddingAfterInput); |
| 4861 | } |
| 4862 | |
| 4863 | armnn::SpaceToBatchNdDescriptor descriptor; |
| 4864 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 4865 | descriptor.m_BlockShape.assign(blockShape.cbegin(), blockShape.cend()); |
| 4866 | descriptor.m_PadList.assign(paddingList.cbegin(), paddingList.cend()); |
| 4867 | |
| 4868 | if(Is12OrLaterOperand(*output)) |
| 4869 | { |
| 4870 | descriptor.m_DataLayout = OptionalDataLayout(operation, 3, model, data); |
| 4871 | } |
| 4872 | |
| 4873 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4874 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4875 | auto validateFunc = [&](const armnn::TensorInfo &outputInfo, bool &isSupported) |
| 4876 | { |
| 4877 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4878 | IsSpaceToBatchNdSupported, |
| 4879 | data.m_Backends, |
| 4880 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4881 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4882 | inputInfo, |
| 4883 | outputInfo, |
| 4884 | descriptor); |
| 4885 | }; |
| 4886 | |
| 4887 | if(IsDynamicTensor(outputInfo)) |
| 4888 | { |
| 4889 | isSupported = AreDynamicTensorsSupported(); |
| 4890 | } else |
| 4891 | { |
| 4892 | validateFunc(outputInfo, isSupported); |
| 4893 | } |
| 4894 | |
| 4895 | if(!isSupported) |
| 4896 | { |
| 4897 | return false; |
| 4898 | } |
| 4899 | |
| 4900 | armnn::IConnectableLayer *const layer = data.m_Network->AddSpaceToBatchNdLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4901 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4902 | assert(layer != nullptr); |
| 4903 | input.Connect(layer->GetInputSlot(0)); |
| 4904 | |
| 4905 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 4906 | } |
| 4907 | |
| 4908 | bool Converter::ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData& data) |
| 4909 | { |
| 4910 | VLOG(DRIVER) << "Converter::ConvertSpaceToDepth()"; |
| 4911 | |
| 4912 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4913 | if (!input.IsValid() ) |
| 4914 | { |
| 4915 | return Fail("%s: Operation has invalid inputs", __func__); |
| 4916 | } |
| 4917 | |
| 4918 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 4919 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 4920 | if (rank != 4) |
| 4921 | { |
| 4922 | return Fail("%s: Only inputs with rank 4 are supported", __func__); |
| 4923 | } |
| 4924 | |
| 4925 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 4926 | if (!output) |
| 4927 | { |
| 4928 | return Fail("%s: Could not read output 0", __func__); |
| 4929 | } |
| 4930 | |
| 4931 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 4932 | |
| 4933 | SpaceToDepthDescriptor desc; |
| 4934 | |
| 4935 | GetInputScalar(operation, 1, OperandType::INT32, desc.m_BlockSize, model, data); |
| 4936 | |
| 4937 | if (desc.m_BlockSize <= 1) |
| 4938 | { |
| 4939 | return Fail("%s: Block size must be at least 1 in all dimensions"); |
| 4940 | } |
| 4941 | |
| 4942 | desc.m_DataLayout = OptionalDataLayout(operation, 2, model, data); |
| 4943 | |
| 4944 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4945 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4946 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 4947 | { |
| 4948 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 4949 | IsSpaceToDepthSupported, |
| 4950 | data.m_Backends, |
| 4951 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4952 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4953 | inputInfo, |
| 4954 | outputInfo, |
| 4955 | desc); |
| 4956 | }; |
| 4957 | |
| 4958 | if(IsDynamicTensor(outputInfo)) |
| 4959 | { |
| 4960 | isSupported = AreDynamicTensorsSupported(); |
| 4961 | } |
| 4962 | else |
| 4963 | { |
| 4964 | validateFunc(outputInfo, isSupported); |
| 4965 | } |
| 4966 | |
| 4967 | if (!isSupported) |
| 4968 | { |
| 4969 | return false; |
| 4970 | } |
| 4971 | |
| 4972 | IConnectableLayer* const layer = data.m_Network->AddSpaceToDepthLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 4973 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 4974 | assert(layer != nullptr); |
| 4975 | input.Connect(layer->GetInputSlot(0)); |
| 4976 | |
| 4977 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 4978 | } |
| 4979 | |
| 4980 | bool Converter::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data) |
| 4981 | { |
| 4982 | VLOG(DRIVER) << "Converter::ConvertSoftmax()"; |
| 4983 | |
| 4984 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 4985 | if (!input.IsValid()) |
| 4986 | { |
| 4987 | return Fail("%s: Operation has invalid inputs", __func__); |
| 4988 | } |
| 4989 | |
| 4990 | const Operand* outputOperand = GetOutputOperand(operation, 0, model); |
| 4991 | if (!outputOperand) |
| 4992 | { |
| 4993 | return Fail("%s: Operation has no outputs", __func__); |
| 4994 | } |
| 4995 | |
| 4996 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand); |
| 4997 | |
| 4998 | SoftmaxDescriptor desc; |
| 4999 | OperandType outputType = outputOperand->type; |
| 5000 | |
| 5001 | // Read beta value |
| 5002 | if (outputType == OperandType::TENSOR_FLOAT16) |
| 5003 | { |
| 5004 | Half value; |
| 5005 | |
| 5006 | if (!GetInputScalar(operation, 1, OperandType::FLOAT16, value, model, data)) |
| 5007 | { |
| 5008 | return Fail("%s: Operation has invalid inputs %d", __func__, outputType); |
| 5009 | } |
| 5010 | |
| 5011 | desc.m_Beta = static_cast<float>(value); |
| 5012 | } |
| 5013 | else |
| 5014 | { |
| 5015 | if (!GetInputFloat32(operation, 1, desc.m_Beta, model, data)) |
| 5016 | { |
| 5017 | return Fail("%s: Operation has invalid inputs %d", __func__, outputType); |
| 5018 | } |
| 5019 | } |
| 5020 | |
| 5021 | if (operation.inputs.size() > 2 && !GetInputScalar(operation, |
| 5022 | 2, |
| 5023 | OperandType::INT32, |
| 5024 | desc.m_Axis, |
| 5025 | model, |
| 5026 | data)) |
| 5027 | { |
| 5028 | return Fail("%s: Operation has invalid inputs", __func__); |
| 5029 | } |
| 5030 | |
| 5031 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5032 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5033 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 5034 | { |
| 5035 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 5036 | IsSoftmaxSupported, |
| 5037 | data.m_Backends, |
| 5038 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5039 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5040 | input.GetTensorInfo(), |
| 5041 | outputInfo, |
| 5042 | desc); |
| 5043 | }; |
| 5044 | |
| 5045 | if(IsDynamicTensor(outputInfo)) |
| 5046 | { |
| 5047 | isSupported = AreDynamicTensorsSupported(); |
| 5048 | } |
| 5049 | else |
| 5050 | { |
| 5051 | validateFunc(outputInfo, isSupported); |
| 5052 | } |
| 5053 | |
| 5054 | if (!isSupported) |
| 5055 | { |
| 5056 | return false; |
| 5057 | } |
| 5058 | |
| 5059 | IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5060 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5061 | assert(layer != nullptr); |
| 5062 | input.Connect(layer->GetInputSlot(0)); |
| 5063 | |
| 5064 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 5065 | } |
| 5066 | |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5067 | bool Converter::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data) |
| 5068 | { |
| 5069 | VLOG(DRIVER) << "Converter::ConvertTanH()"; |
| 5070 | |
| 5071 | armnn::ActivationDescriptor desc; |
| 5072 | desc.m_Function = armnn::ActivationFunction::TanH; |
| 5073 | desc.m_A = 1.0f; // android nn does not support tanH parameters |
| 5074 | desc.m_B = 1.0f; // set to 1.0f for unity scaling |
| 5075 | |
| 5076 | return ConvertToActivation(operation, __func__, desc, model, data); |
| 5077 | } |
| 5078 | |
| 5079 | bool Converter::ConvertTransposeConv2d(const Operation& operation, const Model& model, ConversionData& data) |
| 5080 | { |
| 5081 | VLOG(DRIVER) << "Converter::ConvertTransposeConv2d()"; |
| 5082 | |
| 5083 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 5084 | |
| 5085 | if (!input.IsValid()) |
| 5086 | { |
| 5087 | return Fail("%s: Operation has invalid inputs", __func__); |
| 5088 | } |
| 5089 | |
| 5090 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 5091 | |
| 5092 | if (!output) |
| 5093 | { |
| 5094 | return Fail("%s: Could not read output 0", __func__); |
| 5095 | } |
| 5096 | |
| 5097 | const TensorInfo& inputInfo = input.GetTensorInfo(); |
| 5098 | const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 5099 | |
| 5100 | // ArmNN does not currently support non-fixed weights or bias |
| 5101 | // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ] |
| 5102 | const Operand* weightsOperand = GetInputOperand(operation, 1, model); |
| 5103 | |
| 5104 | if (weightsOperand == nullptr) |
| 5105 | { |
| 5106 | return Fail("%s: Operand is invalid", __func__); |
| 5107 | } |
| 5108 | TransposeConvolution2dDescriptor desc; |
| 5109 | desc.m_DataLayout = DataLayout::NHWC; |
| 5110 | |
| 5111 | // Determine whether padding is implicit or explicit |
| 5112 | bool implicitPadding = operation.inputs.size() == 9; |
| 5113 | |
| 5114 | if (implicitPadding ) |
| 5115 | { |
| 5116 | desc.m_DataLayout = OptionalDataLayout(operation, 8, model, data); |
| 5117 | } |
| 5118 | else |
| 5119 | { |
| 5120 | desc.m_DataLayout = OptionalDataLayout(operation, 10, model, data); |
| 5121 | } |
| 5122 | |
| 5123 | armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout); |
| 5124 | unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); |
| 5125 | unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); |
| 5126 | |
| 5127 | const PermutationVector OHWIToOIHW = {0, 2, 3, 1}; |
| 5128 | |
| 5129 | // The shape of the weight is [depth_out, filter_height, filter_width, depth_in]. |
| 5130 | // We have to permute it to OIHW if the data layout is NCHW. |
| 5131 | const ConstTensorPin weightsPin = (desc.m_DataLayout == DataLayout::NCHW) ? |
| 5132 | ConvertOperationInputToConstTensorPin(operation, 1, |
| 5133 | model, data, OHWIToOIHW) : |
| 5134 | ConvertOperationInputToConstTensorPin(operation, 1, model, data); |
| 5135 | |
| 5136 | // Bias is a 1D tensor |
| 5137 | const ConstTensorPin biasPin = |
| 5138 | ConvertOperationInputToConstTensorPin(operation, 2, model, data); |
| 5139 | |
| 5140 | if (!weightsPin.IsValid()) |
| 5141 | { |
| 5142 | return Fail("%s: Operation has invalid weights", __func__); |
| 5143 | } |
| 5144 | |
| 5145 | if (!biasPin.IsValid()) |
| 5146 | { |
| 5147 | return Fail("%s: Operation has invalid biases", __func__); |
| 5148 | } |
| 5149 | |
| 5150 | ConstTensor weights = weightsPin.GetConstTensor(); |
| 5151 | ConstTensor bias = biasPin.GetConstTensor(); |
| 5152 | SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo); |
| 5153 | |
| 5154 | ActivationFn activation; |
| 5155 | |
| 5156 | if (implicitPadding) |
| 5157 | { |
| 5158 | int32_t strideX{0}; |
| 5159 | int32_t strideY{0}; |
| 5160 | int32_t padLeft{0}; |
| 5161 | int32_t padRight{0}; |
| 5162 | int32_t padTop{0}; |
| 5163 | int32_t padBottom{0}; |
| 5164 | |
| 5165 | ::android::nn::PaddingScheme paddingScheme; |
| 5166 | if (!GetInputPaddingScheme(operation, 4, paddingScheme, model, data) || |
| 5167 | !GetInputScalar(operation, 5, OperandType::INT32, strideX, model, data) || |
| 5168 | !GetInputScalar(operation, 6, OperandType::INT32, strideY, model, data) || |
| 5169 | !GetInputActivationFunction(operation, 7, activation, model, data)) |
| 5170 | { |
| 5171 | return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); |
| 5172 | } |
| 5173 | |
| 5174 | const uint32_t kernelX = weights.GetShape()[widthIndex]; |
| 5175 | const uint32_t kernelY = weights.GetShape()[heightIndex]; |
| 5176 | |
| 5177 | // If output shape has been specified as a parameter then extract it and make it available. |
| 5178 | const Operand* outputShapeOperand = GetInputOperand(operation, 3, model, false); |
| 5179 | std::vector<int32_t> outputShape; |
| 5180 | if ((outputShapeOperand) && (GetTensorInt32Values(*outputShapeOperand, outputShape, model, data))) |
| 5181 | { |
| 5182 | // Change from signed to unsigned int to store in TransposeConvolution2dDescriptor. |
| 5183 | for (int dimension : outputShape) |
| 5184 | { |
| 5185 | desc.m_OutputShape.push_back(static_cast<unsigned int>(dimension)); |
| 5186 | } |
| 5187 | desc.m_OutputShapeEnabled = true; |
| 5188 | } |
| 5189 | |
| 5190 | uint32_t outputX; |
| 5191 | uint32_t outputY; |
| 5192 | |
| 5193 | if (IsDynamicTensor(outputInfo)) |
| 5194 | { |
| 5195 | if (outputShape.size() == 0) |
| 5196 | { |
| 5197 | return Fail("%s: Padding sizes cannot be inferred", __func__); |
| 5198 | } |
| 5199 | |
| 5200 | outputX = outputShape[widthIndex]; |
| 5201 | outputY = outputShape[heightIndex]; |
| 5202 | } |
| 5203 | else |
| 5204 | { |
| 5205 | outputX = outputInfo.GetShape()[widthIndex]; |
| 5206 | outputY = outputInfo.GetShape()[heightIndex]; |
| 5207 | } |
| 5208 | |
| 5209 | CalcPaddingTransposeConv(outputX, kernelX, strideX, padLeft, padRight, paddingScheme); |
| 5210 | CalcPaddingTransposeConv(outputY, kernelY, strideY, padTop, padBottom, paddingScheme); |
| 5211 | |
| 5212 | // NOTE: The Android NN API allows for negative padding values in TransposeConv2d, |
| 5213 | // but Arm NN only supports values >= 0 |
| 5214 | if (padLeft < 0 || padRight < 0 || padTop < 0 || padBottom < 0) |
| 5215 | { |
| 5216 | return Fail("%s: Negative padding values are not supported", __func__); |
| 5217 | } |
| 5218 | |
| 5219 | desc.m_StrideX = armnn::numeric_cast<uint32_t>(strideX); |
| 5220 | desc.m_StrideY = armnn::numeric_cast<uint32_t>(strideY); |
| 5221 | desc.m_PadLeft = armnn::numeric_cast<uint32_t>(padLeft); |
| 5222 | desc.m_PadRight = armnn::numeric_cast<uint32_t>(padRight); |
| 5223 | desc.m_PadTop = armnn::numeric_cast<uint32_t>(padTop); |
| 5224 | desc.m_PadBottom = armnn::numeric_cast<uint32_t>(padBottom); |
| 5225 | } |
| 5226 | else if (operation.inputs.size() == 11) |
| 5227 | { |
| 5228 | // explicit padding |
| 5229 | if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || |
| 5230 | !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || |
| 5231 | !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || |
| 5232 | !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || |
| 5233 | !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || |
| 5234 | !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || |
| 5235 | !GetInputActivationFunction(operation, 9, activation, model, data)) |
| 5236 | { |
| 5237 | return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); |
| 5238 | } |
| 5239 | } |
| 5240 | else |
| 5241 | { |
| 5242 | return Fail("%s: Unsupported number of operation inputs", __func__); |
| 5243 | } |
| 5244 | |
| 5245 | desc.m_BiasEnabled = true; |
| 5246 | Optional<TensorInfo> biases(bias.GetInfo()); |
| 5247 | |
| 5248 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5249 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5250 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 5251 | { |
| 5252 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 5253 | IsTransposeConvolution2dSupported, |
| 5254 | data.m_Backends, |
| 5255 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5256 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5257 | inputInfo, |
| 5258 | outputInfo, |
| 5259 | desc, |
| 5260 | weights.GetInfo(), |
| 5261 | biases); |
| 5262 | }; |
| 5263 | |
| 5264 | if(IsDynamicTensor(outputInfo)) |
| 5265 | { |
| 5266 | isSupported = AreDynamicTensorsSupported(); |
| 5267 | } |
| 5268 | else |
| 5269 | { |
| 5270 | validateFunc(outputInfo, isSupported); |
| 5271 | } |
| 5272 | if (!isSupported) |
| 5273 | { |
| 5274 | return false; |
| 5275 | } |
| 5276 | |
| 5277 | IConnectableLayer* startLayer = |
| 5278 | data.m_Network->AddTransposeConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias)); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5279 | startLayer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5280 | if (!startLayer) |
| 5281 | { |
| 5282 | return Fail("%s: AddTransposeConvolution2dLayer failed", __func__); |
| 5283 | } |
| 5284 | |
| 5285 | input.Connect(startLayer->GetInputSlot(0)); |
| 5286 | |
| 5287 | return SetupAndTrackLayerOutputSlot(operation, 0, *startLayer, model, |
| 5288 | data, nullptr, validateFunc, activation); |
| 5289 | } |
| 5290 | |
| 5291 | bool Converter::ConvertSqrt(const Operation& operation, const Model& model, ConversionData& data) |
| 5292 | { |
| 5293 | VLOG(DRIVER) << "Converter::ConvertSqrt()"; |
| 5294 | ActivationDescriptor desc; |
| 5295 | desc.m_Function = ActivationFunction::Sqrt; |
| 5296 | |
| 5297 | return ::ConvertToActivation(operation, __func__, desc, model, data); |
| 5298 | } |
| 5299 | |
| 5300 | bool Converter::ConvertSqueeze(const Operation& operation, const Model& model, ConversionData& data) |
| 5301 | { |
| 5302 | VLOG(DRIVER) << "Converter::ConvertSqueeze()"; |
| 5303 | |
| 5304 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 5305 | if (!input.IsValid()) |
| 5306 | { |
| 5307 | return Fail("%s: Operation has invalid inputs", __func__); |
| 5308 | } |
| 5309 | |
| 5310 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 5311 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 5312 | if (rank > 4) |
| 5313 | { |
| 5314 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
| 5315 | } |
| 5316 | |
| 5317 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 5318 | if (!output) |
| 5319 | { |
| 5320 | return Fail("%s: Could not read output 0", __func__); |
| 5321 | } |
| 5322 | |
| 5323 | if (IsDynamicTensor(GetTensorInfoForOperand(*output)) && !(AreDynamicTensorsSupported())) |
| 5324 | { |
| 5325 | return Fail("%s: Dynamic output tensors are not supported", __func__); |
| 5326 | } |
| 5327 | |
| 5328 | // NOTE: Axis is an optional parameter to SQUEEZE, therefore we do not want to generate a failure |
| 5329 | // if the operand index is out of bounds. |
| 5330 | const Operand* axisOperand = GetInputOperand(operation, 1, model, false); |
| 5331 | |
| 5332 | const uint32_t dimensionSequence[] = { 0, 1, 2, 3 }; |
| 5333 | |
| 5334 | std::vector<int32_t> axis; |
| 5335 | if (!axisOperand) |
| 5336 | { |
| 5337 | axis.assign(dimensionSequence, |
| 5338 | dimensionSequence + rank); |
| 5339 | } |
| 5340 | else if (!GetTensorInt32Values(*axisOperand, axis, model, data)) |
| 5341 | { |
| 5342 | return Fail("%s: Operation has an invalid or unsupported axis operand", __func__); |
| 5343 | } |
| 5344 | |
| 5345 | std::vector<uint32_t> outputDims; |
| 5346 | for (unsigned int i = 0; i < rank; i++) |
| 5347 | { |
| 5348 | bool skipSqueeze = (std::find(axis.begin(), axis.end(), i) == axis.end()); |
| 5349 | auto currentDimension = inputInfo.GetShape()[i]; |
| 5350 | if (skipSqueeze || currentDimension != 1) |
| 5351 | { |
| 5352 | outputDims.push_back(currentDimension); |
| 5353 | } |
| 5354 | } |
| 5355 | |
| 5356 | armnn::TensorShape outShape = armnn::TensorShape(outputDims.size(), outputDims.data()); |
| 5357 | |
| 5358 | armnn::TensorInfo outputInfo = inputInfo; |
| 5359 | outputInfo.SetShape(outShape); |
| 5360 | |
| 5361 | armnn::ReshapeDescriptor reshapeDesc; |
| 5362 | reshapeDesc.m_TargetShape = outputInfo.GetShape(); |
| 5363 | |
| 5364 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5365 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5366 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 5367 | IsReshapeSupported, |
| 5368 | data.m_Backends, |
| 5369 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5370 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5371 | inputInfo, |
| 5372 | outputInfo, |
| 5373 | reshapeDesc); |
| 5374 | |
| 5375 | if (!isSupported) |
| 5376 | { |
| 5377 | return false; |
| 5378 | } |
| 5379 | |
| 5380 | armnn::IConnectableLayer* const layer = data.m_Network->AddReshapeLayer(reshapeDesc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5381 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5382 | assert(layer != nullptr); |
| 5383 | input.Connect(layer->GetInputSlot(0)); |
| 5384 | |
| 5385 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data); |
| 5386 | } |
| 5387 | |
| 5388 | bool Converter::ConvertStridedSlice(const Operation& operation, const Model& model, ConversionData& data) |
| 5389 | { |
| 5390 | VLOG(DRIVER) << "Converter::ConvertStridedSlice()"; |
| 5391 | |
| 5392 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 5393 | if (!input.IsValid()) |
| 5394 | { |
| 5395 | return Fail("%s: Operation has invalid inputs", __func__); |
| 5396 | } |
| 5397 | |
| 5398 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 5399 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 5400 | if (rank > 4) |
| 5401 | { |
| 5402 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
| 5403 | } |
| 5404 | |
| 5405 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 5406 | if (!output) |
| 5407 | { |
| 5408 | return Fail("%s: Could not read output 0", __func__); |
| 5409 | } |
| 5410 | |
| 5411 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 5412 | |
| 5413 | const Operand* beginOperand = GetInputOperand(operation, 1, model); |
| 5414 | const Operand* endOperand = GetInputOperand(operation, 2, model); |
| 5415 | const Operand* stridesOperand = GetInputOperand(operation, 3, model); |
| 5416 | |
| 5417 | std::vector<int32_t> beginValues; |
| 5418 | std::vector<int32_t> endValues; |
| 5419 | std::vector<int32_t> stridesValues; |
| 5420 | |
| 5421 | // The length of the beginOperand, endOperand and stridesOperand must be of a rank(input) |
| 5422 | auto ValidateInputOperands = [&] (const Operand& operand, std::vector<int32_t>& operandValues) |
| 5423 | { |
| 5424 | if (!GetTensorInt32Values(operand, operandValues, model, data)) |
| 5425 | { |
| 5426 | return false; |
| 5427 | } |
| 5428 | |
| 5429 | if (operandValues.size() != rank) |
| 5430 | { |
| 5431 | return false; |
| 5432 | } |
| 5433 | |
| 5434 | return true; |
| 5435 | }; |
| 5436 | |
| 5437 | if (!ValidateInputOperands(*beginOperand, beginValues) |
| 5438 | || !ValidateInputOperands(*endOperand, endValues) |
| 5439 | || !ValidateInputOperands(*stridesOperand, stridesValues)) |
| 5440 | { |
| 5441 | return Fail("%s: Operation has invalid input operand", __func__); |
| 5442 | } |
| 5443 | |
| 5444 | // Stride cannot have value '0' |
| 5445 | if (std::any_of(stridesValues.cbegin(), stridesValues.cend(), [](int32_t i){ return i == 0; })) |
| 5446 | { |
| 5447 | return Fail("%s: Stride must be non-zero value.", __func__); |
| 5448 | } |
| 5449 | |
| 5450 | armnn::StridedSliceDescriptor descriptor; |
| 5451 | descriptor.m_Begin.assign(beginValues.cbegin(), beginValues.cend()); |
| 5452 | descriptor.m_End.assign(endValues.cbegin(), endValues.cend()); |
| 5453 | descriptor.m_Stride.assign(stridesValues.cbegin(), stridesValues.cend()); |
| 5454 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 5455 | |
| 5456 | // Get the "begin_mask", "end_mask", and "shrink_axis_mask" flags |
| 5457 | if (!GetInputInt32(operation, 4, descriptor.m_BeginMask, model, data) || |
| 5458 | !GetInputInt32(operation, 5, descriptor.m_EndMask, model, data) || |
| 5459 | !GetInputInt32(operation, 6, descriptor.m_ShrinkAxisMask, model, data)) |
| 5460 | { |
| 5461 | return Fail("%s: Operation has invalid inputs", __func__); |
| 5462 | } |
| 5463 | |
| 5464 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5465 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5466 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 5467 | { |
| 5468 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 5469 | IsStridedSliceSupported, |
| 5470 | data.m_Backends, |
| 5471 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5472 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5473 | inputInfo, |
| 5474 | outputInfo, |
| 5475 | descriptor); |
| 5476 | }; |
| 5477 | |
| 5478 | if(IsDynamicTensor(outputInfo)) |
| 5479 | { |
| 5480 | isSupported = AreDynamicTensorsSupported(); |
| 5481 | } |
| 5482 | else |
| 5483 | { |
| 5484 | validateFunc(outputInfo, isSupported); |
| 5485 | } |
| 5486 | |
| 5487 | if (!isSupported) |
| 5488 | { |
| 5489 | return false; |
| 5490 | } |
| 5491 | |
| 5492 | // Check if slice can fit in a inferred output |
| 5493 | armnn::TensorShape inputShape = inputInfo.GetShape(); |
| 5494 | for (unsigned int i = 0; i < inputShape.GetNumDimensions(); i++) |
| 5495 | { |
| 5496 | int stride = descriptor.m_Stride[i]; |
| 5497 | |
| 5498 | if (descriptor.m_ShrinkAxisMask & (1 << i)) |
| 5499 | { |
| 5500 | // If the difference between the start point and the end point of the slice on an axis being shrunk |
| 5501 | // is greater than 1 then throw an error as the output will not be large enough to hold the slice |
| 5502 | if (((descriptor.m_Begin[i] - descriptor.m_End[i]) > 1) |
| 5503 | || ((descriptor.m_Begin[i] - descriptor.m_End[i]) < -1)) |
| 5504 | { |
| 5505 | return Fail("%s: StridedSlice: Output will not be large enough to hold the slice", __func__); |
| 5506 | } |
| 5507 | |
| 5508 | if(stride < 0) |
| 5509 | { |
| 5510 | return Fail("%s: StridedSlice: Stride can not be negative while ShrinkAxisMask is set.", __func__); |
| 5511 | } |
| 5512 | } |
| 5513 | } |
| 5514 | |
| 5515 | armnn::IConnectableLayer* const layer = data.m_Network->AddStridedSliceLayer(descriptor); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5516 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5517 | assert(layer != nullptr); |
| 5518 | input.Connect(layer->GetInputSlot(0)); |
| 5519 | |
| 5520 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 5521 | } |
| 5522 | |
| 5523 | bool Converter::ConvertTranspose(const Operation& operation, const Model& model, ConversionData& data) |
| 5524 | { |
| 5525 | VLOG(DRIVER) << "Converter::ConvertTranspose()"; |
| 5526 | |
| 5527 | LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data); |
| 5528 | if (!input.IsValid()) |
| 5529 | { |
| 5530 | return Fail("%s: Operation has invalid inputs", __func__); |
| 5531 | } |
| 5532 | |
| 5533 | const armnn::TensorInfo& inputInfo = input.GetTensorInfo(); |
| 5534 | unsigned int rank = inputInfo.GetNumDimensions(); |
| 5535 | if (rank > 4) |
| 5536 | { |
| 5537 | Fail("%s: Inputs with rank greater than 4 are not supported", __func__); |
| 5538 | } |
| 5539 | |
| 5540 | // NOTE: Axis is an optional parameter to TRANSPOSE, therefore we do not want to generate a failure |
| 5541 | // if the operand index is out of bounds. |
| 5542 | const Operand* permOperand = GetInputOperand(operation, 1, model, false); |
| 5543 | |
| 5544 | std::vector<int32_t> perm(rank); |
| 5545 | if (!permOperand || (permOperand->lifetime == OperandLifeTime::NO_VALUE)) |
| 5546 | { |
| 5547 | for (unsigned int i = rank; i > 0; i--) |
| 5548 | { |
| 5549 | perm[rank - i] = armnn::numeric_cast<int> (i - 1); |
| 5550 | } |
| 5551 | } |
| 5552 | else if (!GetTensorInt32Values(*permOperand, perm, model, data)) |
| 5553 | { |
| 5554 | return Fail("%s: Operation has an invalid or unsupported permutation operand", __func__); |
| 5555 | } |
| 5556 | |
| 5557 | std::vector<uint32_t> outputDims(perm.begin(), perm.begin() + rank); |
| 5558 | |
| 5559 | armnn::TransposeDescriptor transposeDesc; |
| 5560 | transposeDesc.m_DimMappings = armnn::PermutationVector(outputDims.data(), outputDims.size()); |
| 5561 | |
| 5562 | const Operand* output = GetOutputOperand(operation, 0, model); |
| 5563 | if (!output) |
| 5564 | { |
| 5565 | return Fail("%s: Could not read output 0", __func__); |
| 5566 | } |
| 5567 | |
| 5568 | const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output); |
| 5569 | |
| 5570 | bool isSupported = false; |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5571 | armnn::BackendId setBackend; |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5572 | auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| 5573 | { |
| 5574 | FORWARD_LAYER_SUPPORT_FUNC(__func__, |
| 5575 | IsTransposeSupported, |
| 5576 | data.m_Backends, |
| 5577 | isSupported, |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5578 | setBackend, |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5579 | inputInfo, |
| 5580 | outputInfo, |
| 5581 | transposeDesc); |
| 5582 | }; |
| 5583 | |
| 5584 | if(IsDynamicTensor(outputInfo)) |
| 5585 | { |
| 5586 | isSupported = AreDynamicTensorsSupported(); |
| 5587 | } |
| 5588 | else |
| 5589 | { |
| 5590 | validateFunc(outputInfo, isSupported); |
| 5591 | } |
| 5592 | |
| 5593 | if (!isSupported) |
| 5594 | { |
| 5595 | return false; |
| 5596 | } |
| 5597 | |
| 5598 | armnn::IConnectableLayer* const layer = data.m_Network->AddTransposeLayer(transposeDesc); |
Cathal Corbett | 5383767 | 2022-09-01 11:34:37 +0100 | [diff] [blame] | 5599 | layer->SetBackendId(setBackend); |
Sadik Armagan | 8f397a1 | 2022-06-17 15:38:22 +0100 | [diff] [blame] | 5600 | assert(layer != nullptr); |
| 5601 | input.Connect(layer->GetInputSlot(0)); |
| 5602 | |
| 5603 | return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data, nullptr, validateFunc); |
| 5604 | } |
| 5605 | |
| 5606 | } // namespace armnn_driver |