telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // See LICENSE file in the project root for full license information. |
| 4 | // |
| 5 | #include "CaffeParser.hpp" |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 6 | #include "RecordByRecordCaffeParser.hpp" |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 7 | |
| 8 | #include "armnn/Descriptors.hpp" |
| 9 | #include "armnn/INetwork.hpp" |
| 10 | #include "armnn/Utils.hpp" |
| 11 | #include "armnn/Exceptions.hpp" |
| 12 | |
| 13 | #include "GraphTopologicalSort.hpp" |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 14 | #include "VerificationHelpers.hpp" |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 15 | |
| 16 | #include <boost/numeric/conversion/cast.hpp> |
| 17 | #include <boost/assert.hpp> |
| 18 | #include <boost/format.hpp> |
| 19 | #include <boost/log/trivial.hpp> |
| 20 | |
| 21 | // Caffe |
| 22 | #include "caffe/proto/caffe.pb.h" |
| 23 | |
| 24 | // ProtoBuf |
| 25 | #include <google/protobuf/io/coded_stream.h> |
| 26 | #include <google/protobuf/io/zero_copy_stream.h> |
| 27 | #include <google/protobuf/io/zero_copy_stream_impl.h> |
| 28 | #include <google/protobuf/text_format.h> |
| 29 | #include <google/protobuf/stubs/common.h> |
| 30 | #include <google/protobuf/stubs/once.h> |
| 31 | #include <google/protobuf/io/coded_stream.h> |
| 32 | #include <google/protobuf/wire_format_lite_inl.h> |
| 33 | #include <google/protobuf/descriptor.h> |
| 34 | #include <google/protobuf/generated_message_reflection.h> |
| 35 | #include <google/protobuf/reflection_ops.h> |
| 36 | #include <google/protobuf/wire_format.h> |
| 37 | |
| 38 | #include <cmath> |
| 39 | #include <sstream> |
| 40 | #include <queue> |
| 41 | #include <fcntl.h> |
| 42 | |
| 43 | /// Caffe networks are loaded from protobuf files (binary or text) using the protobuf library and the generated |
| 44 | /// code from caffe.pb.h. This gives us a caffe::NetParameter which is an in-memory version of the file. |
| 45 | /// This contains a flat list of Caffe 'layers' (e.g. convolution, pooling etc.). |
| 46 | /// Each layer has inputs (called "bottoms") and outputs (called "tops"). Data flows from bottom to top. |
| 47 | /// The bottoms of a layer refer to the tops of other layers, not their names. |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 48 | /// The names of layers seem to be arbitrary (you could rename a layer and the network wouldn't |
| 49 | /// need any other changes). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 50 | /// |
| 51 | /// Some layers (e.g. Relu) can be configured so that their top and bottom are both the same. This is called an |
| 52 | /// "in-place" layer and is a Caffe runtime feature used to reduce memory usage by modifying tensors in-place. |
| 53 | /// This isn't relevant to the parser and so we preprocess these layers to convert them to regular layers, to result |
| 54 | /// in a consistent graph structure. |
| 55 | |
| 56 | namespace armnnCaffeParser |
| 57 | { |
| 58 | |
| 59 | using namespace armnn; |
| 60 | using namespace caffe; |
| 61 | using namespace std; |
| 62 | using namespace google::protobuf::io; |
| 63 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 64 | namespace |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 65 | { |
| 66 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 67 | const float* GetArrayPtrFromBlob(const LayerParameter& layerParam, unsigned int blobIndex) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 68 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 69 | auto nBlobs = layerParam.blobs_size(); |
| 70 | if (blobIndex >= boost::numeric_cast<unsigned int>(nBlobs)) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 71 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 72 | throw ParseException( |
| 73 | boost::str( |
| 74 | boost::format( |
| 75 | "Expected data blob at index %1% in layer %2% not found. nBlobs=%2%. %4%") % |
| 76 | blobIndex % |
| 77 | layerParam.name() % |
| 78 | nBlobs % |
| 79 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 80 | } |
| 81 | |
| 82 | const BlobProto& blob = layerParam.blobs(boost::numeric_cast<int>(blobIndex)); |
| 83 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 84 | const float* arrayPtr = blob.data().data(); |
| 85 | return arrayPtr; |
| 86 | } |
| 87 | |
| 88 | void GetDataFromBlob(const LayerParameter& layerParam, vector<float>& outData, unsigned int blobIndex) |
| 89 | { |
| 90 | auto nBlobs = layerParam.blobs_size(); |
| 91 | if (blobIndex >= boost::numeric_cast<unsigned int>(nBlobs)) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 92 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 93 | throw ParseException( |
| 94 | boost::str( |
| 95 | boost::format( |
| 96 | "Expected data blob at index %1% in layer %2% not found. %3%") % |
| 97 | blobIndex % |
| 98 | layerParam.name() % |
| 99 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 100 | } |
| 101 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 102 | const BlobProto& blob = layerParam.blobs(boost::numeric_cast<int>(blobIndex)); |
| 103 | |
| 104 | size_t blobSize = boost::numeric_cast<size_t>(blob.data_size()); |
| 105 | if (blobSize != outData.size()) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 106 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 107 | throw ParseException( |
| 108 | boost::str( |
| 109 | boost::format( |
| 110 | "Data blob at index %1% in layer %2% has an unexpected size. " |
| 111 | "Expected %3% elements but got %4% elements. %5%") % |
| 112 | blobIndex % |
| 113 | layerParam.name() % |
| 114 | outData.size() % |
| 115 | blobSize % |
| 116 | CHECK_LOCATION().AsString())); |
| 117 | } |
| 118 | |
| 119 | int outSizeInt = boost::numeric_cast<int>(outData.size()); |
| 120 | for (int i = 0; i < outSizeInt; ++i) |
| 121 | { |
| 122 | outData[static_cast<size_t>(i)] = blob.data(i); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 123 | } |
| 124 | } |
| 125 | |
| 126 | bool IsInRange(unsigned int value, unsigned int min, unsigned int max) |
| 127 | { |
| 128 | return (value >= min && value <= max) ? true : false; |
| 129 | } |
| 130 | |
| 131 | template <typename T> |
| 132 | size_t SizeOfVectorData(const vector<T>& vec) |
| 133 | { |
| 134 | return vec.size() * sizeof(T); |
| 135 | } |
| 136 | |
| 137 | void ValidateNumInputsOutputs(const caffe::LayerParameter& layerParameter, |
| 138 | unsigned int numInputs, |
| 139 | unsigned int numOutputs) |
| 140 | { |
| 141 | int numInputsActual = layerParameter.bottom_size(); |
| 142 | if (numInputs != boost::numeric_cast<unsigned int>(numInputsActual)) |
| 143 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 144 | throw ParseException( |
| 145 | boost::str( |
| 146 | boost::format("Invalid number of inputs requested %1% for layer %2% " |
| 147 | "while only %3% present. %4%") % |
| 148 | numInputs % |
| 149 | layerParameter.name() % |
| 150 | numInputsActual % |
| 151 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 152 | } |
| 153 | |
| 154 | int numOutputsActual = layerParameter.top_size(); |
| 155 | if (numOutputs != boost::numeric_cast<unsigned int>(numOutputsActual)) |
| 156 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 157 | throw ParseException( |
| 158 | boost::str( |
| 159 | boost::format("Invalid number of outputs requested %1% for layer %2% " |
| 160 | "while only %3% present. %4%") % |
| 161 | numOutputs % |
| 162 | layerParameter.name() % |
| 163 | numOutputsActual % |
| 164 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 165 | } |
| 166 | } |
| 167 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 168 | template <typename ParamType, typename ExtractOptional, typename ExtractFallback, typename ValueType> |
| 169 | ValueType GetOptionalWithFallback(const ParamType& param, |
| 170 | ExtractOptional extractOptional, |
| 171 | ExtractFallback extractFallback, |
| 172 | ValueType defaultValue) |
| 173 | { |
| 174 | auto optValue = extractOptional(param, defaultValue); |
| 175 | if (optValue.first) |
| 176 | { |
| 177 | return optValue.second; |
| 178 | } |
| 179 | auto fallbackValue = extractFallback(param, defaultValue); |
| 180 | return fallbackValue.second; |
| 181 | } |
| 182 | |
| 183 | #define GET_OPTIONAL_WITH_VECTOR_FALLBACK(PARAM, \ |
| 184 | PARAM_TYPE, \ |
| 185 | OPTIONAL_VALUE, \ |
| 186 | FALLBACK_VECTOR, \ |
| 187 | VALUE_TYPE, \ |
| 188 | DEFAULT_VALUE) \ |
| 189 | GetOptionalWithFallback( \ |
| 190 | PARAM, \ |
| 191 | [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \ |
| 192 | { \ |
| 193 | if (param.has_##OPTIONAL_VALUE ()) \ |
| 194 | { \ |
| 195 | return std::make_pair(true, param.OPTIONAL_VALUE ()); \ |
| 196 | } \ |
| 197 | else \ |
| 198 | { \ |
| 199 | return std::make_pair(false, defaultValue); \ |
| 200 | } \ |
| 201 | }, \ |
| 202 | [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \ |
| 203 | { \ |
| 204 | if (param.FALLBACK_VECTOR##_size() > 0) \ |
| 205 | { \ |
| 206 | return std::make_pair(true, (param.FALLBACK_VECTOR ()).Get(0)); \ |
| 207 | } \ |
| 208 | else \ |
| 209 | { \ |
| 210 | return std::make_pair(false, defaultValue); \ |
| 211 | } \ |
| 212 | }, \ |
| 213 | DEFAULT_VALUE) |
| 214 | |
| 215 | #define GET_OPTIONAL_WITH_FALLBACK(PARAM, \ |
| 216 | PARAM_TYPE, \ |
| 217 | OPTIONAL_VALUE, \ |
| 218 | FALLBACK_VALUE, \ |
| 219 | VALUE_TYPE, \ |
| 220 | DEFAULT_VALUE) \ |
| 221 | GetOptionalWithFallback( \ |
| 222 | PARAM, \ |
| 223 | [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \ |
| 224 | { \ |
| 225 | if (param.has_##OPTIONAL_VALUE ()) \ |
| 226 | { \ |
| 227 | return std::make_pair(true, param.OPTIONAL_VALUE ()); \ |
| 228 | } \ |
| 229 | else \ |
| 230 | { \ |
| 231 | return std::make_pair(false, defaultValue); \ |
| 232 | } \ |
| 233 | }, \ |
| 234 | [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \ |
| 235 | { \ |
| 236 | if (param.has_##FALLBACK_VALUE ()) \ |
| 237 | { \ |
| 238 | return std::make_pair(true, param.FALLBACK_VALUE ()); \ |
| 239 | } \ |
| 240 | else \ |
| 241 | { \ |
| 242 | return std::make_pair(false, defaultValue); \ |
| 243 | } \ |
| 244 | }, \ |
| 245 | DEFAULT_VALUE) |
| 246 | |
| 247 | |
| 248 | void ValidateEqualValuesInRange(unsigned int valueA, |
| 249 | const char* valueNameA, |
| 250 | unsigned int valueB, |
| 251 | const char* valueNameB, |
| 252 | unsigned int min, |
| 253 | unsigned int max, |
| 254 | const armnn::CheckLocation& location) |
| 255 | { |
| 256 | if (!IsInRange(valueA, min, max) || !IsInRange(valueB, min, max) || (valueA != valueB)) |
| 257 | { |
| 258 | throw ParseException( |
| 259 | boost::str( |
| 260 | boost::format( |
| 261 | "%1%=%2% and %3%=%4% must be equal and within the valid range" |
| 262 | "of [%5%, %6%] %7%") % |
| 263 | valueNameA % |
| 264 | valueA % |
| 265 | valueNameB % |
| 266 | valueB % |
| 267 | min % |
| 268 | max % |
| 269 | location.AsString())); |
| 270 | } |
| 271 | } |
| 272 | |
| 273 | #define VALIDATE_EQUAL_VALUES_IN_RANGE(A, B, MIN_RANGE, MAX_RANGE) \ |
| 274 | ValidateEqualValuesInRange(A, #A, B, #B, MIN_RANGE, MAX_RANGE, CHECK_LOCATION()) |
| 275 | |
| 276 | } // namespace <anonymous> |
| 277 | |
| 278 | const std::map<std::string, CaffeParserBase::OperationParsingFunction> |
| 279 | CaffeParserBase::ms_CaffeLayerNameToParsingFunctions = { |
| 280 | { "Input", &CaffeParserBase::ParseInputLayer }, |
| 281 | { "Convolution", &CaffeParserBase::ParseConvLayer }, |
| 282 | { "Pooling", &CaffeParserBase::ParsePoolingLayer }, |
| 283 | { "ReLU", &CaffeParserBase::ParseReluLayer }, |
| 284 | { "LRN", &CaffeParserBase::ParseLRNLayer }, |
| 285 | { "InnerProduct", &CaffeParserBase::ParseInnerProductLayer }, |
| 286 | { "Softmax", &CaffeParserBase::ParseSoftmaxLayer }, |
| 287 | { "Eltwise", &CaffeParserBase::ParseEltwiseLayer }, |
| 288 | { "Concat", &CaffeParserBase::ParseConcatLayer }, |
| 289 | { "BatchNorm", &CaffeParserBase::ParseBatchNormLayer }, |
| 290 | { "Scale", &CaffeParserBase::ParseScaleLayer }, |
| 291 | { "Split", &CaffeParserBase::ParseSplitLayer }, |
| 292 | { "Dropout", &CaffeParserBase::ParseDropoutLayer}, |
| 293 | }; |
| 294 | |
| 295 | ICaffeParser* ICaffeParser::CreateRaw() |
| 296 | { |
| 297 | return new RecordByRecordCaffeParser(); |
| 298 | } |
| 299 | |
| 300 | ICaffeParserPtr ICaffeParser::Create() |
| 301 | { |
| 302 | return ICaffeParserPtr(CreateRaw(), &ICaffeParser::Destroy); |
| 303 | } |
| 304 | |
| 305 | void ICaffeParser::Destroy(ICaffeParser* parser) |
| 306 | { |
| 307 | delete parser; |
| 308 | } |
| 309 | |
| 310 | CaffeParserBase::CaffeParserBase() |
| 311 | : m_Network(nullptr, nullptr) |
| 312 | { |
| 313 | |
| 314 | } |
| 315 | |
| 316 | CaffeParser::CaffeParser() |
| 317 | : CaffeParserBase() |
| 318 | { |
| 319 | |
| 320 | } |
| 321 | |
| 322 | BindingPointInfo CaffeParserBase::GetNetworkInputBindingInfo(const std::string& name) const |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 323 | { |
| 324 | return GetBindingInfo(name, "input", m_NetworkInputsBindingInfo); |
| 325 | } |
| 326 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 327 | BindingPointInfo CaffeParserBase::GetNetworkOutputBindingInfo(const std::string& name) const |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 328 | { |
| 329 | return GetBindingInfo(name, "output", m_NetworkOutputsBindingInfo); |
| 330 | } |
| 331 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 332 | std::pair<armnn::LayerBindingId, armnn::TensorInfo> CaffeParserBase::GetBindingInfo(const std::string& layerName, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 333 | const char* bindingPointDesc, |
| 334 | const std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo) |
| 335 | { |
| 336 | auto it = nameToBindingInfo.find(layerName); |
| 337 | if (it == nameToBindingInfo.end()) |
| 338 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 339 | throw InvalidArgumentException( |
| 340 | boost::str( |
| 341 | boost::format( |
| 342 | "Unknown binding %1% for layer '%2%'. %3%") % |
| 343 | bindingPointDesc % |
| 344 | layerName % |
| 345 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 346 | } |
| 347 | return it->second; |
| 348 | } |
| 349 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 350 | TensorInfo CaffeParserBase::BlobShapeToTensorInfo(const caffe::BlobShape& blobShape) const |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 351 | { |
| 352 | std::vector<unsigned int> shape; |
| 353 | for (int j = 0; j < blobShape.dim_size(); ++j) |
| 354 | { |
| 355 | shape.push_back(static_cast<unsigned int>(blobShape.dim(j))); |
| 356 | } |
| 357 | |
| 358 | return TensorInfo(boost::numeric_cast<unsigned int>(shape.size()), shape.data(), DataType::Float32); |
| 359 | } |
| 360 | |
| 361 | BlobShape TensorDescToBlobShape(const TensorInfo& desc) |
| 362 | { |
| 363 | BlobShape ret; |
| 364 | for (unsigned int i = 0; i < desc.GetNumDimensions(); ++i) |
| 365 | { |
| 366 | ret.add_dim(i); |
| 367 | ret.set_dim(boost::numeric_cast<int>(i), desc.GetShape()[i]); |
| 368 | } |
| 369 | |
| 370 | return ret; |
| 371 | } |
| 372 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 373 | // Note: can move to CaffeParser when/if we optimise the text/string format |
| 374 | // to load on a layer by layer basis |
| 375 | vector<const LayerParameter*> CaffeParserBase::GetInputs(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 376 | { |
| 377 | std::vector<const caffe::LayerParameter*> ret; |
| 378 | ret.reserve(boost::numeric_cast<size_t>(layerParam.bottom_size())); |
| 379 | for (int j = 0; j < layerParam.bottom_size(); ++j) |
| 380 | { |
| 381 | std::string inputName = layerParam.bottom(j); |
| 382 | auto inputIt = m_CaffeLayersByTopName.find(inputName); |
| 383 | if (inputIt == m_CaffeLayersByTopName.end()) |
| 384 | { |
| 385 | throw ParseException( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 386 | boost::str( |
| 387 | boost::format( |
| 388 | "Can't find Caffe layer with top called '%1%', " |
| 389 | "which is listed as an input of '%2%'. %3%") % |
| 390 | inputName % |
| 391 | layerParam.name() % |
| 392 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 393 | } |
| 394 | ret.push_back(inputIt->second); |
| 395 | } |
| 396 | |
| 397 | return ret; |
| 398 | } |
| 399 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 400 | void CaffeParserBase::ParseInputLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 401 | { |
| 402 | BOOST_ASSERT(layerParam.type() == "Input"); |
| 403 | ValidateNumInputsOutputs(layerParam, 0, 1); |
| 404 | |
| 405 | const InputParameter& param = layerParam.input_param(); |
| 406 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 407 | const armnn::LayerBindingId inputId = boost::numeric_cast<armnn::LayerBindingId>( |
| 408 | m_NetworkInputsBindingInfo.size()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 409 | armnn::IConnectableLayer* const inputLayer = m_Network->AddInputLayer(inputId, layerParam.name().c_str()); |
| 410 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 411 | // Decides the tensor info for this input. This can be specified in the Caffe network but can also |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 412 | // be overriden by user input (m_inputShapes). |
| 413 | armnn::TensorInfo inputTensorInfo; |
| 414 | |
| 415 | const BlobShape* originalShape = param.shape_size() > 0 && param.shape(0).dim_size() > 0 ? |
| 416 | ¶m.shape(0) : nullptr; |
| 417 | if (originalShape) |
| 418 | { |
| 419 | inputTensorInfo = BlobShapeToTensorInfo(*originalShape); |
| 420 | } |
| 421 | |
| 422 | auto overrideIt = m_InputShapes.find(layerParam.name()); |
| 423 | if (overrideIt != m_InputShapes.end()) |
| 424 | { |
| 425 | const TensorShape& overrideShape = overrideIt->second; |
| 426 | if (originalShape && |
| 427 | ( originalShape->dim(1) != overrideShape[1] |
| 428 | || originalShape->dim(2) != overrideShape[2] |
| 429 | || originalShape->dim(3) != overrideShape[3])) |
| 430 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 431 | throw ParseException( |
| 432 | boost::str( |
| 433 | boost::format( |
| 434 | "Parsed input shape for '%1%' is incompatible with the override provided. %2%") % |
| 435 | layerParam.name() % |
| 436 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 437 | } |
| 438 | inputTensorInfo.SetShape(overrideShape); |
| 439 | } |
| 440 | else if (!originalShape) |
| 441 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 442 | throw ParseException( |
| 443 | boost::str( |
| 444 | boost::format( |
| 445 | "No input descriptor given for '%1%' and no input shape found in caffe model. %2%") % |
| 446 | layerParam.name() % |
| 447 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 448 | } |
| 449 | |
| 450 | TrackInputBinding(inputLayer, inputId, inputTensorInfo); |
| 451 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 452 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), inputLayer->GetOutputSlot(0)); |
| 453 | } |
| 454 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 455 | void CaffeParserBase::AddConvLayerWithSplits(const caffe::LayerParameter& layerParam, |
| 456 | const armnn::Convolution2dDescriptor& desc, |
| 457 | unsigned int kernelW, |
| 458 | unsigned int kernelH) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 459 | { |
| 460 | BOOST_ASSERT(layerParam.type() == "Convolution"); |
| 461 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 462 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 463 | ConvolutionParameter convParam = layerParam.convolution_param(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 464 | BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo()); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 465 | const unsigned int numGroups = convParam.has_group() ? convParam.group() : 1; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 466 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 467 | // asusme these were already verified by the caller ParseConvLayer() function |
| 468 | BOOST_ASSERT(numGroups < inputShape.dim(1)); |
| 469 | BOOST_ASSERT(numGroups > 1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 470 | |
| 471 | // Handle grouping |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 472 | armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)); |
| 473 | |
| 474 | vector<string> convLayerNames(numGroups); |
| 475 | vector<armnn::IConnectableLayer*> convLayers(numGroups); |
| 476 | convLayerNames[0] = layerParam.name(); |
| 477 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 478 | // This convolution is to be applied to chunks of the input data so add a splitter layer |
| 479 | |
| 480 | // Redirect the convolution input to the splitter |
| 481 | unsigned int splitterDimSizes[4] = {static_cast<unsigned int>(inputShape.dim(0)), |
| 482 | static_cast<unsigned int>(inputShape.dim(1)), |
| 483 | static_cast<unsigned int>(inputShape.dim(2)), |
| 484 | static_cast<unsigned int>(inputShape.dim(3))}; |
| 485 | |
| 486 | // Split dimension 1 of the splitter output shape and conv input shapes |
| 487 | // according to the number of groups |
| 488 | |
| 489 | splitterDimSizes[1] /= numGroups; |
| 490 | inputShape.set_dim(1, splitterDimSizes[1]); |
| 491 | |
| 492 | // This is used to describe how the input is to be split |
| 493 | ViewsDescriptor splitterDesc(numGroups); |
| 494 | |
| 495 | // Create an output node for each group, giving each a unique name |
| 496 | for (unsigned int g = 0; g < numGroups; ++g) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 497 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 498 | // Work out the names of the splitter layers child convolutions |
| 499 | stringstream ss; |
| 500 | ss << layerParam.name() << "_" << g; |
| 501 | convLayerNames[g] = ss.str(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 502 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 503 | splitterDesc.SetViewOriginCoord(g, 1, splitterDimSizes[1] * g); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 504 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 505 | // Set the size of the views. |
| 506 | for (unsigned int dimIdx=0; dimIdx < 4; dimIdx++) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 507 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 508 | splitterDesc.SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 509 | } |
| 510 | } |
| 511 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 512 | const std::string splitterLayerName = std::string("splitter_") + layerParam.bottom(0); |
| 513 | armnn::IConnectableLayer* splitterLayer = m_Network->AddSplitterLayer(splitterDesc, splitterLayerName.c_str()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 514 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 515 | inputConnection.Connect(splitterLayer->GetInputSlot(0)); |
| 516 | for (unsigned int i = 0; i < splitterLayer->GetNumOutputSlots(); i++) |
| 517 | { |
| 518 | splitterLayer->GetOutputSlot(i).SetTensorInfo(BlobShapeToTensorInfo(inputShape)); |
| 519 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 520 | |
| 521 | unsigned int numFilters = convParam.num_output(); |
| 522 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 523 | // Populates convolution output tensor descriptor dimensions. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 524 | BlobShape outputShape; |
| 525 | outputShape.add_dim(0); |
| 526 | outputShape.set_dim(0, inputShape.dim(0)); |
| 527 | outputShape.add_dim(1); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 528 | // Ensures that dimension 1 of the convolution output is split according to the number of groups. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 529 | outputShape.set_dim(1, numFilters / numGroups); |
| 530 | outputShape.add_dim(2); |
| 531 | outputShape.set_dim( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 532 | 2, (static_cast<int>( |
| 533 | static_cast<float>(inputShape.dim(2) + 2 * desc.m_PadBottom - kernelH) / |
| 534 | static_cast<float>(desc.m_StrideY)) + 1)); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 535 | outputShape.add_dim(3); |
| 536 | outputShape.set_dim( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 537 | 3, (static_cast<int>( |
| 538 | static_cast<float>(inputShape.dim(3) + 2 * desc.m_PadRight - kernelW) / |
| 539 | static_cast<float>(desc.m_StrideX)) + 1)); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 540 | |
| 541 | // Load the weight data for ALL groups |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 542 | vector<float> weightData(boost::numeric_cast<size_t>(numGroups * |
| 543 | inputShape.dim(1) * // number of input channels |
| 544 | outputShape.dim(1) * // number of output channels |
| 545 | kernelH * |
| 546 | kernelW)); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 547 | GetDataFromBlob(layerParam, weightData, 0); |
| 548 | |
| 549 | const unsigned int weightDimSizes[4] = { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 550 | static_cast<unsigned int>(outputShape.dim(1)), |
| 551 | static_cast<unsigned int>(inputShape.dim(1)), |
| 552 | kernelH, |
| 553 | kernelW}; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 554 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 555 | TensorInfo biasInfo; |
| 556 | vector<float> biasData; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 557 | |
| 558 | if (desc.m_BiasEnabled) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 559 | { |
| 560 | biasData.resize(boost::numeric_cast<size_t>(numGroups * outputShape.dim(1)), 1.f); |
| 561 | GetDataFromBlob(layerParam, biasData, 1); |
| 562 | |
| 563 | const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))}; |
| 564 | biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32); |
| 565 | } |
| 566 | |
| 567 | const unsigned int numWeightsPerGroup = boost::numeric_cast<unsigned int>(weightData.size()) / numGroups; |
| 568 | const unsigned int numBiasesPerGroup = boost::numeric_cast<unsigned int>(biasData.size()) / numGroups; |
| 569 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 570 | for (unsigned int g = 0; g < numGroups; ++g) |
| 571 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 572 | // Sets the slot index, group 0 should be connected to the 0th output of the splitter |
| 573 | // group 1 should be connected to the 1st output of the splitter. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 574 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 575 | // Pulls out the weights for this group from that loaded from the model file earlier. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 576 | ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32), |
| 577 | weightData.data() + numWeightsPerGroup * g); |
| 578 | |
| 579 | IConnectableLayer* convLayer = nullptr; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 580 | if (desc.m_BiasEnabled) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 581 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 582 | // Pulls out the biases for this group from that loaded from the model file earlier. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 583 | ConstTensor biases(biasInfo, biasData.data() + numBiasesPerGroup * g); |
| 584 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 585 | convLayer = |
| 586 | m_Network->AddConvolution2dLayer(desc, weights, biases, convLayerNames[g].c_str()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 587 | } |
| 588 | else |
| 589 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 590 | convLayer = |
| 591 | m_Network->AddConvolution2dLayer(desc, weights, convLayerNames[g].c_str()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 592 | } |
| 593 | convLayers[g] = convLayer; |
| 594 | |
| 595 | // If we have more than one group then the input to the nth convolution the splitter layer's nth output, |
| 596 | // otherwise it's the regular input to this layer. |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 597 | armnn::IOutputSlot& splitterInputConnection = |
| 598 | splitterLayer ? splitterLayer->GetOutputSlot(g) : inputConnection; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 599 | splitterInputConnection.Connect(convLayer->GetInputSlot(0)); |
| 600 | convLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape)); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 601 | } |
| 602 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 603 | // If the convolution was performed in chunks, add a layer to merge the results |
| 604 | |
| 605 | // The merge input shape matches that of the convolution output |
| 606 | unsigned int mergeDimSizes[4] = {static_cast<unsigned int>(outputShape.dim(0)), |
| 607 | static_cast<unsigned int>(outputShape.dim(1)), |
| 608 | static_cast<unsigned int>(outputShape.dim(2)), |
| 609 | static_cast<unsigned int>(outputShape.dim(3))}; |
| 610 | |
| 611 | // This is used to describe how the input is to be merged |
| 612 | OriginsDescriptor mergeDesc(numGroups); |
| 613 | |
| 614 | // Now create an input node for each group, using the name from |
| 615 | // the output of the corresponding convolution |
| 616 | for (unsigned int g = 0; g < numGroups; ++g) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 617 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 618 | mergeDesc.SetViewOriginCoord(g, 1, mergeDimSizes[1] * g); |
| 619 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 620 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 621 | // Make sure the output from the merge is the correct size to hold the data for all groups |
| 622 | mergeDimSizes[1] *= numGroups; |
| 623 | outputShape.set_dim(1, mergeDimSizes[1]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 624 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 625 | // Finally add the merge layer |
| 626 | IConnectableLayer* mergerLayer = m_Network->AddMergerLayer(mergeDesc, layerParam.name().c_str()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 627 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 628 | if (!mergerLayer) |
| 629 | { |
| 630 | throw ParseException( |
| 631 | boost::str( |
| 632 | boost::format( |
| 633 | "Failed to create final merger layer for Split+Convolution+Merger. " |
| 634 | "Layer=%1% #groups=%2% #filters=%3% %4%") % |
| 635 | layerParam.name() % |
| 636 | numGroups % |
| 637 | numFilters % |
| 638 | CHECK_LOCATION().AsString())); |
| 639 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 640 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 641 | for (unsigned int g = 0; g < numGroups; ++g) |
| 642 | { |
| 643 | convLayers[g]->GetOutputSlot(0).Connect(mergerLayer->GetInputSlot(g)); |
| 644 | } |
| 645 | mergerLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo(4, mergeDimSizes, DataType::Float32)); |
| 646 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), mergerLayer->GetOutputSlot(0)); |
| 647 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 648 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 649 | void CaffeParserBase::AddConvLayerWithDepthwiseConv(const caffe::LayerParameter& layerParam, |
| 650 | const armnn::Convolution2dDescriptor& convDesc, |
| 651 | unsigned int kernelW, |
| 652 | unsigned int kernelH) |
| 653 | { |
| 654 | BOOST_ASSERT(layerParam.type() == "Convolution"); |
| 655 | ValidateNumInputsOutputs(layerParam, 1, 1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 656 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 657 | ConvolutionParameter convParam = layerParam.convolution_param(); |
| 658 | BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 659 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 660 | DepthwiseConvolution2dDescriptor desc; |
| 661 | desc.m_PadLeft = convDesc.m_PadLeft; |
| 662 | desc.m_PadRight = convDesc.m_PadRight; |
| 663 | desc.m_PadTop = convDesc.m_PadTop; |
| 664 | desc.m_PadBottom = convDesc.m_PadBottom; |
| 665 | desc.m_StrideX = convDesc.m_StrideX; |
| 666 | desc.m_StrideY = convDesc.m_StrideY; |
| 667 | desc.m_BiasEnabled = convDesc.m_BiasEnabled; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 668 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 669 | unsigned int numFilters = convParam.num_output(); |
| 670 | |
| 671 | BlobShape outputShape; |
| 672 | outputShape.add_dim(0); |
| 673 | outputShape.set_dim(0, inputShape.dim(0)); |
| 674 | outputShape.add_dim(1); |
| 675 | outputShape.set_dim(1, numFilters); |
| 676 | outputShape.add_dim(2); |
| 677 | outputShape.set_dim( |
| 678 | 2, (static_cast<int>( |
| 679 | static_cast<float>(inputShape.dim(2) + 2 * desc.m_PadBottom - kernelH) / |
| 680 | static_cast<float>(desc.m_StrideY)) + 1)); |
| 681 | outputShape.add_dim(3); |
| 682 | outputShape.set_dim( |
| 683 | 3, (static_cast<int>( |
| 684 | static_cast<float>(inputShape.dim(3) + 2 * desc.m_PadRight - kernelW) / |
| 685 | static_cast<float>(desc.m_StrideX)) + 1)); |
| 686 | |
| 687 | // Load the weight data |
| 688 | size_t allWeightsSize = boost::numeric_cast<size_t>(inputShape.dim(1) * kernelH * kernelW); |
| 689 | vector<float> weightData(allWeightsSize); |
| 690 | |
| 691 | GetDataFromBlob(layerParam, weightData, 0); |
| 692 | |
| 693 | // depth multiplier will be 1 for the depthwise convolution |
| 694 | const unsigned int weightDimSizes[4] = { |
| 695 | static_cast<unsigned int>(1), // depth multiplier |
| 696 | static_cast<unsigned int>(inputShape.dim(1)), // #channels |
| 697 | kernelH, |
| 698 | kernelW}; |
| 699 | |
| 700 | armnn::IConnectableLayer* returnLayer = nullptr; |
| 701 | ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32), weightData.data()); |
| 702 | |
| 703 | if (desc.m_BiasEnabled) |
| 704 | { |
| 705 | TensorInfo biasInfo; |
| 706 | vector<float> biasData; |
| 707 | |
| 708 | biasData.resize(boost::numeric_cast<size_t>(outputShape.dim(1)), 1.f); |
| 709 | GetDataFromBlob(layerParam, biasData, 1); |
| 710 | |
| 711 | const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))}; |
| 712 | biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32); |
| 713 | |
| 714 | ConstTensor biases(biasInfo, biasData.data()); |
| 715 | returnLayer = m_Network->AddDepthwiseConvolution2dLayer(desc, weights, biases, layerParam.name().c_str()); |
| 716 | } |
| 717 | else |
| 718 | { |
| 719 | returnLayer = m_Network->AddDepthwiseConvolution2dLayer(desc, weights, layerParam.name().c_str()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 720 | } |
| 721 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 722 | if (!returnLayer) |
| 723 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 724 | throw ParseException( |
| 725 | boost::str( |
| 726 | boost::format( |
| 727 | "Failed to create depthwise convolution layer. " |
| 728 | "Layer=%1% #filters=%2% %3%") % |
| 729 | layerParam.name() % |
| 730 | numFilters % |
| 731 | CHECK_LOCATION().AsString())); |
| 732 | } |
| 733 | armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)); |
| 734 | inputConnection.Connect(returnLayer->GetInputSlot(0)); |
| 735 | returnLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape)); |
| 736 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), returnLayer->GetOutputSlot(0)); |
| 737 | } |
| 738 | |
| 739 | void CaffeParserBase::ParseConvLayer(const LayerParameter& layerParam) |
| 740 | { |
| 741 | // Ignored Caffe Parameters |
| 742 | // * Dilation Size |
| 743 | // * Weight Filler |
| 744 | // * Bias Filler |
| 745 | // * Engine |
| 746 | // * Force nd_im2col |
| 747 | // * Axis |
| 748 | |
| 749 | // Not Available ArmNN Interface Parameters |
| 750 | // * Rounding policy; |
| 751 | |
| 752 | BOOST_ASSERT(layerParam.type() == "Convolution"); |
| 753 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 754 | |
| 755 | ConvolutionParameter convParam = layerParam.convolution_param(); |
| 756 | BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo()); |
| 757 | const unsigned int numGroups = convParam.has_group() ? convParam.group() : 1; |
| 758 | unsigned int numFilters = convParam.num_output(); |
| 759 | |
| 760 | const auto notFound = std::numeric_limits<unsigned int>::max(); |
| 761 | |
| 762 | unsigned int kernelH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter, |
| 763 | kernel_h, kernel_size, unsigned int, notFound); |
| 764 | unsigned int kernelW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter, |
| 765 | kernel_w, kernel_size, unsigned int, notFound); |
| 766 | |
| 767 | unsigned int strideH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter, |
| 768 | stride_h, stride, unsigned int, 1u); |
| 769 | unsigned int strideW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter, |
| 770 | stride_w, stride, unsigned int, 1u); |
| 771 | |
| 772 | unsigned int padH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter, |
| 773 | pad_h, pad, unsigned int, 0u); |
| 774 | unsigned int padW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter, |
| 775 | pad_w, pad, unsigned int, 0u); |
| 776 | |
| 777 | VALIDATE_EQUAL_VALUES_IN_RANGE(kernelH, kernelW, 0, 11); |
| 778 | VALIDATE_EQUAL_VALUES_IN_RANGE(strideH, strideW, 0, 11); |
| 779 | VALIDATE_EQUAL_VALUES_IN_RANGE(padH, padW, 0, 11); |
| 780 | |
| 781 | Convolution2dDescriptor convolution2dDescriptor; |
| 782 | convolution2dDescriptor.m_PadLeft = padW; |
| 783 | convolution2dDescriptor.m_PadRight = padW; |
| 784 | convolution2dDescriptor.m_PadTop = padH; |
| 785 | convolution2dDescriptor.m_PadBottom = padH; |
| 786 | convolution2dDescriptor.m_StrideX = strideW; |
| 787 | convolution2dDescriptor.m_StrideY = strideH; |
| 788 | convolution2dDescriptor.m_BiasEnabled = convParam.has_bias_term() ? convParam.bias_term() : true; |
| 789 | |
| 790 | if (numGroups > numFilters) |
| 791 | { |
| 792 | throw ParseException( |
| 793 | boost::str( |
| 794 | boost::format( |
| 795 | "Error parsing Convolution: %1%. " |
| 796 | "The 'group'=%2% parameter cannot be larger than the " |
| 797 | "number of filters supplied ='%3%'. %4%") % |
| 798 | layerParam.name() % |
| 799 | numGroups % |
| 800 | numFilters % |
| 801 | CHECK_LOCATION().AsString())); |
| 802 | } |
| 803 | |
| 804 | if (inputShape.dim_size() != 4) |
| 805 | { |
| 806 | throw ParseException( |
| 807 | boost::str( |
| 808 | boost::format( |
| 809 | "Convolution input shape is expected to have 4 dimensions. " |
| 810 | "%1%'s input has only %2%. %3%") % |
| 811 | layerParam.name() % |
| 812 | inputShape.dim_size() % |
| 813 | CHECK_LOCATION().AsString())); |
| 814 | } |
| 815 | |
| 816 | if (numGroups > 1) |
| 817 | { |
| 818 | if (numGroups > inputShape.dim(1)) |
| 819 | { |
| 820 | throw ParseException( |
| 821 | boost::str( |
| 822 | boost::format( |
| 823 | "Error parsing Convolution: %1%. " |
| 824 | "The 'group'=%2% parameter cannot be larger than the " |
| 825 | "channel of the input shape=%3% (in NCHW format). %4%") % |
| 826 | layerParam.name() % |
| 827 | numGroups % |
| 828 | inputShape.dim(1) % |
| 829 | CHECK_LOCATION().AsString())); |
| 830 | } |
| 831 | else if (numGroups == inputShape.dim(1)) |
| 832 | { |
| 833 | // we use a depthwise convolution here, because the number of groups equals to the |
| 834 | // input channels |
| 835 | AddConvLayerWithDepthwiseConv(layerParam, convolution2dDescriptor, kernelW, kernelH); |
| 836 | return; |
| 837 | } |
| 838 | else |
| 839 | { |
| 840 | // we split the input by channels into channels/groups separate convolutions |
| 841 | // and merger the results afterwards |
| 842 | AddConvLayerWithSplits(layerParam, convolution2dDescriptor, kernelW, kernelH); |
| 843 | return; |
| 844 | } |
| 845 | } |
| 846 | |
| 847 | // NOTE: at this point we only need to handle #group=1 case, all other cases should be |
| 848 | // handled by the AddConvLayer* helpers |
| 849 | |
| 850 | // Populate convolution output tensor descriptor dimensions |
| 851 | BlobShape outputShape; |
| 852 | outputShape.add_dim(0); |
| 853 | outputShape.set_dim(0, inputShape.dim(0)); |
| 854 | outputShape.add_dim(1); |
| 855 | outputShape.set_dim(1, numFilters); |
| 856 | outputShape.add_dim(2); |
| 857 | outputShape.set_dim( |
| 858 | 2, (static_cast<int>( |
| 859 | static_cast<float>(inputShape.dim(2) + 2 * padH - kernelH) / |
| 860 | static_cast<float>(strideH)) + 1)); |
| 861 | outputShape.add_dim(3); |
| 862 | outputShape.set_dim( |
| 863 | 3, (static_cast<int>( |
| 864 | static_cast<float>(inputShape.dim(3) + 2 * padW - kernelW) / |
| 865 | static_cast<float>(strideW)) + 1)); |
| 866 | |
| 867 | // Load the weight data for ALL groups |
| 868 | vector<float> weightData(boost::numeric_cast<size_t>(inputShape.dim(1) * |
| 869 | outputShape.dim(1) * |
| 870 | kernelH * |
| 871 | kernelW)); |
| 872 | GetDataFromBlob(layerParam, weightData, 0); |
| 873 | |
| 874 | const unsigned int weightDimSizes[4] = { |
| 875 | static_cast<unsigned int>(outputShape.dim(1)), // output channels |
| 876 | static_cast<unsigned int>(inputShape.dim(1)), // input channels |
| 877 | kernelH, |
| 878 | kernelW}; |
| 879 | |
| 880 | armnn::IConnectableLayer* returnLayer = nullptr; |
| 881 | |
| 882 | // Pull out the weights for this group from that loaded from the model file earlier |
| 883 | ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32), weightData.data()); |
| 884 | |
| 885 | if (convolution2dDescriptor.m_BiasEnabled) |
| 886 | { |
| 887 | TensorInfo biasInfo; |
| 888 | vector<float> biasData; |
| 889 | |
| 890 | biasData.resize(boost::numeric_cast<size_t>(outputShape.dim(1)), 1.f); |
| 891 | GetDataFromBlob(layerParam, biasData, 1); |
| 892 | |
| 893 | const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))}; |
| 894 | biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32); |
| 895 | |
| 896 | // Pull out the biases for this group from that loaded from the model file earlier |
| 897 | ConstTensor biases(biasInfo, biasData.data()); |
| 898 | |
| 899 | returnLayer = |
| 900 | m_Network->AddConvolution2dLayer(convolution2dDescriptor, weights, biases, layerParam.name().c_str()); |
| 901 | } |
| 902 | else |
| 903 | { |
| 904 | returnLayer = m_Network->AddConvolution2dLayer(convolution2dDescriptor, weights, layerParam.name().c_str()); |
| 905 | } |
| 906 | |
| 907 | armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)); |
| 908 | inputConnection.Connect(returnLayer->GetInputSlot(0)); |
| 909 | returnLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape)); |
| 910 | |
| 911 | if (!returnLayer) |
| 912 | { |
| 913 | throw ParseException( |
| 914 | boost::str( |
| 915 | boost::format( |
| 916 | "Failed to create Convolution layer. " |
| 917 | "Layer=%1% #groups=%2% #filters=%3% %4%") % |
| 918 | layerParam.name() % |
| 919 | numGroups % |
| 920 | numFilters % |
| 921 | CHECK_LOCATION().AsString())); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 922 | } |
| 923 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 924 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), returnLayer->GetOutputSlot(0)); |
| 925 | } |
| 926 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 927 | void CaffeParserBase::ParsePoolingLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 928 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 929 | // Ignored Caffe Parameters |
| 930 | // Stochastic Pooling |
| 931 | // Engine |
| 932 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 933 | ValidateNumInputsOutputs(layerParam, 1, 1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 934 | PoolingParameter param = layerParam.pooling_param(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 935 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 936 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 937 | const auto notFound = std::numeric_limits<unsigned int>::max(); |
| 938 | |
| 939 | unsigned int kernel_h = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter, |
| 940 | kernel_h, kernel_size, unsigned int, notFound); |
| 941 | unsigned int kernel_w = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter, |
| 942 | kernel_w, kernel_size, unsigned int, notFound); |
| 943 | |
| 944 | if ((kernel_h == notFound || kernel_w == notFound) && param.has_global_pooling()) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 945 | { |
| 946 | kernel_h = inputInfo.GetShape()[2]; |
| 947 | kernel_w = inputInfo.GetShape()[3]; |
| 948 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 949 | |
| 950 | VALIDATE_EQUAL_VALUES_IN_RANGE(kernel_h, kernel_w, 0, 11); |
| 951 | |
| 952 | unsigned int stride_h = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter, |
| 953 | stride_h, stride, unsigned int, notFound); |
| 954 | unsigned int stride_w = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter, |
| 955 | stride_h, stride, unsigned int, notFound); |
| 956 | |
| 957 | if ((stride_h == notFound || stride_w == notFound) && param.has_global_pooling()) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 958 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 959 | stride_h = 1; |
| 960 | stride_w = 1; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 961 | } |
| 962 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 963 | VALIDATE_EQUAL_VALUES_IN_RANGE(stride_h, stride_w, 0, 11); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 964 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 965 | unsigned int pad_h = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter, |
| 966 | pad_h, pad, unsigned int, 0u); |
| 967 | unsigned int pad_w = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter, |
| 968 | pad_w, pad, unsigned int, 0u); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 969 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 970 | VALIDATE_EQUAL_VALUES_IN_RANGE(pad_h, pad_w, 0, 11); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 971 | |
| 972 | // Populate Weight and Bias Filter Descriptor |
| 973 | Pooling2dDescriptor pooling2dDescriptor; |
| 974 | if (param.has_pool()) |
| 975 | { |
| 976 | PoolingParameter_PoolMethod p = param.pool(); |
| 977 | switch (p) |
| 978 | { |
| 979 | case PoolingParameter_PoolMethod_MAX: |
| 980 | { |
| 981 | pooling2dDescriptor.m_PoolType = PoolingAlgorithm::Max; |
| 982 | break; |
| 983 | } |
| 984 | case PoolingParameter_PoolMethod_AVE: |
| 985 | { |
| 986 | pooling2dDescriptor.m_PoolType = PoolingAlgorithm::Average; |
| 987 | break; |
| 988 | } |
| 989 | case PoolingParameter_PoolMethod_STOCHASTIC: |
| 990 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 991 | throw ParseException( |
| 992 | boost::str( |
| 993 | boost::format( |
| 994 | "Pooling Layer: Stochastic Pooling Not Supported. Layer=%1% %2%") % |
| 995 | layerParam.name() % |
| 996 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 997 | } |
| 998 | default: |
| 999 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1000 | throw ParseException( |
| 1001 | boost::str( |
| 1002 | boost::format( |
| 1003 | "Pooling Layer: unknown pooling method: %1% for layer: %2% %3%") % |
| 1004 | p % |
| 1005 | layerParam.name() % |
| 1006 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1007 | } |
| 1008 | } |
| 1009 | } |
| 1010 | else |
| 1011 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1012 | throw ParseException( |
| 1013 | boost::str( |
| 1014 | boost::format( |
| 1015 | "No Pooling Method Defined for %1% %2%") % |
| 1016 | layerParam.name() % |
| 1017 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1018 | } |
| 1019 | |
| 1020 | pooling2dDescriptor.m_PadLeft = pad_w; |
| 1021 | pooling2dDescriptor.m_PadRight = pad_w; |
| 1022 | pooling2dDescriptor.m_PadTop = pad_h; |
| 1023 | pooling2dDescriptor.m_PadBottom = pad_h; |
| 1024 | pooling2dDescriptor.m_StrideX = stride_w; |
| 1025 | pooling2dDescriptor.m_StrideY = stride_h; |
| 1026 | pooling2dDescriptor.m_PoolWidth = kernel_w; |
| 1027 | pooling2dDescriptor.m_PoolHeight = kernel_h; |
| 1028 | |
| 1029 | pooling2dDescriptor.m_OutputShapeRounding = OutputShapeRounding::Ceiling; |
| 1030 | pooling2dDescriptor.m_PaddingMethod = PaddingMethod::IgnoreValue; |
| 1031 | |
| 1032 | armnn::IConnectableLayer* poolingLayer = m_Network->AddPooling2dLayer(pooling2dDescriptor, |
| 1033 | layerParam.name().c_str()); |
| 1034 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1035 | TensorInfo outputInfo( |
| 1036 | { inputInfo.GetShape()[0], |
| 1037 | inputInfo.GetShape()[1], |
| 1038 | static_cast<unsigned int>(ceil( |
| 1039 | static_cast<float>(inputInfo.GetShape()[2] + 2 * pad_h - kernel_h) / |
| 1040 | boost::numeric_cast<float>(stride_h))) + 1, |
| 1041 | static_cast<unsigned int>(ceil( |
| 1042 | static_cast<float>(inputInfo.GetShape()[3] + 2 * pad_w - kernel_w) / |
| 1043 | boost::numeric_cast<float>(stride_w))) + 1 }, |
| 1044 | DataType::Float32); |
| 1045 | |
| 1046 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(poolingLayer->GetInputSlot(0)); |
| 1047 | poolingLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 1048 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), poolingLayer->GetOutputSlot(0)); |
| 1049 | } |
| 1050 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1051 | void CaffeParserBase::ParseReluLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1052 | { |
| 1053 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 1054 | |
| 1055 | const string& name = layerParam.name(); |
| 1056 | const ReLUParameter& param = layerParam.relu_param(); |
| 1057 | |
| 1058 | ActivationDescriptor activationDescriptor; |
| 1059 | const float negativeSlope = param.negative_slope(); |
| 1060 | if (negativeSlope == 0.0f) |
| 1061 | { |
| 1062 | activationDescriptor.m_Function = ActivationFunction::ReLu; |
| 1063 | } |
| 1064 | else |
| 1065 | { |
| 1066 | activationDescriptor.m_Function = ActivationFunction::LeakyReLu; |
| 1067 | activationDescriptor.m_A = negativeSlope; |
| 1068 | } |
| 1069 | |
| 1070 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 1071 | IConnectableLayer* const activationLayer = m_Network->AddActivationLayer(activationDescriptor, name.c_str()); |
| 1072 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(activationLayer->GetInputSlot(0)); |
| 1073 | activationLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 1074 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), activationLayer->GetOutputSlot(0)); |
| 1075 | } |
| 1076 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1077 | void CaffeParserBase::ParseLRNLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1078 | { |
| 1079 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 1080 | |
| 1081 | LRNParameter param = layerParam.lrn_param(); |
| 1082 | |
| 1083 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 1084 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1085 | // Ignored BATCH NORMALIZATION Caffe Parameters. |
| 1086 | // Ignored MVN Caffe Parameters. |
| 1087 | // Ignored LRN Caffe Parameters. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1088 | // Engine |
| 1089 | |
| 1090 | NormalizationDescriptor normalizationDescriptor; |
| 1091 | if (param.has_norm_region()) |
| 1092 | { |
| 1093 | LRNParameter_NormRegion n = param.norm_region(); |
| 1094 | switch (n) |
| 1095 | { |
| 1096 | case LRNParameter_NormRegion_ACROSS_CHANNELS: |
| 1097 | { |
| 1098 | normalizationDescriptor.m_NormChannelType = NormalizationAlgorithmChannel::Across; |
| 1099 | break; |
| 1100 | } |
| 1101 | case LRNParameter_NormRegion_WITHIN_CHANNEL: |
| 1102 | { |
| 1103 | normalizationDescriptor.m_NormChannelType = NormalizationAlgorithmChannel::Within; |
| 1104 | break; |
| 1105 | } |
| 1106 | default: |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1107 | { |
| 1108 | throw ParseException( |
| 1109 | boost::str( |
| 1110 | boost::format( |
| 1111 | "Unknown region %1% for LRN layer %2% %3%") % |
| 1112 | n % |
| 1113 | layerParam.name() % |
| 1114 | CHECK_LOCATION().AsString())); |
| 1115 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1116 | } |
| 1117 | } |
| 1118 | else |
| 1119 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1120 | // Caffe defaults to normalization across channels. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1121 | normalizationDescriptor.m_NormChannelType = NormalizationAlgorithmChannel::Across; |
| 1122 | } |
| 1123 | |
| 1124 | normalizationDescriptor.m_NormMethodType = NormalizationAlgorithmMethod::LocalBrightness; |
| 1125 | if (param.has_local_size()) |
| 1126 | { |
| 1127 | normalizationDescriptor.m_NormSize = param.local_size(); |
| 1128 | } |
| 1129 | else |
| 1130 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1131 | throw ParseException( |
| 1132 | boost::str( |
| 1133 | boost::format( |
| 1134 | "local_size not defined for LRN layer %1% %2%") % |
| 1135 | layerParam.name() % |
| 1136 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1137 | } |
| 1138 | |
| 1139 | if (param.has_alpha()) |
| 1140 | { |
| 1141 | normalizationDescriptor.m_Alpha = param.alpha(); |
| 1142 | normalizationDescriptor.m_Alpha /= boost::numeric_cast<float>(param.local_size()); |
| 1143 | } |
| 1144 | else |
| 1145 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1146 | throw ParseException( |
| 1147 | boost::str( |
| 1148 | boost::format( |
| 1149 | "Alpha not defined for LRN layer %1% %2%") % |
| 1150 | layerParam.name() % |
| 1151 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1152 | } |
| 1153 | if (param.has_beta()) |
| 1154 | { |
| 1155 | normalizationDescriptor.m_Beta = param.beta(); |
| 1156 | } |
| 1157 | else |
| 1158 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1159 | throw ParseException( |
| 1160 | boost::str( |
| 1161 | boost::format( |
| 1162 | "Beta not defined for LRN layer %1% %2%") % |
| 1163 | layerParam.name() % |
| 1164 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1165 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1166 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1167 | if (param.has_k()) |
| 1168 | { |
| 1169 | normalizationDescriptor.m_K = param.k(); |
| 1170 | } |
| 1171 | else |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1172 | { |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1173 | normalizationDescriptor.m_K = 1; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1174 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1175 | |
| 1176 | IConnectableLayer* const normLayer = m_Network->AddNormalizationLayer(normalizationDescriptor, |
| 1177 | layerParam.name().c_str()); |
| 1178 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(normLayer->GetInputSlot(0)); |
| 1179 | normLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 1180 | |
| 1181 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), normLayer->GetOutputSlot(0)); |
| 1182 | } |
| 1183 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1184 | void CaffeParserBase::ParseInnerProductLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1185 | { |
| 1186 | InnerProductParameter param = layerParam.inner_product_param(); |
| 1187 | |
| 1188 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 1189 | |
| 1190 | unsigned int outputSize = param.num_output(); |
| 1191 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1192 | // Ignored Caffe Parameters: |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1193 | // Weight Filler |
| 1194 | // Bias Filler |
| 1195 | // Engine |
| 1196 | // Axis |
| 1197 | |
| 1198 | FullyConnectedDescriptor tensorFullyConnectedDescriptor; |
| 1199 | |
| 1200 | if (param.has_transpose()) |
| 1201 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1202 | // If true, assumes transposed weights. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1203 | tensorFullyConnectedDescriptor.m_TransposeWeightMatrix = param.transpose(); |
| 1204 | } |
| 1205 | else |
| 1206 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1207 | // Caffe defaults to transposed. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1208 | tensorFullyConnectedDescriptor.m_TransposeWeightMatrix = true; |
| 1209 | } |
| 1210 | |
| 1211 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 1212 | |
| 1213 | TensorInfo weightInfo; |
| 1214 | TensorInfo biasInfo; |
| 1215 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1216 | // Allows implicit flattening of extra dimensions. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1217 | unsigned int inputSize = inputInfo.GetShape()[1]; |
| 1218 | for (unsigned int i = 2; i < inputInfo.GetNumDimensions(); ++i) |
| 1219 | { |
| 1220 | inputSize *= inputInfo.GetShape()[i]; |
| 1221 | } |
| 1222 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1223 | const float* weightDataPtr = GetArrayPtrFromBlob(layerParam, 0); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1224 | const unsigned int swTD[2] = { outputSize, inputSize }; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1225 | ConstTensor weights(TensorInfo(2, swTD, DataType::Float32), weightDataPtr); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1226 | |
| 1227 | tensorFullyConnectedDescriptor.m_BiasEnabled = true; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1228 | // Todo: check whether bias enabled. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1229 | armnn::IConnectableLayer* fullyConnectedLayer = nullptr; |
| 1230 | if (tensorFullyConnectedDescriptor.m_BiasEnabled) |
| 1231 | { |
| 1232 | // BIAS VALUE |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1233 | const float* biasDataPtr = GetArrayPtrFromBlob(layerParam, 1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1234 | |
| 1235 | const unsigned int sbTD[1] = { outputSize }; |
| 1236 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1237 | ConstTensor biases(TensorInfo(1, sbTD, DataType::Float32), biasDataPtr); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1238 | |
| 1239 | fullyConnectedLayer = m_Network->AddFullyConnectedLayer(tensorFullyConnectedDescriptor, weights, biases, |
| 1240 | layerParam.name().c_str()); |
| 1241 | } |
| 1242 | else |
| 1243 | { |
| 1244 | fullyConnectedLayer = m_Network->AddFullyConnectedLayer(tensorFullyConnectedDescriptor, weights, |
| 1245 | layerParam.name().c_str()); |
| 1246 | } |
| 1247 | |
| 1248 | TensorInfo outputInfo({ inputInfo.GetShape()[0], outputSize }, DataType::Float32); |
| 1249 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(fullyConnectedLayer->GetInputSlot(0)); |
| 1250 | fullyConnectedLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 1251 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), fullyConnectedLayer->GetOutputSlot(0)); |
| 1252 | } |
| 1253 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1254 | void CaffeParserBase::ParseSoftmaxLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1255 | { |
| 1256 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 1257 | |
| 1258 | SoftmaxParameter param = layerParam.softmax_param(); |
| 1259 | |
| 1260 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 1261 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1262 | // Ignored Caffe Parameters: |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1263 | // axis |
| 1264 | // Engine |
| 1265 | |
| 1266 | armnn::SoftmaxDescriptor softmaxDescriptor; |
| 1267 | armnn::IConnectableLayer* const softmaxLayer = m_Network->AddSoftmaxLayer( |
| 1268 | softmaxDescriptor, |
| 1269 | layerParam.name().c_str()); |
| 1270 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(softmaxLayer->GetInputSlot(0)); |
| 1271 | softmaxLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 1272 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), softmaxLayer->GetOutputSlot(0)); |
| 1273 | } |
| 1274 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1275 | void CaffeParserBase::ParseEltwiseLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1276 | { |
| 1277 | ValidateNumInputsOutputs(layerParam, 2, 1); |
| 1278 | |
| 1279 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 1280 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1281 | // Ignored Caffe Parameters: |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1282 | // coeff |
| 1283 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1284 | EltwiseParameter_EltwiseOp operation = EltwiseParameter_EltwiseOp_SUM; // Defaults to sum as per caffe. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1285 | |
| 1286 | if (layerParam.has_eltwise_param() && layerParam.eltwise_param().has_operation()) |
| 1287 | { |
| 1288 | operation = layerParam.eltwise_param().operation(); |
| 1289 | } |
| 1290 | |
| 1291 | armnn::IConnectableLayer* newLayer = nullptr; |
| 1292 | switch (operation) |
| 1293 | { |
| 1294 | case EltwiseParameter_EltwiseOp_SUM: |
| 1295 | { |
| 1296 | newLayer = m_Network->AddAdditionLayer(layerParam.name().c_str()); |
| 1297 | break; |
| 1298 | } |
| 1299 | case EltwiseParameter_EltwiseOp_PROD: |
| 1300 | { |
| 1301 | newLayer = m_Network->AddMultiplicationLayer(layerParam.name().c_str()); |
| 1302 | break; |
| 1303 | } |
| 1304 | default: |
| 1305 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1306 | throw ParseException( |
| 1307 | boost::str( |
| 1308 | boost::format( |
| 1309 | "Unsupported operation %1% in Eltwise layer %2% %3%") % |
| 1310 | operation % |
| 1311 | layerParam.name() % |
| 1312 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1313 | } |
| 1314 | } |
| 1315 | |
| 1316 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(newLayer->GetInputSlot(0)); |
| 1317 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(1)).Connect(newLayer->GetInputSlot(1)); |
| 1318 | newLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 1319 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), newLayer->GetOutputSlot(0)); |
| 1320 | } |
| 1321 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1322 | void CaffeParserBase::ParseConcatLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1323 | { |
| 1324 | unsigned int numInputs = static_cast<unsigned int>(layerParam.bottom_size()); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1325 | // We assume concat happens along the channel dimension, which is 1 in (0, 1, 2, 3). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1326 | unsigned int concatDim = 1; |
| 1327 | unsigned int numOfDims = 4; |
| 1328 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1329 | // we only consider 4-D tensor here |
| 1330 | OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numInputs), numOfDims); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1331 | std::vector<unsigned int>mergeDimSizes(numOfDims, 0u); |
| 1332 | |
| 1333 | unsigned int mergeDim = 0; |
| 1334 | for (unsigned int viewIndex = 0; viewIndex < numInputs; ++viewIndex) |
| 1335 | { |
| 1336 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop( |
| 1337 | layerParam.bottom(boost::numeric_cast<int>(viewIndex))).GetTensorInfo(); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1338 | // Checks whether the dimensions of the input tensors are actually 4. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1339 | if (inputInfo.GetNumDimensions()!=4) |
| 1340 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1341 | throw ParseException( |
| 1342 | boost::str( |
| 1343 | boost::format( |
| 1344 | "The number of dimensions for input tensors of " |
| 1345 | "the concatenation op should be 4. Inputs of %1% has " |
| 1346 | "%2% dimensions. %3%") % |
| 1347 | layerParam.name() % |
| 1348 | inputInfo.GetNumDimensions() % |
| 1349 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1350 | } |
| 1351 | |
| 1352 | mergeDimSizes[0] = inputInfo.GetShape()[0]; |
| 1353 | mergeDimSizes[1] = inputInfo.GetShape()[1]; |
| 1354 | mergeDimSizes[2] = inputInfo.GetShape()[2]; |
| 1355 | mergeDimSizes[3] = inputInfo.GetShape()[3]; |
| 1356 | |
| 1357 | for (unsigned int j = 0; j < concatDim; ++j) |
| 1358 | { |
| 1359 | concatDescriptor.SetViewOriginCoord(viewIndex, j, 0); |
| 1360 | } |
| 1361 | |
| 1362 | concatDescriptor.SetViewOriginCoord(viewIndex, concatDim, mergeDim); |
| 1363 | mergeDim += mergeDimSizes[concatDim]; |
| 1364 | |
| 1365 | for (unsigned int j = concatDim+1; j < numOfDims; ++j) |
| 1366 | { |
| 1367 | concatDescriptor.SetViewOriginCoord(viewIndex, j, 0); |
| 1368 | } |
| 1369 | } |
| 1370 | mergeDimSizes[concatDim] = mergeDim; |
| 1371 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1372 | armnn::IConnectableLayer* concatlayer = m_Network->AddMergerLayer(concatDescriptor, layerParam.name().c_str()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1373 | for (unsigned int i = 0; i < numInputs; ++i) |
| 1374 | { |
| 1375 | armnn::IOutputSlot& outputSlot = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(boost::numeric_cast<int>(i))); |
| 1376 | outputSlot.Connect(concatlayer->GetInputSlot(i)); |
| 1377 | } |
| 1378 | |
| 1379 | concatlayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo(numOfDims, mergeDimSizes.data(), DataType::Float32)); |
| 1380 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), concatlayer->GetOutputSlot(0)); |
| 1381 | } |
| 1382 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1383 | void CaffeParserBase::ParseBatchNormLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1384 | { |
| 1385 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 1386 | |
| 1387 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 1388 | |
| 1389 | string name = layerParam.name(); |
| 1390 | |
| 1391 | BatchNormParameter param = layerParam.batch_norm_param(); |
| 1392 | // If use_global_stats is not explicitly set in the model, assume it to be true (its default value |
| 1393 | // when the network is in the testing phase). |
| 1394 | if (param.has_use_global_stats()) |
| 1395 | { |
| 1396 | if (!param.use_global_stats()) |
| 1397 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1398 | throw ParseException( |
| 1399 | boost::str( |
| 1400 | boost::format( |
| 1401 | "Error parsing Batch Norm layer '%1%': " |
| 1402 | "Parameter 'use_global_stats' is set to false, which is " |
| 1403 | "unsupported (value used for training). %2%") % |
| 1404 | name % |
| 1405 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1406 | } |
| 1407 | } |
| 1408 | |
| 1409 | BatchNormalizationDescriptor desc; |
| 1410 | desc.m_Eps = param.eps(); |
| 1411 | |
| 1412 | unsigned int channels = inputInfo.GetShape()[1]; |
| 1413 | unsigned int shape[] = {channels}; |
| 1414 | |
| 1415 | vector<float> meanData(channels); |
| 1416 | GetDataFromBlob(layerParam, meanData, 0); |
| 1417 | |
| 1418 | vector<float> varianceData(channels); |
| 1419 | GetDataFromBlob(layerParam, varianceData, 1); |
| 1420 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1421 | // Reads moving average factor and applies scaling (if required). |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1422 | const BlobProto& blob = layerParam.blobs(boost::numeric_cast<int>(2)); |
| 1423 | const float movingAverageFactor = blob.data(boost::numeric_cast<int>(0)); |
| 1424 | if(movingAverageFactor != 0.0f) |
| 1425 | { |
| 1426 | const float scaleFactor = 1.0f / movingAverageFactor; |
| 1427 | auto scaleFunction = [scaleFactor](float f) -> float { return f * scaleFactor; }; |
| 1428 | |
| 1429 | std::transform(varianceData.begin(), varianceData.end(), varianceData.begin(), scaleFunction); |
| 1430 | std::transform(meanData.begin(), meanData.end(), meanData.begin(), scaleFunction); |
| 1431 | } |
| 1432 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1433 | // Identifies scale operation. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1434 | vector<float> betaData(channels, 0.0f); |
| 1435 | vector<float> gammaData(channels, 1.0f); |
| 1436 | |
| 1437 | ConstTensor mean(TensorInfo(1, shape, armnn::DataType::Float32), meanData); |
| 1438 | ConstTensor variance(TensorInfo(1, shape, armnn::DataType::Float32), varianceData); |
| 1439 | ConstTensor beta(TensorInfo(1, shape, armnn::DataType::Float32), betaData); |
| 1440 | ConstTensor gamma(TensorInfo(1, shape, armnn::DataType::Float32), gammaData); |
| 1441 | |
| 1442 | armnn::IConnectableLayer* const batchNormLayer = m_Network->AddBatchNormalizationLayer(desc, |
| 1443 | mean, variance, beta, gamma, name.c_str()); |
| 1444 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(batchNormLayer->GetInputSlot(0)); |
| 1445 | batchNormLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 1446 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), batchNormLayer->GetOutputSlot(0)); |
| 1447 | } |
| 1448 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1449 | void CaffeParserBase::ParseScaleLayer(const LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1450 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1451 | // Current unoptimal solution: add a batchnormalization layer with 0 mean and 1 variance. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1452 | ValidateNumInputsOutputs(layerParam, 1, 1); |
| 1453 | |
| 1454 | const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo(); |
| 1455 | |
| 1456 | string name = layerParam.name(); |
| 1457 | |
| 1458 | ScaleParameter param = layerParam.scale_param(); |
| 1459 | if (param.axis() != 1) |
| 1460 | { |
| 1461 | // Would have to use something other than BatchNormalizationLayer in this case |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1462 | throw ParseException( |
| 1463 | boost::str( |
| 1464 | boost::format( |
| 1465 | "Loading Scale Layer: Only axis 1 is supported currently. " |
| 1466 | "Layer=%1% Axis=%2% %3%") % |
| 1467 | layerParam.name() % |
| 1468 | param.axis() % |
| 1469 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1470 | } |
| 1471 | |
| 1472 | unsigned int channels = inputInfo.GetShape()[1]; |
| 1473 | unsigned int shape[] = {channels}; |
| 1474 | |
| 1475 | BatchNormalizationDescriptor desc; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1476 | desc.m_Eps = 0.0f; // Don't need epsilon if variance is 1. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1477 | vector<float> meanData(channels, 0.0f); |
| 1478 | vector<float> varianceData(channels, 1.0f); |
| 1479 | vector<float> betaData(channels, 0.0f); |
| 1480 | vector<float> gammaData(channels); |
| 1481 | |
| 1482 | GetDataFromBlob(layerParam, gammaData, 0); |
| 1483 | |
| 1484 | if(param.has_bias_term()) |
| 1485 | { |
| 1486 | GetDataFromBlob(layerParam, betaData, 1); |
| 1487 | } |
| 1488 | |
| 1489 | ConstTensor mean(TensorInfo(1, shape, armnn::DataType::Float32), meanData); |
| 1490 | ConstTensor variance(TensorInfo(1, shape, armnn::DataType::Float32), varianceData); |
| 1491 | ConstTensor beta(TensorInfo(1, shape, armnn::DataType::Float32), betaData); |
| 1492 | ConstTensor gamma(TensorInfo(1, shape, armnn::DataType::Float32), gammaData); |
| 1493 | |
| 1494 | armnn::IConnectableLayer* const batchNormLayer = m_Network->AddBatchNormalizationLayer(desc, |
| 1495 | mean, variance, beta, gamma, name.c_str()); |
| 1496 | GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(batchNormLayer->GetInputSlot(0)); |
| 1497 | batchNormLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 1498 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), batchNormLayer->GetOutputSlot(0)); |
| 1499 | } |
| 1500 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1501 | void CaffeParserBase::ParseSplitLayer(const caffe::LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1502 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1503 | // Used in caffe to duplicate memory - not necessary in armnn. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1504 | if (layerParam.bottom_size() != 1) |
| 1505 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1506 | throw ParseException( |
| 1507 | boost::str( |
| 1508 | boost::format( |
| 1509 | "Split layer '%1%' should have exactly 1 bottom. " |
| 1510 | "#bottoms=%2% %3%") % |
| 1511 | layerParam.name() % |
| 1512 | layerParam.bottom_size() % |
| 1513 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1514 | } |
| 1515 | armnn::IOutputSlot& outputSlot = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)); |
| 1516 | for (int i = 0; i < layerParam.top_size(); i++) |
| 1517 | { |
| 1518 | SetArmnnOutputSlotForCaffeTop(layerParam.top(i), outputSlot); |
| 1519 | } |
| 1520 | } |
| 1521 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1522 | void CaffeParserBase::ParseDropoutLayer(const caffe::LayerParameter& layerParam) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1523 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1524 | // Ignored for inference, so patch the single input to its single output. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1525 | if (layerParam.bottom_size() != 1 || layerParam.top_size() != 1) |
| 1526 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1527 | throw ParseException( |
| 1528 | boost::str( |
| 1529 | boost::format( |
| 1530 | "Dropout layer '%1%' should have exactly 1 bottom and 1 top. " |
| 1531 | "#bottoms=%2% #tops=%3% %4%") % |
| 1532 | layerParam.name() % |
| 1533 | layerParam.bottom_size() % |
| 1534 | layerParam.top_size() % |
| 1535 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1536 | } |
| 1537 | SetArmnnOutputSlotForCaffeTop(layerParam.top(0), GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0))); |
| 1538 | } |
| 1539 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1540 | void CaffeParserBase::TrackInputBinding(armnn::IConnectableLayer* layer, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1541 | armnn::LayerBindingId id, |
| 1542 | const armnn::TensorInfo& tensorInfo) |
| 1543 | { |
| 1544 | return TrackBindingPoint(layer, id, tensorInfo, layer->GetName(), m_NetworkInputsBindingInfo); |
| 1545 | } |
| 1546 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1547 | void CaffeParserBase::TrackOutputBinding(armnn::IConnectableLayer* layer, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1548 | armnn::LayerBindingId id, |
| 1549 | const armnn::TensorInfo& tensorInfo) |
| 1550 | { |
| 1551 | return TrackBindingPoint(layer, id, tensorInfo, layer->GetName(), m_NetworkOutputsBindingInfo); |
| 1552 | } |
| 1553 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1554 | void CaffeParserBase::TrackBindingPoint(armnn::IConnectableLayer* layer, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1555 | armnn::LayerBindingId id, |
| 1556 | const armnn::TensorInfo& tensorInfo, |
| 1557 | const char* bindingPointDesc, |
| 1558 | std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo) |
| 1559 | { |
| 1560 | const std::string layerName = layer->GetName(); |
| 1561 | auto it = nameToBindingInfo.find(layerName); |
| 1562 | if (it == nameToBindingInfo.end()) |
| 1563 | { |
| 1564 | nameToBindingInfo[layerName] = std::make_pair(id, tensorInfo); |
| 1565 | } |
| 1566 | else |
| 1567 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1568 | throw ParseException( |
| 1569 | boost::str( |
| 1570 | boost::format( |
| 1571 | "Id %1% used by more than one %2% layer %3%") % |
| 1572 | id % |
| 1573 | bindingPointDesc % |
| 1574 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1575 | } |
| 1576 | } |
| 1577 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1578 | armnn::IOutputSlot& CaffeParserBase::GetArmnnOutputSlotForCaffeTop(const std::string& caffeTopName) const |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1579 | { |
| 1580 | auto it = m_ArmnnOutputSlotForCaffeTop.find(caffeTopName); |
| 1581 | if (it != m_ArmnnOutputSlotForCaffeTop.end()) |
| 1582 | { |
| 1583 | return *it->second; |
| 1584 | } |
| 1585 | else |
| 1586 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1587 | throw ParseException( |
| 1588 | boost::str( |
| 1589 | boost::format( |
| 1590 | "Could not find armnn output slot for Caffe top '%1%' %2%") % |
| 1591 | caffeTopName % |
| 1592 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1593 | } |
| 1594 | } |
| 1595 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1596 | void CaffeParserBase::SetArmnnOutputSlotForCaffeTop( |
| 1597 | const std::string& caffeTopName, armnn::IOutputSlot& armnnOutputSlot) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1598 | { |
| 1599 | auto it = m_ArmnnOutputSlotForCaffeTop.find(caffeTopName); |
| 1600 | if (it == m_ArmnnOutputSlotForCaffeTop.end()) |
| 1601 | { |
| 1602 | m_ArmnnOutputSlotForCaffeTop[caffeTopName] = &armnnOutputSlot; |
| 1603 | } |
| 1604 | else |
| 1605 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1606 | throw ParseException( |
| 1607 | boost::str( |
| 1608 | boost::format( |
| 1609 | "Attempting to add duplicate entry for Caffe top '%1%' %2%") % |
| 1610 | caffeTopName % |
| 1611 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1612 | } |
| 1613 | } |
| 1614 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1615 | // Note: can move to CaffeParser when/if we optimise the text/string format |
| 1616 | // to load on a layer by layer basis |
| 1617 | void CaffeParserBase::ResolveInPlaceLayers(caffe::NetParameter& netParameter) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1618 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1619 | // Finds layers with the same top. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1620 | std::map<std::string, std::vector<caffe::LayerParameter*>> layersByTop; |
| 1621 | for (int layerIdx = 0; layerIdx < netParameter.layer_size(); ++layerIdx) |
| 1622 | { |
| 1623 | caffe::LayerParameter& layer = *netParameter.mutable_layer(layerIdx); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1624 | std::string name = layer.name(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1625 | for (int i = 0; i < layer.top_size(); ++i) |
| 1626 | { |
| 1627 | layersByTop[layer.top(i)].push_back(&layer); |
| 1628 | } |
| 1629 | } |
| 1630 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1631 | // For each set of layers with the same top, resolves them to a linear chain rather than in-place layers. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1632 | // Note that for 'regular' layers, there will be a single layer in each group and so this will be a no-op. |
| 1633 | for (auto layersWithSameTopIt : layersByTop) |
| 1634 | { |
| 1635 | const std::string& top = layersWithSameTopIt.first; |
| 1636 | const std::vector<caffe::LayerParameter*>& layersWithSameTop = layersWithSameTopIt.second; |
| 1637 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1638 | // Chains the layers together in the order that they are listed in the prototxt (hopefully this is correct). |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1639 | // Note that the last layer will not have its top modified so that other layers will continue to reference it. |
| 1640 | for (unsigned int layerIdx = 0; layerIdx < layersWithSameTop.size() - 1; ++layerIdx) |
| 1641 | { |
| 1642 | caffe::LayerParameter& layer1 = *layersWithSameTop[layerIdx]; |
| 1643 | caffe::LayerParameter& layer2 = *layersWithSameTop[layerIdx+1]; |
| 1644 | if (layer1.top_size() != 1) |
| 1645 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1646 | throw ParseException( |
| 1647 | boost::str( |
| 1648 | boost::format( |
| 1649 | "Node '%1%' is an in-place layer but doesn't have exactly one " |
| 1650 | "top. It has %2% instead. %3%") % |
| 1651 | layer1.name() % |
| 1652 | layer1.top_size() % |
| 1653 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1654 | } |
| 1655 | std::string newTop = layer1.name() + "_top"; |
| 1656 | layer1.set_top(0, newTop); |
| 1657 | if (layer2.bottom_size() != 1 || layer2.bottom(0) != top) |
| 1658 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1659 | throw ParseException( |
| 1660 | boost::str( |
| 1661 | boost::format( |
| 1662 | "Node '%1%' is an in-place layer but " |
| 1663 | "doesn't have exactly one bottom, or it doesn't match its top. " |
| 1664 | "#bottoms=%2%, first bottom is %3%, top is %4% %5%") % |
| 1665 | layer2.name() % |
| 1666 | layer2.bottom(0) % |
| 1667 | top % |
| 1668 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1669 | } |
| 1670 | layer2.set_bottom(0, newTop); |
| 1671 | } |
| 1672 | } |
| 1673 | } |
| 1674 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1675 | // Note: can move to CaffeParser when/if we optimise the text/string format |
| 1676 | // to load on a layer by layer basis |
| 1677 | void CaffeParserBase::LoadNetParam(NetParameter& netParameter) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1678 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1679 | // Caffe models sometimes have an implicit input layer. |
| 1680 | // In that case, add an explicit one. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1681 | if (netParameter.input_size() > 0) |
| 1682 | { |
| 1683 | LayerParameter* newLayer = netParameter.add_layer(); |
| 1684 | |
| 1685 | newLayer->set_type("Input"); |
| 1686 | newLayer->set_name(netParameter.input(0)); |
| 1687 | newLayer->add_top(netParameter.input(0)); |
| 1688 | |
| 1689 | InputParameter* inputParam = newLayer->mutable_input_param(); |
| 1690 | BlobShape* shape = inputParam->add_shape(); |
| 1691 | |
| 1692 | int dim_size = netParameter.input_dim_size(); |
| 1693 | for (int i = 0; i < dim_size; ++i) |
| 1694 | { |
| 1695 | shape->add_dim(netParameter.input_dim(i)); |
| 1696 | } |
| 1697 | } |
| 1698 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1699 | // Replaces in-place layers with regular ones to make the rest of the parsing easier. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1700 | ResolveInPlaceLayers(netParameter); |
| 1701 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1702 | // Creates a lookup of Caffe layers by name. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1703 | for (int i = 0; i < netParameter.layer_size(); ++i) |
| 1704 | { |
| 1705 | const caffe::LayerParameter& layer = netParameter.layer(i); |
| 1706 | for (int i = 0; i < layer.top_size(); ++i) |
| 1707 | { |
| 1708 | m_CaffeLayersByTopName[layer.top(i)] = &layer; |
| 1709 | } |
| 1710 | } |
| 1711 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1712 | // Finds the output layers the user requested. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1713 | std::vector<const caffe::LayerParameter*> targetLayers; |
| 1714 | for (const std::string& requestedOutputName : m_RequestedOutputs) |
| 1715 | { |
| 1716 | auto nodeIt = m_CaffeLayersByTopName.find(requestedOutputName); |
| 1717 | if (nodeIt == m_CaffeLayersByTopName.end()) |
| 1718 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1719 | throw ParseException( |
| 1720 | boost::str( |
| 1721 | boost::format( |
| 1722 | "Couldn't find requested output layer '%1%' in graph %2%") % |
| 1723 | requestedOutputName % |
| 1724 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1725 | } |
| 1726 | targetLayers.push_back(nodeIt->second); |
| 1727 | } |
| 1728 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1729 | // Sorts them into a linear ordering such that all inputs of a node are before the node itself. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1730 | std::vector<const caffe::LayerParameter*> sortedNodes; |
| 1731 | if (!armnnUtils::GraphTopologicalSort<const caffe::LayerParameter*>( |
| 1732 | targetLayers, |
| 1733 | [this](const caffe::LayerParameter* node) |
| 1734 | { |
| 1735 | return GetInputs(*node); |
| 1736 | }, |
| 1737 | sortedNodes)) |
| 1738 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1739 | throw ParseException( |
| 1740 | boost::str( |
| 1741 | boost::format( |
| 1742 | "Cycle detected in graph. #nodes: %1% %2%") % |
| 1743 | sortedNodes.size() % |
| 1744 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1745 | } |
| 1746 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1747 | // Parses each node in order, knowing that all inputs of a node will be processed before the node itself. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1748 | for (const caffe::LayerParameter* current : sortedNodes) |
| 1749 | { |
| 1750 | auto it = ms_CaffeLayerNameToParsingFunctions.find(current->type()); |
| 1751 | if (it == ms_CaffeLayerNameToParsingFunctions.end()) |
| 1752 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1753 | throw ParseException( |
| 1754 | boost::str( |
| 1755 | boost::format("Unsupported layer type: '%1%' for layer %2% %3%") % |
| 1756 | current->type() % |
| 1757 | current->name() % |
| 1758 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1759 | } |
| 1760 | auto func = it->second; |
| 1761 | (this->*func)(*current); |
| 1762 | } |
| 1763 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1764 | // Adds ArmNN output layers connected to each requested output. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1765 | for (const std::string& requestedOutput : m_RequestedOutputs) |
| 1766 | { |
| 1767 | armnn::IOutputSlot& outputSlot = GetArmnnOutputSlotForCaffeTop(requestedOutput); |
| 1768 | |
| 1769 | const armnn::LayerBindingId outputId = boost::numeric_cast<armnn::LayerBindingId>( |
| 1770 | m_NetworkOutputsBindingInfo.size()); |
| 1771 | armnn::IConnectableLayer* const outputLayer = m_Network->AddOutputLayer(outputId, requestedOutput.c_str()); |
| 1772 | outputSlot.Connect(outputLayer->GetInputSlot(0)); |
| 1773 | |
| 1774 | TrackOutputBinding(outputLayer, outputId, outputLayer->GetInputSlot(0).GetConnection()->GetTensorInfo()); |
| 1775 | } |
| 1776 | } |
| 1777 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1778 | INetworkPtr CaffeParserBase::CreateNetworkFromTextFile(const char* graphFile, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1779 | const std::map<std::string, armnn::TensorShape>& inputShapes, |
| 1780 | const std::vector<std::string>& requestedOutputs) |
| 1781 | { |
| 1782 | FILE* fd = fopen(graphFile, "r"); |
| 1783 | |
| 1784 | if (fd == nullptr) |
| 1785 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1786 | throw FileNotFoundException( |
| 1787 | boost::str( |
| 1788 | boost::format( |
| 1789 | "Failed to open graph file: %1% %2%") % |
| 1790 | graphFile % |
| 1791 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1792 | } |
| 1793 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1794 | // Parses the file into a message. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1795 | NetParameter netParam; |
| 1796 | auto input = new google::protobuf::io::FileInputStream(fileno(fd)); |
| 1797 | bool success = google::protobuf::TextFormat::Parse(input, &netParam); |
| 1798 | delete input; |
| 1799 | fclose(fd); |
| 1800 | |
| 1801 | if (!success) |
| 1802 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1803 | throw ParseException( |
| 1804 | boost::str( |
| 1805 | boost::format( |
| 1806 | "Failed to parse graph file: %1% %2%") % |
| 1807 | graphFile % |
| 1808 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1809 | } |
| 1810 | |
| 1811 | return CreateNetworkFromNetParameter(netParam, inputShapes, requestedOutputs); |
| 1812 | } |
| 1813 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1814 | INetworkPtr CaffeParserBase::CreateNetworkFromString(const char* protoText, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1815 | const std::map<std::string, armnn::TensorShape>& inputShapes, |
| 1816 | const std::vector<std::string>& requestedOutputs) |
| 1817 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1818 | // Parses the string into a message. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1819 | NetParameter netParam; |
| 1820 | bool success = google::protobuf::TextFormat::ParseFromString(protoText, &netParam); |
| 1821 | |
| 1822 | if (!success) |
| 1823 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1824 | throw ParseException( |
| 1825 | boost::str( |
| 1826 | boost::format( |
| 1827 | "Failed to parse graph string %1%") % |
| 1828 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1829 | } |
| 1830 | |
| 1831 | return CreateNetworkFromNetParameter(netParam, inputShapes, requestedOutputs); |
| 1832 | } |
| 1833 | |
| 1834 | INetworkPtr CaffeParser::CreateNetworkFromBinaryFile(const char* graphFile, |
| 1835 | const std::map<std::string, armnn::TensorShape>& inputShapes, |
| 1836 | const std::vector<std::string>& requestedOutputs) |
| 1837 | { |
| 1838 | FILE* fd = fopen(graphFile, "rb"); |
| 1839 | |
| 1840 | if (fd == nullptr) |
| 1841 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1842 | throw FileNotFoundException( |
| 1843 | boost::str( |
| 1844 | boost::format( |
| 1845 | "Failed to open graph file at: %1% %2%") % |
| 1846 | graphFile % |
| 1847 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1848 | } |
| 1849 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1850 | // Parses the file into a message. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1851 | NetParameter netParam; |
| 1852 | |
| 1853 | FileInputStream inStream(fileno(fd)); |
| 1854 | CodedInputStream codedStream(&inStream); |
| 1855 | codedStream.SetTotalBytesLimit(INT_MAX, INT_MAX); |
| 1856 | bool success = netParam.ParseFromCodedStream(&codedStream); |
| 1857 | fclose(fd); |
| 1858 | |
| 1859 | if (!success) |
| 1860 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1861 | throw ParseException( |
| 1862 | boost::str( |
| 1863 | boost::format( |
| 1864 | "Failed to parse protobuf file: %1% %2%") % |
| 1865 | graphFile % |
| 1866 | CHECK_LOCATION().AsString())); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1867 | } |
| 1868 | |
| 1869 | return CreateNetworkFromNetParameter(netParam, inputShapes, requestedOutputs); |
| 1870 | } |
| 1871 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1872 | // Note: can move to CaffeParser when/if we optimise the text/string format |
| 1873 | // to load on a layer by layer basis |
| 1874 | INetworkPtr CaffeParserBase::CreateNetworkFromNetParameter(NetParameter& netParam, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1875 | const std::map<std::string, armnn::TensorShape>& inputShapes, |
| 1876 | const std::vector<std::string>& requestedOutputs) |
| 1877 | { |
| 1878 | m_NetworkInputsBindingInfo.clear(); |
| 1879 | m_NetworkOutputsBindingInfo.clear(); |
| 1880 | |
| 1881 | m_Network = INetwork::Create(); |
| 1882 | |
| 1883 | m_InputShapes = inputShapes; |
| 1884 | if (requestedOutputs.size() == 0) |
| 1885 | { |
| 1886 | throw ParseException("requestedOutputs must have at least one entry"); |
| 1887 | } |
| 1888 | m_RequestedOutputs = requestedOutputs; |
| 1889 | |
| 1890 | try |
| 1891 | { |
| 1892 | LoadNetParam(netParam); |
| 1893 | } |
| 1894 | catch (const ParseException& e) |
| 1895 | { |
| 1896 | Cleanup(); |
| 1897 | throw e; |
| 1898 | } |
| 1899 | |
| 1900 | Cleanup(); |
| 1901 | |
| 1902 | return move(m_Network); |
| 1903 | } |
| 1904 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1905 | void CaffeParserBase::Cleanup() { |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1906 | // cleanup, in case we reuse this parser |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1907 | m_InputShapes.clear(); |
| 1908 | m_RequestedOutputs.clear(); |
| 1909 | m_ArmnnOutputSlotForCaffeTop.clear(); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame^] | 1910 | // NOTE: when we get the text/string format |
| 1911 | // optimised for memory then this data structure can |
| 1912 | // also move to the CaffeParser class |
| 1913 | m_CaffeLayersByTopName.clear(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1914 | } |
| 1915 | |
| 1916 | } |