telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4 | // |
| 5 | #include "TfLiteParser.hpp" |
| 6 | |
| 7 | #include <armnn/ArmNN.hpp> |
| 8 | #include <armnn/Exceptions.hpp> |
| 9 | #include <armnn/TypesUtils.hpp> |
| 10 | #include <boost/filesystem.hpp> |
| 11 | |
| 12 | // armnnUtils: |
Sadik Armagan | 479045b | 2018-10-01 11:51:37 +0100 | [diff] [blame] | 13 | #include <ParserHelper.hpp> |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 14 | #include <Permute.hpp> |
| 15 | #include <VerificationHelpers.hpp> |
| 16 | |
| 17 | // The generated code based on the Tf Lite schema: |
| 18 | #include <schema_generated.h> |
| 19 | |
| 20 | #include <boost/core/ignore_unused.hpp> |
| 21 | #include <boost/assert.hpp> |
| 22 | #include <boost/format.hpp> |
| 23 | #include <boost/log/trivial.hpp> |
| 24 | |
| 25 | #include <fstream> |
| 26 | #include <algorithm> |
| 27 | #include <limits> |
Sadik | b94967b | 2018-09-19 15:30:00 +0100 | [diff] [blame] | 28 | #include <numeric> |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 29 | |
| 30 | using namespace armnn; |
| 31 | using armnn::CheckLocation; |
| 32 | namespace armnnTfLiteParser |
| 33 | { |
| 34 | namespace |
| 35 | { |
| 36 | const PermutationVector NHWCToArmNN = { 0, 2, 3, 1 }; |
| 37 | const PermutationVector ArmNNToNHWC = { 0, 3, 1, 2 }; |
| 38 | |
| 39 | const uint32_t VIRTUAL_OPERATOR_ID = std::numeric_limits<uint32_t>::max(); |
| 40 | |
| 41 | void CheckSubgraph(const TfLiteParser::ModelPtr & model, |
| 42 | size_t subgraphIndex, |
| 43 | const CheckLocation & location) |
| 44 | { |
| 45 | if (model.get() == nullptr) |
| 46 | { |
| 47 | throw ParseException( |
| 48 | boost::str( |
| 49 | boost::format("%1% was called with invalid (null) model. " |
| 50 | "Possible reason is that the model is not yet loaded and Unpack(ed). " |
| 51 | "subgraph:%2% at %3%") % |
| 52 | location.m_Function % |
| 53 | subgraphIndex % |
| 54 | location.FileLine())); |
| 55 | } |
| 56 | else if (subgraphIndex >= model->subgraphs.size()) |
| 57 | { |
| 58 | throw ParseException( |
| 59 | boost::str( |
| 60 | boost::format("%1% was called with an invalid subgraph index. " |
| 61 | "subgraph:%2% at %3%") % |
| 62 | location.m_Function % |
| 63 | subgraphIndex % |
| 64 | location.FileLine())); |
| 65 | } |
| 66 | } |
| 67 | |
| 68 | #define CHECK_SUBGRAPH(MODEL, SUBGRAPH_INDEX) \ |
| 69 | CheckSubgraph(MODEL, SUBGRAPH_INDEX, CHECK_LOCATION()) |
| 70 | |
| 71 | void CheckModel(const TfLiteParser::ModelPtr & model, |
| 72 | size_t subgraphIndex, |
| 73 | size_t operatorIndex, |
| 74 | const CheckLocation & location) |
| 75 | { |
| 76 | if (model.get() == nullptr) |
| 77 | { |
| 78 | throw ParseException( |
| 79 | boost::str( |
| 80 | boost::format("%1% was called with invalid (null) model. " |
| 81 | "Possible reason is that the model is not yet loaded and Unpack(ed). " |
| 82 | "subgraph:%2% operator:%3% at %4%") % |
| 83 | location.m_Function % |
| 84 | subgraphIndex % |
| 85 | operatorIndex % |
| 86 | location.FileLine())); |
| 87 | } |
| 88 | else if (subgraphIndex >= model->subgraphs.size()) |
| 89 | { |
| 90 | throw ParseException( |
| 91 | boost::str( |
| 92 | boost::format("%1% was called with an invalid subgraph index. " |
| 93 | "subgraph:%2% operator:%3% at %4%") % |
| 94 | location.m_Function % |
| 95 | subgraphIndex % |
| 96 | operatorIndex % |
| 97 | location.FileLine())); |
| 98 | } |
| 99 | else if (operatorIndex >= model->subgraphs[subgraphIndex]->operators.size() && |
| 100 | operatorIndex != VIRTUAL_OPERATOR_ID) |
| 101 | { |
| 102 | throw ParseException( |
| 103 | boost::str( |
| 104 | boost::format("%1% was called with an invalid operator index. " |
| 105 | "subgraph:%2% operator:%3% at %4%") % |
| 106 | location.m_Function % |
| 107 | subgraphIndex % |
| 108 | operatorIndex % |
| 109 | location.FileLine())); |
| 110 | } |
| 111 | } |
| 112 | |
| 113 | #define CHECK_MODEL(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX) \ |
| 114 | CheckModel(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX, CHECK_LOCATION()) |
| 115 | |
| 116 | void CheckTensor(const TfLiteParser::ModelPtr & model, |
| 117 | size_t subgraphIndex, |
| 118 | size_t tensorIndex, |
| 119 | const CheckLocation & location) |
| 120 | { |
| 121 | // not checking model, because I assume CHECK_MODEL already run |
| 122 | // and checked that. An assert would do. |
| 123 | BOOST_ASSERT_MSG(model.get() != nullptr, "Expecting a valid model in this function"); |
| 124 | |
| 125 | // also subgraph index should be checked by CHECK_MODEL so |
| 126 | // I only add an assert here |
| 127 | BOOST_ASSERT_MSG(subgraphIndex < model->subgraphs.size(), "Expecting a valid subgraph index"); |
| 128 | |
| 129 | // the tensor index is the only one to check here |
| 130 | if (tensorIndex >= model->subgraphs[subgraphIndex]->tensors.size()) |
| 131 | { |
| 132 | throw ParseException( |
| 133 | boost::str( |
| 134 | boost::format("%1% was called with an invalid tensor index. " |
| 135 | "subgraph:%2% tensor:%3% at %4%") % |
| 136 | location.m_Function % |
| 137 | subgraphIndex % |
| 138 | tensorIndex % |
| 139 | location.FileLine())); |
| 140 | } |
| 141 | } |
| 142 | |
| 143 | #define CHECK_TENSOR(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX) \ |
| 144 | CheckTensor(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX, CHECK_LOCATION()) |
| 145 | |
| 146 | void CheckTensorPtr(TfLiteParser::TensorRawPtr rawPtr, |
| 147 | const CheckLocation & location) |
| 148 | { |
| 149 | if (rawPtr == nullptr) |
| 150 | { |
| 151 | throw ParseException( |
| 152 | boost::str( |
| 153 | boost::format("%1% was called with a null tensor pointer. " |
| 154 | "at %2%") % |
| 155 | location.m_Function % |
| 156 | location.FileLine())); |
| 157 | |
| 158 | } |
| 159 | } |
| 160 | |
| 161 | #define CHECK_TENSOR_PTR(TENSOR_PTR) \ |
| 162 | CheckTensorPtr(TENSOR_PTR, CHECK_LOCATION()) |
| 163 | |
| 164 | void CheckBuffer(const TfLiteParser::ModelPtr & model, |
| 165 | size_t bufferIndex, |
| 166 | const CheckLocation & location) |
| 167 | { |
| 168 | if (model.get() == nullptr) |
| 169 | { |
| 170 | throw ParseException( |
| 171 | boost::str( |
| 172 | boost::format("%1% was called with invalid (null) model. " |
| 173 | "Possible reason is that the model is not yet loaded and Unpack(ed). " |
| 174 | "buffer:%2% at %3%") % |
| 175 | location.m_Function % |
| 176 | bufferIndex % |
| 177 | location.FileLine())); |
| 178 | } |
| 179 | else if (bufferIndex >= model->buffers.size()) |
| 180 | { |
| 181 | throw ParseException( |
| 182 | boost::str( |
| 183 | boost::format("%1% was called with an invalid buffer index. " |
| 184 | "buffer index:%2% at %3%") % |
| 185 | location.m_Function % |
| 186 | bufferIndex % |
| 187 | location.FileLine())); |
| 188 | } |
| 189 | else if (model->buffers[bufferIndex].get() == nullptr) |
| 190 | { |
| 191 | throw ParseException( |
| 192 | boost::str( |
| 193 | boost::format("The buffer #%1% is null. %3%") % |
| 194 | bufferIndex % |
| 195 | location.AsString())); |
| 196 | } |
| 197 | } |
| 198 | |
| 199 | #define CHECK_BUFFER(MODEL, BUFFER_INDEX) \ |
| 200 | CheckBuffer(MODEL, BUFFER_INDEX, CHECK_LOCATION()) |
| 201 | |
| 202 | void CheckBufferSize(TfLiteParser::BufferRawPtr bufferPtr, |
| 203 | const armnn::TensorInfo & tensorInfo, |
| 204 | uint32_t bufferId, |
| 205 | const CheckLocation & location) |
| 206 | { |
| 207 | if (bufferPtr == nullptr) |
| 208 | { |
| 209 | throw ParseException( |
| 210 | boost::str( |
| 211 | boost::format("BufferPtr is null for buffer:%1%. %2%") % |
| 212 | bufferId % |
| 213 | location.AsString())); |
| 214 | } |
| 215 | else if(tensorInfo.GetNumElements() > bufferPtr->data.size() || |
| 216 | tensorInfo.GetNumBytes() > bufferPtr->data.size()) |
| 217 | { |
| 218 | std::stringstream ss; |
| 219 | ss << "Buffer #" << bufferId << " has " << bufferPtr->data.size() << " bytes. " |
| 220 | << "For tensor: " << tensorInfo.GetShape() |
| 221 | << " expecting: " << tensorInfo.GetNumBytes() << " bytes and " |
| 222 | << tensorInfo.GetNumElements() << " elements. " << location.AsString(); |
| 223 | throw ParseException(ss.str()); |
| 224 | } |
| 225 | } |
| 226 | |
| 227 | #define CHECK_BUFFER_SIZE(BUFFER_PTR, TENSOR_INFO, BUFFER_ID) \ |
| 228 | CheckBufferSize(BUFFER_PTR, TENSOR_INFO, BUFFER_ID, CHECK_LOCATION()) |
| 229 | |
| 230 | bool IsActivationSupported(tflite::ActivationFunctionType activationType) |
| 231 | { |
| 232 | switch(activationType) |
| 233 | { |
| 234 | case tflite::ActivationFunctionType_NONE: |
| 235 | case tflite::ActivationFunctionType_RELU: |
| 236 | case tflite::ActivationFunctionType_RELU6: |
| 237 | case tflite::ActivationFunctionType_TANH: |
| 238 | { |
| 239 | return true; |
| 240 | } |
| 241 | default: |
| 242 | { |
| 243 | return false; |
| 244 | } |
| 245 | } |
| 246 | } |
| 247 | |
| 248 | #define CHECK_SUPPORTED_FUSED_ACTIVATION(OPTION, SUBGRAPH_INDEX, OPERATOR_INDEX) \ |
| 249 | do { \ |
| 250 | if (IsActivationSupported(OPTION->fused_activation_function) == false) \ |
| 251 | { \ |
| 252 | throw ParseException( \ |
| 253 | boost::str( \ |
| 254 | boost::format("TfLite parser doesn't suppport fused activation: " \ |
| 255 | "%1%/%2% in %3% subgraph:%4% operator:%5% at %6%") % \ |
| 256 | OPTION->fused_activation_function % \ |
| 257 | tflite::EnumNameActivationFunctionType(\ |
| 258 | OPTION->fused_activation_function) % \ |
| 259 | __func__ % \ |
| 260 | SUBGRAPH_INDEX % \ |
| 261 | OPERATOR_INDEX % \ |
| 262 | CHECK_LOCATION().FileLine())); \ |
| 263 | } \ |
| 264 | } while(false) |
| 265 | |
| 266 | |
| 267 | std::vector<unsigned int> AsUnsignedVector(const std::vector<int32_t> & in) |
| 268 | { |
| 269 | std::vector<unsigned int> result; |
| 270 | result.reserve(in.size()); |
| 271 | for (auto & i : in) |
| 272 | { |
| 273 | result.push_back(CHECKED_NON_NEGATIVE(i)); |
| 274 | } |
| 275 | return result; |
| 276 | } |
| 277 | |
| 278 | void CalcPadding(uint32_t inputSize, |
| 279 | uint32_t filterSize, |
| 280 | uint32_t stride, |
| 281 | uint32_t& paddingFront, |
| 282 | uint32_t& paddingBack, |
| 283 | tflite::Padding padding) |
| 284 | { |
| 285 | paddingFront = 0; |
| 286 | paddingBack = 0; |
| 287 | if (padding == tflite::Padding_SAME) |
| 288 | { |
| 289 | uint32_t outputSize = (inputSize + stride - 1) / stride; |
| 290 | uint32_t temp = (outputSize - 1) * stride + filterSize; |
| 291 | if (temp > inputSize) |
| 292 | { |
| 293 | paddingFront = (temp - inputSize) / 2; |
| 294 | paddingBack = (temp - inputSize) - paddingFront; |
| 295 | } |
| 296 | } |
| 297 | } |
| 298 | |
| 299 | armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr) |
| 300 | { |
| 301 | armnn::DataType type; |
| 302 | CHECK_TENSOR_PTR(tensorPtr); |
| 303 | |
| 304 | switch (tensorPtr->type) |
| 305 | { |
| 306 | case tflite::TensorType_UINT8: |
| 307 | type = armnn::DataType::QuantisedAsymm8; |
| 308 | break; |
| 309 | case tflite::TensorType_FLOAT32: |
| 310 | type = armnn::DataType::Float32; |
| 311 | break; |
| 312 | case tflite::TensorType_INT32: |
| 313 | type = armnn::DataType::Signed32; |
| 314 | break; |
| 315 | |
| 316 | default: |
| 317 | { |
| 318 | CheckLocation location = CHECK_LOCATION(); |
| 319 | throw ParseException( |
| 320 | boost::str( |
| 321 | boost::format("Unsupported data type %1% = %2% for tensor: %3%. %4%") % |
| 322 | tensorPtr->type % |
| 323 | tflite::EnumNameTensorType(tensorPtr->type) % |
| 324 | tensorPtr->name % |
| 325 | location.AsString())); |
| 326 | } |
| 327 | } |
| 328 | |
| 329 | float quantizationScale = 0.0f; |
| 330 | int32_t quantizationOffset = 0; |
| 331 | |
| 332 | if (tensorPtr->quantization.get()) |
| 333 | { |
| 334 | CHECK_VALID_SIZE(tensorPtr->quantization->scale.size(), 0, 1); |
| 335 | CHECK_VALID_SIZE(tensorPtr->quantization->zero_point.size(), 0, 1); |
| 336 | |
| 337 | if (tensorPtr->quantization->scale.size() == 1) |
| 338 | { |
| 339 | quantizationScale = tensorPtr->quantization->scale[0]; |
| 340 | } |
| 341 | if (tensorPtr->quantization->zero_point.size() == 1) |
| 342 | { |
| 343 | // NOTE: we lose precision here when converting from 64 bit to 32 |
| 344 | // but this is what we support at the monent in ArmNN |
| 345 | quantizationOffset = static_cast<int32_t>(tensorPtr->quantization->zero_point[0]); |
| 346 | } |
| 347 | } |
| 348 | |
| 349 | auto const & dimensions = AsUnsignedVector(tensorPtr->shape); |
| 350 | |
| 351 | // two statements (on purpose) for easier debugging: |
| 352 | armnn::TensorInfo result(static_cast<unsigned int>(tensorPtr->shape.size()), |
| 353 | dimensions.data(), |
| 354 | type, |
| 355 | quantizationScale, |
| 356 | quantizationOffset); |
| 357 | return result; |
| 358 | } |
| 359 | |
| 360 | template<typename T> |
| 361 | std::pair<armnn::ConstTensor, std::unique_ptr<T[]>> |
| 362 | CreateConstTensorImpl(TfLiteParser::BufferRawPtr bufferPtr, |
| 363 | TfLiteParser::TensorRawPtr tensorPtr, |
| 364 | armnn::TensorInfo & tensorInfo, |
| 365 | bool convertFromTfToArmnnFormat) |
| 366 | { |
| 367 | BOOST_ASSERT_MSG(tensorPtr != nullptr, "tensorPtr is null"); |
| 368 | BOOST_ASSERT_MSG(bufferPtr != nullptr, |
| 369 | boost::str( |
| 370 | boost::format("Buffer for buffer:%1% is null") % tensorPtr->buffer).c_str()); |
| 371 | |
| 372 | std::unique_ptr<T[]> data(new T[tensorInfo.GetNumElements()]); |
| 373 | |
| 374 | if (convertFromTfToArmnnFormat) |
| 375 | { |
| 376 | tensorInfo = armnnUtils::Permuted(tensorInfo, NHWCToArmNN); |
| 377 | armnnUtils::Permute(tensorInfo.GetShape(), |
| 378 | NHWCToArmNN, |
| 379 | reinterpret_cast<const T *>(bufferPtr->data.data()), |
| 380 | data.get()); |
| 381 | } |
| 382 | else |
| 383 | { |
| 384 | ::memcpy(data.get(), bufferPtr->data.data(), tensorInfo.GetNumBytes()); |
| 385 | } |
| 386 | return std::make_pair(ConstTensor(tensorInfo, data.get()), std::move(data)); |
| 387 | } |
| 388 | |
| 389 | IConnectableLayer* SwizzleIn(INetwork& network, |
| 390 | IConnectableLayer* layer, |
| 391 | unsigned int inputSlotIndex, |
| 392 | const TensorInfo & inputInfo) |
| 393 | { |
| 394 | BOOST_ASSERT(layer != nullptr); |
| 395 | // Add swizzle layer |
| 396 | std::stringstream name; |
| 397 | name << "swizzle_for-" << layer->GetName() << ":in" << inputSlotIndex; |
| 398 | IConnectableLayer* const swizzleLayer = network.AddPermuteLayer(NHWCToArmNN, name.str().c_str()); |
| 399 | // Set swizzled output shape |
| 400 | const TensorInfo swizzleOutInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN); |
| 401 | swizzleLayer->GetOutputSlot(0).SetTensorInfo(swizzleOutInfo); |
| 402 | // Connect the swizzle layer to the actual layer |
| 403 | swizzleLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(inputSlotIndex)); |
| 404 | |
| 405 | return swizzleLayer; |
| 406 | } |
| 407 | |
| 408 | IConnectableLayer* DeswizzleOut(INetwork& network, |
| 409 | IConnectableLayer* layer, |
| 410 | unsigned int outputSlotIndex, |
| 411 | const TensorInfo & outputInfo) |
| 412 | { |
| 413 | BOOST_ASSERT(layer != nullptr); |
| 414 | // Add deswizzle layer |
| 415 | std::stringstream name; |
| 416 | name << "deswizzle_for-" << layer->GetName() << ":out" << outputSlotIndex; |
| 417 | IConnectableLayer* const deswizzleLayer = network.AddPermuteLayer(ArmNNToNHWC, name.str().c_str()); |
| 418 | // Set deswizzled output shape |
| 419 | deswizzleLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 420 | // Set original layer output shape |
| 421 | const TensorInfo deswizzleOutInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN); |
| 422 | layer->GetOutputSlot(outputSlotIndex).SetTensorInfo(deswizzleOutInfo); |
| 423 | // Connect the actual layer to the deswizzle layer |
| 424 | layer->GetOutputSlot(outputSlotIndex).Connect(deswizzleLayer->GetInputSlot(0)); |
| 425 | |
| 426 | return deswizzleLayer; |
| 427 | } |
| 428 | |
| 429 | std::pair<IConnectableLayer*, IConnectableLayer*> SwizzleInDeswizzleOut(INetwork& network, |
| 430 | IConnectableLayer* layer, |
| 431 | unsigned int inputSlotIndex, |
| 432 | const TensorInfo & inputInfo, |
| 433 | unsigned int outputSlotIndex, |
| 434 | const TensorInfo & outputInfo) |
| 435 | { |
| 436 | IConnectableLayer* const swizzleLayer = SwizzleIn(network, layer, inputSlotIndex, inputInfo); |
| 437 | IConnectableLayer* const deswizzleLayer = DeswizzleOut(network, layer, outputSlotIndex, outputInfo); |
| 438 | return std::make_pair(swizzleLayer, deswizzleLayer); |
| 439 | } |
| 440 | |
| 441 | armnn::LayerBindingId GenerateLayerBindingId(size_t subgraphIndex, size_t tensorIndex) |
| 442 | { |
| 443 | // generate the binding id by shifting the tensor id by 8 bit |
| 444 | // and add the subgraph id, which allows 256 subgraphs |
| 445 | return static_cast<armnn::LayerBindingId>((tensorIndex<<8)+subgraphIndex); |
| 446 | } |
| 447 | |
| 448 | } // <anonymous> |
| 449 | |
| 450 | TfLiteParser::TfLiteParser() |
| 451 | : m_Network(nullptr, nullptr) |
| 452 | , m_ParserFunctions(tflite::BuiltinOperator_MAX+1, &TfLiteParser::ParseUnsupportedOperator) |
| 453 | { |
| 454 | // register supported operators |
| 455 | m_ParserFunctions[tflite::BuiltinOperator_AVERAGE_POOL_2D] = &TfLiteParser::ParseAveragePool2D; |
Sadik Armagan | 479045b | 2018-10-01 11:51:37 +0100 | [diff] [blame] | 456 | m_ParserFunctions[tflite::BuiltinOperator_CONCATENATION] = &TfLiteParser::ParseConcatenation; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 457 | m_ParserFunctions[tflite::BuiltinOperator_CONV_2D] = &TfLiteParser::ParseConv2D; |
| 458 | m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = &TfLiteParser::ParseDepthwiseConv2D; |
Sadik Armagan | 8853c1f | 2018-10-22 09:04:18 +0100 | [diff] [blame] | 459 | m_ParserFunctions[tflite::BuiltinOperator_FULLY_CONNECTED] = &TfLiteParser::ParseFullyConnected; |
Nattapat Chaimanowong | b66504b | 2018-10-17 15:19:14 +0100 | [diff] [blame] | 460 | m_ParserFunctions[tflite::BuiltinOperator_MAX_POOL_2D] = &TfLiteParser::ParseMaxPool2D; |
Sadik Armagan | 58f3919 | 2018-09-17 14:14:39 +0100 | [diff] [blame] | 461 | m_ParserFunctions[tflite::BuiltinOperator_RELU] = &TfLiteParser::ParseRelu; |
| 462 | m_ParserFunctions[tflite::BuiltinOperator_RELU6] = &TfLiteParser::ParseRelu6; |
Sadik | b94967b | 2018-09-19 15:30:00 +0100 | [diff] [blame] | 463 | m_ParserFunctions[tflite::BuiltinOperator_RESHAPE] = &TfLiteParser::ParseReshape; |
Sadik Armagan | 479045b | 2018-10-01 11:51:37 +0100 | [diff] [blame] | 464 | m_ParserFunctions[tflite::BuiltinOperator_SOFTMAX] = &TfLiteParser::ParseSoftmax; |
| 465 | m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE] = &TfLiteParser::ParseSqueeze; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 466 | } |
| 467 | |
| 468 | void TfLiteParser::ResetParser() |
| 469 | { |
| 470 | m_Network = armnn::INetworkPtr(nullptr, nullptr); |
| 471 | m_Model = nullptr; |
| 472 | m_SubgraphConnections.clear(); |
| 473 | } |
| 474 | |
| 475 | INetworkPtr TfLiteParser::CreateNetworkFromBinaryFile(const char* graphFile) |
| 476 | { |
| 477 | ResetParser(); |
| 478 | m_Model = LoadModelFromFile(graphFile); |
| 479 | return CreateNetworkFromModel(); |
| 480 | } |
| 481 | |
| 482 | INetworkPtr TfLiteParser::CreateNetworkFromBinary(const std::vector<uint8_t> & binaryContent) |
| 483 | { |
| 484 | ResetParser(); |
| 485 | m_Model = LoadModelFromBinary(binaryContent.data(), binaryContent.size()); |
| 486 | return CreateNetworkFromModel(); |
| 487 | } |
| 488 | |
| 489 | INetworkPtr TfLiteParser::CreateNetworkFromModel() |
| 490 | { |
| 491 | m_Network = INetwork::Create(); |
| 492 | BOOST_ASSERT(m_Model.get() != nullptr); |
| 493 | |
| 494 | bool failedToCreate = false; |
| 495 | std::stringstream errors; |
| 496 | |
| 497 | if (m_Model->subgraphs.size() != 1) |
| 498 | { |
| 499 | throw ParseException( |
| 500 | boost::str( |
| 501 | boost::format("Current TfLite parser only supports 1 subgraph. Current one has: %1% %2%") % |
| 502 | m_Model->subgraphs.size() % |
| 503 | CHECK_LOCATION().AsString())); |
| 504 | } |
| 505 | |
| 506 | size_t subgraphIndex = 0; |
| 507 | for (SubGraphPtr const & subgraph : m_Model->subgraphs) |
| 508 | { |
| 509 | m_SubgraphConnections.emplace_back(subgraph->tensors.size()); |
| 510 | |
| 511 | size_t operatorIndex = 0; |
| 512 | for (OperatorPtr const & op : subgraph->operators) |
| 513 | { |
| 514 | try |
| 515 | { |
| 516 | if (op->custom_options.size() > 0) |
| 517 | { |
| 518 | throw ParseException( |
| 519 | boost::str( |
| 520 | boost::format("Custom options for op: %1% is not supported. " |
| 521 | "It has %2% bytes of custom options. %3%") % |
| 522 | op->opcode_index % |
| 523 | op->custom_options.size() % |
| 524 | CHECK_LOCATION().AsString())); |
| 525 | } |
| 526 | |
| 527 | auto const & opCodePtr = m_Model->operator_codes[op->opcode_index]; |
| 528 | auto builtinCode = opCodePtr->builtin_code; |
| 529 | |
| 530 | if (builtinCode > tflite::BuiltinOperator_MAX) |
| 531 | { |
| 532 | throw ParseException( |
| 533 | boost::str( |
| 534 | boost::format("Operator code %1% is out of range 0-%2%. " |
| 535 | "subgraph:%3% operator idx:%4%. %5%") % |
| 536 | builtinCode % |
| 537 | tflite::BuiltinOperator_MAX % |
| 538 | subgraphIndex % |
| 539 | operatorIndex % |
| 540 | CHECK_LOCATION().AsString())); |
| 541 | } |
| 542 | |
| 543 | // lookup and call the parser function |
| 544 | auto & parserFunction = m_ParserFunctions[builtinCode]; |
| 545 | (this->*parserFunction)(subgraphIndex, operatorIndex); |
| 546 | } |
| 547 | catch (const ParseException& e) |
| 548 | { |
| 549 | failedToCreate = true; |
| 550 | std::stringstream errorString; |
| 551 | |
| 552 | errorString << "Failed to parse operator #" << operatorIndex |
| 553 | << " within subgraph #" << subgraphIndex |
| 554 | << " error: " << e.what(); |
| 555 | BOOST_LOG_TRIVIAL(error) << errorString.str(); |
| 556 | |
| 557 | errors << errorString.str() << "\n"; |
| 558 | } |
| 559 | ++operatorIndex; |
| 560 | } |
| 561 | |
| 562 | SetupInputLayers(subgraphIndex); |
| 563 | SetupOutputLayers(subgraphIndex); |
| 564 | |
| 565 | ++subgraphIndex; |
| 566 | } |
| 567 | |
| 568 | if (failedToCreate) |
| 569 | { |
| 570 | // we can skip everything and let the outer exception handler deal with the error |
| 571 | throw ParseException(errors.str()); |
| 572 | } |
| 573 | |
| 574 | // establish the connections from the layer outputs to the inputs of the subsequent layers |
| 575 | for (size_t subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex) |
| 576 | { |
| 577 | for (size_t tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex) |
| 578 | { |
| 579 | if (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot != nullptr) |
| 580 | { |
| 581 | for (size_t inputSlotIdx = 0; |
| 582 | inputSlotIdx < m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size(); |
| 583 | ++inputSlotIdx) |
| 584 | { |
| 585 | m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot->Connect( |
| 586 | *(m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots[inputSlotIdx])); |
| 587 | } |
| 588 | } |
| 589 | } |
| 590 | } |
| 591 | |
| 592 | return std::move(m_Network); |
| 593 | } |
| 594 | |
| 595 | void TfLiteParser::RegisterProducerOfTensor(size_t subgraphIndex, |
| 596 | size_t tensorIndex, |
| 597 | armnn::IOutputSlot* slot) |
| 598 | { |
| 599 | CHECK_TENSOR(m_Model, subgraphIndex, tensorIndex); |
| 600 | BOOST_ASSERT(m_SubgraphConnections.size() > subgraphIndex); |
| 601 | BOOST_ASSERT(m_SubgraphConnections[subgraphIndex].size() > tensorIndex); |
| 602 | |
| 603 | TensorSlots & tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex]; |
| 604 | |
| 605 | // assuming there is only one producer for that tensor |
| 606 | if (tensorSlots.outputSlot != nullptr) |
| 607 | { |
| 608 | throw ParseException(boost::str( |
| 609 | boost::format("Another layer has already registered itself as the producer of " |
| 610 | "subgraph:%1% tensor:%2% %3%") % |
| 611 | subgraphIndex % |
| 612 | tensorIndex % |
| 613 | CHECK_LOCATION().AsString())); |
| 614 | } |
| 615 | |
| 616 | tensorSlots.outputSlot = slot; |
| 617 | } |
| 618 | |
| 619 | void TfLiteParser::RegisterConsumerOfTensor(size_t subgraphIndex, |
| 620 | size_t tensorIndex, |
| 621 | armnn::IInputSlot* slot) |
| 622 | { |
| 623 | CHECK_TENSOR(m_Model, subgraphIndex, tensorIndex); |
| 624 | BOOST_ASSERT(m_SubgraphConnections.size() > subgraphIndex); |
| 625 | BOOST_ASSERT(m_SubgraphConnections[subgraphIndex].size() > tensorIndex); |
| 626 | |
| 627 | TensorSlots & tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex]; |
| 628 | tensorSlots.inputSlots.push_back(slot); |
| 629 | } |
| 630 | |
| 631 | void TfLiteParser::ParseUnsupportedOperator(size_t subgraphIndex, size_t operatorIndex) |
| 632 | { |
| 633 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 634 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 635 | // |
| 636 | auto opcodeIndex = operatorPtr->opcode_index; |
| 637 | auto opcode = m_Model->operator_codes[opcodeIndex]->builtin_code; |
| 638 | |
| 639 | throw ParseException( |
| 640 | boost::str( |
| 641 | boost::format("Operator not supported. " |
| 642 | "subgraph:%1% operator:%2% " |
| 643 | "opcode_index:%3% opcode:%4% / %5% %6%") % |
| 644 | subgraphIndex % |
| 645 | operatorIndex % |
| 646 | opcodeIndex % |
| 647 | opcode % |
| 648 | tflite::EnumNameBuiltinOperator(opcode) % |
| 649 | CHECK_LOCATION().AsString())); |
| 650 | } |
| 651 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 652 | void TfLiteParser::ParseConv2D(size_t subgraphIndex, size_t operatorIndex) |
| 653 | { |
| 654 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 655 | |
| 656 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 657 | const auto * options = operatorPtr->builtin_options.AsConv2DOptions(); |
| 658 | |
| 659 | CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| 660 | |
| 661 | Convolution2dDescriptor desc; |
| 662 | desc.m_BiasEnabled = false; |
| 663 | desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w); |
| 664 | desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h); |
| 665 | |
| 666 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 667 | CHECK_VALID_SIZE(inputs.size(), 2, 3); |
| 668 | |
| 669 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 670 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 671 | |
| 672 | armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| 673 | armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]); |
| 674 | |
| 675 | // assuming input is NHWC |
| 676 | unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| 677 | unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| 678 | |
| 679 | // assuming the filter is OHWI : Output, H, W, Input |
| 680 | // which is essentially the same as NHWC |
| 681 | unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| 682 | unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| 683 | |
| 684 | CalcPadding(inputHeight, filterHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, options->padding); |
| 685 | CalcPadding(inputWidth, filterWidth, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, options->padding); |
| 686 | |
| 687 | auto filterTensorAndData = CreateConstTensor(inputs[1], filterTensorInfo, true); |
| 688 | armnn::IConnectableLayer* layer; |
| 689 | |
| 690 | auto layerName = boost::str(boost::format("Conv2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| 691 | |
| 692 | if (inputs.size() == 3) |
| 693 | { |
| 694 | desc.m_BiasEnabled = true; |
| 695 | armnn::TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); |
| 696 | auto biasTensorAndData = CreateConstTensor(inputs[2], biasTensorInfo, false); |
| 697 | layer = m_Network->AddConvolution2dLayer(desc, |
| 698 | filterTensorAndData.first, |
| 699 | biasTensorAndData.first, |
| 700 | layerName.c_str()); |
| 701 | } |
| 702 | else |
| 703 | { |
| 704 | layer = m_Network->AddConvolution2dLayer(desc, |
| 705 | filterTensorAndData.first, |
| 706 | layerName.c_str()); |
| 707 | } |
| 708 | |
| 709 | BOOST_ASSERT(layer != nullptr); |
| 710 | |
| 711 | // add permute layers to swizzle the input and deswizzle the output |
| 712 | armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 713 | std::pair<IConnectableLayer*, IConnectableLayer*> permuteLayers = |
| 714 | SwizzleInDeswizzleOut(*m_Network, layer, 0, inputTensorInfo, 0, outputTensorInfo); |
| 715 | |
| 716 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 717 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 718 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 719 | RegisterInputSlots(subgraphIndex, operatorIndex, permuteLayers.first, {inputTensorIndexes[0]}); |
| 720 | |
| 721 | // we need to add the activation layer and fortunately we don't need to care about the data layout |
| 722 | // beause the activation function is element-wise, so it is OK to have the activation after the trailing |
| 723 | // swizzle layer |
Sadik Armagan | 58f3919 | 2018-09-17 14:14:39 +0100 | [diff] [blame] | 724 | layer = AddFusedActivationLayer(permuteLayers.second, 0, options->fused_activation_function); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 725 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 726 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 727 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 728 | } |
| 729 | |
| 730 | void TfLiteParser::ParseDepthwiseConv2D(size_t subgraphIndex, size_t operatorIndex) |
| 731 | { |
| 732 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 733 | |
| 734 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 735 | const auto * options = operatorPtr->builtin_options.AsDepthwiseConv2DOptions(); |
| 736 | |
| 737 | CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| 738 | |
| 739 | DepthwiseConvolution2dDescriptor desc; |
| 740 | desc.m_BiasEnabled = false; |
| 741 | desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w); |
| 742 | desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h); |
| 743 | // ACL only supports a depth (channel) multiplier of 1, it is not currently stored in the descriptor |
| 744 | CHECK_VALID_SIZE(CHECKED_NON_NEGATIVE(options->depth_multiplier), 1); |
| 745 | |
| 746 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 747 | CHECK_VALID_SIZE(inputs.size(), 2, 3); |
| 748 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 749 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 750 | |
| 751 | armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| 752 | armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]); |
| 753 | |
| 754 | // assuming input is NHWC |
| 755 | unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| 756 | unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| 757 | // assuming the filter is OHWI : Output, H, W, Input |
| 758 | unsigned int filterHeight = filterTensorInfo.GetShape()[1]; |
| 759 | unsigned int filterWidth = filterTensorInfo.GetShape()[2]; |
| 760 | |
| 761 | CalcPadding(inputHeight, filterHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, options->padding); |
| 762 | CalcPadding(inputWidth, filterWidth, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, options->padding); |
| 763 | |
| 764 | auto filterTensorAndData = CreateConstTensor(inputs[1], filterTensorInfo, true); |
| 765 | armnn::IConnectableLayer* layer; |
| 766 | auto layerName = boost::str(boost::format("DepthwiseConv2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| 767 | |
| 768 | if (inputs.size() == 3) |
| 769 | { |
| 770 | desc.m_BiasEnabled = true; |
| 771 | TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); |
| 772 | auto biasTensorAndData = CreateConstTensor(inputs[2], biasTensorInfo, false); |
| 773 | layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| 774 | filterTensorAndData.first, |
| 775 | biasTensorAndData.first, |
| 776 | layerName.c_str()); |
| 777 | } |
| 778 | else |
| 779 | { |
| 780 | layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| 781 | filterTensorAndData.first, |
| 782 | layerName.c_str()); |
| 783 | } |
| 784 | BOOST_ASSERT(layer != nullptr); |
| 785 | |
| 786 | // add permute layers to swizzle the input and deswizzle the output |
| 787 | armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 788 | std::pair<IConnectableLayer*, IConnectableLayer*> permuteLayers = |
| 789 | SwizzleInDeswizzleOut(*m_Network, layer, 0, inputTensorInfo, 0, outputTensorInfo); |
| 790 | |
| 791 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 792 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 793 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 794 | RegisterInputSlots(subgraphIndex, operatorIndex, permuteLayers.first, {inputTensorIndexes[0]}); |
| 795 | |
| 796 | // we need to add the activation layer and fortunately we don't need to care about the data layout |
| 797 | // beause the activation function is element-wise, so it is OK to have the activation after the trailing |
| 798 | // swizzle layer |
Sadik Armagan | 58f3919 | 2018-09-17 14:14:39 +0100 | [diff] [blame] | 799 | layer = AddFusedActivationLayer(permuteLayers.second, 0, options->fused_activation_function); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 800 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 801 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 802 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 803 | } |
| 804 | |
Nattapat Chaimanowong | b66504b | 2018-10-17 15:19:14 +0100 | [diff] [blame] | 805 | void TfLiteParser::ParseAveragePool2D(size_t subgraphIndex, size_t operatorIndex) |
| 806 | { |
| 807 | ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Average); |
| 808 | } |
| 809 | |
| 810 | void TfLiteParser::ParseMaxPool2D(size_t subgraphIndex, size_t operatorIndex) |
| 811 | { |
| 812 | ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Max); |
| 813 | } |
| 814 | |
| 815 | void TfLiteParser::ParsePool(size_t subgraphIndex, |
| 816 | size_t operatorIndex, |
| 817 | PoolingAlgorithm algorithm) |
| 818 | { |
| 819 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 820 | |
| 821 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 822 | const auto * options = operatorPtr->builtin_options.AsPool2DOptions(); |
| 823 | |
| 824 | CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| 825 | |
| 826 | std::string layerName; |
| 827 | |
| 828 | switch (algorithm) |
| 829 | { |
| 830 | case PoolingAlgorithm::Average: |
| 831 | layerName = |
| 832 | boost::str(boost::format("AveragePool2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| 833 | break; |
| 834 | case PoolingAlgorithm::Max: |
| 835 | layerName = |
| 836 | boost::str(boost::format("MaxPool2D:%1%:%2%") % subgraphIndex % operatorIndex); |
| 837 | break; |
| 838 | default: |
| 839 | BOOST_ASSERT_MSG(false, "Unsupported Pooling Algorithm"); |
| 840 | } |
| 841 | |
| 842 | Pooling2dDescriptor desc; |
| 843 | |
| 844 | desc.m_PoolType = algorithm; |
| 845 | desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w); |
| 846 | desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h); |
| 847 | desc.m_PoolWidth = CHECKED_NON_NEGATIVE(options->filter_width); |
| 848 | desc.m_PoolHeight = CHECKED_NON_NEGATIVE(options->filter_height); |
| 849 | desc.m_PaddingMethod = PaddingMethod::Exclude; |
| 850 | desc.m_OutputShapeRounding = OutputShapeRounding::Floor; |
| 851 | |
| 852 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 853 | CHECK_VALID_SIZE(inputs.size(), 1); |
| 854 | armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| 855 | |
| 856 | // assuming input is NHWC |
| 857 | unsigned int inputHeight = inputTensorInfo.GetShape()[1]; |
| 858 | unsigned int inputWidth = inputTensorInfo.GetShape()[2]; |
| 859 | |
| 860 | CalcPadding(inputHeight, desc.m_PoolHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, options->padding); |
| 861 | CalcPadding(inputWidth, desc.m_PoolWidth, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, options->padding); |
| 862 | |
| 863 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 864 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 865 | armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 866 | |
| 867 | IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, layerName.c_str()); |
| 868 | |
| 869 | BOOST_ASSERT(layer != nullptr); |
| 870 | |
| 871 | // add permute layers to swizzle the input and deswizzle the output |
| 872 | std::pair<IConnectableLayer*, IConnectableLayer*> permuteLayers = |
| 873 | SwizzleInDeswizzleOut(*m_Network, layer, 0, inputTensorInfo, 0, outputTensorInfo); |
| 874 | |
| 875 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 876 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 877 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 878 | RegisterInputSlots(subgraphIndex, operatorIndex, permuteLayers.first, {inputTensorIndexes[0]}); |
| 879 | |
| 880 | // we need to add the activation layer and fortunately we don't need to care about the data layout |
| 881 | // beause the activation function is element-wise, so it is OK to have the activation after the trailing |
| 882 | // swizzle layer |
| 883 | layer = AddFusedActivationLayer(permuteLayers.second, 0, options->fused_activation_function); |
| 884 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 885 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 886 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 887 | } |
| 888 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 889 | void TfLiteParser::ParseSoftmax(size_t subgraphIndex, size_t operatorIndex) |
| 890 | { |
| 891 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 892 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 893 | const auto * options = operatorPtr->builtin_options.AsSoftmaxOptions(); |
| 894 | |
| 895 | SoftmaxDescriptor desc; |
| 896 | desc.m_Beta = options->beta; |
| 897 | |
| 898 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 899 | CHECK_VALID_SIZE(inputs.size(), 1); |
| 900 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 901 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 902 | |
| 903 | auto layerName = boost::str(boost::format("Softmax:%1%:%2%") % subgraphIndex % operatorIndex); |
| 904 | IConnectableLayer* const layer = m_Network->AddSoftmaxLayer(desc, layerName.c_str()); |
| 905 | |
| 906 | armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 907 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 908 | |
| 909 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 910 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 911 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 912 | RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| 913 | |
| 914 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 915 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 916 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 917 | } |
| 918 | |
| 919 | armnn::TensorInfo TfLiteParser::OutputShapeOfSqueeze(const std::vector<uint32_t> & squeezeDimsIn, |
| 920 | const armnn::TensorInfo & inputTensorInfo) |
| 921 | { |
| 922 | CHECK_VALID_SIZE(squeezeDimsIn.size(), 0, 1, 2, 3, 4); |
| 923 | std::vector<uint32_t> squeezeDims = squeezeDimsIn; |
| 924 | static const uint32_t dimensionSequence[] = { 0, 1, 2, 3 }; |
| 925 | |
| 926 | if (inputTensorInfo.GetNumDimensions() > 4) |
| 927 | { |
| 928 | std::stringstream ss; |
| 929 | ss << "Input tensor has unexpected number of dimensions:" << inputTensorInfo.GetNumDimensions() |
| 930 | << " shape:" << inputTensorInfo.GetShape() << " " |
| 931 | << CHECK_LOCATION().AsString(); |
| 932 | throw ParseException(ss.str()); |
| 933 | } |
| 934 | |
| 935 | if (squeezeDims.empty()) |
| 936 | { |
| 937 | squeezeDims.assign(dimensionSequence, |
| 938 | dimensionSequence+inputTensorInfo.GetNumDimensions()); |
| 939 | } |
| 940 | |
| 941 | std::vector<uint32_t> outputDims; |
| 942 | for(unsigned int i = 0; i < inputTensorInfo.GetNumDimensions(); i++) |
| 943 | { |
| 944 | bool skipSqueeze = (std::find(squeezeDims.begin(), squeezeDims.end(), i) == squeezeDims.end()); |
| 945 | auto currentDimension = inputTensorInfo.GetShape()[i]; |
| 946 | if (skipSqueeze || currentDimension != 1) |
| 947 | { |
| 948 | outputDims.push_back(currentDimension); |
| 949 | } |
| 950 | } |
| 951 | |
| 952 | if (outputDims.size() > 4) |
| 953 | { |
| 954 | std::stringstream ss; |
| 955 | ss << "Output tensor has unexpected number of dimensions:" << inputTensorInfo.GetNumDimensions() |
| 956 | << " shape:" << inputTensorInfo.GetShape() << " " |
| 957 | << CHECK_LOCATION().AsString(); |
| 958 | throw ParseException(ss.str()); |
| 959 | } |
| 960 | |
| 961 | TensorShape outShape = TensorShape(static_cast<unsigned int>(outputDims.size()), |
| 962 | outputDims.data()); |
| 963 | |
| 964 | // we need to preserve the tensor type and the quantization data as well |
| 965 | TensorInfo outTensorInfo = inputTensorInfo; |
| 966 | outTensorInfo.SetShape(outShape); |
| 967 | |
| 968 | return outTensorInfo; |
| 969 | } |
| 970 | |
| 971 | void TfLiteParser::ParseSqueeze(size_t subgraphIndex, size_t operatorIndex) |
| 972 | { |
| 973 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 974 | |
| 975 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 976 | CHECK_VALID_SIZE(inputs.size(), 1); |
| 977 | |
| 978 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 979 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 980 | |
| 981 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 982 | const auto * options = operatorPtr->builtin_options.AsSqueezeOptions(); |
| 983 | |
| 984 | armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| 985 | armnn::TensorInfo outputTensorInfo = |
| 986 | TfLiteParser::OutputShapeOfSqueeze(AsUnsignedVector(options->squeeze_dims), |
| 987 | inputTensorInfo); |
| 988 | |
| 989 | ReshapeDescriptor reshapeDesc; |
| 990 | reshapeDesc.m_TargetShape = outputTensorInfo.GetShape(); |
| 991 | |
| 992 | auto layerName = boost::str(boost::format("Squeeze:%1%:%2%") % subgraphIndex % operatorIndex); |
| 993 | IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
| 994 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 995 | |
| 996 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 997 | RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| 998 | |
| 999 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1000 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 1001 | } |
| 1002 | |
Sadik Armagan | 58f3919 | 2018-09-17 14:14:39 +0100 | [diff] [blame] | 1003 | void TfLiteParser::ParseRelu(size_t subgraphIndex, size_t operatorIndex) |
| 1004 | { |
| 1005 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 1006 | |
| 1007 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 1008 | boost::ignore_unused(operatorPtr); |
| 1009 | |
| 1010 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 1011 | CHECK_VALID_SIZE(inputs.size(), 1); |
| 1012 | |
| 1013 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 1014 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 1015 | |
| 1016 | auto layerName = str(boost::format("Activation:RELU:%1%:%2%") % subgraphIndex % operatorIndex); |
| 1017 | ActivationDescriptor activationDesc; |
| 1018 | activationDesc.m_Function = ActivationFunction::ReLu; |
| 1019 | IConnectableLayer* const layer = |
| 1020 | m_Network->AddActivationLayer(activationDesc, layerName.c_str()); |
| 1021 | |
| 1022 | TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 1023 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 1024 | |
| 1025 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1026 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1027 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1028 | RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| 1029 | |
| 1030 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1031 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1032 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 1033 | } |
| 1034 | |
| 1035 | void TfLiteParser::ParseRelu6(size_t subgraphIndex, size_t operatorIndex) |
| 1036 | { |
| 1037 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 1038 | |
| 1039 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 1040 | boost::ignore_unused(operatorPtr); |
| 1041 | |
| 1042 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 1043 | CHECK_VALID_SIZE(inputs.size(), 1); |
| 1044 | |
| 1045 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 1046 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 1047 | |
| 1048 | auto layerName = str(boost::format("Activation:RELU6:%1%:%2%") % subgraphIndex % operatorIndex); |
| 1049 | ActivationDescriptor activationDesc; |
| 1050 | activationDesc.m_Function = ActivationFunction::BoundedReLu; |
| 1051 | activationDesc.m_A = 6.0f; |
| 1052 | activationDesc.m_B = 0.0f; |
| 1053 | IConnectableLayer* const layer = |
| 1054 | m_Network->AddActivationLayer(activationDesc, layerName.c_str()); |
| 1055 | |
| 1056 | TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 1057 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 1058 | |
| 1059 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1060 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1061 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1062 | RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| 1063 | |
| 1064 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1065 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1066 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 1067 | } |
| 1068 | |
Sadik | b94967b | 2018-09-19 15:30:00 +0100 | [diff] [blame] | 1069 | armnn::TensorInfo TfLiteParser::OutputShapeOfReshape(const armnn::TensorInfo & inputTensorInfo, |
| 1070 | const std::vector<int32_t> & targetDimsIn) |
| 1071 | { |
| 1072 | std::vector<unsigned int> outputDims(targetDimsIn.begin(), targetDimsIn.end()); |
| 1073 | const auto stretchDim = std::find(targetDimsIn.begin(), targetDimsIn.end(), -1); |
| 1074 | |
| 1075 | if (stretchDim != targetDimsIn.end()) |
| 1076 | { |
| 1077 | if (std::find(std::next(stretchDim), targetDimsIn.end(), -1) != targetDimsIn.end()) |
| 1078 | { |
| 1079 | throw ParseException( |
| 1080 | boost::str( |
| 1081 | boost::format("At most one component of shape can be -1 %1%") % CHECK_LOCATION().AsString())); |
| 1082 | } |
| 1083 | |
| 1084 | auto targetNumElements = |
| 1085 | boost::numeric_cast<unsigned int>( |
| 1086 | std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>())); |
| 1087 | |
| 1088 | auto stretchIndex = static_cast<size_t>(std::distance(targetDimsIn.begin(), stretchDim)); |
| 1089 | outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements; |
| 1090 | } |
| 1091 | |
| 1092 | TensorShape outputShape = TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data()); |
| 1093 | |
| 1094 | TensorInfo reshapeInfo = inputTensorInfo; |
| 1095 | reshapeInfo.SetShape(outputShape); |
| 1096 | |
| 1097 | return reshapeInfo; |
| 1098 | } |
| 1099 | |
| 1100 | void TfLiteParser::ParseReshape(size_t subgraphIndex, size_t operatorIndex) |
| 1101 | { |
| 1102 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 1103 | |
| 1104 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 1105 | CHECK_VALID_SIZE(inputs.size(), 1); |
| 1106 | |
| 1107 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 1108 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 1109 | |
| 1110 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 1111 | const auto * options = operatorPtr->builtin_options.AsReshapeOptions(); |
| 1112 | |
| 1113 | armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); |
| 1114 | armnn::TensorInfo outputTensorInfo = |
| 1115 | TfLiteParser::OutputShapeOfReshape(inputTensorInfo, options->new_shape); |
| 1116 | |
| 1117 | ReshapeDescriptor reshapeDesc; |
| 1118 | reshapeDesc.m_TargetShape = outputTensorInfo.GetShape(); |
| 1119 | |
| 1120 | auto layerName = boost::str(boost::format("Reshape:%1%:%2%") % subgraphIndex % operatorIndex); |
| 1121 | IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
| 1122 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 1123 | |
| 1124 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1125 | RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| 1126 | |
| 1127 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1128 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 1129 | } |
| 1130 | |
Sadik Armagan | 479045b | 2018-10-01 11:51:37 +0100 | [diff] [blame] | 1131 | void TfLiteParser::ParseConcatenation(size_t subgraphIndex, size_t operatorIndex) |
| 1132 | { |
| 1133 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 1134 | |
| 1135 | const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 1136 | const auto * options = operatorPtr->builtin_options.AsConcatenationOptions(); |
| 1137 | |
| 1138 | CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| 1139 | |
| 1140 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 1141 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 1142 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 1143 | |
| 1144 | unsigned int numInputs = static_cast<unsigned int>(inputs.size()); |
| 1145 | unsigned int numConcatView = numInputs; |
| 1146 | |
| 1147 | OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatView), MaxNumOfTensorDimensions); |
| 1148 | std::vector<unsigned int>mergeDimSizes(MaxNumOfTensorDimensions, 0u); |
| 1149 | |
| 1150 | unsigned int mergeDim = 0; |
| 1151 | |
| 1152 | // This concatDim indicates the data format: 3 is the NHWC, 1 is the NCHW. |
| 1153 | // axis could also be negative numbers. Negative axis are interpreted as counting from the end of the rank, |
| 1154 | // i.e., axis + rank(values)-th dimension. |
| 1155 | int32_t inputRank = static_cast<int32_t>(ToTensorInfo(inputs[0]).GetNumDimensions()); |
| 1156 | const unsigned int concatDimInput = static_cast<unsigned int>((inputRank + options->axis) % inputRank); |
| 1157 | |
| 1158 | // ArmNN supports concatenation along the channel dimension for data formats NHWC and NCHW. |
| 1159 | if (concatDimInput == 0 || concatDimInput == 2) |
| 1160 | { |
| 1161 | throw ParseException( |
| 1162 | boost::str( |
| 1163 | boost::format( |
| 1164 | "Dimension %1% for concatenation is not supported by Armnn. " |
| 1165 | "Node %2%") |
| 1166 | % concatDimInput |
| 1167 | % CHECK_LOCATION().AsString())); |
| 1168 | } |
| 1169 | |
| 1170 | for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) |
| 1171 | { |
| 1172 | TensorInfo inputTensorInfo = ToTensorInfo(inputs[viewIndex]); |
| 1173 | |
| 1174 | // process the input tensor info |
| 1175 | armnnUtils::ProcessConcatInputTensorInfo(inputTensorInfo, concatDescriptor, |
| 1176 | concatDimInput, viewIndex, mergeDimSizes, mergeDim); |
| 1177 | } |
| 1178 | |
| 1179 | auto layerName = boost::str(boost::format("Concatenation:%1%:%2%") % subgraphIndex % operatorIndex); |
| 1180 | IConnectableLayer* layer = m_Network->AddMergerLayer(concatDescriptor, layerName.c_str()); |
| 1181 | |
| 1182 | BOOST_ASSERT(layer != nullptr); |
| 1183 | |
| 1184 | armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 1185 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1186 | if (concatDimInput == 3) |
| 1187 | { |
| 1188 | // Adding Fused Activation Layer after this moment.... |
| 1189 | for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) |
| 1190 | { |
| 1191 | // add permute layers to swizzle the inputs |
| 1192 | armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[viewIndex]); |
| 1193 | IConnectableLayer* const swizzleLayer = SwizzleIn(*m_Network, layer, viewIndex, inputTensorInfo); |
| 1194 | |
| 1195 | BOOST_ASSERT(swizzleLayer != nullptr); |
| 1196 | |
| 1197 | // register the input connection slots for the layer |
| 1198 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1199 | RegisterInputSlots(subgraphIndex, operatorIndex, swizzleLayer, {inputTensorIndexes[viewIndex]}); |
| 1200 | } |
| 1201 | |
| 1202 | // add permute layer to deswizzle the output |
| 1203 | IConnectableLayer* const deswizzleLayer = DeswizzleOut(*m_Network, layer, 0, outputTensorInfo); |
| 1204 | |
| 1205 | // add fused activation layer after the trailing swizzle layer |
| 1206 | layer = AddFusedActivationLayer(deswizzleLayer, 0, options->fused_activation_function); |
| 1207 | } |
| 1208 | else |
| 1209 | { |
| 1210 | // set the layer output tensor info |
| 1211 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 1212 | |
| 1213 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1214 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1215 | RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes}); |
| 1216 | } |
| 1217 | |
| 1218 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1219 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1220 | RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); |
| 1221 | } |
| 1222 | |
Sadik Armagan | 8853c1f | 2018-10-22 09:04:18 +0100 | [diff] [blame] | 1223 | void TfLiteParser::ParseFullyConnected(size_t subgraphIndex, size_t operatorIndex) |
| 1224 | { |
| 1225 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 1226 | |
| 1227 | const auto & operatorRfr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; |
| 1228 | const auto options = operatorRfr->builtin_options.AsFullyConnectedOptions(); |
| 1229 | |
| 1230 | CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex); |
| 1231 | |
| 1232 | FullyConnectedDescriptor desc; |
| 1233 | desc.m_BiasEnabled = false; |
| 1234 | |
| 1235 | auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); |
| 1236 | auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); |
| 1237 | CHECK_VALID_SIZE(outputs.size(), 1); |
| 1238 | |
| 1239 | armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]); |
| 1240 | |
| 1241 | // Fully Connected Layer accepts two dimensional weights input |
| 1242 | int32_t weightsDimension = static_cast<int32_t>(filterTensorInfo.GetNumDimensions()); |
| 1243 | if (weightsDimension != 2) |
| 1244 | { |
| 1245 | throw ParseException( |
| 1246 | boost::str( |
| 1247 | boost::format( |
| 1248 | "Dimension %1% for Fully Connected weights is not supported by Armnn. " |
| 1249 | "Node %2%") |
| 1250 | % weightsDimension |
| 1251 | % CHECK_LOCATION().AsString())); |
| 1252 | } |
| 1253 | |
| 1254 | auto filterTensorAndData = CreateConstTensor(inputs[1], filterTensorInfo, false); |
| 1255 | armnn::IConnectableLayer* layer; |
| 1256 | auto layerName = boost::str(boost::format("FullyConnected:%1%:%2%") % subgraphIndex % operatorIndex); |
| 1257 | |
| 1258 | if (inputs.size() == 3) |
| 1259 | { |
| 1260 | desc.m_BiasEnabled = true; |
| 1261 | TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); |
| 1262 | auto biasTensorAndData = CreateConstTensor(inputs[2], biasTensorInfo, false); |
| 1263 | layer = m_Network->AddFullyConnectedLayer(desc, |
| 1264 | filterTensorAndData.first, |
| 1265 | biasTensorAndData.first, |
| 1266 | layerName.c_str()); |
| 1267 | } |
| 1268 | else |
| 1269 | { |
| 1270 | layer = m_Network->AddFullyConnectedLayer(desc, |
| 1271 | filterTensorAndData.first, |
| 1272 | layerName.c_str()); |
| 1273 | } |
| 1274 | BOOST_ASSERT(layer != nullptr); |
| 1275 | |
| 1276 | armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); |
| 1277 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 1278 | |
| 1279 | // register the input connection slot for the layer |
| 1280 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1281 | auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1282 | RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); |
| 1283 | |
| 1284 | // we need to add the activation layer and fortunately we don't need to care about the data layout |
| 1285 | armnn::IConnectableLayer* fusedActivationLayer = AddFusedActivationLayer(layer, 0, |
| 1286 | options->fused_activation_function); |
| 1287 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1288 | auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); |
| 1289 | RegisterOutputSlots(subgraphIndex, operatorIndex, fusedActivationLayer, {outputTensorIndexes[0]}); |
| 1290 | } |
| 1291 | |
Sadik Armagan | 58f3919 | 2018-09-17 14:14:39 +0100 | [diff] [blame] | 1292 | armnn::IConnectableLayer* TfLiteParser::AddFusedActivationLayer(armnn::IConnectableLayer* prevLayer, |
| 1293 | unsigned int outputSlot, |
| 1294 | tflite::ActivationFunctionType activationType) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1295 | { |
| 1296 | ActivationDescriptor activationDesc; |
| 1297 | std::string layerName = prevLayer->GetName(); |
| 1298 | |
| 1299 | switch(activationType) |
| 1300 | { |
| 1301 | case tflite::ActivationFunctionType_NONE: |
| 1302 | { |
| 1303 | // this is a no-op: return previous layer |
| 1304 | return prevLayer; |
| 1305 | } |
| 1306 | case tflite::ActivationFunctionType_RELU: |
| 1307 | { |
| 1308 | activationDesc.m_Function = ActivationFunction::ReLu; |
| 1309 | layerName += ":RELU"; |
| 1310 | break; |
| 1311 | } |
| 1312 | case tflite::ActivationFunctionType_RELU6: |
| 1313 | { |
| 1314 | activationDesc.m_Function = ActivationFunction::BoundedReLu; |
| 1315 | activationDesc.m_A = 6.0f; |
| 1316 | activationDesc.m_B = 0.0f; |
| 1317 | layerName += ":RELU6"; |
| 1318 | break; |
| 1319 | } |
| 1320 | case tflite::ActivationFunctionType_TANH: |
| 1321 | { |
| 1322 | activationDesc.m_Function = ActivationFunction::TanH; |
| 1323 | activationDesc.m_A = 1.0f; |
| 1324 | activationDesc.m_B = 1.0f; |
| 1325 | layerName += ":TANH"; |
| 1326 | break; |
| 1327 | } |
| 1328 | |
| 1329 | // I only put these here as a reminder what others we could support |
| 1330 | case tflite::ActivationFunctionType_RELU_N1_TO_1: |
| 1331 | case tflite::ActivationFunctionType_SIGN_BIT: |
| 1332 | default: |
| 1333 | { |
| 1334 | throw ParseException( |
| 1335 | boost::str( |
| 1336 | boost::format("TfLite parser doesn't suppport fused activation: " |
| 1337 | "%1%/%2% %3% ") % |
| 1338 | activationType % |
| 1339 | tflite::EnumNameActivationFunctionType(activationType) % |
| 1340 | CHECK_LOCATION().AsString())); |
| 1341 | |
| 1342 | } |
| 1343 | } |
| 1344 | |
| 1345 | IConnectableLayer* activationLayer = |
| 1346 | m_Network->AddActivationLayer(activationDesc, layerName.c_str()); |
| 1347 | |
| 1348 | auto & prevOutputSlot = prevLayer->GetOutputSlot(outputSlot); |
| 1349 | prevOutputSlot.Connect(activationLayer->GetInputSlot(0)); |
| 1350 | activationLayer->GetOutputSlot(0).SetTensorInfo(prevOutputSlot.GetTensorInfo()); |
| 1351 | return activationLayer; |
| 1352 | } |
| 1353 | |
| 1354 | TfLiteParser::ModelPtr TfLiteParser::LoadModelFromFile(const char * fileName) |
| 1355 | { |
| 1356 | if (fileName == nullptr) |
| 1357 | { |
| 1358 | throw InvalidArgumentException(boost::str(boost::format("Invalid (null) file name %1%") % |
| 1359 | CHECK_LOCATION().AsString())); |
| 1360 | } |
| 1361 | boost::system::error_code errorCode; |
| 1362 | boost::filesystem::path pathToFile(fileName); |
| 1363 | if (!boost::filesystem::exists(pathToFile, errorCode)) |
| 1364 | { |
| 1365 | throw FileNotFoundException(boost::str(boost::format("Cannot find the file (%1%) errorCode: %2% %3%") % |
| 1366 | fileName % |
| 1367 | errorCode % |
| 1368 | CHECK_LOCATION().AsString())); |
| 1369 | } |
| 1370 | std::ifstream file(fileName, std::ios::binary); |
| 1371 | std::string fileContent((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>()); |
| 1372 | return LoadModelFromBinary(reinterpret_cast<const uint8_t *>(fileContent.c_str()), |
| 1373 | fileContent.size()); |
| 1374 | } |
| 1375 | |
| 1376 | TfLiteParser::ModelPtr TfLiteParser::LoadModelFromBinary(const uint8_t * binaryContent, size_t len) |
| 1377 | { |
| 1378 | if (binaryContent == nullptr) |
| 1379 | { |
| 1380 | throw InvalidArgumentException(boost::str(boost::format("Invalid (null) binary content %1%") % |
| 1381 | CHECK_LOCATION().AsString())); |
| 1382 | } |
| 1383 | flatbuffers::Verifier verifier(binaryContent, len); |
| 1384 | if (verifier.VerifyBuffer<tflite::Model>() == false) |
| 1385 | { |
| 1386 | throw ParseException( |
| 1387 | boost::str(boost::format("Buffer doesn't conform to the expected Tensorflow Lite " |
| 1388 | "flatbuffers format. size:%1% %2%") % |
| 1389 | len % |
| 1390 | CHECK_LOCATION().AsString())); |
| 1391 | } |
| 1392 | return tflite::UnPackModel(binaryContent); |
| 1393 | } |
| 1394 | |
| 1395 | TfLiteParser::TensorRawPtrVector TfLiteParser::GetInputs(const ModelPtr & model, |
| 1396 | size_t subgraphIndex, |
| 1397 | size_t operatorIndex) |
| 1398 | { |
| 1399 | CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| 1400 | |
| 1401 | const auto & subGraphPtr = model->subgraphs[subgraphIndex]; |
| 1402 | const auto & operatorPtr = subGraphPtr->operators[operatorIndex]; |
| 1403 | |
| 1404 | size_t inputCount = operatorPtr->inputs.size(); |
| 1405 | TensorRawPtrVector result(inputCount); |
| 1406 | for (size_t i=0; i<inputCount; ++i) |
| 1407 | { |
| 1408 | uint32_t inputId = CHECKED_NON_NEGATIVE(operatorPtr->inputs[i]); |
| 1409 | result[i] = subGraphPtr->tensors[inputId].get(); |
| 1410 | } |
| 1411 | return result; |
| 1412 | } |
| 1413 | |
| 1414 | TfLiteParser::TensorRawPtrVector TfLiteParser::GetOutputs(const ModelPtr & model, |
| 1415 | size_t subgraphIndex, |
| 1416 | size_t operatorIndex) |
| 1417 | { |
| 1418 | CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| 1419 | |
| 1420 | const auto & subGraphPtr = model->subgraphs[subgraphIndex]; |
| 1421 | const auto & operatorPtr = subGraphPtr->operators[operatorIndex]; |
| 1422 | |
| 1423 | size_t outputCount = operatorPtr->outputs.size(); |
| 1424 | TensorRawPtrVector result(outputCount); |
| 1425 | for (size_t i=0; i<outputCount; ++i) |
| 1426 | { |
| 1427 | uint32_t outputId = CHECKED_NON_NEGATIVE(operatorPtr->outputs[i]); |
| 1428 | CHECK_TENSOR(model, subgraphIndex, outputId); |
| 1429 | result[i] = subGraphPtr->tensors[outputId].get(); |
| 1430 | } |
| 1431 | return result; |
| 1432 | } |
| 1433 | |
| 1434 | TfLiteParser::TensorIdRawPtrVector TfLiteParser::GetSubgraphInputs(const ModelPtr & model, |
| 1435 | size_t subgraphIndex) |
| 1436 | { |
| 1437 | CHECK_SUBGRAPH(model, subgraphIndex); |
| 1438 | const auto & subGraphPtr = model->subgraphs[subgraphIndex]; |
| 1439 | |
| 1440 | size_t inputCount = subGraphPtr->inputs.size(); |
| 1441 | TensorIdRawPtrVector result(inputCount); |
| 1442 | for (size_t i=0; i<inputCount; ++i) |
| 1443 | { |
| 1444 | uint32_t inputId = CHECKED_NON_NEGATIVE(subGraphPtr->inputs[i]); |
| 1445 | CHECK_TENSOR(model, subgraphIndex, inputId); |
| 1446 | result[i] = std::make_pair(inputId, subGraphPtr->tensors[inputId].get()); |
| 1447 | } |
| 1448 | return result; |
| 1449 | } |
| 1450 | |
| 1451 | TfLiteParser::TensorIdRawPtrVector TfLiteParser::GetSubgraphOutputs(const ModelPtr & model, |
| 1452 | size_t subgraphIndex) |
| 1453 | { |
| 1454 | CHECK_SUBGRAPH(model, subgraphIndex); |
| 1455 | const auto & subGraphPtr = model->subgraphs[subgraphIndex]; |
| 1456 | |
| 1457 | size_t outputCount = subGraphPtr->outputs.size(); |
| 1458 | TensorIdRawPtrVector result(outputCount); |
| 1459 | for (size_t i=0; i<outputCount; ++i) |
| 1460 | { |
| 1461 | uint32_t outputId = CHECKED_NON_NEGATIVE(subGraphPtr->outputs[i]); |
| 1462 | result[i] = std::make_pair(outputId, subGraphPtr->tensors[outputId].get()); |
| 1463 | } |
| 1464 | return result; |
| 1465 | } |
| 1466 | |
| 1467 | std::vector<int32_t>& TfLiteParser::GetInputTensorIds(const ModelPtr& model, |
| 1468 | size_t subgraphIndex, |
| 1469 | size_t operatorIndex) |
| 1470 | { |
| 1471 | CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| 1472 | const auto & subGraphPtr = model->subgraphs[subgraphIndex]; |
| 1473 | const auto & operatorPtr = subGraphPtr->operators[operatorIndex]; |
| 1474 | return operatorPtr->inputs; |
| 1475 | } |
| 1476 | |
| 1477 | std::vector<int32_t>& TfLiteParser::GetOutputTensorIds(const ModelPtr& model, |
| 1478 | size_t subgraphIndex, |
| 1479 | size_t operatorIndex) |
| 1480 | { |
| 1481 | CHECK_MODEL(model, subgraphIndex, operatorIndex); |
| 1482 | const auto & subGraphPtr = model->subgraphs[subgraphIndex]; |
| 1483 | const auto & operatorPtr = subGraphPtr->operators[operatorIndex]; |
| 1484 | return operatorPtr->outputs; |
| 1485 | } |
| 1486 | |
| 1487 | void TfLiteParser::RegisterInputSlots(size_t subgraphIndex, |
| 1488 | size_t operatorIndex, |
| 1489 | IConnectableLayer* layer, |
| 1490 | const std::vector<unsigned int>& tensorIndexes) |
| 1491 | { |
| 1492 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 1493 | BOOST_ASSERT(layer != nullptr); |
| 1494 | if (tensorIndexes.size() != layer->GetNumInputSlots()) |
| 1495 | { |
| 1496 | throw ParseException( |
| 1497 | boost::str(boost::format("The number of tensor inputs (%1%) does not match the number expected (%2%)" |
| 1498 | " for subgraph:%3% operator index:%4% %5%") % |
| 1499 | tensorIndexes.size() % |
| 1500 | layer->GetNumInputSlots() % |
| 1501 | subgraphIndex % |
| 1502 | operatorIndex % |
| 1503 | CHECK_LOCATION().AsString())); |
| 1504 | } |
| 1505 | |
| 1506 | for (unsigned int slotIndex = 0; slotIndex < layer->GetNumInputSlots(); ++slotIndex) |
| 1507 | { |
| 1508 | unsigned int tensorIndex = tensorIndexes[slotIndex]; |
| 1509 | armnn::IInputSlot* slot = &(layer->GetInputSlot(slotIndex)); |
| 1510 | RegisterConsumerOfTensor(subgraphIndex, tensorIndex, slot); |
| 1511 | } |
| 1512 | } |
| 1513 | |
| 1514 | void TfLiteParser::RegisterOutputSlots(size_t subgraphIndex, |
| 1515 | size_t operatorIndex, |
| 1516 | IConnectableLayer* layer, |
| 1517 | const std::vector<unsigned int>& tensorIndexes) |
| 1518 | { |
| 1519 | CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); |
| 1520 | BOOST_ASSERT(layer != nullptr); |
| 1521 | if (tensorIndexes.size() != layer->GetNumOutputSlots()) |
| 1522 | { |
| 1523 | throw ParseException( |
| 1524 | boost::str(boost::format("The number of tensor outputs (%1%) does not match the number expected (%2%)" |
| 1525 | " for subgraph:%3% operator index:%4% %5%") % |
| 1526 | tensorIndexes.size() % |
| 1527 | layer->GetNumOutputSlots() % |
| 1528 | subgraphIndex % |
| 1529 | operatorIndex % |
| 1530 | CHECK_LOCATION().AsString())); |
| 1531 | } |
| 1532 | |
| 1533 | for (unsigned int slotIndex = 0; slotIndex < layer->GetNumOutputSlots(); ++slotIndex) |
| 1534 | { |
| 1535 | unsigned int tensorIndex = tensorIndexes[slotIndex]; |
| 1536 | armnn::IOutputSlot* slot = &(layer->GetOutputSlot(slotIndex)); |
| 1537 | RegisterProducerOfTensor(subgraphIndex, tensorIndex, slot); |
| 1538 | } |
| 1539 | } |
| 1540 | |
| 1541 | void TfLiteParser::SetupInputLayers(size_t subgraphIndex) |
| 1542 | { |
| 1543 | CHECK_SUBGRAPH(m_Model, subgraphIndex); |
| 1544 | |
| 1545 | auto inputs = GetSubgraphInputs(m_Model, subgraphIndex); |
| 1546 | for (auto const & tensorIdAndPtr : inputs) |
| 1547 | { |
| 1548 | auto bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first); |
| 1549 | IConnectableLayer* layer = |
| 1550 | m_Network->AddInputLayer(bindingId, tensorIdAndPtr.second->name.c_str()); |
| 1551 | |
| 1552 | auto tensorInfo = ToTensorInfo(tensorIdAndPtr.second); |
| 1553 | layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 1554 | |
| 1555 | RegisterOutputSlots(subgraphIndex, |
| 1556 | VIRTUAL_OPERATOR_ID, |
| 1557 | layer, |
| 1558 | { static_cast<uint32_t>(tensorIdAndPtr.first) }); |
| 1559 | } |
| 1560 | } |
| 1561 | |
| 1562 | void TfLiteParser::SetupOutputLayers(size_t subgraphIndex) |
| 1563 | { |
| 1564 | CHECK_SUBGRAPH(m_Model, subgraphIndex); |
| 1565 | |
| 1566 | auto outputs = GetSubgraphOutputs(m_Model, subgraphIndex); |
| 1567 | for (auto const & tensorIdAndPtr : outputs) |
| 1568 | { |
| 1569 | auto bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first); |
| 1570 | IConnectableLayer* layer = |
| 1571 | m_Network->AddOutputLayer(bindingId, tensorIdAndPtr.second->name.c_str()); |
| 1572 | |
| 1573 | RegisterInputSlots(subgraphIndex, |
| 1574 | VIRTUAL_OPERATOR_ID, |
| 1575 | layer, |
| 1576 | { static_cast<uint32_t>(tensorIdAndPtr.first) }); |
| 1577 | } |
| 1578 | } |
| 1579 | |
| 1580 | // example usage: BufferRawPtr bufferPtr = GetBuffer(m_Model, inputs[0]->buffer); |
| 1581 | TfLiteParser::BufferRawPtr TfLiteParser::GetBuffer(const ModelPtr& model, size_t bufferIndex) |
| 1582 | { |
| 1583 | CHECK_BUFFER(model, bufferIndex); |
| 1584 | return model->buffers[bufferIndex].get(); |
| 1585 | } |
| 1586 | |
| 1587 | std::pair<armnn::ConstTensor, TfLiteParser::SupportedDataStorage> |
| 1588 | TfLiteParser::CreateConstTensor(TensorRawPtr tensorPtr, |
| 1589 | armnn::TensorInfo & tensorInfo, |
| 1590 | bool convertFromTfToArmnnFormat) |
| 1591 | { |
| 1592 | CHECK_TENSOR_PTR(tensorPtr); |
| 1593 | auto bufferPtr = GetBuffer(m_Model, tensorPtr->buffer); |
| 1594 | CHECK_BUFFER_SIZE(bufferPtr, tensorInfo, tensorPtr->buffer); |
| 1595 | |
| 1596 | switch (tensorInfo.GetDataType()) |
| 1597 | { |
| 1598 | case armnn::DataType::Float32: |
| 1599 | { |
| 1600 | auto constData = CreateConstTensorImpl<float>(bufferPtr, |
| 1601 | tensorPtr, |
| 1602 | tensorInfo, |
| 1603 | convertFromTfToArmnnFormat); |
| 1604 | SupportedDataStorage storage(std::move(constData.second)); |
| 1605 | return std::make_pair(constData.first, std::move(storage)); |
| 1606 | } |
| 1607 | case armnn::DataType::QuantisedAsymm8: |
| 1608 | { |
| 1609 | auto constData = CreateConstTensorImpl<uint8_t>(bufferPtr, |
| 1610 | tensorPtr, |
| 1611 | tensorInfo, |
| 1612 | convertFromTfToArmnnFormat); |
| 1613 | SupportedDataStorage storage(std::move(constData.second)); |
| 1614 | return std::make_pair(constData.first, std::move(storage)); |
| 1615 | } |
| 1616 | case armnn::DataType::Signed32: |
| 1617 | { |
| 1618 | auto constData = CreateConstTensorImpl<int32_t>(bufferPtr, |
| 1619 | tensorPtr, |
| 1620 | tensorInfo, |
| 1621 | convertFromTfToArmnnFormat); |
| 1622 | SupportedDataStorage storage(std::move(constData.second)); |
| 1623 | return std::make_pair(constData.first, std::move(storage)); |
| 1624 | } |
| 1625 | default: |
| 1626 | { |
| 1627 | std::stringstream errString; |
| 1628 | errString << "Unexpected datatype when creating const tensor: " |
| 1629 | << armnn::GetDataTypeName(tensorInfo.GetDataType()) |
| 1630 | << " shape:" << tensorInfo.GetShape() |
| 1631 | << CHECK_LOCATION().AsString(); |
| 1632 | throw ParseException(errString.str()); |
| 1633 | } |
| 1634 | } |
| 1635 | } |
| 1636 | |
| 1637 | BindingPointInfo TfLiteParser::GetNetworkInputBindingInfo(size_t subgraphId, |
| 1638 | const std::string& name) const |
| 1639 | { |
| 1640 | CHECK_SUBGRAPH(m_Model, subgraphId); |
| 1641 | auto inputs = GetSubgraphInputs(m_Model, subgraphId); |
| 1642 | for (auto const & input : inputs) |
| 1643 | { |
| 1644 | if (input.second->name == name) |
| 1645 | { |
| 1646 | auto bindingId = GenerateLayerBindingId(subgraphId, input.first); |
| 1647 | return std::make_pair(bindingId, ToTensorInfo(input.second)); |
| 1648 | } |
| 1649 | } |
| 1650 | |
| 1651 | std::stringstream bindings; |
| 1652 | for (auto const & input : inputs) |
| 1653 | { |
| 1654 | bindings << "'" << input.second->name << "' "; |
| 1655 | } |
| 1656 | |
| 1657 | throw ParseException( |
| 1658 | boost::str( |
| 1659 | boost::format("No input binding found for subgraph:%1% and name:%2%. " |
| 1660 | "Possible inputs are: [%3%] %4%") % |
| 1661 | subgraphId % |
| 1662 | name % |
| 1663 | bindings.str() % |
| 1664 | CHECK_LOCATION().AsString())); |
| 1665 | } |
| 1666 | |
| 1667 | BindingPointInfo TfLiteParser::GetNetworkOutputBindingInfo(size_t subgraphId, |
| 1668 | const std::string& name) const |
| 1669 | { |
| 1670 | CHECK_SUBGRAPH(m_Model, subgraphId); |
| 1671 | auto outputs = GetSubgraphOutputs(m_Model, subgraphId); |
| 1672 | for (auto const & output : outputs) |
| 1673 | { |
| 1674 | if (output.second->name == name) |
| 1675 | { |
| 1676 | auto bindingId = GenerateLayerBindingId(subgraphId, output.first); |
| 1677 | return std::make_pair(bindingId, ToTensorInfo(output.second)); |
| 1678 | } |
| 1679 | } |
| 1680 | |
| 1681 | std::stringstream bindings; |
| 1682 | for (auto const & output : outputs) |
| 1683 | { |
| 1684 | bindings << "'" << output.second->name << "' "; |
| 1685 | } |
| 1686 | |
| 1687 | throw ParseException( |
| 1688 | boost::str( |
| 1689 | boost::format("No output binding found for subgraph:%1% and name:%2%. " |
| 1690 | "Possible outputs are: [%3%] %4%") % |
| 1691 | subgraphId % |
| 1692 | name % |
| 1693 | bindings.str() % |
| 1694 | CHECK_LOCATION().AsString())); |
| 1695 | } |
| 1696 | |
| 1697 | size_t TfLiteParser::GetSubgraphCount() const |
| 1698 | { |
| 1699 | return m_Model->subgraphs.size(); |
| 1700 | } |
| 1701 | |
| 1702 | std::vector<std::string> TfLiteParser::GetSubgraphInputTensorNames(size_t subgraphId) const |
| 1703 | { |
| 1704 | CHECK_SUBGRAPH(m_Model, subgraphId); |
| 1705 | auto inputs = GetSubgraphInputs(m_Model, subgraphId); |
| 1706 | std::vector<std::string> result; |
| 1707 | result.reserve(inputs.size()); |
| 1708 | for (auto const & input : inputs) |
| 1709 | { |
| 1710 | result.push_back(input.second->name); |
| 1711 | } |
| 1712 | return result; |
| 1713 | } |
| 1714 | |
| 1715 | std::vector<std::string> TfLiteParser::GetSubgraphOutputTensorNames(size_t subgraphId) const |
| 1716 | { |
| 1717 | CHECK_SUBGRAPH(m_Model, subgraphId); |
| 1718 | auto outputs = GetSubgraphOutputs(m_Model, subgraphId); |
| 1719 | std::vector<std::string> result; |
| 1720 | result.reserve(outputs.size()); |
| 1721 | for (auto const & output : outputs) |
| 1722 | { |
| 1723 | result.push_back(output.second->name); |
| 1724 | } |
| 1725 | return result; |
| 1726 | } |
| 1727 | |
| 1728 | ITfLiteParser* ITfLiteParser::CreateRaw() |
| 1729 | { |
| 1730 | return new TfLiteParser(); |
| 1731 | } |
| 1732 | |
| 1733 | ITfLiteParserPtr ITfLiteParser::Create() |
| 1734 | { |
| 1735 | return ITfLiteParserPtr(CreateRaw(), &ITfLiteParser::Destroy); |
| 1736 | } |
| 1737 | |
| 1738 | void ITfLiteParser::Destroy(ITfLiteParser* parser) |
| 1739 | { |
| 1740 | delete parser; |
| 1741 | } |
| 1742 | |
| 1743 | TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<float[]> && data) |
| 1744 | : m_FloatData(std::move(data)) |
| 1745 | , m_Uint8Data(nullptr) |
| 1746 | , m_Int32Data(nullptr) |
| 1747 | { |
| 1748 | } |
| 1749 | |
| 1750 | TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<uint8_t[]> && data) |
| 1751 | : m_FloatData(nullptr) |
| 1752 | , m_Uint8Data(std::move(data)) |
| 1753 | , m_Int32Data(nullptr) |
| 1754 | { |
| 1755 | } |
| 1756 | |
| 1757 | TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<int32_t[]> && data) |
| 1758 | : m_FloatData(nullptr) |
| 1759 | , m_Uint8Data(nullptr) |
| 1760 | , m_Int32Data(std::move(data)) |
| 1761 | { |
| 1762 | } |
| 1763 | |
| 1764 | } // armnnTfLiteParser |