surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4 | // |
| 5 | #include "TfParser.hpp" |
| 6 | |
| 7 | #include <armnn/INetwork.hpp> |
| 8 | #include <armnn/Utils.hpp> |
| 9 | #include <armnn/TypesUtils.hpp> |
| 10 | #include <armnn/Exceptions.hpp> |
| 11 | #include <armnn/Descriptors.hpp> |
| 12 | |
| 13 | #include <GraphTopologicalSort.hpp> |
Sadik Armagan | 479045b | 2018-10-01 11:51:37 +0100 | [diff] [blame] | 14 | #include <ParserHelper.hpp> |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 15 | #include <Permute.hpp> |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 16 | #include <VerificationHelpers.hpp> |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 17 | #include <DataLayoutIndexed.hpp> |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 18 | |
| 19 | #include <google/protobuf/io/zero_copy_stream_impl.h> |
| 20 | #include <google/protobuf/text_format.h> |
| 21 | |
| 22 | #include "tensorflow/core/framework/graph.pb.h" |
| 23 | #include "tensorflow/core/framework/node_def.pb.h" |
| 24 | #include "tensorflow/core/framework/types.pb.h" |
| 25 | #include "tensorflow/core/framework/tensor.pb.h" |
| 26 | #include "tensorflow/core/framework/tensor_shape.pb.h" |
| 27 | |
| 28 | #include <boost/assert.hpp> |
| 29 | #include <boost/format.hpp> |
| 30 | #include <boost/core/ignore_unused.hpp> |
| 31 | #include <boost/log/trivial.hpp> |
| 32 | #include <boost/numeric/conversion/cast.hpp> |
| 33 | #include <boost/polymorphic_cast.hpp> |
| 34 | |
| 35 | #include <memory> |
| 36 | #include <sstream> |
| 37 | #include <numeric> |
| 38 | #include <functional> |
| 39 | |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 40 | using namespace armnnUtils; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 41 | using namespace armnn; |
| 42 | |
| 43 | namespace armnnTfParser |
| 44 | { |
| 45 | namespace |
| 46 | { |
| 47 | |
| 48 | const PermutationVector NHWCToArmNN = { 0, 2, 3, 1 }; |
| 49 | const PermutationVector ArmNNToNHWC = { 0, 3, 1, 2 }; |
| 50 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 51 | |
| 52 | template <typename Callable> |
| 53 | void ReadMandatoryNodeAttributeImpl(const tensorflow::NodeDef& nodeDef, |
| 54 | const std::string& attribName, |
| 55 | tensorflow::AttrValue::ValueCase expectedValueCase, |
| 56 | Callable callable) |
| 57 | { |
| 58 | auto iter = nodeDef.attr().find(attribName); |
| 59 | if (iter != nodeDef.attr().end()) |
| 60 | { |
| 61 | const auto& attrValue = iter->second; |
| 62 | if (attrValue.value_case() == expectedValueCase) |
| 63 | { |
| 64 | callable(attrValue); |
| 65 | } |
| 66 | else |
| 67 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 68 | throw ParseException( |
| 69 | boost::str( |
| 70 | boost::format( |
| 71 | "Attribute %1% of node %2% expected to have %3% as tensorflow::AttrValue::ValueCase, " |
| 72 | "but found %4% instead %5%") |
| 73 | % attribName |
| 74 | % nodeDef.name() |
| 75 | % static_cast<int>(expectedValueCase) |
| 76 | % static_cast<int>(attrValue.value_case()) |
| 77 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 78 | } |
| 79 | } |
| 80 | else |
| 81 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 82 | throw ParseException( |
| 83 | boost::str( |
| 84 | boost::format( |
| 85 | "Could not find required attribute %1% in node %2% %3%") |
| 86 | % attribName |
| 87 | % nodeDef.name() |
| 88 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 89 | } |
| 90 | } |
| 91 | |
| 92 | template <typename Callable> |
| 93 | void ReadOptionalNodeAttributeImpl(const tensorflow::NodeDef& nodeDef, |
| 94 | const std::string& attribName, |
| 95 | tensorflow::AttrValue::ValueCase expectedValueCase, |
| 96 | Callable callable) |
| 97 | { |
| 98 | auto iter = nodeDef.attr().find(attribName); |
| 99 | if (iter != nodeDef.attr().end()) |
| 100 | { |
| 101 | const auto& attrValue = iter->second; |
| 102 | if (attrValue.value_case() == expectedValueCase) |
| 103 | { |
| 104 | callable(attrValue); |
| 105 | } |
| 106 | else |
| 107 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 108 | throw ParseException( |
| 109 | boost::str( |
| 110 | boost::format( |
| 111 | "Attribute %1% of node %2% expected to have %3% as tensorflow::AttrValue::ValueCase, " |
| 112 | "but found %4% instead %5%") |
| 113 | % attribName |
| 114 | % nodeDef.name() |
| 115 | % static_cast<int>(expectedValueCase) |
| 116 | % static_cast<int>(attrValue.value_case()) |
| 117 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 118 | } |
| 119 | } |
| 120 | } |
| 121 | |
| 122 | float ReadMandatoryNodeFloatAttribute(const tensorflow::NodeDef& nodeDef, const std::string& name) |
| 123 | { |
| 124 | float attribValue = 0.0f; |
| 125 | ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kF, |
| 126 | [&attribValue](const tensorflow::AttrValue& attrValue) |
| 127 | { |
| 128 | attribValue = attrValue.f(); |
| 129 | }); |
| 130 | return attribValue; |
| 131 | } |
| 132 | |
Conor Kennedy | c2130a0 | 2018-12-05 11:05:54 +0000 | [diff] [blame] | 133 | int32_t ReadMandatoryNodeInt32Attribute(const tensorflow::NodeDef& nodeDef, const std::string& name) |
| 134 | { |
| 135 | int32_t attribValue = 0u; |
| 136 | ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kI, |
| 137 | [&attribValue](const tensorflow::AttrValue& attrValue) |
| 138 | { |
| 139 | attribValue = static_cast<int32_t>(attrValue.i()); |
| 140 | }); |
| 141 | return attribValue; |
| 142 | } |
| 143 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 144 | uint32_t ReadMandatoryNodeUint32Attribute(const tensorflow::NodeDef& nodeDef, const std::string& name) |
| 145 | { |
| 146 | uint32_t attribValue = 0u; |
| 147 | ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kI, |
| 148 | [&attribValue](const tensorflow::AttrValue& attrValue) |
| 149 | { |
| 150 | attribValue = static_cast<uint32_t>(attrValue.i()); |
| 151 | }); |
| 152 | return attribValue; |
| 153 | } |
| 154 | |
| 155 | std::string ReadMandatoryNodeStringAttribute(const tensorflow::NodeDef& nodeDef, const std::string& name) |
| 156 | { |
| 157 | std::string attribValue = ""; |
| 158 | ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kS, |
| 159 | [&attribValue](const tensorflow::AttrValue& attrValue) |
| 160 | { |
| 161 | attribValue = attrValue.s(); |
| 162 | }); |
| 163 | return attribValue; |
| 164 | } |
| 165 | |
| 166 | std::vector<uint32_t> ReadMandatoryNodeUint32ListAttribute(const tensorflow::NodeDef& nodeDef, |
| 167 | const std::string& name) |
| 168 | { |
| 169 | std::vector<uint32_t> attriList; |
| 170 | ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kList, |
| 171 | [&attriList](const tensorflow::AttrValue& attrValue) |
| 172 | { |
| 173 | for (int attriNum = 0; attriNum < attrValue.list().i_size(); ++attriNum) |
| 174 | { |
| 175 | attriList.push_back(static_cast<uint32_t>(attrValue.list().i(attriNum))); |
| 176 | } |
| 177 | }); |
| 178 | |
| 179 | return attriList; |
| 180 | } |
| 181 | |
| 182 | std::vector<uint32_t> ReadOptionalNodeUint32ListAttribute(const tensorflow::NodeDef& nodeDef, |
| 183 | const std::string& name) |
| 184 | { |
| 185 | std::vector<uint32_t> attriList; |
| 186 | ReadOptionalNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kList, |
| 187 | [&attriList](const tensorflow::AttrValue& attrValue) |
| 188 | { |
| 189 | for (int attriNum = 0; attriNum < attrValue.list().i_size(); ++attriNum) |
| 190 | { |
| 191 | attriList.push_back(static_cast<uint32_t>(attrValue.list().i(attriNum))); |
| 192 | } |
| 193 | }); |
| 194 | |
| 195 | return attriList; |
| 196 | } |
| 197 | |
| 198 | bool ReadOptionalNodeBoolAttribute(const tensorflow::NodeDef& nodeDef, |
| 199 | const std::string& name, |
| 200 | bool defaultValue = false) |
| 201 | { |
| 202 | bool attribValue = defaultValue; |
| 203 | ReadOptionalNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kB, |
| 204 | [&attribValue](const tensorflow::AttrValue& attrValue) |
| 205 | { |
| 206 | attribValue = attrValue.b(); |
| 207 | }); |
| 208 | return attribValue; |
| 209 | } |
| 210 | |
| 211 | tensorflow::DataType ReadMandatoryNodeTypeAttribute(const tensorflow::NodeDef& nodeDef, const std::string& name) |
| 212 | { |
| 213 | tensorflow::DataType attribValue = tensorflow::DT_INVALID; |
| 214 | ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kType, |
| 215 | [&attribValue](const tensorflow::AttrValue& attrValue) |
| 216 | { |
| 217 | attribValue = attrValue.type(); |
| 218 | }); |
| 219 | return attribValue; |
| 220 | } |
| 221 | |
| 222 | TensorInfo PrepareReshape(const TensorInfo& input, const std::vector<int32_t>& targetDims) |
| 223 | { |
| 224 | std::vector<unsigned int> outDims(targetDims.begin(), targetDims.end()); |
| 225 | const auto stretchDim = std::find(targetDims.begin(), targetDims.end(), -1); |
| 226 | |
| 227 | if (stretchDim != targetDims.end()) |
| 228 | { |
| 229 | if (std::find(std::next(stretchDim), targetDims.end(), -1) != targetDims.end()) |
| 230 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 231 | throw ParseException( |
| 232 | boost::str( |
| 233 | boost::format( |
| 234 | "At most one component of shape can be -1 %1%") |
| 235 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 236 | } |
| 237 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 238 | auto targetNumElements = |
| 239 | boost::numeric_cast<unsigned int>( |
| 240 | std::accumulate(targetDims.begin(), targetDims.end(), -1, std::multiplies<int32_t>())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 241 | auto stretchIndex = static_cast<size_t>(std::distance(targetDims.begin(), stretchDim)); |
| 242 | outDims[stretchIndex] = input.GetNumElements() / targetNumElements; |
| 243 | } |
| 244 | |
| 245 | TensorInfo reshapeInfo = input; |
| 246 | reshapeInfo.SetShape(TensorShape{ static_cast<unsigned int>(outDims.size()), outDims.data() }); |
| 247 | |
| 248 | return reshapeInfo; |
| 249 | } |
| 250 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 251 | // We need the input0Slot to guide the reshape for input1Slot. |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 252 | IOutputSlot* AddBroadcastReshapeLayer(IOutputSlot* input0Slot, IOutputSlot* input1Slot, bool isNHWC, |
| 253 | INetwork& m_Network, const tensorflow::NodeDef& nodeDef) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 254 | { |
| 255 | const TensorInfo& input1Info = input1Slot->GetTensorInfo(); |
| 256 | const TensorInfo inputTensorInfo = input0Slot->GetTensorInfo(); |
| 257 | const unsigned int matchDim = inputTensorInfo.GetNumDimensions() - (isNHWC ? 1 : 3); |
| 258 | std::array<unsigned int, MaxNumOfTensorDimensions> reshapedDimensions; |
| 259 | std::fill_n(reshapedDimensions.begin(), inputTensorInfo.GetNumDimensions(), 1); |
| 260 | reshapedDimensions[matchDim] = input1Info.GetShape()[0]; |
| 261 | |
| 262 | armnn::TensorInfo reshapedInfo = input1Info; |
| 263 | reshapedInfo.SetShape(TensorShape{ inputTensorInfo.GetNumDimensions(), reshapedDimensions.data() }); |
| 264 | |
| 265 | const std::string reshapeLayerName = "reshape_for-" + nodeDef.name(); |
| 266 | ReshapeDescriptor reshapeDesc; |
| 267 | reshapeDesc.m_TargetShape = reshapedInfo.GetShape(); |
| 268 | IConnectableLayer* const reshapeLayer = m_Network.AddReshapeLayer(reshapeDesc, reshapeLayerName.c_str()); |
| 269 | |
| 270 | input1Slot->Connect(reshapeLayer->GetInputSlot(0)); |
| 271 | reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo); |
| 272 | |
| 273 | input1Slot = &reshapeLayer->GetOutputSlot(0); |
| 274 | |
| 275 | return input1Slot; |
| 276 | } |
| 277 | |
| 278 | OutputId ParseOutputId(const std::string & name) |
| 279 | { |
| 280 | unsigned int outputNum = 0; |
| 281 | size_t colonPos = name.find_last_of(":"); |
| 282 | if (colonPos != std::string::npos) |
| 283 | { |
| 284 | int n = std::stoi(name.substr(colonPos+1)); |
| 285 | if (n<0 || n>100) |
| 286 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 287 | throw ParseException( |
| 288 | boost::str( |
| 289 | boost::format( |
| 290 | "Output tensor id is out of range for %1% %2%") |
| 291 | % name |
| 292 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 293 | } |
| 294 | outputNum = static_cast<unsigned int>(n); |
| 295 | } |
| 296 | return OutputId(name.substr(0,colonPos),outputNum); |
| 297 | } |
| 298 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 299 | #define CHECK_DATA_FORMAT(NODE_DEF, FORMAT, NODE_TYPE) \ |
| 300 | if( FORMAT != "NHWC" && FORMAT != "NCHW" ) \ |
| 301 | { \ |
| 302 | throw ParseException( \ |
| 303 | boost::str( \ |
| 304 | boost::format( \ |
| 305 | "Unsupported data format %1% passed for %2% node %3%. " \ |
| 306 | "Only NHWC and NCHW supported %4%") \ |
| 307 | % FORMAT \ |
| 308 | % NODE_TYPE \ |
| 309 | % NODE_DEF.name() \ |
| 310 | % CHECK_LOCATION().AsString())); \ |
| 311 | } |
| 312 | |
| 313 | #define CHECK_PADDING_TYPE(NODE_DEF, PADDING) \ |
| 314 | if(PADDING != "SAME" && PADDING != "VALID" ) \ |
| 315 | { \ |
| 316 | throw ParseException( \ |
| 317 | boost::str( \ |
| 318 | boost::format( \ |
| 319 | "Only 'SAME' and 'VALID' padding supported. Got %1% for %2% %3%") \ |
| 320 | % PADDING \ |
| 321 | % NODE_DEF.name() \ |
| 322 | % CHECK_LOCATION().AsString())); \ |
| 323 | } \ |
| 324 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 325 | } // namespace |
| 326 | |
| 327 | const std::map<std::string, TfParser::OperationParsingFunction> TfParser::ms_OperationNameToParsingFunctions = { |
| 328 | { "Const", &TfParser::ParseConst }, |
| 329 | { "Add", &TfParser::ParseAdd }, |
Ferran Balaguer | fbdad03 | 2018-12-28 18:15:24 +0000 | [diff] [blame] | 330 | { "AddN", &TfParser::ParseAddN }, |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 331 | { "BiasAdd", &TfParser::ParseBiasAdd }, |
| 332 | { "Identity", &TfParser::ParseIdentity }, |
| 333 | { "Conv2D", &TfParser::ParseConv2D }, |
| 334 | { "DepthwiseConv2dNative", &TfParser::ParseDepthwiseConv2D }, |
Conor Kennedy | c2130a0 | 2018-12-05 11:05:54 +0000 | [diff] [blame] | 335 | { "ExpandDims", &TfParser::ParseExpandDims }, |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 336 | { "FusedBatchNorm", &TfParser::ParseFusedBatchNorm }, |
jimfly01 | a06bf31 | 2018-12-18 16:24:51 +0000 | [diff] [blame] | 337 | { "Greater", &TfParser::ParseGreater}, |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 338 | { "ConcatV2", &TfParser::ParseConcat }, |
| 339 | { "LRN", &TfParser::ParseLrn }, |
| 340 | { "MatMul", &TfParser::ParseMatMul }, |
| 341 | { "Mul", &TfParser::ParseMul }, |
| 342 | { "Placeholder", &TfParser::ParsePlaceholder }, |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 343 | { "RealDiv", &TfParser::ParseRealDiv }, |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 344 | { "Relu", &TfParser::ParseRelu }, |
| 345 | { "Relu6", &TfParser::ParseRelu6 }, |
| 346 | { "Reshape", &TfParser::ParseReshape }, |
| 347 | { "ResizeBilinear", &TfParser::ParseResizeBilinear }, |
| 348 | { "Shape", &TfParser::ParseShape }, |
| 349 | { "Squeeze", &TfParser::ParseSqueeze }, |
| 350 | { "Sigmoid", &TfParser::ParseSigmoid }, |
| 351 | { "Softmax", &TfParser::ParseSoftmax }, |
| 352 | { "Softplus", &TfParser::ParseSoftplus }, |
Sadik Armagan | 2ad6cb4 | 2018-12-27 11:23:44 +0000 | [diff] [blame] | 353 | { "Split", &TfParser::ParseSplit }, |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 354 | { "Tanh", &TfParser::ParseTanh }, |
| 355 | { "MaxPool", &TfParser::ParseMaxPool }, |
| 356 | { "AvgPool", &TfParser::ParseAvgPool }, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 357 | { "Maximum", &TfParser::ParseMaximum }, |
Nattapat Chaimanowong | 24df822 | 2018-12-04 13:47:02 +0000 | [diff] [blame] | 358 | { "Minimum", &TfParser::ParseMinimum }, |
jimfly01 | 84c70e6 | 2018-12-19 13:14:46 +0000 | [diff] [blame] | 359 | { "Equal", &TfParser::ParseEqual }, |
jimfly01 | f6ba747 | 2018-12-04 10:09:52 +0000 | [diff] [blame] | 360 | { "Pad", &TfParser::ParsePad }, |
narpra01 | 6f37f83 | 2018-12-21 18:30:00 +0000 | [diff] [blame] | 361 | { "Sub", &TfParser::ParseSub } |
| 362 | }; |
| 363 | |
| 364 | const std::list<std::string> TfParser::m_ControlInputs = { |
| 365 | "Assert" |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 366 | }; |
| 367 | |
| 368 | ITfParser* ITfParser::CreateRaw() |
| 369 | { |
| 370 | return new TfParser(); |
| 371 | } |
| 372 | |
| 373 | ITfParserPtr ITfParser::Create() |
| 374 | { |
| 375 | return ITfParserPtr(CreateRaw(), &ITfParser::Destroy); |
| 376 | } |
| 377 | |
| 378 | void ITfParser::Destroy(ITfParser* parser) |
| 379 | { |
| 380 | delete parser; |
| 381 | } |
| 382 | |
| 383 | inline void CalculateSamePadding(uint32_t inputSize, uint32_t stride, |
| 384 | uint32_t filterSize, bool samePadding, |
| 385 | uint32_t* paddingFront, uint32_t* paddingBack) { |
| 386 | *paddingFront = 0; |
| 387 | *paddingBack = 0; |
| 388 | |
| 389 | if (samePadding) { |
| 390 | uint32_t outputSize = (inputSize + stride - 1) / stride; |
| 391 | uint32_t temp = (outputSize - 1) * stride + filterSize; |
| 392 | if (temp > inputSize) { |
| 393 | *paddingFront = (temp - inputSize) / 2; |
| 394 | *paddingBack = (temp - inputSize) - *paddingFront; |
| 395 | } |
| 396 | } |
| 397 | } |
| 398 | |
| 399 | void CalcPadding(uint32_t input, uint32_t kernel, uint32_t stride, uint32_t& outPadHead, uint32_t& outPadTail, |
| 400 | bool samePadding) |
| 401 | { |
| 402 | CalculateSamePadding(input, stride, kernel, samePadding, &outPadHead, &outPadTail); |
| 403 | } |
| 404 | |
| 405 | /// An Abstract base class which represents a single tensorflow operation (node) |
| 406 | /// that has been (potentially partially) converted to Armnn. |
| 407 | /// It may not yet have been fully converted into actual Armnn layers. |
| 408 | class ParsedTfOperation |
| 409 | { |
| 410 | public: |
| 411 | ParsedTfOperation(TfParser* parser, const tensorflow::NodeDef& node) |
| 412 | : m_Parser(parser) |
| 413 | , m_Node(node) |
| 414 | { |
| 415 | } |
| 416 | |
| 417 | virtual ~ParsedTfOperation() {}; |
| 418 | |
| 419 | const tensorflow::NodeDef& GetNode() const { return m_Node; } |
| 420 | |
| 421 | /// Gets the ArmNN IOutputSlot corresponding to the given output index of the Tensorflow operation. |
| 422 | /// This may result in the creation of Armnn layers if this was deferred (e.g. see ParsedConstTfOperation). |
| 423 | virtual IOutputSlot& ResolveArmnnOutputSlot(unsigned int tfOutputIndex) = 0; |
| 424 | |
| 425 | /// If this operation is an Identity then this will follow return the 'parent' operation (recursively). |
| 426 | virtual ParsedTfOperation* ResolveIdentityOperations() |
| 427 | { |
| 428 | return this; |
| 429 | } |
| 430 | |
| 431 | protected: |
| 432 | TfParser* m_Parser; |
| 433 | const tensorflow::NodeDef& m_Node; |
| 434 | }; |
| 435 | |
| 436 | /// An ParsedTfOperation where the Armnn equivalent is a single layer, |
| 437 | /// with output slots that correspond directly to the Tf node outputs. |
| 438 | class SingleLayerParsedTfOperation : public ParsedTfOperation |
| 439 | { |
| 440 | public: |
| 441 | SingleLayerParsedTfOperation(TfParser* parser, const tensorflow::NodeDef& node, IConnectableLayer* layer) |
| 442 | : ParsedTfOperation(parser, node) |
| 443 | , m_Layer(layer) |
| 444 | { |
| 445 | } |
| 446 | |
| 447 | IOutputSlot& ResolveArmnnOutputSlot(unsigned int tfOutputIndex) override |
| 448 | { |
| 449 | BOOST_ASSERT(m_Layer); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 450 | // Assumes one-to-one mapping between Tf and armnn output slots. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 451 | unsigned int armnnOutputSlotIdx = tfOutputIndex; |
| 452 | if (armnnOutputSlotIdx >= m_Layer->GetNumOutputSlots()) |
| 453 | { |
| 454 | throw ParseException( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 455 | boost::str( |
| 456 | boost::format( |
| 457 | "The requested output slot #%1% " |
| 458 | "for %2% does not exist %3%") |
| 459 | % armnnOutputSlotIdx |
| 460 | % m_Layer->GetName() |
| 461 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 462 | } |
| 463 | return m_Layer->GetOutputSlot(armnnOutputSlotIdx); |
| 464 | } |
| 465 | |
| 466 | protected: |
| 467 | IConnectableLayer* m_Layer; |
| 468 | }; |
| 469 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 470 | /// A SingleLayerParsedTfOperation for deferred layer creation. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 471 | class DeferredSingleLayerParsedTfOperation : public SingleLayerParsedTfOperation |
| 472 | { |
| 473 | public: |
| 474 | DeferredSingleLayerParsedTfOperation(TfParser* parser, const tensorflow::NodeDef& node) |
| 475 | : SingleLayerParsedTfOperation(parser, node, nullptr) |
| 476 | { |
| 477 | } |
| 478 | |
| 479 | IOutputSlot& ResolveArmnnOutputSlot(unsigned int tfOutputIndex) override |
| 480 | { |
| 481 | if (!m_Layer) |
| 482 | { |
| 483 | CreateLayerDeferred(); |
| 484 | } |
| 485 | return SingleLayerParsedTfOperation::ResolveArmnnOutputSlot(tfOutputIndex); |
| 486 | } |
| 487 | |
| 488 | private: |
| 489 | virtual void CreateLayerDeferred() = 0; |
| 490 | }; |
| 491 | |
| 492 | |
| 493 | TfParser::TfParser() |
| 494 | : m_Network(nullptr, nullptr) |
| 495 | { |
| 496 | } |
| 497 | |
| 498 | |
| 499 | const tensorflow::NodeDef* TfParser::ResolveIdentityNode(const tensorflow::NodeDef* nodeDef) |
| 500 | { |
| 501 | if (nodeDef->op() != "Identity") |
| 502 | { |
| 503 | return nodeDef; |
| 504 | } |
| 505 | |
| 506 | if (nodeDef->input_size() != 1) |
| 507 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 508 | throw ParseException( |
| 509 | boost::str( |
| 510 | boost::format( |
| 511 | "Identity node should have a single input! %1% has %2% inputs %3%") |
| 512 | % nodeDef->name() |
| 513 | % nodeDef->input_size() |
| 514 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 515 | } |
| 516 | |
| 517 | auto it = m_NodesByName.find(nodeDef->input(0)); |
| 518 | if (it != m_NodesByName.end()) |
| 519 | { |
| 520 | const tensorflow::NodeDef* inputNode = it->second; |
| 521 | return ResolveIdentityNode(inputNode); |
| 522 | } |
| 523 | else |
| 524 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 525 | throw ParseException( |
| 526 | boost::str( |
| 527 | boost::format( |
| 528 | "Cannot find what the Identity node %1% is linked to! %2%") |
| 529 | % nodeDef->name() |
| 530 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 531 | } |
| 532 | } |
| 533 | |
| 534 | std::vector<OutputOfConstNodeDef> |
| 535 | TfParser::GetTfInputNodes(const tensorflow::NodeDef& nodeDef) const |
| 536 | { |
| 537 | std::vector<OutputOfConstNodeDef> ret; |
| 538 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 539 | if (nodeDef.op() == "Const") |
| 540 | { |
| 541 | // For some reason const node can have "Control Inputs". We ignore them for now. |
| 542 | return ret; |
| 543 | } |
| 544 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 545 | ret.reserve(boost::numeric_cast<size_t>(nodeDef.input_size())); |
| 546 | for (int j = 0; j < nodeDef.input_size(); ++j) |
| 547 | { |
| 548 | OutputId outputId = ParseOutputId(nodeDef.input(j)); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 549 | |
| 550 | if (nodeDef.input(j)[0] == '^') // I couldn't find a better test for control inputs. |
| 551 | { |
narpra01 | 6f37f83 | 2018-12-21 18:30:00 +0000 | [diff] [blame] | 552 | // We currently allow Control Input from TensorFlow graph but we ignore them from ArmNN graph. |
| 553 | continue; |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 554 | } |
| 555 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 556 | auto inputIt = m_NodesByName.find(outputId.m_IndexedValue); |
| 557 | if (inputIt == m_NodesByName.end()) |
| 558 | { |
| 559 | throw ParseException( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 560 | boost::str( |
| 561 | boost::format( |
| 562 | "Can't find node '%1%', which is listed as an input of '%2%' %3%") |
| 563 | % nodeDef.input(j) |
| 564 | % nodeDef.name() |
| 565 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 566 | } |
| 567 | ret.push_back(OutputOfConstNodeDef(inputIt->second,outputId.m_Index)); |
| 568 | } |
| 569 | |
| 570 | return ret; |
| 571 | } |
| 572 | |
| 573 | std::vector<OutputOfParsedTfOperation> |
| 574 | TfParser::GetInputParsedTfOperationsChecked(const tensorflow::NodeDef& nodeDef, |
| 575 | std::size_t expectedNumInputs) |
| 576 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 577 | // Fetches the tensorflow nodes connected as inputs and validate the size. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 578 | std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef); |
| 579 | const std::size_t numInputs = nodes.size(); |
| 580 | if (numInputs != expectedNumInputs) |
| 581 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 582 | throw ParseException( |
| 583 | boost::str( |
| 584 | boost::format( |
| 585 | "Unexpected number of inputs for node %1%. Expected %2%, found %3% %4%") |
| 586 | % nodeDef.name() |
| 587 | % expectedNumInputs |
| 588 | % numInputs |
| 589 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 590 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 591 | // Fetches the corresponding ParsedTfOperation operations |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 592 | std::vector<OutputOfParsedTfOperation> result; |
| 593 | for (auto&& node : nodes) |
| 594 | { |
| 595 | auto it = m_ParsedTfOperations.find(node.m_IndexedValue->name()); |
| 596 | if (it == m_ParsedTfOperations.end()) |
| 597 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 598 | throw ParseException( |
| 599 | boost::str( |
| 600 | boost::format( |
| 601 | "Node with name '%1%' has not been parsed %2%") |
| 602 | % node.m_IndexedValue->name() |
| 603 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 604 | } |
| 605 | ParsedTfOperation* parsedOp = it->second.get(); |
| 606 | // Transparently 'skip' any Identity operations. This simplifies the logic inside the ParseXXX() functions. |
| 607 | parsedOp = parsedOp->ResolveIdentityOperations(); |
| 608 | result.push_back(OutputOfParsedTfOperation(parsedOp,node.m_Index)); |
| 609 | } |
| 610 | return result; |
| 611 | } |
| 612 | |
Ferran Balaguer | fbdad03 | 2018-12-28 18:15:24 +0000 | [diff] [blame] | 613 | IConnectableLayer* TfParser::CreateAdditionLayer( |
| 614 | const tensorflow::NodeDef& nodeDef, |
| 615 | IOutputSlot* input0Slot, |
| 616 | IOutputSlot* input1Slot, |
| 617 | const std::string& layerName) |
| 618 | { |
| 619 | const TensorInfo& input0Info = input0Slot->GetTensorInfo(); |
| 620 | const TensorInfo& input1Info = input1Slot->GetTensorInfo(); |
| 621 | |
| 622 | const unsigned int input0Dim = input0Info.GetNumDimensions(); |
| 623 | const unsigned int input1Dim = input1Info.GetNumDimensions(); |
| 624 | if (input0Dim != input1Dim) |
| 625 | { |
| 626 | // broadcasting where input0 and input1 have different number of dimensions |
| 627 | // is only supported for 1D and 4D tensors pair |
| 628 | if (input0Dim == 1 && input1Dim == 4) |
| 629 | { |
| 630 | input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, true, *m_Network, nodeDef); |
| 631 | } |
| 632 | else if (input0Dim == 4 && input1Dim == 1) |
| 633 | { |
| 634 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, true, *m_Network, nodeDef); |
| 635 | } |
| 636 | else |
| 637 | { |
| 638 | throw ParseException( |
| 639 | boost::str( |
| 640 | boost::format("Unsupported broadcast configuration for %1% operation %2% %3%") |
| 641 | % layerName |
| 642 | % nodeDef.name() |
| 643 | % CHECK_LOCATION().AsString())); |
| 644 | } |
| 645 | } |
| 646 | IConnectableLayer* const layer = m_Network->AddAdditionLayer(layerName.c_str()); |
| 647 | |
| 648 | input0Slot->Connect(layer->GetInputSlot(0)); |
| 649 | input1Slot->Connect(layer->GetInputSlot(1)); |
| 650 | |
| 651 | // Ensure the output tensor has the correct dimensions even if a broadcast has been done |
| 652 | TensorInfo outputInfo = input0Slot->GetTensorInfo(); |
| 653 | std::vector<unsigned int> outputShape; |
| 654 | |
| 655 | const TensorShape& input0Shape = input0Slot->GetTensorInfo().GetShape(); |
| 656 | const TensorShape& input1Shape = input1Slot->GetTensorInfo().GetShape(); |
| 657 | |
| 658 | for (unsigned int i = 0; i < input0Shape.GetNumDimensions(); i++) |
| 659 | { |
| 660 | outputShape.push_back(std::max(input0Shape[i], input1Shape[i])); |
| 661 | } |
| 662 | |
| 663 | outputInfo.SetShape(TensorShape(input0Shape.GetNumDimensions(), outputShape.data())); |
| 664 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 665 | |
| 666 | return layer; |
| 667 | } |
| 668 | |
| 669 | IConnectableLayer* TfParser::CreateAdditionLayer( |
| 670 | const tensorflow::NodeDef& nodeDef, |
| 671 | IConnectableLayer* layerOne, |
| 672 | IConnectableLayer* layerTwo, |
| 673 | unsigned int numberOfAddition, |
| 674 | unsigned long numberOfLayersToConnect, |
| 675 | bool isOdd) |
| 676 | { |
| 677 | IOutputSlot* input0Slot = &layerOne->GetOutputSlot(0); |
| 678 | IOutputSlot* input1Slot = &layerTwo->GetOutputSlot(0); |
| 679 | std::string layerName(nodeDef.name()); |
| 680 | if (isOdd || numberOfLayersToConnect != 2) |
| 681 | { |
| 682 | // we are not connecting the final layer |
| 683 | layerName.append("_addN_").append(std::to_string(numberOfAddition)); |
| 684 | } |
| 685 | return CreateAdditionLayer(nodeDef, input0Slot, input1Slot, layerName); |
| 686 | } |
| 687 | |
| 688 | IConnectableLayer* TfParser::CreateAdditionLayer( |
| 689 | const tensorflow::NodeDef& nodeDef, |
| 690 | const OutputOfParsedTfOperation& opOne, |
| 691 | const OutputOfParsedTfOperation& opTwo, |
| 692 | unsigned int numberOfAddition) |
| 693 | { |
| 694 | IOutputSlot* input0Slot = &opOne.m_IndexedValue->ResolveArmnnOutputSlot(opOne.m_Index); |
| 695 | IOutputSlot* input1Slot = &opTwo.m_IndexedValue->ResolveArmnnOutputSlot(opTwo.m_Index); |
| 696 | std::string layerName(nodeDef.name()); |
| 697 | layerName.append("_addN_").append(std::to_string(numberOfAddition)); |
| 698 | return CreateAdditionLayer(nodeDef, input0Slot, input1Slot, layerName); |
| 699 | } |
| 700 | |
| 701 | IConnectableLayer* TfParser::CreateAdditionLayer( |
| 702 | const tensorflow::NodeDef& nodeDef, |
| 703 | const OutputOfParsedTfOperation& op, |
| 704 | IConnectableLayer* layer) |
| 705 | { |
| 706 | IOutputSlot* input0Slot = &op.m_IndexedValue->ResolveArmnnOutputSlot(op.m_Index); |
| 707 | IOutputSlot* input1Slot = &layer->GetOutputSlot(0); |
| 708 | return CreateAdditionLayer(nodeDef, input0Slot, input1Slot, nodeDef.name()); |
| 709 | } |
| 710 | |
| 711 | ParsedTfOperationPtr TfParser::ParseAddN(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 712 | { |
| 713 | uint32_t numberOfInputs = ReadMandatoryNodeUint32Attribute(nodeDef, "N"); |
| 714 | if (numberOfInputs < 2) |
| 715 | { |
| 716 | // should never happen |
| 717 | throw ParseException( |
| 718 | boost::str( |
| 719 | boost::format( |
| 720 | "AddN Node with name '%1%' has less than two (%2) inputs %3%") |
| 721 | % nodeDef.name() |
| 722 | % std::to_string(numberOfInputs) |
| 723 | % CHECK_LOCATION().AsString())); |
| 724 | } |
| 725 | else if (numberOfInputs == 2) |
| 726 | { |
| 727 | //this is the same as a simple Add operation |
| 728 | return AddAdditionLayer(nodeDef, false); |
| 729 | } |
| 730 | else |
| 731 | { |
| 732 | // build a binary tree of Add layers and return the final Add as the return from the function |
| 733 | // if we have an odd number of inputs then the final Add will consist of a layer connecting to an |
| 734 | // OutputOfParsedTfOperation, otherwise it will be two layers being added together |
| 735 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numberOfInputs); |
| 736 | unsigned int numberOfAdditions = 0; |
| 737 | std::vector<IConnectableLayer*> layers; |
| 738 | // NOTE: at this point we will have a minimum of three inputs |
| 739 | for (unsigned int i = 0; i < numberOfInputs; ++i) |
| 740 | { |
| 741 | // every time i is odd we have two inputs to process. |
| 742 | bool onSecondItem = i % 2; |
| 743 | if (onSecondItem) |
| 744 | { |
| 745 | ++numberOfAdditions; |
| 746 | IConnectableLayer* newLayer = CreateAdditionLayer( |
| 747 | nodeDef, inputs[ i - 1], inputs[i], numberOfAdditions); |
| 748 | layers.push_back(newLayer); |
| 749 | } |
| 750 | } |
| 751 | |
| 752 | std::vector<IConnectableLayer*> layersToConnect(layers); |
| 753 | unsigned long numberOfLayersToConnect = layersToConnect.size(); |
| 754 | bool isOdd = numberOfInputs % 2; |
| 755 | |
| 756 | while (numberOfLayersToConnect > 1) |
| 757 | { |
| 758 | layers.clear(); |
| 759 | for (unsigned long i = 0; i < numberOfLayersToConnect; ++i) { |
| 760 | bool onSecondItem = i % 2; |
| 761 | if (onSecondItem) { |
| 762 | ++numberOfAdditions; |
| 763 | IConnectableLayer* newLayer = CreateAdditionLayer( |
| 764 | nodeDef, |
| 765 | layersToConnect[i - 1], |
| 766 | layersToConnect[i], |
| 767 | numberOfAdditions, |
| 768 | numberOfLayersToConnect, |
| 769 | isOdd); |
| 770 | layers.push_back(newLayer); |
| 771 | } |
| 772 | } |
| 773 | //OK... need to go again... maybe |
| 774 | layersToConnect = layers; |
| 775 | numberOfLayersToConnect = layersToConnect.size(); |
| 776 | } |
| 777 | IConnectableLayer* finalLayer = layersToConnect[0]; |
| 778 | // if we had an odd number of inputs we need to connect the final layer to the |
| 779 | // last OutputOfParsedTfOperation in order to create the last Add layer we will |
| 780 | // be handing back. |
| 781 | if (isOdd) |
| 782 | { |
| 783 | // connect the final layer to the last op |
| 784 | finalLayer = CreateAdditionLayer(nodeDef, inputs[numberOfInputs - 1], finalLayer); |
| 785 | } |
| 786 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, finalLayer); |
| 787 | } |
| 788 | } |
| 789 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 790 | ParsedTfOperationPtr TfParser::ParseAdd(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 791 | { |
| 792 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 793 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 794 | // If one of the inputs is a MatMul and the other is a const, then we handle both nodes |
| 795 | // together as FullyConnected. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 796 | if (inputs[0].m_IndexedValue->GetNode().op() == "MatMul" && |
| 797 | HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name())) |
| 798 | { |
| 799 | IConnectableLayer* layer = |
| 800 | AddFullyConnectedLayer(inputs[0].m_IndexedValue->GetNode(), |
| 801 | &nodeDef,nodeDef.name().c_str()); |
| 802 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 803 | } |
| 804 | else if (HasParsedConstTensor<float>(inputs[0].m_IndexedValue->GetNode().name()) && |
| 805 | inputs[1].m_IndexedValue->GetNode().op() == "MatMul") |
| 806 | { |
| 807 | IConnectableLayer* layer = |
| 808 | AddFullyConnectedLayer(inputs[1].m_IndexedValue->GetNode(), |
| 809 | &nodeDef,nodeDef.name().c_str()); |
| 810 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 811 | } |
| 812 | else |
| 813 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 814 | // Otherwise it's just a regular addition. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 815 | return AddAdditionLayer(nodeDef); |
| 816 | } |
| 817 | } |
| 818 | |
| 819 | ParsedTfOperationPtr TfParser::ParseBiasAdd(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 820 | { |
| 821 | return AddAdditionLayer(nodeDef, true); |
| 822 | } |
| 823 | |
| 824 | /// An ParsedTfOperation which forwards to another (used for Identity nodes). |
| 825 | class ParsedIdentityTfOperation : public ParsedTfOperation |
| 826 | { |
| 827 | public: |
| 828 | ParsedIdentityTfOperation(TfParser* parser, const tensorflow::NodeDef& node, ParsedTfOperation* representative) |
| 829 | : ParsedTfOperation(parser, node) |
| 830 | , m_Representative(representative) |
| 831 | { |
| 832 | } |
| 833 | |
| 834 | virtual IOutputSlot& ResolveArmnnOutputSlot(unsigned int tfOutputIndex) override |
| 835 | { |
| 836 | BOOST_ASSERT(m_Representative); |
| 837 | return m_Representative->ResolveArmnnOutputSlot(tfOutputIndex); |
| 838 | } |
| 839 | |
| 840 | virtual ParsedTfOperation* ResolveIdentityOperations() override |
| 841 | { |
| 842 | return m_Representative->ResolveIdentityOperations(); |
| 843 | } |
| 844 | |
| 845 | private: |
| 846 | ParsedTfOperation* m_Representative; |
| 847 | }; |
| 848 | |
| 849 | ParsedTfOperationPtr TfParser::ParseIdentity(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 850 | { |
| 851 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 852 | // Any requests for the output slots of this node should be forwarded to the node connected as input. |
| 853 | return std::make_unique<ParsedIdentityTfOperation>(this, nodeDef, inputs[0].m_IndexedValue); |
| 854 | } |
| 855 | |
| 856 | /// An ParsedTfOperation for a Const node. |
| 857 | /// Creation of the armnn ConstLayer is deferred until it is actually needed, because Const nodes are mostly used |
| 858 | /// for weight inputs to MatMul/Conv2D nodes and in these cases armnn doesn't need a ConstLayer. |
| 859 | template <typename T> |
| 860 | class ParsedConstTfOperation : public DeferredSingleLayerParsedTfOperation |
| 861 | { |
| 862 | public: |
| 863 | ParsedConstTfOperation(TfParser* parser, const tensorflow::NodeDef& node, |
| 864 | const T* tensorData, const TensorInfo& tensorInfo) |
| 865 | : DeferredSingleLayerParsedTfOperation(parser, node), |
| 866 | m_Storage(tensorData, tensorData + tensorInfo.GetNumElements()), |
| 867 | m_TensorInfo(tensorInfo) |
| 868 | { |
| 869 | BOOST_ASSERT(tensorInfo.GetDataType() == GetDataType<T>()); |
| 870 | } |
| 871 | |
| 872 | void CreateLayerDeferred() override |
| 873 | { |
| 874 | BOOST_ASSERT(m_Layer == nullptr); |
| 875 | m_Layer = m_Parser->m_Network->AddConstantLayer(ConstTensor(m_TensorInfo, m_Storage), m_Node.name().c_str()); |
| 876 | m_Layer->GetOutputSlot(0).SetTensorInfo(m_TensorInfo); |
| 877 | } |
| 878 | |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 879 | ConstTensor GetConstTensor(std::vector<T>& outputTensorData) const |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 880 | { |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 881 | outputTensorData.resize(m_TensorInfo.GetNumElements()); |
| 882 | |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 883 | memcpy(outputTensorData.data(), m_Storage.data(), m_TensorInfo.GetNumBytes()); |
| 884 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 885 | // Updates the result to point to the user provided storage. |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 886 | ConstTensor constTensor(m_TensorInfo, outputTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 887 | return constTensor; |
| 888 | } |
| 889 | |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 890 | const T* GetStorage() const |
| 891 | { |
| 892 | return m_Storage.data(); |
| 893 | } |
| 894 | |
| 895 | const TensorInfo& GetTensorInfo() const |
| 896 | { |
| 897 | return m_TensorInfo; |
| 898 | } |
| 899 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 900 | private: |
| 901 | ///< Manages the lifetime of the tensor data. |
| 902 | std::vector<T> m_Storage; |
| 903 | ///< Describes the layout of the tensor and points to the data in m_Storage. |
| 904 | TensorInfo m_TensorInfo; |
| 905 | }; |
| 906 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 907 | DataType ConvertTfTensorDataType(const tensorflow::DataType tfDataType, |
| 908 | const tensorflow::NodeDef& nodeDef) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 909 | { |
| 910 | switch (tfDataType) |
| 911 | { |
| 912 | case tensorflow::DT_FLOAT: |
| 913 | return DataType::Float32; |
| 914 | break; |
| 915 | case tensorflow::DT_INT32: |
| 916 | return DataType::Signed32; |
| 917 | break; |
| 918 | default: |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 919 | throw ParseException( |
| 920 | boost::str( |
| 921 | boost::format( |
| 922 | "Unknown DataType %1% for node %2% %3%") |
| 923 | % tensorflow::DataType_Name(tfDataType) |
| 924 | % nodeDef.name() |
| 925 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 926 | } |
| 927 | } |
| 928 | |
| 929 | struct ParseTfTensorValueList |
| 930 | { |
| 931 | template<typename DataType> |
| 932 | static void Parse( |
| 933 | const tensorflow::TensorProto& tfTensor, |
| 934 | unsigned int dstElements, |
| 935 | std::vector<int8_t>& outputData); |
| 936 | |
| 937 | template <typename DataType> |
| 938 | static void ReadData(const void* srcData, unsigned int numSrcElements, |
| 939 | std::vector<int8_t>& dstData, unsigned int numDstElements) |
| 940 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 941 | // If there are no entries in the list, perform no action. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 942 | if (numSrcElements == 0) |
| 943 | { |
| 944 | return; |
| 945 | } |
| 946 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 947 | // If no size was provided, use the length of the value list. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 948 | if (numDstElements == 0) |
| 949 | { |
| 950 | numDstElements = numSrcElements; |
| 951 | } |
| 952 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 953 | // Allocates memory. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 954 | dstData.resize(std::max(numSrcElements, numDstElements) * sizeof(DataType)); |
| 955 | |
| 956 | const DataType* srcTensor = reinterpret_cast<const DataType*>(srcData); |
| 957 | DataType* dstTensor = reinterpret_cast<DataType*>(dstData.data()); |
| 958 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 959 | // Copies the value list entries into the destination. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 960 | std::copy(srcTensor, srcTensor + numSrcElements, dstTensor); |
| 961 | |
| 962 | if (numDstElements > numSrcElements) |
| 963 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 964 | // Uses the last element in the list to fill the remaining entries. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 965 | std::fill(dstTensor + numSrcElements, dstTensor + numDstElements, srcTensor[numSrcElements - 1]); |
| 966 | } |
| 967 | } |
| 968 | |
| 969 | }; |
| 970 | |
| 971 | template <> |
| 972 | void ParseTfTensorValueList::Parse<float>(const tensorflow::TensorProto& tfTensor, |
| 973 | unsigned int dstElements, std::vector<int8_t>& outputData) |
| 974 | { |
| 975 | ReadData<float>(tfTensor.float_val().data(), static_cast<unsigned int>(tfTensor.float_val_size()), |
| 976 | outputData, dstElements); |
| 977 | } |
| 978 | |
| 979 | template <> |
| 980 | void ParseTfTensorValueList::Parse<int32_t>(const tensorflow::TensorProto& tfTensor, |
| 981 | unsigned int dstElements, std::vector<int8_t>& outputData) |
| 982 | { |
| 983 | ReadData<int32_t>(tfTensor.int_val().data(), static_cast<unsigned int>(tfTensor.int_val_size()), |
| 984 | outputData, dstElements); |
| 985 | } |
| 986 | |
| 987 | template <template<typename> class OperatorType, typename T = int8_t> |
| 988 | struct MakeTfOperation |
| 989 | { |
| 990 | template<typename DataType, class... Args> |
| 991 | inline static std::unique_ptr<OperatorType<DataType>> Parse(TfParser* parser, const tensorflow::NodeDef& node, |
| 992 | Args&&... args) |
| 993 | { |
| 994 | return std::make_unique<OperatorType<DataType>>(parser, node, std::forward<Args>(args)...); |
| 995 | } |
| 996 | }; |
| 997 | |
| 998 | template <> |
| 999 | struct MakeTfOperation<ParsedConstTfOperation> |
| 1000 | { |
| 1001 | template<typename DataType, class... Args> |
| 1002 | inline static std::unique_ptr<ParsedConstTfOperation<DataType>> Parse(TfParser* parser, |
| 1003 | const tensorflow::NodeDef& node, const std::vector<int8_t>& tensorData, const TensorInfo& tensorInfo) |
| 1004 | { |
| 1005 | return std::make_unique<ParsedConstTfOperation<DataType>>(parser, node, |
| 1006 | reinterpret_cast<const DataType*>(tensorData.data()), tensorInfo); |
| 1007 | } |
| 1008 | }; |
| 1009 | |
| 1010 | template <class FuncType> |
| 1011 | struct InvokeParseFunction |
| 1012 | { |
| 1013 | template<class ResType, class... Args> |
| 1014 | inline static ResType Result(DataType dataType, Args&&... args) |
| 1015 | { |
| 1016 | if (dataType == DataType::Float32) |
| 1017 | { |
| 1018 | return FuncType::template Parse<float>(std::forward<Args>(args)...); |
| 1019 | } |
| 1020 | else if (dataType == DataType::Signed32) |
| 1021 | { |
| 1022 | return FuncType::template Parse<int32_t>(std::forward<Args>(args)...); |
| 1023 | } |
| 1024 | |
| 1025 | return ResType(); |
| 1026 | } |
| 1027 | |
| 1028 | template<class... Args> |
| 1029 | inline static void Result(DataType dataType, Args&&... args) |
| 1030 | { |
| 1031 | if (dataType == DataType::Float32) |
| 1032 | { |
| 1033 | FuncType::template Parse<float>(std::forward<Args>(args)...); |
| 1034 | } |
| 1035 | else if (dataType == DataType::Signed32) |
| 1036 | { |
| 1037 | FuncType::template Parse<int32_t>(std::forward<Args>(args)...); |
| 1038 | } |
| 1039 | } |
| 1040 | }; |
| 1041 | |
| 1042 | ParsedTfOperationPtr TfParser::ParseConst(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 1043 | { |
| 1044 | BOOST_ASSERT(nodeDef.op() == "Const"); |
| 1045 | |
| 1046 | if (nodeDef.attr().count("value") == 0) |
| 1047 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1048 | throw ParseException( |
| 1049 | boost::str( |
| 1050 | boost::format( |
| 1051 | "Value not found for Const node - %1% %2%") |
| 1052 | % nodeDef.name() |
| 1053 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1054 | } |
| 1055 | |
| 1056 | const tensorflow::TensorProto& tfTensor = nodeDef.attr().at("value").tensor(); |
| 1057 | const tensorflow::TensorShapeProto& tfTensorShape = tfTensor.tensor_shape(); |
| 1058 | const tensorflow::DataType tfDataType = ReadMandatoryNodeTypeAttribute(nodeDef, "dtype"); |
| 1059 | |
| 1060 | const auto GetDimensionSize = [](auto& d) { return d.size(); }; |
| 1061 | |
| 1062 | std::vector<unsigned int> dimensionSizes; |
| 1063 | std::transform(tfTensorShape.dim().begin(), tfTensorShape.dim().end(), |
| 1064 | std::back_inserter(dimensionSizes), GetDimensionSize); |
| 1065 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1066 | // Calculates number of elements. |
| 1067 | const DataType dataType = ConvertTfTensorDataType(tfDataType, nodeDef); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1068 | unsigned int numElements = 0U; |
| 1069 | |
| 1070 | if (!dimensionSizes.empty()) |
| 1071 | { |
| 1072 | numElements = std::accumulate(dimensionSizes.begin(), dimensionSizes.end(), |
| 1073 | 1U, std::multiplies<unsigned int>()); |
| 1074 | } |
| 1075 | |
| 1076 | std::vector<int8_t> tensorData; |
| 1077 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1078 | // Get tensor data from the list of values attribute. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1079 | if (tfTensor.tensor_content().empty()) |
| 1080 | { |
| 1081 | InvokeParseFunction<ParseTfTensorValueList>::Result<void>(dataType, tfTensor, numElements, tensorData); |
| 1082 | |
| 1083 | // If the tensor shape is not defined, but there is a value list, then interpret the data as a 1D |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1084 | // tensor of the provided number of elements. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1085 | if (numElements == 0) |
| 1086 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1087 | const unsigned int tfNumElements = |
| 1088 | static_cast<unsigned int>(tensorData.size()) / GetDataTypeSize(dataType); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1089 | dimensionSizes.push_back(tfNumElements); |
| 1090 | } |
| 1091 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1092 | // Gets tensor data from tensor content attribute. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1093 | else |
| 1094 | { |
| 1095 | tensorData.assign(tfTensor.tensor_content().begin(), tfTensor.tensor_content().end()); |
| 1096 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1097 | // Checks if a tensor shape is defined for the tensor content. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1098 | if (numElements == 0) |
| 1099 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1100 | throw ParseException( |
| 1101 | boost::str( |
| 1102 | boost::format( |
| 1103 | "No tensor shape found for Const node - %1% %2%") |
| 1104 | % nodeDef.name() |
| 1105 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1106 | } |
| 1107 | } |
| 1108 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1109 | // Const node requires at least a list of values or a content attribute. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1110 | if (tensorData.empty()) |
| 1111 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1112 | throw ParseException( |
| 1113 | boost::str( |
| 1114 | boost::format( |
| 1115 | "No tensor data found for Const node - %1% %2%") |
| 1116 | % nodeDef.name() |
| 1117 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1118 | } |
| 1119 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1120 | const TensorInfo tensorInfo(static_cast<unsigned int>(dimensionSizes.size()), |
| 1121 | dimensionSizes.data(), |
| 1122 | dataType); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1123 | |
| 1124 | // If we have a list of values, then the length of the list must be |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1125 | // less than or equal to the number of elements implied by the shape argument. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1126 | if (tensorData.size() > tensorInfo.GetNumBytes()) |
| 1127 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1128 | throw ParseException( |
| 1129 | boost::str( |
| 1130 | boost::format( |
| 1131 | "Number of elements (%1%) should be less than or equal " |
| 1132 | "to the number of elements implied by the shape argument (%2%) for Const node - %3% %4%") |
| 1133 | % (tensorData.size() / GetDataTypeSize(dataType)) |
| 1134 | % tensorInfo.GetNumElements() |
| 1135 | % nodeDef.name() |
| 1136 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1137 | } |
| 1138 | |
| 1139 | return InvokeParseFunction<MakeTfOperation<ParsedConstTfOperation>>::Result<ParsedTfOperationPtr>( |
| 1140 | dataType, this, nodeDef, tensorData, tensorInfo); |
| 1141 | } |
| 1142 | |
| 1143 | template<typename Type> |
| 1144 | bool TfParser::HasParsedConstTensor(const std::string & nodeName) const |
| 1145 | { |
| 1146 | auto it = m_ParsedTfOperations.find(nodeName); |
jimfly01 | f6ba747 | 2018-12-04 10:09:52 +0000 | [diff] [blame] | 1147 | if (it == m_ParsedTfOperations.end()) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1148 | { |
| 1149 | return false; |
| 1150 | } |
jimfly01 | f6ba747 | 2018-12-04 10:09:52 +0000 | [diff] [blame] | 1151 | return dynamic_cast<ParsedConstTfOperation<Type>*>(it->second.get()) != nullptr; |
| 1152 | } |
| 1153 | |
| 1154 | template<typename Type> |
| 1155 | bool TfParser::HasParsedConstTensor(ParsedTfOperation* parsedTfOpPtr) const |
| 1156 | { |
| 1157 | return dynamic_cast<ParsedConstTfOperation<Type>*>(parsedTfOpPtr) != nullptr; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1158 | } |
| 1159 | |
| 1160 | ParsedTfOperationPtr TfParser::ParseConv2D(const tensorflow::NodeDef& nodeDef, |
| 1161 | const tensorflow::GraphDef& graphDef) |
| 1162 | { |
| 1163 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 1164 | IOutputSlot& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 1165 | TensorInfo inputTensorInfo = inputSlot.GetTensorInfo(); |
| 1166 | |
| 1167 | if (!HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name())) |
| 1168 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1169 | throw ParseException( |
| 1170 | boost::str( |
| 1171 | boost::format( |
| 1172 | "ArmNN only supports Convolution layers with constant weights for %1%, input %2% %3%") |
| 1173 | % nodeDef.name() |
| 1174 | % inputs[1].m_IndexedValue->GetNode().name() |
| 1175 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1176 | } |
| 1177 | ParsedConstTfOperation<float>* weightNode = |
| 1178 | boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[1].m_IndexedValue); |
| 1179 | |
| 1180 | std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, "padding"); |
| 1181 | std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, "data_format"); |
| 1182 | std::vector<uint32_t> strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, "strides"); |
| 1183 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1184 | // Read the dilations, if present - only [1,1,1,1] (the default) is supported. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1185 | std::vector<uint32_t> dilations = ReadOptionalNodeUint32ListAttribute(nodeDef, "dilations"); |
| 1186 | if (!dilations.empty()) |
| 1187 | { |
| 1188 | for (auto dilation : dilations) |
| 1189 | { |
| 1190 | if (dilation != 1u) |
| 1191 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1192 | throw ParseException( |
| 1193 | boost::str( |
| 1194 | boost::format( |
| 1195 | "ArmNN only supports Convolution layers with dilations [1,1,1,1] for %1% %2%") |
| 1196 | % nodeDef.name() |
| 1197 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1198 | } |
| 1199 | } |
| 1200 | } |
| 1201 | |
| 1202 | Convolution2dDescriptor desc; |
| 1203 | desc.m_BiasEnabled = false; |
| 1204 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1205 | CHECK_DATA_FORMAT(nodeDef, dataFormat, "Conv2D"); |
| 1206 | |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1207 | DataLayout dataLayout = dataFormat == "NHWC" ? DataLayout::NHWC : DataLayout::NCHW; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1208 | |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1209 | desc.m_DataLayout = dataLayout; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1210 | |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1211 | DataLayoutIndexed dataLayoutIndexed(dataLayout); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1212 | |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1213 | desc.m_StrideX = strides[dataLayoutIndexed.GetWidthIndex()]; |
| 1214 | desc.m_StrideY = strides[dataLayoutIndexed.GetHeightIndex()]; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1215 | |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1216 | uint32_t inputHeight = inputTensorInfo.GetShape()[dataLayoutIndexed.GetHeightIndex()]; |
| 1217 | uint32_t inputWidth = inputTensorInfo.GetShape()[dataLayoutIndexed.GetWidthIndex()]; |
| 1218 | |
| 1219 | // Mappings from TensorFlow filter tensors to the ArmNN filter tensors. |
| 1220 | // Tensorflow weights are [H, W, In, Out]. |
| 1221 | // ArmNN weights have to be [Out, H, W, In] when the data layout is NHWC, |
| 1222 | // and [Out, In, H, W] when the data layout is NCHW. |
| 1223 | PermutationVector permutationVector = |
| 1224 | dataLayout == DataLayout::NHWC ? |
| 1225 | std::initializer_list<unsigned int>{ 1, 2, 3, 0 } : // NHWC: [H, W, In, Out] -> [Out, H, W, In] |
| 1226 | std::initializer_list<unsigned int>{ 2, 3, 1, 0 }; // NCHW: [H, W, In, Out] -> [Out, In, H, W] |
| 1227 | |
| 1228 | // Swizzle the tensor using the given permutation vector. |
| 1229 | const TensorInfo& weightTensorInfo = weightNode->GetTensorInfo(); |
| 1230 | const TensorInfo weightTensorSwizzledInfo = armnnUtils::Permuted(weightTensorInfo, permutationVector); |
| 1231 | |
| 1232 | // Swizzles the content of the tensor's permanent storage into a local storage. |
| 1233 | std::vector<float> weightTensorSwizzledData(weightTensorInfo.GetNumElements()); |
| 1234 | armnnUtils::Permute(weightTensorSwizzledInfo.GetShape(), permutationVector, |
Matteo Martincigh | d5b9e64 | 2019-01-04 18:01:21 +0000 | [diff] [blame] | 1235 | weightNode->GetStorage(), weightTensorSwizzledData.data(), sizeof(float)); |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1236 | |
| 1237 | // Create a weight tensor with the newly swizzled data. |
| 1238 | ConstTensor weightTensor(weightTensorSwizzledInfo, weightTensorSwizzledData); |
| 1239 | |
| 1240 | uint32_t weightHeight = weightTensor.GetShape()[dataLayoutIndexed.GetHeightIndex()]; |
| 1241 | uint32_t weightWidth = weightTensor.GetShape()[dataLayoutIndexed.GetWidthIndex()]; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1242 | |
| 1243 | bool padding = false; |
| 1244 | TensorInfo outputInfo; |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1245 | unsigned int outputHeight = 0; |
| 1246 | unsigned int outputWidth = 0; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1247 | |
| 1248 | CHECK_PADDING_TYPE(nodeDef, paddingString); |
| 1249 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1250 | if (paddingString == "SAME") |
| 1251 | { |
| 1252 | padding = true; |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1253 | |
| 1254 | outputHeight = static_cast<uint32_t>(ceil(static_cast<float>(inputHeight) / |
| 1255 | static_cast<float>(desc.m_StrideY))); |
| 1256 | outputWidth = static_cast<uint32_t>(ceil(static_cast<float>(inputWidth) / |
| 1257 | static_cast<float>(desc.m_StrideX))); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1258 | } |
| 1259 | else if (paddingString == "VALID") |
| 1260 | { |
| 1261 | padding = false; |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1262 | |
| 1263 | outputHeight = static_cast<uint32_t>(ceil(static_cast<float>(inputHeight - weightHeight + 1) / |
| 1264 | static_cast<float>(desc.m_StrideY))); |
| 1265 | outputWidth = static_cast<uint32_t>(ceil(static_cast<float>(inputWidth - weightWidth + 1) / |
| 1266 | static_cast<float>(desc.m_StrideX))); |
| 1267 | } |
| 1268 | |
| 1269 | switch (dataLayout) |
| 1270 | { |
| 1271 | case DataLayout::NHWC: |
| 1272 | outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], |
| 1273 | outputHeight, |
| 1274 | outputWidth, |
| 1275 | weightTensor.GetShape()[0] }, |
| 1276 | DataType::Float32); |
| 1277 | break; |
| 1278 | case DataLayout::NCHW: |
| 1279 | default: |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1280 | outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], |
| 1281 | weightTensor.GetShape()[0], |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1282 | outputHeight, |
| 1283 | outputWidth }, |
| 1284 | DataType::Float32); |
| 1285 | break; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1286 | } |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1287 | |
| 1288 | CalcPadding(inputHeight, weightHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, padding); |
| 1289 | CalcPadding(inputWidth, weightWidth, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, padding); |
| 1290 | |
| 1291 | IConnectableLayer* layer = m_Network->AddConvolution2dLayer(desc, weightTensor, nodeDef.name().c_str()); |
| 1292 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
Matteo Martincigh | 4631582 | 2018-11-28 16:22:36 +0000 | [diff] [blame] | 1293 | inputSlot.Connect(layer->GetInputSlot(0)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1294 | |
| 1295 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1296 | } |
| 1297 | |
| 1298 | ParsedTfOperationPtr TfParser::ParseDepthwiseConv2D(const tensorflow::NodeDef& nodeDef, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1299 | const tensorflow::GraphDef& graphDef) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1300 | { |
| 1301 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 1302 | IOutputSlot& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 1303 | TensorInfo inputTensorInfo = inputSlot.GetTensorInfo(); |
| 1304 | |
| 1305 | if (!HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name())) |
| 1306 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1307 | throw ParseException( |
| 1308 | boost::str( |
| 1309 | boost::format( |
| 1310 | "ArmNN only supports Depthwise Convolution layer with constant weights. " |
| 1311 | "Non const input found %1% for node %2% %3%") |
| 1312 | % inputs[1].m_IndexedValue->GetNode().name() |
| 1313 | % nodeDef.name() |
| 1314 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1315 | } |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1316 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1317 | ParsedConstTfOperation<float>* weightNode = |
| 1318 | boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[1].m_IndexedValue); |
| 1319 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1320 | std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, "padding"); |
| 1321 | std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, "data_format"); |
| 1322 | std::vector<uint32_t> strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, "strides"); |
| 1323 | |
| 1324 | DepthwiseConvolution2dDescriptor desc; |
| 1325 | desc.m_BiasEnabled = false; |
| 1326 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1327 | CHECK_DATA_FORMAT(nodeDef, dataFormat, "DepthwiseConv2dNative"); |
| 1328 | |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1329 | DataLayout dataLayout = dataFormat == "NHWC" ? DataLayout::NHWC : DataLayout::NCHW; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1330 | |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1331 | desc.m_DataLayout = dataLayout; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1332 | |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1333 | DataLayoutIndexed dataLayoutIndexed(dataLayout); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1334 | |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1335 | desc.m_StrideX = strides[dataLayoutIndexed.GetWidthIndex()]; |
| 1336 | desc.m_StrideY = strides[dataLayoutIndexed.GetHeightIndex()]; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1337 | |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1338 | uint32_t inputHeight = inputTensorInfo.GetShape()[dataLayoutIndexed.GetHeightIndex()]; |
| 1339 | uint32_t inputWidth = inputTensorInfo.GetShape()[dataLayoutIndexed.GetWidthIndex()]; |
| 1340 | |
| 1341 | // Mappings from TensorFlow filter tensors to the ArmNN filter tensors. |
Matteo Martincigh | 747ef82 | 2018-12-18 09:26:39 +0000 | [diff] [blame] | 1342 | // Tensorflow weights come in the format [H, W, I, M]. |
| 1343 | // ArmNN weights have to be [M, I, H, W]. |
| 1344 | PermutationVector permutationVector{ 2, 3, 1, 0 }; // [H, W, I, M] -> [M, I, H, W] |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1345 | |
| 1346 | // Swizzle the tensor using the given permutation vector. |
| 1347 | const TensorInfo& weightTensorInfo = weightNode->GetTensorInfo(); |
| 1348 | const TensorInfo weightTensorSwizzledInfo = armnnUtils::Permuted(weightTensorInfo, permutationVector); |
| 1349 | |
| 1350 | // Swizzles the content of the tensor's permanent storage into a local storage. |
| 1351 | std::vector<float> weightTensorSwizzledData(weightTensorInfo.GetNumElements()); |
| 1352 | armnnUtils::Permute(weightTensorSwizzledInfo.GetShape(), permutationVector, |
Matteo Martincigh | d5b9e64 | 2019-01-04 18:01:21 +0000 | [diff] [blame] | 1353 | weightNode->GetStorage(), weightTensorSwizzledData.data(), sizeof(float)); |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1354 | |
| 1355 | // Create a weight tensor with the newly swizzled data. |
| 1356 | ConstTensor weightTensor(weightTensorSwizzledInfo, weightTensorSwizzledData); |
| 1357 | |
Matteo Martincigh | 747ef82 | 2018-12-18 09:26:39 +0000 | [diff] [blame] | 1358 | uint32_t weightHeight = weightTensor.GetShape()[2]; |
| 1359 | uint32_t weightWidth = weightTensor.GetShape()[3]; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1360 | |
| 1361 | bool padding = false; |
| 1362 | TensorInfo outputInfo; |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1363 | unsigned int outputHeight = 0; |
| 1364 | unsigned int outputWidth = 0; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1365 | |
| 1366 | CHECK_PADDING_TYPE(nodeDef, paddingString); |
| 1367 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1368 | if (paddingString == "SAME") |
| 1369 | { |
| 1370 | padding = true; |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1371 | |
| 1372 | outputHeight = static_cast<uint32_t>(ceil(static_cast<float>(inputHeight) / |
| 1373 | static_cast<float>(desc.m_StrideY))); |
| 1374 | outputWidth = static_cast<uint32_t>(ceil(static_cast<float>(inputWidth) / |
| 1375 | static_cast<float>(desc.m_StrideX))); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1376 | } |
| 1377 | else if (paddingString == "VALID") |
| 1378 | { |
| 1379 | padding = false; |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1380 | |
| 1381 | outputHeight = static_cast<uint32_t>(ceil(static_cast<float>(inputHeight - weightHeight + 1) / |
| 1382 | static_cast<float>(desc.m_StrideY))); |
| 1383 | outputWidth = static_cast<uint32_t>(ceil(static_cast<float>(inputWidth - weightWidth + 1) / |
| 1384 | static_cast<float>(desc.m_StrideX))); |
| 1385 | } |
| 1386 | |
| 1387 | switch (dataLayout) |
| 1388 | { |
| 1389 | case DataLayout::NHWC: |
| 1390 | outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], |
| 1391 | outputHeight, |
| 1392 | outputWidth, |
Matteo Martincigh | 747ef82 | 2018-12-18 09:26:39 +0000 | [diff] [blame] | 1393 | weightTensor.GetShape()[0] * weightTensor.GetShape()[1]}, |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1394 | DataType::Float32); |
| 1395 | break; |
| 1396 | case DataLayout::NCHW: |
| 1397 | default: |
| 1398 | outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], |
| 1399 | weightTensor.GetShape()[0] * weightTensor.GetShape()[1], |
| 1400 | outputHeight, |
| 1401 | outputWidth }, |
| 1402 | DataType::Float32); |
| 1403 | break; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1404 | } |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1405 | |
| 1406 | CalcPadding(inputHeight, weightHeight, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, padding); |
| 1407 | CalcPadding(inputWidth, weightWidth, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, padding); |
| 1408 | |
| 1409 | IConnectableLayer* layer = m_Network->AddDepthwiseConvolution2dLayer(desc, weightTensor, nodeDef.name().c_str()); |
| 1410 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
Ferran Balaguer | 6a669d7 | 2018-12-11 10:29:05 +0000 | [diff] [blame] | 1411 | inputSlot.Connect(layer->GetInputSlot(0)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1412 | |
| 1413 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1414 | } |
| 1415 | |
Conor Kennedy | c2130a0 | 2018-12-05 11:05:54 +0000 | [diff] [blame] | 1416 | TensorInfo OutputShapeOfExpandDims(const tensorflow::NodeDef& nodeDef, TensorInfo inputTensorInfo) |
| 1417 | { |
| 1418 | BOOST_ASSERT(nodeDef.op() == "ExpandDims"); |
| 1419 | |
| 1420 | if (inputTensorInfo.GetNumDimensions() > 4) { |
| 1421 | throw ParseException( |
| 1422 | boost::str( |
| 1423 | boost::format( |
| 1424 | "Unsupported number of dimensions: %1% for input shape for ExpandDims %2% %3%") |
| 1425 | % inputTensorInfo.GetNumDimensions() |
| 1426 | % nodeDef.name() |
| 1427 | % CHECK_LOCATION().AsString())); |
| 1428 | } |
| 1429 | |
| 1430 | std::int32_t expandDim = ReadMandatoryNodeInt32Attribute(nodeDef, "Tdim"); |
| 1431 | |
| 1432 | std::int32_t inputDimSize = boost::numeric_cast<int32_t>(inputTensorInfo.GetNumDimensions()); |
| 1433 | std::vector<uint32_t> outputDims; |
| 1434 | |
| 1435 | // expandDim operation requires: -1-input.dims() <= dim <= input.dims() |
| 1436 | if (expandDim >= -1 - inputDimSize && expandDim <= inputDimSize) |
| 1437 | { |
| 1438 | // add current input shape to outputDims |
| 1439 | for (unsigned int i = 0; i < inputTensorInfo.GetNumDimensions(); ++i) { |
| 1440 | auto currentDimension = inputTensorInfo.GetShape()[i]; |
| 1441 | outputDims.push_back(currentDimension); |
| 1442 | } |
| 1443 | |
| 1444 | // insert a dimension of 1 at index 'expandDim' of inputs shape |
| 1445 | if (expandDim >= 0) |
| 1446 | { |
| 1447 | auto getPosition = std::next(outputDims.begin() + 0, expandDim); |
| 1448 | outputDims.insert(getPosition, 1); |
| 1449 | } |
| 1450 | |
| 1451 | // if negative number for 'expandDim' then count backwards from the last element |
| 1452 | // and insert 1 dimension at index 'expandDim' |
| 1453 | if (expandDim < 0) |
| 1454 | { |
Matteo Martincigh | d7cceeb | 2018-12-06 09:06:29 +0000 | [diff] [blame] | 1455 | int outputDimSize = boost::numeric_cast<int>(outputDims.size() + 1); |
Conor Kennedy | c2130a0 | 2018-12-05 11:05:54 +0000 | [diff] [blame] | 1456 | auto getPosition = std::next(outputDims.begin() + outputDimSize, expandDim); |
| 1457 | outputDims.insert(getPosition, 1); |
| 1458 | } |
| 1459 | } |
| 1460 | else |
| 1461 | { |
| 1462 | throw InvalidArgumentException( |
| 1463 | boost::str( |
| 1464 | boost::format( |
| 1465 | "Cannot expand dimension %1% in input tensor with %2% dimension %3%") |
| 1466 | % expandDim |
| 1467 | % inputDimSize |
| 1468 | % CHECK_LOCATION().AsString())); |
| 1469 | } |
| 1470 | |
| 1471 | if (outputDims.size() > 4) |
| 1472 | { |
| 1473 | throw ParseException( |
| 1474 | boost::str( |
| 1475 | boost::format( |
| 1476 | "Unsupported number of dimensions: %1% for output shape for ExpandDims %2% %3%") |
| 1477 | % outputDims.size() |
| 1478 | % nodeDef.name() |
| 1479 | % CHECK_LOCATION().AsString())); |
| 1480 | } |
| 1481 | |
| 1482 | TensorShape outShape = TensorShape(static_cast<unsigned int>(outputDims.size()), |
| 1483 | outputDims.data()); |
| 1484 | |
| 1485 | TensorInfo outTensorInfo = inputTensorInfo; |
| 1486 | outTensorInfo.SetShape(outShape); |
| 1487 | |
| 1488 | return outTensorInfo; |
| 1489 | } |
| 1490 | |
| 1491 | ParsedTfOperationPtr TfParser::ParseExpandDims(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 1492 | { |
| 1493 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 1494 | |
| 1495 | IOutputSlot& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 1496 | TensorInfo inputTensorInfo = prevLayerOutputSlot.GetTensorInfo(); |
| 1497 | |
| 1498 | TensorInfo outputInfo; |
| 1499 | outputInfo = OutputShapeOfExpandDims(nodeDef, inputTensorInfo); |
| 1500 | |
| 1501 | ReshapeDescriptor reshapeDesc; |
| 1502 | reshapeDesc.m_TargetShape = outputInfo.GetShape(); |
| 1503 | IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, nodeDef.name().c_str()); |
| 1504 | prevLayerOutputSlot.Connect(layer->GetInputSlot(0)); |
| 1505 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 1506 | |
| 1507 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1508 | } |
| 1509 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1510 | ParsedTfOperationPtr TfParser::ParseFusedBatchNorm(const tensorflow::NodeDef& nodeDef, |
| 1511 | const tensorflow::GraphDef& graphDef) |
| 1512 | { |
| 1513 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 5); |
| 1514 | |
| 1515 | if (!HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name())) |
| 1516 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1517 | throw ParseException( |
| 1518 | boost::str( |
| 1519 | boost::format( |
| 1520 | "ArmNN only supports FusedBatchNormalization layers with constant scale. " |
| 1521 | "Input %1%. Node %2% %3%") |
| 1522 | % inputs[1].m_IndexedValue->GetNode().name() |
| 1523 | % nodeDef.name() |
| 1524 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1525 | } |
| 1526 | ParsedConstTfOperation<float>* scaleNode = |
| 1527 | boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[1].m_IndexedValue); |
| 1528 | |
| 1529 | if (!HasParsedConstTensor<float>(inputs[2].m_IndexedValue->GetNode().name())) |
| 1530 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1531 | throw ParseException( |
| 1532 | boost::str( |
| 1533 | boost::format( |
| 1534 | "ArmNN only supports FusedBatchNormalization layers with constant offset. " |
| 1535 | "Input %1%. Node %2% %3%") |
| 1536 | % inputs[2].m_IndexedValue->GetNode().name() |
| 1537 | % nodeDef.name() |
| 1538 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1539 | } |
| 1540 | ParsedConstTfOperation<float>* offsetNode = |
| 1541 | boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[2].m_IndexedValue); |
| 1542 | |
| 1543 | if (!HasParsedConstTensor<float>(inputs[3].m_IndexedValue->GetNode().name())) |
| 1544 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1545 | throw ParseException( |
| 1546 | boost::str( |
| 1547 | boost::format( |
| 1548 | "ArmNN only supports FusedBatchNormalization layers with constant mean. " |
| 1549 | "Input %1%. Node %2% %3%") |
| 1550 | % inputs[3].m_IndexedValue->GetNode().name() |
| 1551 | % nodeDef.name() |
| 1552 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1553 | } |
| 1554 | ParsedConstTfOperation<float>* meanNode = |
| 1555 | boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[3].m_IndexedValue); |
| 1556 | |
| 1557 | if (!HasParsedConstTensor<float>(inputs[4].m_IndexedValue->GetNode().name())) |
| 1558 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1559 | throw ParseException( |
| 1560 | boost::str( |
| 1561 | boost::format( |
| 1562 | "ArmNN only supports FusedBatchNormalization layers with constant variance. " |
| 1563 | "Input %1%. Node %2% %3%") |
| 1564 | % inputs[4].m_IndexedValue->GetNode().name() |
| 1565 | % nodeDef.name() |
| 1566 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1567 | } |
| 1568 | ParsedConstTfOperation<float>* varianceNode = |
| 1569 | boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[4].m_IndexedValue); |
| 1570 | |
Matteo Martincigh | 075c750 | 2018-12-05 13:10:45 +0000 | [diff] [blame] | 1571 | const std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, "data_format"); |
| 1572 | |
| 1573 | CHECK_DATA_FORMAT(nodeDef, dataFormat, "FusedBatchNorm"); |
| 1574 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1575 | // The descriptor only has the epsilon attribute. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1576 | BatchNormalizationDescriptor desc; |
| 1577 | desc.m_Eps = ReadMandatoryNodeFloatAttribute(nodeDef, "epsilon"); |
Matteo Martincigh | 075c750 | 2018-12-05 13:10:45 +0000 | [diff] [blame] | 1578 | desc.m_DataLayout = dataFormat == "NHWC" ? DataLayout::NHWC : DataLayout::NCHW; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1579 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1580 | // Data for the parsed tensor args (scale, offset, mean, variance) must be stored |
| 1581 | // locally until the layer is added. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1582 | std::vector<float> scaleTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 1583 | ConstTensor scaleTensor = scaleNode->GetConstTensor(scaleTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1584 | |
| 1585 | std::vector<float> offsetTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 1586 | ConstTensor offsetTensor = offsetNode->GetConstTensor(offsetTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1587 | |
| 1588 | std::vector<float> meanTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 1589 | ConstTensor meanTensor = meanNode->GetConstTensor(meanTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1590 | |
| 1591 | std::vector<float> varianceTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 1592 | ConstTensor varianceTensor = varianceNode->GetConstTensor(varianceTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1593 | |
| 1594 | IConnectableLayer* layer = m_Network->AddBatchNormalizationLayer(desc, |
| 1595 | meanTensor, |
| 1596 | varianceTensor, |
| 1597 | offsetTensor, |
| 1598 | scaleTensor, |
| 1599 | nodeDef.name().c_str()); |
| 1600 | |
| 1601 | IOutputSlot& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 1602 | |
Matteo Martincigh | 075c750 | 2018-12-05 13:10:45 +0000 | [diff] [blame] | 1603 | layer->GetOutputSlot(0).SetTensorInfo(inputSlot.GetTensorInfo()); |
| 1604 | inputSlot.Connect(layer->GetInputSlot(0)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1605 | |
| 1606 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1607 | } |
| 1608 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1609 | bool TfParser::IsSupportedLeakyReluPattern(const tensorflow::NodeDef& mulNodeDef, |
| 1610 | size_t alphaLayerIndex, |
| 1611 | const OutputOfParsedTfOperation& otherOp, |
| 1612 | armnn::IOutputSlot** outputOfLeakyRelu, |
| 1613 | armnn::ActivationDescriptor & desc) |
| 1614 | { |
| 1615 | const tensorflow::NodeDef& otherNodeDef = otherOp.m_IndexedValue->GetNode(); |
| 1616 | |
| 1617 | // Verifying all these assumptions hold: |
| 1618 | // |
| 1619 | // 1, the mulNodeDef is an elementwise multiplication node "Mul" |
| 1620 | // 2, the alphaLayerIndex selects a constant node from the inputs of the "Mul" node |
| 1621 | // 3, the inputLayerIndex selects a layer which has the same name as otherNodeDef |
| 1622 | // |
| 1623 | |
| 1624 | if (mulNodeDef.op() == "Mul") |
| 1625 | { |
| 1626 | size_t otherLayerIndex = (alphaLayerIndex == 0 ? 1 : 0); |
| 1627 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(mulNodeDef, 2); |
| 1628 | |
| 1629 | BOOST_ASSERT(inputs.size() == 2); |
| 1630 | BOOST_ASSERT((otherLayerIndex == 0 || alphaLayerIndex == 0)); |
| 1631 | BOOST_ASSERT((otherLayerIndex == 1 || alphaLayerIndex == 1)); |
| 1632 | BOOST_ASSERT(((otherLayerIndex + alphaLayerIndex) == 1)); |
| 1633 | |
| 1634 | if (inputs[otherLayerIndex].m_IndexedValue->GetNode().name() == otherNodeDef.name()) |
| 1635 | { |
| 1636 | if (HasParsedConstTensor<float>(inputs[alphaLayerIndex].m_IndexedValue->GetNode().name())) |
| 1637 | { |
| 1638 | ParsedConstTfOperation<float>* alpha = |
| 1639 | boost::polymorphic_downcast<ParsedConstTfOperation<float> *>( |
| 1640 | inputs[alphaLayerIndex].m_IndexedValue); |
| 1641 | |
| 1642 | std::vector<float> const_data; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 1643 | ConstTensor const_tensor = alpha->GetConstTensor(const_data); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1644 | |
| 1645 | if (const_data.size() == 1) |
| 1646 | { |
| 1647 | desc.m_Function = ActivationFunction::LeakyReLu; |
| 1648 | desc.m_A = const_data[0]; |
| 1649 | |
| 1650 | *outputOfLeakyRelu = &(otherOp.m_IndexedValue->ResolveArmnnOutputSlot(otherOp.m_Index)); |
| 1651 | return true; |
| 1652 | } |
| 1653 | } |
| 1654 | } |
| 1655 | } |
| 1656 | return false; |
| 1657 | } |
| 1658 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1659 | ParsedTfOperationPtr TfParser::ParseMaximum(const tensorflow::NodeDef& nodeDef, |
| 1660 | const tensorflow::GraphDef& graphDef) |
| 1661 | { |
| 1662 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
Sadik Armagan | 975c09a | 2018-12-04 10:02:08 +0000 | [diff] [blame] | 1663 | if (inputs.size() != 2) |
| 1664 | { |
| 1665 | throw ParseException( |
| 1666 | boost::str( |
| 1667 | boost::format( |
| 1668 | "Maximum expects two inputs!. Got %1% for Node %2% %3%") |
| 1669 | % inputs.size() |
| 1670 | % nodeDef.name() |
| 1671 | % CHECK_LOCATION().AsString())); |
| 1672 | } |
| 1673 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1674 | auto inputNode0 = inputs[0].m_IndexedValue->GetNode(); |
| 1675 | auto inputNode1 = inputs[1].m_IndexedValue->GetNode(); |
| 1676 | IOutputSlot* outputOfLeakyRelu = nullptr; |
| 1677 | |
| 1678 | ActivationDescriptor desc; |
| 1679 | |
Sadik Armagan | 975c09a | 2018-12-04 10:02:08 +0000 | [diff] [blame] | 1680 | // A max node may be part of a LeakyRelu, with one input as a multiplication with a scalar constant, |
| 1681 | // i.e. one of the four possible scenarios: |
| 1682 | // 1, max(mul(a, x), x) |
| 1683 | // 2, max(mul(x, a), x) |
| 1684 | // 3, max(x, mul(a, x)) |
| 1685 | // 4, max(x, mul(x, a)) |
| 1686 | // These are handled by an activation layer. |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1687 | |
| 1688 | if (IsSupportedLeakyReluPattern(inputNode0, 0, inputs[1], &outputOfLeakyRelu, desc) || |
| 1689 | IsSupportedLeakyReluPattern(inputNode0, 1, inputs[1], &outputOfLeakyRelu, desc) || |
| 1690 | IsSupportedLeakyReluPattern(inputNode1, 0, inputs[0], &outputOfLeakyRelu, desc) || |
| 1691 | IsSupportedLeakyReluPattern(inputNode1, 1, inputs[0], &outputOfLeakyRelu, desc)) |
| 1692 | { |
| 1693 | BOOST_ASSERT(outputOfLeakyRelu != nullptr); |
| 1694 | |
| 1695 | IConnectableLayer* const layer = m_Network->AddActivationLayer(desc, nodeDef.name().c_str()); |
| 1696 | outputOfLeakyRelu->Connect(layer->GetInputSlot(0)); |
| 1697 | layer->GetOutputSlot(0).SetTensorInfo(outputOfLeakyRelu->GetTensorInfo()); |
| 1698 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1699 | } |
| 1700 | else |
| 1701 | { |
Sadik Armagan | 975c09a | 2018-12-04 10:02:08 +0000 | [diff] [blame] | 1702 | // Anything else is just a maximum layer. |
| 1703 | |
| 1704 | return AddMaximumLayer(nodeDef); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1705 | } |
| 1706 | } |
| 1707 | |
jimfly01 | 84c70e6 | 2018-12-19 13:14:46 +0000 | [diff] [blame] | 1708 | std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> TfParser::ProcessElementwiseInputSlots( |
| 1709 | const tensorflow::NodeDef& nodeDef, const std::string& layerName) |
Nattapat Chaimanowong | 24df822 | 2018-12-04 13:47:02 +0000 | [diff] [blame] | 1710 | { |
| 1711 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 1712 | |
| 1713 | IOutputSlot* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 1714 | IOutputSlot* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index); |
| 1715 | const unsigned int input0Dim = input0Slot->GetTensorInfo().GetNumDimensions(); |
| 1716 | const unsigned int input1Dim = input1Slot->GetTensorInfo().GetNumDimensions(); |
| 1717 | |
| 1718 | if (input0Dim != input1Dim) |
| 1719 | { |
| 1720 | // broadcasting where input0 and input1 have different number of dimensions |
| 1721 | // is only supported for 1D and 4D tensors pair |
| 1722 | if (input0Dim == 1 && input1Dim == 4) |
| 1723 | { |
| 1724 | input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, true, *m_Network, nodeDef); |
| 1725 | } |
| 1726 | else if (input0Dim == 4 && input1Dim == 1) |
| 1727 | { |
| 1728 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, true, *m_Network, nodeDef); |
| 1729 | } |
| 1730 | else |
| 1731 | { |
| 1732 | throw ParseException( |
jimfly01 | 84c70e6 | 2018-12-19 13:14:46 +0000 | [diff] [blame] | 1733 | boost::str( |
| 1734 | boost::format("Unsupported broadcast configuration for %1% operation %2% %3%") |
| 1735 | % layerName |
| 1736 | % nodeDef.name() |
| 1737 | % CHECK_LOCATION().AsString())); |
Nattapat Chaimanowong | 24df822 | 2018-12-04 13:47:02 +0000 | [diff] [blame] | 1738 | } |
| 1739 | } |
jimfly01 | 84c70e6 | 2018-12-19 13:14:46 +0000 | [diff] [blame] | 1740 | return {input0Slot, input1Slot}; |
| 1741 | } |
Nattapat Chaimanowong | 24df822 | 2018-12-04 13:47:02 +0000 | [diff] [blame] | 1742 | |
jimfly01 | 84c70e6 | 2018-12-19 13:14:46 +0000 | [diff] [blame] | 1743 | ParsedTfOperationPtr TfParser::ProcessElementwiseLayer( |
| 1744 | IOutputSlot* input0Slot, |
| 1745 | IOutputSlot* input1Slot, |
| 1746 | IConnectableLayer* const layer, |
| 1747 | const tensorflow::NodeDef& nodeDef) |
| 1748 | { |
Nattapat Chaimanowong | 24df822 | 2018-12-04 13:47:02 +0000 | [diff] [blame] | 1749 | input0Slot->Connect(layer->GetInputSlot(0)); |
| 1750 | input1Slot->Connect(layer->GetInputSlot(1)); |
| 1751 | |
| 1752 | TensorInfo outputInfo = input0Slot->GetTensorInfo(); |
| 1753 | std::vector<unsigned int> outputShape; |
| 1754 | |
| 1755 | const TensorShape& input0Shape = input0Slot->GetTensorInfo().GetShape(); |
| 1756 | const TensorShape& input1Shape = input1Slot->GetTensorInfo().GetShape(); |
| 1757 | |
| 1758 | for (unsigned int i = 0; i < input0Shape.GetNumDimensions(); i++) |
| 1759 | { |
| 1760 | outputShape.push_back(std::max(input0Shape[i], input1Shape[i])); |
| 1761 | } |
| 1762 | |
| 1763 | outputInfo.SetShape(TensorShape(input0Shape.GetNumDimensions(), outputShape.data())); |
| 1764 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 1765 | |
| 1766 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1767 | } |
| 1768 | |
jimfly01 | a06bf31 | 2018-12-18 16:24:51 +0000 | [diff] [blame] | 1769 | ParsedTfOperationPtr TfParser::ParseGreater(const tensorflow::NodeDef& nodeDef, |
| 1770 | const tensorflow::GraphDef& graphDef) |
| 1771 | { |
| 1772 | std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> inputLayers = ProcessElementwiseInputSlots(nodeDef, "Greater"); |
| 1773 | IOutputSlot* input0Slot = inputLayers.first; |
| 1774 | IOutputSlot* input1Slot = inputLayers.second; |
| 1775 | |
| 1776 | IConnectableLayer* const layer = m_Network->AddGreaterLayer(nodeDef.name().c_str()); |
| 1777 | |
| 1778 | return ProcessElementwiseLayer(input0Slot, input1Slot, layer, nodeDef); |
| 1779 | } |
| 1780 | |
jimfly01 | 84c70e6 | 2018-12-19 13:14:46 +0000 | [diff] [blame] | 1781 | ParsedTfOperationPtr TfParser::ParseEqual(const tensorflow::NodeDef& nodeDef, |
| 1782 | const tensorflow::GraphDef& graphDef) |
| 1783 | { |
| 1784 | std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> inputLayers = ProcessElementwiseInputSlots(nodeDef, "Equal"); |
| 1785 | IOutputSlot* input0Slot = inputLayers.first; |
| 1786 | IOutputSlot* input1Slot = inputLayers.second; |
| 1787 | |
| 1788 | IConnectableLayer* const layer = m_Network->AddEqualLayer(nodeDef.name().c_str()); |
| 1789 | |
| 1790 | return ProcessElementwiseLayer(input0Slot, input1Slot, layer, nodeDef); |
| 1791 | } |
| 1792 | |
| 1793 | ParsedTfOperationPtr TfParser::ParseMinimum(const tensorflow::NodeDef& nodeDef, |
| 1794 | const tensorflow::GraphDef& graphDef) |
| 1795 | { |
| 1796 | std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> inputLayers = ProcessElementwiseInputSlots(nodeDef, "Minimum"); |
| 1797 | IOutputSlot* input0Slot = inputLayers.first; |
| 1798 | IOutputSlot* input1Slot = inputLayers.second; |
| 1799 | |
| 1800 | IConnectableLayer* const layer = m_Network->AddMinimumLayer(nodeDef.name().c_str()); |
| 1801 | |
| 1802 | return ProcessElementwiseLayer(input0Slot, input1Slot, layer, nodeDef); |
| 1803 | } |
| 1804 | |
jimfly01 | 23be07e | 2018-12-04 17:47:22 +0000 | [diff] [blame] | 1805 | ParsedTfOperationPtr TfParser::ParseSub(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 1806 | { |
| 1807 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 1808 | |
| 1809 | IOutputSlot* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 1810 | IOutputSlot* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index); |
| 1811 | |
| 1812 | const TensorInfo& input0Info = input0Slot->GetTensorInfo(); |
| 1813 | const TensorInfo& input1Info = input1Slot->GetTensorInfo(); |
| 1814 | |
| 1815 | if (input0Info.GetNumDimensions() == 1) |
| 1816 | { |
| 1817 | const bool isNHWC = true; |
| 1818 | input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef); |
| 1819 | } |
| 1820 | |
| 1821 | if (input1Info.GetNumDimensions() == 1) |
| 1822 | { |
| 1823 | const bool isNHWC = true; |
| 1824 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef); |
| 1825 | } |
| 1826 | |
| 1827 | IConnectableLayer* const layer = m_Network->AddSubtractionLayer(nodeDef.name().c_str()); |
| 1828 | |
| 1829 | input0Slot->Connect(layer->GetInputSlot(0)); |
| 1830 | input1Slot->Connect(layer->GetInputSlot(1)); |
| 1831 | |
| 1832 | if (input0Info.GetNumDimensions() == 1) |
| 1833 | { |
| 1834 | layer->GetOutputSlot(0).SetTensorInfo(input1Slot->GetTensorInfo()); |
| 1835 | } |
| 1836 | else |
| 1837 | { |
| 1838 | layer->GetOutputSlot(0).SetTensorInfo(input0Slot->GetTensorInfo()); |
| 1839 | } |
| 1840 | |
| 1841 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1842 | } |
| 1843 | |
jimfly01 | f6ba747 | 2018-12-04 10:09:52 +0000 | [diff] [blame] | 1844 | unsigned int CheckPaddingTensor(const ConstTensor& paddingTensor, |
| 1845 | const TensorInfo& inputTensorInfo, |
| 1846 | const std::string& nodeName) |
| 1847 | { |
| 1848 | unsigned int rank = paddingTensor.GetShape()[0]; |
| 1849 | unsigned int expectedRank = inputTensorInfo.GetNumDimensions(); |
| 1850 | if (rank != expectedRank) |
| 1851 | { |
| 1852 | throw ParseException( |
| 1853 | boost::str( |
| 1854 | boost::format( |
| 1855 | "Expected the padding tensor to be of rank %1 not %2 on Node %3 %4.") |
| 1856 | % expectedRank |
| 1857 | % rank |
| 1858 | % nodeName |
| 1859 | % CHECK_LOCATION().AsString())); |
| 1860 | } |
| 1861 | unsigned int second = paddingTensor.GetShape()[1]; |
| 1862 | if (second != 2) |
| 1863 | { |
| 1864 | throw ParseException( |
| 1865 | boost::str( |
| 1866 | boost::format( |
| 1867 | "Expected the padding tensor to be of dimensions [%1, 2] not [%1, %2] on Node %3 %4.") |
| 1868 | % rank |
| 1869 | % second |
| 1870 | % nodeName |
| 1871 | % CHECK_LOCATION().AsString())); |
| 1872 | } |
| 1873 | return rank; |
| 1874 | } |
| 1875 | |
| 1876 | TensorInfo CalculatePaddedOutputTensorInfo(const TensorInfo& inputTensorInfo, |
| 1877 | const std::vector<std::pair<unsigned int, unsigned int>>& padList) |
| 1878 | { |
| 1879 | unsigned int numDims = inputTensorInfo.GetNumDimensions(); |
| 1880 | std::vector<unsigned int> outDims; |
| 1881 | for (unsigned int i = 0; i < numDims; ++i) |
| 1882 | { |
| 1883 | unsigned int dimSize = inputTensorInfo.GetShape()[i]; |
| 1884 | const std::pair<unsigned int, unsigned int>& dimPadding = padList[i]; |
| 1885 | dimSize += dimPadding.first; |
| 1886 | dimSize += dimPadding.second; |
| 1887 | outDims.push_back(dimSize); |
| 1888 | } |
| 1889 | TensorInfo paddedTensorInfo = inputTensorInfo; |
| 1890 | unsigned int outDimsSize = static_cast<unsigned int>(outDims.size()); |
| 1891 | paddedTensorInfo.SetShape(TensorShape{ outDimsSize, outDims.data() }); |
| 1892 | return paddedTensorInfo; |
| 1893 | } |
| 1894 | |
| 1895 | ParsedTfOperationPtr TfParser::ParsePad(const tensorflow::NodeDef& nodeDef, |
| 1896 | const tensorflow::GraphDef& graphDef) |
| 1897 | { |
| 1898 | // input consists of: |
| 1899 | // input[0] the tensor which will be padded |
| 1900 | // input[1] the tensor holding the padding values |
| 1901 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 1902 | IOutputSlot& previousLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 1903 | TensorInfo inputTensorInfo = previousLayerOutputSlot.GetTensorInfo(); |
| 1904 | if (!HasParsedConstTensor<int32_t>(inputs[1].m_IndexedValue)) |
| 1905 | { |
| 1906 | throw ParseException( |
| 1907 | boost::str( |
| 1908 | boost::format( |
| 1909 | "ArmNN only supports Pad with constant padding. " |
| 1910 | "Input %1%. Node %2% %3%") |
| 1911 | % inputs[1].m_IndexedValue->GetNode().name() |
| 1912 | % nodeDef.name() |
| 1913 | % CHECK_LOCATION().AsString())); |
| 1914 | |
| 1915 | } |
| 1916 | ParsedConstTfOperation<int32_t>* paddingTensorOp = |
| 1917 | boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue); |
| 1918 | |
| 1919 | std::vector<int32_t> paddingTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 1920 | ConstTensor paddingTensor = paddingTensorOp->GetConstTensor(paddingTensorData); |
jimfly01 | f6ba747 | 2018-12-04 10:09:52 +0000 | [diff] [blame] | 1921 | // paddings is an integer tensor with shape [n, 2], where n is the rank of tensor |
| 1922 | // and should match the rank of the input tensor that is being padded. |
| 1923 | // For each dimension D of input, paddings[D, 0] indicates how many values to add |
| 1924 | // before the contents of tensor in that dimension, and paddings[D, 1] indicates how |
| 1925 | // many values to add after the contents of tensor in that dimension |
| 1926 | // This needs to be translated into a padList for ACL |
| 1927 | std::vector<std::pair<unsigned int, unsigned int>> padList; |
| 1928 | unsigned int rank = CheckPaddingTensor(paddingTensor, inputTensorInfo, nodeDef.name()); |
| 1929 | for (unsigned int i = 0; i < rank; ++i) |
| 1930 | { |
| 1931 | std::pair<unsigned int, unsigned int> paddingForDim; |
| 1932 | for (unsigned int j = 0; j < 2; j++) |
| 1933 | { |
| 1934 | unsigned int index = (i * 2) + j; |
| 1935 | int paddingAmount = paddingTensorData[index]; |
| 1936 | // make sure we can cast to an unsigned value |
| 1937 | if (paddingAmount < 0) |
| 1938 | { |
| 1939 | throw ParseException( |
| 1940 | boost::str( |
| 1941 | boost::format( |
| 1942 | "Negative amount %1 specified at [%2, %3] of padding tensor on Node %4 %5.") |
| 1943 | % paddingAmount |
| 1944 | % i |
| 1945 | % j |
| 1946 | % nodeDef.name() |
| 1947 | % CHECK_LOCATION().AsString())); |
| 1948 | } |
| 1949 | if (j == 0) |
| 1950 | { |
| 1951 | paddingForDim.first = static_cast<unsigned int>(paddingAmount); |
| 1952 | } |
| 1953 | else |
| 1954 | { |
| 1955 | paddingForDim.second = static_cast<unsigned int>(paddingAmount); |
| 1956 | } |
| 1957 | } |
| 1958 | padList.push_back(paddingForDim); |
| 1959 | } |
| 1960 | PadDescriptor padDescriptor(padList); |
| 1961 | IConnectableLayer* layer = m_Network->AddPadLayer(padDescriptor, nodeDef.name().c_str()); |
| 1962 | previousLayerOutputSlot.Connect(layer->GetInputSlot(0)); |
| 1963 | // Use the padding to calculate the new output tensor shape |
| 1964 | TensorInfo outputTensorInfo = CalculatePaddedOutputTensorInfo(inputTensorInfo, padList); |
| 1965 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 1966 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 1967 | } |
| 1968 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1969 | ParsedTfOperationPtr TfParser::ParseConcat(const tensorflow::NodeDef& nodeDef, |
| 1970 | const tensorflow::GraphDef& graphDef) |
| 1971 | { |
| 1972 | std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef); |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 1973 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1974 | // In tensorflow, we have the last input of the Concat layer as the axis for concatenation. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1975 | unsigned int numInputs = static_cast<unsigned int>(nodes.size()); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1976 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1977 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numInputs); |
| 1978 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1979 | // The last input is the axis for concatenation. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1980 | if (!HasParsedConstTensor<int32_t>(inputs[numInputs - 1].m_IndexedValue->GetNode().name())) |
| 1981 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1982 | throw ParseException( |
| 1983 | boost::str( |
| 1984 | boost::format( |
| 1985 | "ArmNN only supports Concat with constant axis. " |
| 1986 | "Input %1%. Node %2% %3%") |
| 1987 | % inputs[numInputs - 1].m_IndexedValue->GetNode().name() |
| 1988 | % nodeDef.name() |
| 1989 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1990 | } |
| 1991 | ParsedConstTfOperation<int32_t>* shapeNode = |
| 1992 | boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[numInputs - 1].m_IndexedValue); |
| 1993 | |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 1994 | // Get the axis tensor data |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1995 | std::vector<int32_t> axisTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 1996 | shapeNode->GetConstTensor(axisTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1997 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1998 | // This concatDim indicates the data format: 3 is the NHWC, 1 is the NCHW. |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 1999 | const unsigned int concatDim = static_cast<unsigned int>(axisTensorData[0]); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2000 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2001 | // Armnn supports concatenation along the channel dimension for data formats NHWC and NCHW. |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2002 | if (concatDim == 0 || concatDim == 2) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2003 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2004 | throw ParseException( |
| 2005 | boost::str( |
| 2006 | boost::format( |
| 2007 | "Dimension %1% for concatenation is not supported by Armnn. " |
| 2008 | "Node %2% %3%") |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2009 | % concatDim |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2010 | % nodeDef.name() |
| 2011 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2012 | } |
| 2013 | |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2014 | unsigned int numConcatViews = numInputs - 1; |
| 2015 | OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatViews), MaxNumOfTensorDimensions); |
| 2016 | concatDescriptor.SetConcatAxis(concatDim); |
| 2017 | TensorShape mergeDims(MaxNumOfTensorDimensions); |
| 2018 | unsigned int mergeDim = 0; |
| 2019 | for (unsigned int viewIndex = 0; viewIndex < numConcatViews; ++viewIndex) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2020 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2021 | // Need to double check whether it should be |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2022 | IOutputSlot& inputSlot = inputs[viewIndex].m_IndexedValue->ResolveArmnnOutputSlot(inputs[viewIndex].m_Index); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2023 | TensorInfo inputTensorInfo = inputSlot.GetTensorInfo(); |
| 2024 | |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2025 | // Double check dimensions of the tensors |
| 2026 | if (inputTensorInfo.GetNumDimensions() != MaxNumOfTensorDimensions) |
| 2027 | { |
| 2028 | throw armnn::ParseException( |
| 2029 | boost::str( |
| 2030 | boost::format( |
| 2031 | "The number of dimensions: %1% for input tensors of the " |
| 2032 | "concatenation op should be %2% %3%") |
| 2033 | % inputTensorInfo.GetNumDimensions() |
| 2034 | % MaxNumOfTensorDimensions |
| 2035 | % CHECK_LOCATION().AsString())); |
| 2036 | } |
| 2037 | |
| 2038 | // Copy the input tensor shape to mergeDimSizes and initialize the view origin coordinates for the current input |
| 2039 | mergeDims = inputTensorInfo.GetShape(); |
| 2040 | unsigned int* viewOrigin = const_cast<unsigned int*>(concatDescriptor.GetViewOrigin(viewIndex)); |
| 2041 | std::fill(viewOrigin, viewOrigin + MaxNumOfTensorDimensions, 0); |
| 2042 | |
| 2043 | // Update the view origin coordinates and the merge dimension value |
| 2044 | concatDescriptor.SetViewOriginCoord(viewIndex, concatDim, mergeDim); |
| 2045 | mergeDim += mergeDims[concatDim]; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2046 | } |
| 2047 | |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2048 | // Update the output shape |
| 2049 | mergeDims[concatDim] = mergeDim; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2050 | armnn::IConnectableLayer *layer = m_Network->AddMergerLayer(concatDescriptor, nodeDef.name().c_str()); |
| 2051 | |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2052 | layer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo(mergeDims, DataType::Float32)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2053 | |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2054 | for (unsigned int viewIndex = 0; viewIndex < numConcatViews; ++viewIndex) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2055 | { |
Matteo Martincigh | f9afc79 | 2018-12-06 12:03:17 +0000 | [diff] [blame] | 2056 | IOutputSlot& inputSlot = inputs[viewIndex].m_IndexedValue->ResolveArmnnOutputSlot(inputs[viewIndex].m_Index); |
| 2057 | inputSlot.Connect(layer->GetInputSlot(viewIndex)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2058 | } |
| 2059 | |
| 2060 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2061 | } |
| 2062 | |
| 2063 | ParsedTfOperationPtr TfParser::ParseShape(const tensorflow::NodeDef& nodeDef, |
| 2064 | const tensorflow::GraphDef& graphDef) |
| 2065 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2066 | // Note: the Shape layer is handled in a special way, because: |
| 2067 | // 1. ARMNN doesn't support int32 tensors which it outputs. |
| 2068 | // 2. ARMNN works with statically shaped tensors which are known at parse time. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2069 | // 3. because of 1. and 2. we treat the output of Shape as a temporary const int32 |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2070 | // tensor which may be used as an input to other ops, most likely a Reshape. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2071 | |
| 2072 | const tensorflow::DataType tfDataType = ReadMandatoryNodeTypeAttribute(nodeDef, "out_type"); |
| 2073 | if (tfDataType != tensorflow::DT_INT32) |
| 2074 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2075 | throw ParseException( |
| 2076 | boost::str( |
| 2077 | boost::format( |
| 2078 | "Armnn only supports DT_INT32 as out_type. Got %1% for Node %2% %3%") |
| 2079 | % tensorflow::DataType_Name(tfDataType) |
| 2080 | % nodeDef.name() |
| 2081 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2082 | } |
| 2083 | |
| 2084 | const std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 2085 | IOutputSlot& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2086 | const TensorInfo& prevLayerTensorInfo = prevLayerOutputSlot.GetTensorInfo(); |
| 2087 | unsigned int prevLayerDimensions = prevLayerTensorInfo.GetNumDimensions(); |
| 2088 | |
| 2089 | std::vector<int32_t> shapeTensorData; |
| 2090 | shapeTensorData.reserve(prevLayerDimensions); |
| 2091 | |
| 2092 | for (unsigned int i=0; i<prevLayerDimensions; ++i) |
| 2093 | { |
| 2094 | shapeTensorData.push_back(static_cast<int32_t>(prevLayerTensorInfo.GetShape()[i])); |
| 2095 | } |
| 2096 | |
| 2097 | TensorInfo shapeTensorInfo(1, &prevLayerDimensions, DataType::Signed32); |
| 2098 | |
| 2099 | return std::make_unique<ParsedConstTfOperation<int32_t>>(this, |
| 2100 | nodeDef, |
| 2101 | &shapeTensorData[0], |
| 2102 | shapeTensorInfo); |
| 2103 | } |
| 2104 | |
| 2105 | ParsedTfOperationPtr TfParser::ParseReshape(const tensorflow::NodeDef& nodeDef, |
| 2106 | const tensorflow::GraphDef& graphDef) |
| 2107 | { |
| 2108 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 2109 | ParsedTfOperation* inputNode = inputs[0].m_IndexedValue; |
| 2110 | |
| 2111 | if (!HasParsedConstTensor<int32_t>(inputs[1].m_IndexedValue->GetNode().name())) |
| 2112 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2113 | throw ParseException( |
| 2114 | boost::str( |
| 2115 | boost::format( |
| 2116 | "ArmNN only supports Reshape layers with constant shapes. " |
| 2117 | "Input %1% Node %2% %3%") |
| 2118 | % inputs[1].m_IndexedValue->GetNode().name() |
| 2119 | % nodeDef.name() |
| 2120 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2121 | } |
| 2122 | ParsedConstTfOperation<int32_t>* shapeNode = |
| 2123 | boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue); |
| 2124 | |
| 2125 | armnn::IOutputSlot& prevLayerOutputSlot = inputNode->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2126 | TensorInfo inputTensorInfo = prevLayerOutputSlot.GetTensorInfo(); |
| 2127 | |
| 2128 | std::vector<int32_t> shapeTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 2129 | ConstTensor shapeTensor = shapeNode->GetConstTensor(shapeTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2130 | const TensorInfo outputTensorInfo = PrepareReshape(inputTensorInfo, shapeTensorData); |
| 2131 | |
| 2132 | TensorShape targetShape = outputTensorInfo.GetShape(); |
| 2133 | ReshapeDescriptor reshapeDesc; |
| 2134 | reshapeDesc.m_TargetShape = targetShape; |
| 2135 | |
| 2136 | IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, nodeDef.name().c_str()); |
| 2137 | prevLayerOutputSlot.Connect(layer->GetInputSlot(0)); |
| 2138 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 2139 | |
| 2140 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2141 | } |
| 2142 | |
| 2143 | ParsedTfOperationPtr TfParser::ParseResizeBilinear(const tensorflow::NodeDef& nodeDef, |
| 2144 | const tensorflow::GraphDef& graphDef) |
| 2145 | { |
| 2146 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 2147 | |
| 2148 | if (!HasParsedConstTensor<int32_t>(inputs[1].m_IndexedValue->GetNode().name())) |
| 2149 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2150 | throw ParseException( |
| 2151 | boost::str( |
| 2152 | boost::format( |
| 2153 | "ArmNN only supports ResizeBilinear layers with constant sizes. " |
| 2154 | "Input %1%. Node %2% %3%") |
| 2155 | % inputs[1].m_IndexedValue->GetNode().name() |
| 2156 | % nodeDef.name() |
| 2157 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2158 | } |
| 2159 | ParsedConstTfOperation<int32_t>* sizeNode = |
| 2160 | boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue); |
| 2161 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2162 | // Checks the align_corners attribute is not set. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2163 | if (ReadOptionalNodeBoolAttribute(nodeDef, "align_corners", false)) |
| 2164 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2165 | throw ParseException( |
| 2166 | boost::str( |
| 2167 | boost::format( |
| 2168 | "ArmNN only supports ResizeBilinear layers with align_corners set to false. " |
| 2169 | "Node %1% %2%") |
| 2170 | % nodeDef.name() |
| 2171 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2172 | } |
| 2173 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2174 | // Data for the parsed tensor args (size) must be stored locally. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2175 | std::vector<int32_t> sizeTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 2176 | ConstTensor sizeTensor = sizeNode->GetConstTensor(sizeTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2177 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2178 | // The descriptor only has target height and width attributes, which we get from the size tensor. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2179 | ResizeBilinearDescriptor desc; |
| 2180 | desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]); |
| 2181 | desc.m_TargetWidth = static_cast<uint32_t> (sizeTensorData[1]); |
jimfly01 | 8a12150 | 2018-12-06 16:19:52 +0000 | [diff] [blame] | 2182 | desc.m_DataLayout = armnn::DataLayout::NHWC; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2183 | |
| 2184 | IConnectableLayer* layer = m_Network->AddResizeBilinearLayer(desc, nodeDef.name().c_str()); |
| 2185 | |
| 2186 | IOutputSlot& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2187 | TensorInfo inputTensorInfo = inputSlot.GetTensorInfo(); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2188 | // The input shape is always in BHWC format, this will be swizzled below; for now, |
| 2189 | // get the batch and channels to make up the ArmNN output shape with the target size. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2190 | unsigned int outBatch = inputTensorInfo.GetShape()[0]; |
| 2191 | unsigned int outChannels = inputTensorInfo.GetShape()[3]; |
| 2192 | unsigned int outHeight = desc.m_TargetHeight; |
| 2193 | unsigned int outWidth = desc.m_TargetWidth; |
jimfly01 | 8a12150 | 2018-12-06 16:19:52 +0000 | [diff] [blame] | 2194 | TensorShape outShape({outBatch, outHeight, outWidth, outChannels }); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2195 | // The output DataType is always Float32, regardless of the input DataType. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2196 | const TensorInfo outputTensorInfo(outShape, armnn::DataType::Float32); |
| 2197 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 2198 | |
jimfly01 | 8a12150 | 2018-12-06 16:19:52 +0000 | [diff] [blame] | 2199 | inputSlot.Connect(layer->GetInputSlot(0)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2200 | |
| 2201 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2202 | } |
| 2203 | |
| 2204 | TensorInfo OutputShapeOfSqueeze(const tensorflow::NodeDef& nodeDef, TensorInfo inputTensorInfo) |
| 2205 | { |
| 2206 | BOOST_ASSERT(nodeDef.op() == "Squeeze"); |
| 2207 | tensorflow::DataType tfDataType = ReadMandatoryNodeTypeAttribute(nodeDef, "T"); |
| 2208 | |
| 2209 | DataType type; |
| 2210 | if (tfDataType == tensorflow::DT_FLOAT) |
| 2211 | { |
| 2212 | type = DataType::Float32; |
| 2213 | } |
| 2214 | else if (tfDataType == tensorflow::DT_INT32) |
| 2215 | { |
| 2216 | type = DataType::Signed32; |
| 2217 | } |
| 2218 | else |
| 2219 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2220 | throw ParseException( |
| 2221 | boost::str( |
| 2222 | boost::format("Unsupported DataType %1% for Squeeze operation %2% %3%") |
| 2223 | % tensorflow::DataType_Name(tfDataType) |
| 2224 | % nodeDef.name() |
| 2225 | % CHECK_LOCATION().AsString())); |
| 2226 | } |
| 2227 | |
| 2228 | |
| 2229 | if (inputTensorInfo.GetNumDimensions() > 4) |
| 2230 | { |
| 2231 | throw ParseException( |
| 2232 | boost::str( |
| 2233 | boost::format( |
| 2234 | "Unsupported number of dimensions: %1% for input shape for Squeeze %2% %3%") |
| 2235 | % inputTensorInfo.GetNumDimensions() |
| 2236 | % nodeDef.name() |
| 2237 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2238 | } |
| 2239 | |
| 2240 | std::vector<uint32_t> squeezeDims = ReadOptionalNodeUint32ListAttribute(nodeDef, "squeeze_dims"); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2241 | static const uint32_t dimensionSequence[] = { 0, 1, 2, 3 }; |
| 2242 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2243 | if (squeezeDims.empty()) |
| 2244 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2245 | squeezeDims.assign(dimensionSequence, |
| 2246 | dimensionSequence+inputTensorInfo.GetNumDimensions()); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2247 | } |
| 2248 | |
| 2249 | std::vector<uint32_t> outputDims; |
| 2250 | for(unsigned int i = 0; i < inputTensorInfo.GetNumDimensions(); i++) |
| 2251 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2252 | bool skipSqueeze = (std::find(squeezeDims.begin(), squeezeDims.end(), i) == squeezeDims.end()); |
| 2253 | auto currentDimension = inputTensorInfo.GetShape()[i]; |
| 2254 | if (skipSqueeze || currentDimension != 1) |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2255 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2256 | outputDims.push_back(currentDimension); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2257 | } |
| 2258 | } |
| 2259 | |
| 2260 | if (outputDims.size() > 4) |
| 2261 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2262 | throw ParseException( |
| 2263 | boost::str( |
| 2264 | boost::format( |
| 2265 | "Unsupported number of dimensions: %1% for output shape for Squeeze %2% %3%") |
| 2266 | % outputDims.size() |
| 2267 | % nodeDef.name() |
| 2268 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2269 | } |
| 2270 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2271 | TensorShape outShape = TensorShape(static_cast<unsigned int>(outputDims.size()), |
| 2272 | outputDims.data()); |
| 2273 | |
| 2274 | TensorInfo outTensorInfo = inputTensorInfo; |
| 2275 | outTensorInfo.SetShape(outShape); |
| 2276 | outTensorInfo.SetDataType(type); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2277 | |
| 2278 | return outTensorInfo; |
| 2279 | } |
| 2280 | |
| 2281 | ParsedTfOperationPtr TfParser::ParseSqueeze(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 2282 | { |
| 2283 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 2284 | |
| 2285 | IOutputSlot& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2286 | TensorInfo inputTensorInfo = prevLayerOutputSlot.GetTensorInfo(); |
| 2287 | |
| 2288 | TensorInfo outputInfo; |
| 2289 | outputInfo = OutputShapeOfSqueeze(nodeDef, inputTensorInfo); |
| 2290 | |
| 2291 | ReshapeDescriptor reshapeDesc; |
| 2292 | reshapeDesc.m_TargetShape = outputInfo.GetShape(); |
| 2293 | IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, nodeDef.name().c_str()); |
| 2294 | prevLayerOutputSlot.Connect(layer->GetInputSlot(0)); |
| 2295 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 2296 | |
| 2297 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2298 | } |
| 2299 | |
| 2300 | ParsedTfOperationPtr TfParser::ParseLrn(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 2301 | { |
| 2302 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 2303 | |
| 2304 | NormalizationDescriptor normalizationDescriptor; |
| 2305 | normalizationDescriptor.m_NormMethodType = NormalizationAlgorithmMethod::LocalBrightness; |
| 2306 | normalizationDescriptor.m_NormChannelType = NormalizationAlgorithmChannel::Across; |
| 2307 | normalizationDescriptor.m_Alpha = ReadMandatoryNodeFloatAttribute(nodeDef, "alpha"); |
| 2308 | normalizationDescriptor.m_Beta = ReadMandatoryNodeFloatAttribute(nodeDef, "beta"); |
| 2309 | normalizationDescriptor.m_K = ReadMandatoryNodeFloatAttribute(nodeDef, "bias"); |
| 2310 | normalizationDescriptor.m_NormSize = ReadMandatoryNodeUint32Attribute(nodeDef, "depth_radius"); |
ruoyan01 | 8174f36 | 2018-12-04 18:24:08 +0000 | [diff] [blame] | 2311 | normalizationDescriptor.m_DataLayout = armnn::DataLayout::NHWC; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2312 | |
| 2313 | // The window size must be an odd value. For a window size of (2 * n + 1), TensorFlow defines depth_radius = n. |
| 2314 | normalizationDescriptor.m_NormSize = normalizationDescriptor.m_NormSize * 2 + 1; |
| 2315 | |
| 2316 | IOutputSlot& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2317 | IConnectableLayer* layer = m_Network->AddNormalizationLayer(normalizationDescriptor, |
| 2318 | nodeDef.name().c_str()); |
ruoyan01 | 8174f36 | 2018-12-04 18:24:08 +0000 | [diff] [blame] | 2319 | prevLayerOutputSlot.Connect(layer->GetInputSlot(0)); |
| 2320 | layer->GetOutputSlot(0).SetTensorInfo(prevLayerOutputSlot.GetTensorInfo()); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2321 | |
| 2322 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2323 | } |
| 2324 | |
| 2325 | /// An ParsedTfOperation for a MatMul node. |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2326 | /// Creation of the armnn FullyConnected layer is deferred until it is actually needed, because |
| 2327 | /// MatMul nodes are often used for the first part of a biased FullyConnected (MatMul followed |
| 2328 | /// by Add) and in these cases armnn doesn't need a separate layer for the MatMul. |
| 2329 | /// |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2330 | class ParsedMatMulTfOperation : public DeferredSingleLayerParsedTfOperation |
| 2331 | { |
| 2332 | public: |
| 2333 | ParsedMatMulTfOperation(TfParser* parser, const tensorflow::NodeDef& node) |
| 2334 | : DeferredSingleLayerParsedTfOperation(parser, node) |
| 2335 | { |
| 2336 | } |
| 2337 | |
| 2338 | void CreateLayerDeferred() override |
| 2339 | { |
| 2340 | BOOST_ASSERT(m_Layer == nullptr); |
| 2341 | m_Layer = m_Parser->AddFullyConnectedLayer(m_Node, nullptr, m_Node.name().c_str()); |
| 2342 | } |
| 2343 | }; |
| 2344 | |
| 2345 | ParsedTfOperationPtr TfParser::ParseMatMul(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 2346 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2347 | // Defers the creation of the layer (see ParsedMatMulTfOperation). |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2348 | return std::make_unique<ParsedMatMulTfOperation>(this, nodeDef); |
| 2349 | } |
| 2350 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2351 | /// An ParsedTfOperation for a Mul node. |
| 2352 | /// Creation of the armnn Mul layer is deferred until it is actually needed, because Mul nodes |
| 2353 | /// are also used for the first part of a leaky relu activation function (Mul followed by Maximum) |
| 2354 | /// and in these cases armnn doesn't need a separate layer for the Mul. |
| 2355 | /// |
| 2356 | class ParsedMulTfOperation : public DeferredSingleLayerParsedTfOperation |
| 2357 | { |
| 2358 | public: |
| 2359 | ParsedMulTfOperation(TfParser* parser, const tensorflow::NodeDef& node) |
| 2360 | : DeferredSingleLayerParsedTfOperation(parser, node) |
| 2361 | { |
| 2362 | } |
| 2363 | |
| 2364 | void CreateLayerDeferred() override |
| 2365 | { |
| 2366 | BOOST_ASSERT(m_Layer == nullptr); |
| 2367 | m_Layer = m_Parser->AddMultiplicationLayer(m_Node); |
| 2368 | } |
| 2369 | }; |
| 2370 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2371 | ParsedTfOperationPtr TfParser::ParseMul(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 2372 | { |
| 2373 | boost::ignore_unused(graphDef); |
| 2374 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2375 | return std::make_unique<ParsedMulTfOperation>(this, nodeDef); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2376 | } |
| 2377 | |
| 2378 | ParsedTfOperationPtr TfParser::ParsePlaceholder(const tensorflow::NodeDef& nodeDef, |
| 2379 | const tensorflow::GraphDef& graphDef) |
| 2380 | { |
| 2381 | boost::ignore_unused(graphDef); |
| 2382 | |
| 2383 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 0); |
| 2384 | |
| 2385 | const LayerBindingId layerId = boost::numeric_cast<LayerBindingId>(m_NetworkInputsBindingInfo.size()); |
| 2386 | |
| 2387 | auto it = m_InputShapes.find(nodeDef.name()); |
| 2388 | if (it == m_InputShapes.end()) |
| 2389 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2390 | throw ParseException( |
| 2391 | boost::str( |
| 2392 | boost::format( |
| 2393 | "Missing input shape for Placeholder '%1%' %2%") |
| 2394 | % nodeDef.name() |
| 2395 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2396 | } |
| 2397 | TensorInfo tensorInfo(it->second, DataType::Float32); |
| 2398 | |
| 2399 | IConnectableLayer* const layer = m_Network->AddInputLayer(layerId, nodeDef.name().c_str()); |
| 2400 | |
| 2401 | layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 2402 | |
| 2403 | TrackInputBinding(layer, layerId, tensorInfo); |
| 2404 | |
| 2405 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2406 | } |
| 2407 | |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 2408 | ParsedTfOperationPtr TfParser::ParseRealDiv(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 2409 | { |
| 2410 | boost::ignore_unused(graphDef); |
| 2411 | return AddRealDivLayer(nodeDef); |
| 2412 | } |
| 2413 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2414 | ParsedTfOperationPtr TfParser::ParseRelu(const tensorflow::NodeDef& nodeDef, |
| 2415 | const tensorflow::GraphDef& graphDef) |
| 2416 | { |
| 2417 | boost::ignore_unused(graphDef); |
| 2418 | |
| 2419 | ActivationDescriptor activationDesc; |
| 2420 | activationDesc.m_Function = ActivationFunction::ReLu; |
| 2421 | return AddActivationLayer(nodeDef, activationDesc); |
| 2422 | } |
| 2423 | |
| 2424 | ParsedTfOperationPtr TfParser::ParseRelu6(const tensorflow::NodeDef& nodeDef, |
| 2425 | const tensorflow::GraphDef& graphDef) |
| 2426 | { |
| 2427 | boost::ignore_unused(graphDef); |
| 2428 | |
| 2429 | ActivationDescriptor activationDesc; |
| 2430 | activationDesc.m_Function = ActivationFunction::BoundedReLu; |
| 2431 | activationDesc.m_A = 6.0f; |
| 2432 | activationDesc.m_B = 0.0f; |
| 2433 | |
| 2434 | return AddActivationLayer(nodeDef, activationDesc); |
| 2435 | } |
| 2436 | |
| 2437 | ParsedTfOperationPtr TfParser::ParseSigmoid(const tensorflow::NodeDef& nodeDef, |
| 2438 | const tensorflow::GraphDef& graphDef) |
| 2439 | { |
| 2440 | boost::ignore_unused(graphDef); |
| 2441 | |
| 2442 | ActivationDescriptor activationDesc; |
| 2443 | activationDesc.m_Function = ActivationFunction::Sigmoid; |
| 2444 | |
| 2445 | return AddActivationLayer(nodeDef, activationDesc); |
| 2446 | } |
| 2447 | |
| 2448 | ParsedTfOperationPtr TfParser::ParseSoftmax(const tensorflow::NodeDef& nodeDef, |
| 2449 | const tensorflow::GraphDef& graphDef) |
| 2450 | { |
| 2451 | boost::ignore_unused(graphDef); |
| 2452 | |
| 2453 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 2454 | |
| 2455 | SoftmaxDescriptor softmaxDescriptor; |
| 2456 | IConnectableLayer* const layer = m_Network->AddSoftmaxLayer(softmaxDescriptor, nodeDef.name().c_str()); |
| 2457 | |
| 2458 | IOutputSlot& prevLayerSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2459 | prevLayerSlot.Connect(layer->GetInputSlot(0)); |
| 2460 | layer->GetOutputSlot(0).SetTensorInfo(prevLayerSlot.GetTensorInfo()); |
| 2461 | |
| 2462 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2463 | } |
| 2464 | |
Sadik Armagan | 2ad6cb4 | 2018-12-27 11:23:44 +0000 | [diff] [blame] | 2465 | ParsedTfOperationPtr TfParser::ParseSplit(const tensorflow::NodeDef& nodeDef, |
| 2466 | const tensorflow::GraphDef& graphDef) |
| 2467 | { |
| 2468 | boost::ignore_unused(graphDef); |
| 2469 | |
| 2470 | std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef); |
| 2471 | unsigned int numInputs = static_cast<unsigned int>(nodes.size()); |
| 2472 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numInputs); |
| 2473 | |
| 2474 | // The last input is the axis for split operation. |
| 2475 | if (!HasParsedConstTensor<int32_t>(inputs[numInputs - 1].m_IndexedValue->GetNode().name())) |
| 2476 | { |
| 2477 | throw ParseException( |
| 2478 | boost::str( |
| 2479 | boost::format( |
| 2480 | "ArmNN only supports split with constant axis. " |
| 2481 | "Input %1%. Node %2% %3%") |
| 2482 | % inputs[numInputs - 1].m_IndexedValue->GetNode().name() |
| 2483 | % nodeDef.name() |
| 2484 | % CHECK_LOCATION().AsString())); |
| 2485 | } |
| 2486 | ParsedConstTfOperation<int32_t>* shapeNode = |
| 2487 | boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[numInputs - 1].m_IndexedValue); |
| 2488 | |
| 2489 | // Get the axis tensor data |
| 2490 | std::vector<int32_t> axisTensorData; |
| 2491 | shapeNode->GetConstTensor(axisTensorData); |
| 2492 | |
| 2493 | // This splitDim indicates the data format: 3 is the NHWC, 1 is the NCHW. |
| 2494 | const unsigned int splitDim = static_cast<unsigned int>(axisTensorData[0]); |
| 2495 | |
| 2496 | // Armnn supports split along the channel dimension for data formats NHWC and NCHW. |
| 2497 | if (splitDim == 0 || splitDim == 2) |
| 2498 | { |
| 2499 | throw ParseException( |
| 2500 | boost::str( |
| 2501 | boost::format( |
| 2502 | "Dimension %1% for split is not supported by Armnn. " |
| 2503 | "Node %2% %3%") |
| 2504 | % splitDim |
| 2505 | % nodeDef.name() |
| 2506 | % CHECK_LOCATION().AsString())); |
| 2507 | } |
| 2508 | |
| 2509 | // As Armnn only supports splitter outputs of the same shape, therefore num_splits will be limited to an integer. |
| 2510 | uint32_t num_split = ReadMandatoryNodeUint32Attribute(nodeDef, "num_or_size_splits"); |
| 2511 | |
| 2512 | IOutputSlot& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2513 | TensorInfo inputTensorInfo = inputSlot.GetTensorInfo(); |
| 2514 | |
| 2515 | if (inputTensorInfo.GetNumDimensions() != MaxNumOfTensorDimensions) |
| 2516 | { |
| 2517 | throw armnn::ParseException( |
| 2518 | boost::str( |
| 2519 | boost::format( |
| 2520 | "The number of dimensions: %1% for input tensors of the " |
| 2521 | "splitter op should be %2% %3%") |
| 2522 | % inputTensorInfo.GetNumDimensions() |
| 2523 | % MaxNumOfTensorDimensions |
| 2524 | % CHECK_LOCATION().AsString())); |
| 2525 | } |
| 2526 | auto inputDimSize = inputTensorInfo.GetNumDimensions(); |
| 2527 | |
| 2528 | std::vector<unsigned int> splitterDimSizes(inputDimSize); |
| 2529 | |
| 2530 | // Add current input shape to splitterDimSizes |
| 2531 | for (unsigned int i = 0; i < inputDimSize; ++i) |
| 2532 | { |
| 2533 | splitterDimSizes[i] = inputTensorInfo.GetShape()[i]; |
| 2534 | } |
| 2535 | |
| 2536 | if (splitterDimSizes[splitDim] % num_split != 0) |
| 2537 | { |
| 2538 | throw ParseException("Number of splits must evenly divide the dimension"); |
| 2539 | } |
| 2540 | splitterDimSizes[splitDim] /= num_split; |
| 2541 | |
| 2542 | SplitterDescriptor splitDesc(num_split); |
| 2543 | for (unsigned int g = 0; g < num_split; ++g) |
| 2544 | { |
| 2545 | // Set the size of the views. |
| 2546 | for (unsigned int dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx) |
| 2547 | { |
| 2548 | splitDesc.SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]); |
| 2549 | } |
| 2550 | splitDesc.SetViewOriginCoord(g, splitDim, splitterDimSizes[splitDim] * g); |
| 2551 | } |
| 2552 | |
| 2553 | IConnectableLayer *layer = m_Network->AddSplitterLayer(splitDesc, nodeDef.name().c_str()); |
| 2554 | |
| 2555 | inputSlot.Connect(layer->GetInputSlot(0)); |
| 2556 | |
| 2557 | TensorShape outShape = TensorShape(static_cast<unsigned int>(splitterDimSizes.size()), |
| 2558 | splitterDimSizes.data()); |
| 2559 | |
| 2560 | for (unsigned int i = 0; i < layer->GetNumOutputSlots(); ++i) |
| 2561 | { |
| 2562 | layer->GetOutputSlot(i).SetTensorInfo(armnn::TensorInfo(outShape, inputTensorInfo.GetDataType())); |
| 2563 | } |
| 2564 | |
| 2565 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2566 | } |
| 2567 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2568 | ParsedTfOperationPtr TfParser::ParseSoftplus(const tensorflow::NodeDef& nodeDef, |
| 2569 | const tensorflow::GraphDef& graphDef) |
| 2570 | { |
| 2571 | boost::ignore_unused(graphDef); |
| 2572 | |
| 2573 | ActivationDescriptor activationDesc; |
| 2574 | activationDesc.m_Function = ActivationFunction::SoftReLu; |
| 2575 | |
| 2576 | return AddActivationLayer(nodeDef, activationDesc); |
| 2577 | } |
| 2578 | |
| 2579 | ParsedTfOperationPtr TfParser::ParseTanh(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 2580 | { |
| 2581 | boost::ignore_unused(graphDef); |
| 2582 | |
| 2583 | ActivationDescriptor activationDesc; |
| 2584 | activationDesc.m_Function = ActivationFunction::TanH; |
| 2585 | activationDesc.m_A = 1.0f; |
| 2586 | activationDesc.m_B = 1.0f; |
| 2587 | |
| 2588 | return AddActivationLayer(nodeDef, activationDesc); |
| 2589 | } |
| 2590 | |
| 2591 | ParsedTfOperationPtr TfParser::AddActivationLayer(const tensorflow::NodeDef& nodeDef, |
| 2592 | ActivationDescriptor& activationDesc) |
| 2593 | { |
| 2594 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 2595 | |
| 2596 | IConnectableLayer* const layer = m_Network->AddActivationLayer(activationDesc, nodeDef.name().c_str()); |
| 2597 | |
| 2598 | IOutputSlot& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2599 | prevLayerOutputSlot.Connect(layer->GetInputSlot(0)); |
| 2600 | layer->GetOutputSlot(0).SetTensorInfo(prevLayerOutputSlot.GetTensorInfo()); |
| 2601 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2602 | } |
| 2603 | |
| 2604 | ParsedTfOperationPtr TfParser::ParseMaxPool(const tensorflow::NodeDef& nodeDef, |
| 2605 | const tensorflow::GraphDef& graphDef) |
| 2606 | { |
| 2607 | return ParsePooling2d(nodeDef, graphDef, PoolingAlgorithm::Max); |
| 2608 | } |
| 2609 | |
| 2610 | ParsedTfOperationPtr TfParser::ParseAvgPool(const tensorflow::NodeDef& nodeDef, |
| 2611 | const tensorflow::GraphDef& graphDef) |
| 2612 | { |
| 2613 | return ParsePooling2d(nodeDef, graphDef, PoolingAlgorithm::Average); |
| 2614 | } |
| 2615 | |
| 2616 | ParsedTfOperationPtr TfParser::ParsePooling2d(const tensorflow::NodeDef& nodeDef, |
| 2617 | const tensorflow::GraphDef& graphDef, PoolingAlgorithm pooltype) |
| 2618 | { |
| 2619 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1); |
| 2620 | IOutputSlot& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2621 | TensorInfo inputTensorInfo = inputSlot.GetTensorInfo(); |
| 2622 | |
| 2623 | if (inputs.size() != 1) |
| 2624 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2625 | throw ParseException( |
| 2626 | boost::str( |
| 2627 | boost::format( |
| 2628 | "2D Pooling expects one input!. Got %1% for Node %2% %3%") |
| 2629 | % inputs.size() |
| 2630 | % nodeDef.name() |
| 2631 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2632 | } |
| 2633 | |
| 2634 | std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, "padding"); |
| 2635 | std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, "data_format"); |
| 2636 | std::vector<uint32_t> strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, "strides"); |
| 2637 | std::vector<uint32_t> ksize = ReadMandatoryNodeUint32ListAttribute(nodeDef, "ksize"); // size of pool windows |
| 2638 | |
| 2639 | Pooling2dDescriptor pooling2dDescriptor; |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2640 | pooling2dDescriptor.m_PoolType = pooltype; |
| 2641 | pooling2dDescriptor.m_PaddingMethod = PaddingMethod::Exclude; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2642 | pooling2dDescriptor.m_OutputShapeRounding = OutputShapeRounding::Floor; |
| 2643 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2644 | CHECK_DATA_FORMAT(nodeDef, dataFormat, "Pooling2D"); |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2645 | DataLayout dataLayout = dataFormat == "NHWC" ? DataLayout::NHWC : DataLayout::NCHW; |
| 2646 | pooling2dDescriptor.m_DataLayout = dataLayout; |
| 2647 | DataLayoutIndexed dataLayoutIndexed(dataLayout); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2648 | |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2649 | pooling2dDescriptor.m_StrideX = strides[dataLayoutIndexed.GetWidthIndex()]; |
| 2650 | pooling2dDescriptor.m_StrideY = strides[dataLayoutIndexed.GetHeightIndex()]; |
| 2651 | pooling2dDescriptor.m_PoolWidth = ksize[dataLayoutIndexed.GetWidthIndex()]; |
| 2652 | pooling2dDescriptor.m_PoolHeight = ksize[dataLayoutIndexed.GetHeightIndex()]; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2653 | |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2654 | uint32_t inputHeight = inputTensorInfo.GetShape()[dataLayoutIndexed.GetHeightIndex()]; |
| 2655 | uint32_t inputWidth = inputTensorInfo.GetShape()[dataLayoutIndexed.GetWidthIndex()]; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2656 | |
| 2657 | bool padding = false; |
| 2658 | TensorInfo outputInfo; |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2659 | unsigned int outputHeight = 0; |
| 2660 | unsigned int outputWidth = 0; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2661 | |
| 2662 | CHECK_PADDING_TYPE(nodeDef, paddingString); |
| 2663 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2664 | if (paddingString == "SAME") |
| 2665 | { |
| 2666 | padding = true; |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2667 | |
| 2668 | outputHeight = static_cast<uint32_t>(ceil(static_cast<float>(inputHeight) / |
| 2669 | static_cast<float>(pooling2dDescriptor.m_StrideY))); |
| 2670 | outputWidth = static_cast<uint32_t>(ceil(static_cast<float>(inputWidth) / |
| 2671 | static_cast<float>(pooling2dDescriptor.m_StrideX))); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2672 | } |
| 2673 | else if (paddingString == "VALID") |
| 2674 | { |
| 2675 | padding = false; |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2676 | |
| 2677 | outputHeight = static_cast<uint32_t>(ceil( |
| 2678 | static_cast<float>(inputHeight - pooling2dDescriptor.m_PoolHeight + 1) / |
| 2679 | static_cast<float>(pooling2dDescriptor.m_StrideY))); |
| 2680 | outputWidth = static_cast<uint32_t>(ceil( |
| 2681 | static_cast<float>(inputWidth - pooling2dDescriptor.m_PoolWidth + 1) / |
| 2682 | static_cast<float>(pooling2dDescriptor.m_StrideX))); |
| 2683 | } |
| 2684 | |
| 2685 | switch (dataLayout) |
| 2686 | { |
| 2687 | case DataLayout::NHWC: |
| 2688 | outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], |
| 2689 | outputHeight, |
| 2690 | outputWidth, |
| 2691 | inputTensorInfo.GetShape()[3] }, |
| 2692 | DataType::Float32); |
| 2693 | break; |
| 2694 | case DataLayout::NCHW: |
| 2695 | outputInfo = TensorInfo({ inputTensorInfo.GetShape()[0], |
| 2696 | inputTensorInfo.GetShape()[1], |
| 2697 | outputHeight, |
| 2698 | outputWidth }, |
| 2699 | DataType::Float32); |
| 2700 | break; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2701 | } |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2702 | |
| 2703 | CalcPadding(inputWidth, pooling2dDescriptor.m_PoolWidth, pooling2dDescriptor.m_StrideX, |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2704 | pooling2dDescriptor.m_PadLeft, pooling2dDescriptor.m_PadRight, padding); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2705 | CalcPadding(inputHeight, pooling2dDescriptor.m_PoolHeight, pooling2dDescriptor.m_StrideY, |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2706 | pooling2dDescriptor.m_PadTop, pooling2dDescriptor.m_PadBottom, padding); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2707 | |
| 2708 | |
| 2709 | IConnectableLayer* layer = m_Network->AddPooling2dLayer(pooling2dDescriptor, nodeDef.name().c_str()); |
| 2710 | if (layer == nullptr) |
| 2711 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2712 | throw ParseException( |
| 2713 | boost::str( |
| 2714 | boost::format( |
| 2715 | "Failed to add pooling2d layer for %1% %2%") |
| 2716 | % nodeDef.name() |
| 2717 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2718 | } |
| 2719 | |
| 2720 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 2721 | |
FrancisMurtagh | f005e31 | 2018-12-06 15:26:04 +0000 | [diff] [blame] | 2722 | inputSlot.Connect(layer->GetInputSlot(0)); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2723 | |
| 2724 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2725 | } |
| 2726 | |
| 2727 | ParsedTfOperationPtr TfParser::AddAdditionLayer(const tensorflow::NodeDef& nodeDef, bool isBiasAdd) |
| 2728 | { |
| 2729 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 2730 | |
| 2731 | IOutputSlot* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2732 | IOutputSlot* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index); |
| 2733 | |
| 2734 | const TensorInfo& input0Info = input0Slot->GetTensorInfo(); |
| 2735 | const TensorInfo& input1Info = input1Slot->GetTensorInfo(); |
| 2736 | |
| 2737 | if (isBiasAdd) |
| 2738 | { |
| 2739 | // BiasAdd takes bias as a 1D tensor. We need to add a reshape layer to create a 4D tensor |
| 2740 | // with the same data in the correct dimension for broadcast in addition. |
| 2741 | if(input1Info.GetNumDimensions() != 1) |
| 2742 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2743 | throw ParseException( |
| 2744 | boost::str( |
| 2745 | boost::format( |
| 2746 | "Unsupported bias for BiasAdd. It should be a 1D vector. " |
| 2747 | "Got %1% dimensions for input %2%. Node %3% %4%") |
| 2748 | % input1Info.GetNumDimensions() |
| 2749 | % inputs[1].m_IndexedValue->GetNode().name() |
| 2750 | % nodeDef.name() |
| 2751 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2752 | } |
| 2753 | |
| 2754 | const std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, "data_format"); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2755 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2756 | CHECK_DATA_FORMAT(nodeDef, dataFormat, "BiasAdd"); |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 2757 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, dataFormat == "NHWC", *m_Network, nodeDef); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2758 | } |
| 2759 | else |
| 2760 | { |
| 2761 | if (input0Info.GetNumDimensions() == 1) |
| 2762 | { |
| 2763 | const bool isNHWC = true; |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 2764 | input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2765 | } |
| 2766 | |
| 2767 | if (input1Info.GetNumDimensions() == 1) |
| 2768 | { |
| 2769 | const bool isNHWC = true; |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 2770 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2771 | } |
| 2772 | } |
| 2773 | |
| 2774 | IConnectableLayer* const layer = m_Network->AddAdditionLayer(nodeDef.name().c_str()); |
| 2775 | |
| 2776 | input0Slot->Connect(layer->GetInputSlot(0)); |
| 2777 | input1Slot->Connect(layer->GetInputSlot(1)); |
| 2778 | |
| 2779 | if (input0Info.GetNumDimensions() == 1 && isBiasAdd == false) |
| 2780 | { |
| 2781 | layer->GetOutputSlot(0).SetTensorInfo(input1Slot->GetTensorInfo()); |
| 2782 | } |
| 2783 | else |
| 2784 | { |
| 2785 | layer->GetOutputSlot(0).SetTensorInfo(input0Slot->GetTensorInfo()); |
| 2786 | } |
| 2787 | |
| 2788 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2789 | } |
| 2790 | |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 2791 | ParsedTfOperationPtr TfParser::AddRealDivLayer(const tensorflow::NodeDef& nodeDef) |
| 2792 | { |
| 2793 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 2794 | |
| 2795 | IConnectableLayer* const layer = m_Network->AddDivisionLayer(nodeDef.name().c_str()); |
| 2796 | IOutputSlot* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2797 | IOutputSlot* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index); |
| 2798 | |
| 2799 | auto const input0NumDims = input0Slot->GetTensorInfo().GetNumDimensions(); |
| 2800 | auto const input1NumDims = input1Slot->GetTensorInfo().GetNumDimensions(); |
| 2801 | |
| 2802 | |
| 2803 | if (input0NumDims < input1NumDims) |
| 2804 | { |
| 2805 | const bool isNHWC = true; |
| 2806 | input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef); |
| 2807 | } |
| 2808 | if (input1NumDims < input0NumDims) |
| 2809 | { |
| 2810 | const bool isNHWC = true; |
| 2811 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef); |
| 2812 | } |
| 2813 | |
| 2814 | input0Slot->Connect(layer->GetInputSlot(0)); |
| 2815 | input1Slot->Connect(layer->GetInputSlot(1)); |
| 2816 | |
| 2817 | if (input0NumDims < input1NumDims) |
| 2818 | { |
| 2819 | layer->GetOutputSlot(0).SetTensorInfo(input1Slot->GetTensorInfo()); |
| 2820 | } |
| 2821 | else |
| 2822 | { |
| 2823 | layer->GetOutputSlot(0).SetTensorInfo(input0Slot->GetTensorInfo()); |
| 2824 | |
| 2825 | } |
| 2826 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2827 | } |
| 2828 | |
Sadik Armagan | 975c09a | 2018-12-04 10:02:08 +0000 | [diff] [blame] | 2829 | ParsedTfOperationPtr TfParser::AddMaximumLayer(const tensorflow::NodeDef& nodeDef) |
| 2830 | { |
| 2831 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 2832 | |
| 2833 | IOutputSlot* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2834 | IOutputSlot* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index); |
| 2835 | |
| 2836 | auto const input0NumDims = input0Slot->GetTensorInfo().GetNumDimensions(); |
| 2837 | auto const input1NumDims = input1Slot->GetTensorInfo().GetNumDimensions(); |
| 2838 | |
| 2839 | if (input0NumDims < input1NumDims) |
| 2840 | { |
| 2841 | const bool isNHWC = true; |
| 2842 | input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef); |
| 2843 | } |
| 2844 | if (input1NumDims < input0NumDims) |
| 2845 | { |
| 2846 | const bool isNHWC = true; |
| 2847 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef); |
| 2848 | } |
| 2849 | |
| 2850 | IConnectableLayer* const layer = m_Network->AddMaximumLayer(nodeDef.name().c_str()); |
| 2851 | |
| 2852 | input0Slot->Connect(layer->GetInputSlot(0)); |
| 2853 | input1Slot->Connect(layer->GetInputSlot(1)); |
| 2854 | |
| 2855 | TensorInfo outputInfo = input0Slot->GetTensorInfo(); |
| 2856 | std::vector<unsigned int> outputShape; |
| 2857 | |
| 2858 | const TensorShape& input0Shape = input0Slot->GetTensorInfo().GetShape(); |
| 2859 | const TensorShape& input1Shape = input1Slot->GetTensorInfo().GetShape(); |
| 2860 | |
| 2861 | for (unsigned int i = 0; i < input0Shape.GetNumDimensions(); i++) |
| 2862 | { |
| 2863 | outputShape.push_back(std::max(input0Shape[i], input1Shape[i])); |
| 2864 | } |
| 2865 | |
| 2866 | outputInfo.SetShape(TensorShape(input0Shape.GetNumDimensions(), outputShape.data())); |
| 2867 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 2868 | |
| 2869 | return std::make_unique<SingleLayerParsedTfOperation>(this, nodeDef, layer); |
| 2870 | } |
| 2871 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2872 | IConnectableLayer* TfParser::AddMultiplicationLayer(const tensorflow::NodeDef& nodeDef) |
| 2873 | { |
| 2874 | std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2); |
| 2875 | |
| 2876 | IConnectableLayer* const layer = m_Network->AddMultiplicationLayer(nodeDef.name().c_str()); |
| 2877 | IOutputSlot* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index); |
| 2878 | IOutputSlot* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index); |
| 2879 | |
| 2880 | auto const input0NumDims = input0Slot->GetTensorInfo().GetNumDimensions(); |
| 2881 | auto const input1NumDims = input1Slot->GetTensorInfo().GetNumDimensions(); |
| 2882 | |
| 2883 | if (input0NumDims < input1NumDims) |
| 2884 | { |
| 2885 | const bool isNHWC = true; |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 2886 | input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2887 | } |
| 2888 | if (input1NumDims < input0NumDims) |
| 2889 | { |
| 2890 | const bool isNHWC = true; |
saoste01 | bbd4061 | 2018-08-28 15:41:51 +0100 | [diff] [blame] | 2891 | input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2892 | } |
| 2893 | |
| 2894 | input0Slot->Connect(layer->GetInputSlot(0)); |
| 2895 | input1Slot->Connect(layer->GetInputSlot(1)); |
| 2896 | |
| 2897 | if (input0NumDims < input1NumDims) |
| 2898 | { |
| 2899 | layer->GetOutputSlot(0).SetTensorInfo(input1Slot->GetTensorInfo()); |
| 2900 | } |
| 2901 | else |
| 2902 | { |
| 2903 | layer->GetOutputSlot(0).SetTensorInfo(input0Slot->GetTensorInfo()); |
| 2904 | } |
| 2905 | return layer; |
| 2906 | } |
| 2907 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2908 | IConnectableLayer* TfParser::AddFullyConnectedLayer(const tensorflow::NodeDef& matMulNodeDef, |
| 2909 | const tensorflow::NodeDef* addNodeDef, const char* armnnLayerName) |
| 2910 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2911 | // Finds bias const (if applicable). |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2912 | ParsedConstTfOperation<float>* biasNode = nullptr; |
| 2913 | if (addNodeDef != nullptr) |
| 2914 | { |
| 2915 | std::vector<OutputOfParsedTfOperation> addInputs = GetInputParsedTfOperationsChecked(*addNodeDef, 2); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2916 | // Finds our inputs. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2917 | if (HasParsedConstTensor<float>(addInputs[0].m_IndexedValue->GetNode().name())) |
| 2918 | { |
| 2919 | biasNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(addInputs[0].m_IndexedValue); |
| 2920 | } |
| 2921 | else if (HasParsedConstTensor<float>(addInputs[1].m_IndexedValue->GetNode().name())) |
| 2922 | { |
| 2923 | biasNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(addInputs[1].m_IndexedValue); |
| 2924 | } |
| 2925 | else |
| 2926 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2927 | throw ParseException( |
| 2928 | boost::str( |
| 2929 | boost::format( |
| 2930 | "ArmNN only supports fully connected layers with constant bias. " |
| 2931 | "Inputs %1% and %2%. AddNode %3%. MatMulNode %4% %5%") |
| 2932 | % addInputs[0].m_IndexedValue->GetNode().name() |
| 2933 | % addInputs[1].m_IndexedValue->GetNode().name() |
| 2934 | % addNodeDef->name() |
| 2935 | % matMulNodeDef.name() |
| 2936 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2937 | } |
| 2938 | } |
| 2939 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2940 | // Finds matmul inputs. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2941 | ParsedConstTfOperation<float>* weightNode = nullptr; |
| 2942 | ParsedTfOperation* inputNode = nullptr; |
| 2943 | unsigned int inputIdx = 0; |
| 2944 | std::vector<OutputOfParsedTfOperation> mulInputs = GetInputParsedTfOperationsChecked(matMulNodeDef, 2); |
| 2945 | if (HasParsedConstTensor<float>(mulInputs[0].m_IndexedValue->GetNode().name())) |
| 2946 | { |
| 2947 | weightNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(mulInputs[0].m_IndexedValue); |
| 2948 | inputNode = mulInputs[1].m_IndexedValue; |
| 2949 | inputIdx = mulInputs[1].m_Index; |
| 2950 | } |
| 2951 | else if (HasParsedConstTensor<float>(mulInputs[1].m_IndexedValue->GetNode().name())) |
| 2952 | { |
| 2953 | weightNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(mulInputs[1].m_IndexedValue); |
| 2954 | inputNode = mulInputs[0].m_IndexedValue; |
| 2955 | inputIdx = mulInputs[0].m_Index; |
| 2956 | } |
| 2957 | else |
| 2958 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2959 | throw ParseException( |
| 2960 | boost::str( |
| 2961 | boost::format( |
| 2962 | "ArmNN only supports fully connected layers with constant weights. " |
| 2963 | "Inputs %1% and %2%. MatMulNode %3% %4%") |
| 2964 | % mulInputs[0].m_IndexedValue->GetNode().name() |
| 2965 | % mulInputs[1].m_IndexedValue->GetNode().name() |
| 2966 | % matMulNodeDef.name() |
| 2967 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2968 | } |
| 2969 | |
| 2970 | std::vector<float> weightTensorData; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2971 | // Handles weight. |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 2972 | ConstTensor weights = weightNode->GetConstTensor(weightTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2973 | |
| 2974 | FullyConnectedDescriptor desc; |
| 2975 | desc.m_BiasEnabled = addNodeDef != nullptr; |
| 2976 | |
| 2977 | IConnectableLayer* layer = nullptr; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2978 | // Makes the layer. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2979 | if (addNodeDef != nullptr) |
| 2980 | { |
| 2981 | std::vector<float> biasTensorData; |
Matteo Martincigh | 482ca85 | 2018-12-12 09:20:55 +0000 | [diff] [blame] | 2982 | ConstTensor biases = biasNode->GetConstTensor(biasTensorData); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2983 | |
| 2984 | if (weights.GetShape()[1] != biases.GetShape()[0]) |
| 2985 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2986 | throw ParseException( |
| 2987 | boost::str( |
| 2988 | boost::format( |
| 2989 | "Shape of matmul weights and bias do not match. " |
| 2990 | "AddNode %1%. MatMulNode %2% %3%") |
| 2991 | % addNodeDef->name() |
| 2992 | % matMulNodeDef.name() |
| 2993 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 2994 | } |
| 2995 | |
| 2996 | layer = m_Network->AddFullyConnectedLayer(desc, weights, biases, armnnLayerName); |
| 2997 | } |
| 2998 | else |
| 2999 | { |
| 3000 | layer = m_Network->AddFullyConnectedLayer(desc, weights, armnnLayerName); |
| 3001 | } |
| 3002 | |
| 3003 | BOOST_ASSERT(layer != nullptr); |
| 3004 | |
| 3005 | inputNode->ResolveArmnnOutputSlot(inputIdx).Connect(layer->GetInputSlot(0)); |
| 3006 | unsigned int batches = inputNode->ResolveArmnnOutputSlot(inputIdx).GetTensorInfo().GetShape()[0]; |
| 3007 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3008 | // Handles output. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3009 | TensorInfo outputInfo({ batches, weights.GetShape()[1] }, DataType::Float32); |
| 3010 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 3011 | return layer; |
| 3012 | } |
| 3013 | |
| 3014 | void TfParser::LoadNodeDef(const tensorflow::NodeDef& nodeDef, const tensorflow::GraphDef& graphDef) |
| 3015 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3016 | // Gets the type of the node (assume float). |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3017 | tensorflow::DataType type = tensorflow::DT_FLOAT; |
| 3018 | if (nodeDef.attr().count("T") != 0) |
| 3019 | { |
| 3020 | auto attr = nodeDef.attr().at("T"); |
| 3021 | type = attr.type(); |
| 3022 | } |
| 3023 | else if (nodeDef.attr().count("dtype") != 0) |
| 3024 | { |
| 3025 | auto attr = nodeDef.attr().at("dtype"); |
| 3026 | type = attr.type(); |
| 3027 | } |
| 3028 | |
| 3029 | if (type != tensorflow::DT_FLOAT && nodeDef.op() != "Const") |
| 3030 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3031 | throw ParseException( |
| 3032 | boost::str( |
| 3033 | boost::format( |
| 3034 | "Currently only FLOAT is supported for tensorflow nodes (apart from Const). " |
| 3035 | "Got %1% for Node %2% %3%") |
| 3036 | % tensorflow::DataType_Name(type) |
| 3037 | % nodeDef.name() |
| 3038 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3039 | } |
| 3040 | |
| 3041 | const std::string& operation = nodeDef.op(); |
narpra01 | 6f37f83 | 2018-12-21 18:30:00 +0000 | [diff] [blame] | 3042 | auto itControlInput = std::find(m_ControlInputs.begin(), m_ControlInputs.end(), operation); |
| 3043 | if (itControlInput != m_ControlInputs.end()) |
| 3044 | { |
| 3045 | // We currently allow Control Input from TensorFlow graph but we ignore them from ArmNN graph. |
| 3046 | return; |
| 3047 | } |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3048 | auto it = ms_OperationNameToParsingFunctions.find(operation); |
| 3049 | if (it != ms_OperationNameToParsingFunctions.end()) |
| 3050 | { |
| 3051 | auto func = it->second; |
| 3052 | ParsedTfOperationPtr parsedTfOperation = (this->*func)(nodeDef, graphDef); |
| 3053 | ParsedTfOperation* parsedTfOperationRaw = parsedTfOperation.get(); |
| 3054 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3055 | // Stores the parsed operation so that dependent layers can connect to it. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3056 | auto it = m_ParsedTfOperations.find(nodeDef.name()); |
| 3057 | if (it != m_ParsedTfOperations.end()) |
| 3058 | { |
| 3059 | throw ParseException(boost::str(boost::format("Name %1% used by more than one node") % nodeDef.name())); |
| 3060 | } |
| 3061 | m_ParsedTfOperations[nodeDef.name()] = std::move(parsedTfOperation); |
| 3062 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3063 | // If this node was requested as an output from the network, then adds an ArmNN output layer. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3064 | if (std::find(m_RequestedOutputs.begin(), m_RequestedOutputs.end(), nodeDef.name()) != |
| 3065 | m_RequestedOutputs.end()) |
| 3066 | { |
| 3067 | auto outId = ParseOutputId(nodeDef.name()); |
| 3068 | const LayerBindingId layerId = boost::numeric_cast<LayerBindingId>(m_NetworkOutputsBindingInfo.size()); |
| 3069 | IOutputSlot& prevSlot = parsedTfOperationRaw->ResolveArmnnOutputSlot(outId.m_Index); |
| 3070 | |
| 3071 | TensorInfo tensorInfo = prevSlot.GetTensorInfo(); |
| 3072 | |
| 3073 | IConnectableLayer* outputLayer = m_Network->AddOutputLayer(layerId, nodeDef.name().c_str()); |
| 3074 | |
| 3075 | prevSlot.Connect(outputLayer->GetInputSlot(0)); |
| 3076 | |
| 3077 | TrackOutputBinding(outputLayer, layerId, tensorInfo); |
| 3078 | } |
| 3079 | } |
| 3080 | else |
| 3081 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3082 | throw ParseException( |
| 3083 | boost::str( |
| 3084 | boost::format( |
| 3085 | "Unsupported operation %1% in tensorflow::GraphDef %2%") |
| 3086 | % operation |
| 3087 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3088 | } |
| 3089 | } |
| 3090 | |
| 3091 | void TfParser::LoadGraphDef(const tensorflow::GraphDef& graphDef) |
| 3092 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3093 | // Adds all nodes to our map. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3094 | m_NodesByName.clear(); |
| 3095 | m_NetworkInputsBindingInfo.clear(); |
| 3096 | m_NetworkOutputsBindingInfo.clear(); |
| 3097 | |
| 3098 | for (int i = 0; i < graphDef.node_size(); ++i) |
| 3099 | { |
| 3100 | const tensorflow::NodeDef& node = graphDef.node(i); |
| 3101 | m_NodesByName[node.name()] = &node; |
| 3102 | } |
| 3103 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3104 | // Finds the output nodes the user requested. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3105 | std::vector<const tensorflow::NodeDef*> targetNodes; |
| 3106 | for (const std::string& requestedOutputName : m_RequestedOutputs) |
| 3107 | { |
| 3108 | auto nodeIt = m_NodesByName.find(requestedOutputName); |
| 3109 | if (nodeIt == m_NodesByName.end()) |
| 3110 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3111 | throw ParseException( |
| 3112 | boost::str( |
| 3113 | boost::format( |
| 3114 | "Couldn't find requested output node '%1%' in graph %2%") |
| 3115 | % requestedOutputName |
| 3116 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3117 | } |
| 3118 | targetNodes.push_back(nodeIt->second); |
| 3119 | } |
| 3120 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3121 | // Sorts them into a linear ordering such that all inputs of a node are before the node itself. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3122 | std::vector<const tensorflow::NodeDef*> sortedNodes; |
| 3123 | if (!armnnUtils::GraphTopologicalSort<const tensorflow::NodeDef*>( |
| 3124 | targetNodes, |
| 3125 | [this](const tensorflow::NodeDef* node) |
| 3126 | { |
| 3127 | auto outputs = GetTfInputNodes(*node); |
| 3128 | std::vector<const tensorflow::NodeDef*> nodesOnly; |
| 3129 | for (const auto & o : outputs) { |
| 3130 | nodesOnly.push_back(o.m_IndexedValue); |
| 3131 | } |
| 3132 | return nodesOnly; |
| 3133 | }, |
| 3134 | sortedNodes)) |
| 3135 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3136 | throw ParseException( |
| 3137 | boost::str( |
| 3138 | boost::format( |
| 3139 | "Cycle detected in graph %1%") |
| 3140 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3141 | } |
| 3142 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3143 | // Parses each node in order, knowing that all inputs of a node will be processed before the node itself. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3144 | for (const auto& it : sortedNodes) |
| 3145 | { |
| 3146 | const tensorflow::NodeDef& currentNode = *it; |
| 3147 | LoadNodeDef(currentNode, graphDef); |
| 3148 | } |
| 3149 | } |
| 3150 | |
| 3151 | INetworkPtr TfParser::CreateNetworkFromTextFile(const char* graphFile, |
| 3152 | const std::map<std::string, TensorShape>& inputShapes, |
| 3153 | const std::vector<std::string>& requestedOutputs) |
| 3154 | { |
| 3155 | FILE* fd = fopen(graphFile, "r"); |
| 3156 | |
| 3157 | if (fd == nullptr) |
| 3158 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3159 | throw FileNotFoundException( |
| 3160 | boost::str( |
| 3161 | boost::format( |
| 3162 | "Graph file %1% failed to open %2%") |
| 3163 | % graphFile |
| 3164 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3165 | } |
| 3166 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3167 | // Parses the file into a message. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3168 | tensorflow::GraphDef graphDef; |
| 3169 | auto input = new google::protobuf::io::FileInputStream(fileno(fd)); |
| 3170 | bool success = google::protobuf::TextFormat::Parse(input, &graphDef); |
| 3171 | delete input; |
| 3172 | fclose(fd); |
| 3173 | |
| 3174 | if (!success) |
| 3175 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3176 | throw ParseException( |
| 3177 | boost::str( |
| 3178 | boost::format( |
| 3179 | "Failed to parse graph file %1%") |
| 3180 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3181 | } |
| 3182 | |
| 3183 | return CreateNetworkFromGraphDef(graphDef, inputShapes, requestedOutputs); |
| 3184 | } |
| 3185 | |
| 3186 | INetworkPtr TfParser::CreateNetworkFromString(const char* protoText, |
| 3187 | const std::map<std::string, TensorShape>& inputShapes, |
| 3188 | const std::vector<std::string>& requestedOutputs) |
| 3189 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3190 | // Parses the string into a message. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3191 | tensorflow::GraphDef graphDef; |
| 3192 | bool success = google::protobuf::TextFormat::ParseFromString(protoText, &graphDef); |
| 3193 | |
| 3194 | if (!success) |
| 3195 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3196 | throw ParseException( |
| 3197 | boost::str( |
| 3198 | boost::format( |
| 3199 | "Failed to parse graph file %1%") |
| 3200 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3201 | } |
| 3202 | |
| 3203 | return CreateNetworkFromGraphDef(graphDef, inputShapes, requestedOutputs); |
| 3204 | } |
| 3205 | |
| 3206 | INetworkPtr TfParser::CreateNetworkFromBinaryFile(const char* graphFile, |
| 3207 | const std::map<std::string, TensorShape>& inputShapes, |
| 3208 | const std::vector<std::string>& requestedOutputs) |
| 3209 | { |
| 3210 | FILE* fd = fopen(graphFile, "rb"); |
| 3211 | |
| 3212 | if (fd == nullptr) |
| 3213 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3214 | throw FileNotFoundException( |
| 3215 | boost::str( |
| 3216 | boost::format( |
| 3217 | "Graph file %1% failed to open %2%") |
| 3218 | % graphFile |
| 3219 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3220 | } |
| 3221 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3222 | // Parses the file into a message. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3223 | tensorflow::GraphDef graphDef; |
| 3224 | |
| 3225 | google::protobuf::io::FileInputStream inStream(fileno(fd)); |
| 3226 | google::protobuf::io::CodedInputStream codedStream(&inStream); |
| 3227 | codedStream.SetTotalBytesLimit(INT_MAX, INT_MAX); |
| 3228 | bool success = graphDef.ParseFromCodedStream(&codedStream); |
| 3229 | fclose(fd); |
| 3230 | |
| 3231 | if (!success) |
| 3232 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3233 | throw ParseException( |
| 3234 | boost::str( |
| 3235 | boost::format( |
| 3236 | "Failed to parse protobuf file %1% %2%") |
| 3237 | % graphFile |
| 3238 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3239 | } |
| 3240 | |
| 3241 | return CreateNetworkFromGraphDef(graphDef, inputShapes, requestedOutputs); |
| 3242 | } |
| 3243 | |
| 3244 | INetworkPtr TfParser::CreateNetworkFromGraphDef(const tensorflow::GraphDef& graphDef, |
| 3245 | const std::map<std::string, TensorShape>& inputShapes, |
| 3246 | const std::vector<std::string>& requestedOutputs) |
| 3247 | { |
| 3248 | m_Network = INetwork::Create(); |
| 3249 | |
| 3250 | m_InputShapes = inputShapes; |
| 3251 | if (requestedOutputs.size() == 0) |
| 3252 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3253 | throw ParseException( |
| 3254 | boost::str( |
| 3255 | boost::format( |
| 3256 | "requestedOutputs must have at least one entry %1%") |
| 3257 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3258 | } |
| 3259 | m_RequestedOutputs = requestedOutputs; |
| 3260 | |
| 3261 | try |
| 3262 | { |
| 3263 | LoadGraphDef(graphDef); |
| 3264 | } |
| 3265 | catch (const ParseException& e) |
| 3266 | { |
| 3267 | Cleanup(); |
| 3268 | throw e; |
| 3269 | } |
| 3270 | |
| 3271 | Cleanup(); |
| 3272 | |
| 3273 | return std::move(m_Network); |
| 3274 | } |
| 3275 | |
| 3276 | void TfParser::Cleanup() |
| 3277 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3278 | // Cleanup, in case we reuse this parser. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3279 | m_InputShapes.clear(); |
| 3280 | m_RequestedOutputs.clear(); |
| 3281 | m_NodesByName.clear(); |
| 3282 | m_ParsedTfOperations.clear(); |
| 3283 | } |
| 3284 | |
| 3285 | BindingPointInfo TfParser::GetNetworkInputBindingInfo(const std::string& name) const |
| 3286 | { |
| 3287 | return GetBindingInfo(name, "input", m_NetworkInputsBindingInfo); |
| 3288 | } |
| 3289 | |
| 3290 | BindingPointInfo TfParser::GetNetworkOutputBindingInfo(const std::string& name) const |
| 3291 | { |
| 3292 | return GetBindingInfo(name, "output", m_NetworkOutputsBindingInfo); |
| 3293 | } |
| 3294 | |
| 3295 | std::pair<LayerBindingId, TensorInfo> TfParser::GetBindingInfo(const std::string& layerName, |
| 3296 | const char* bindingPointDesc, |
| 3297 | const std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo) |
| 3298 | { |
| 3299 | auto it = nameToBindingInfo.find(layerName); |
| 3300 | if (it == nameToBindingInfo.end()) |
| 3301 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3302 | throw InvalidArgumentException( |
| 3303 | boost::str( |
| 3304 | boost::format( |
| 3305 | "Unknown %1% '%2%' %3%") |
| 3306 | % bindingPointDesc |
| 3307 | % layerName |
| 3308 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3309 | } |
| 3310 | return it->second; |
| 3311 | } |
| 3312 | |
| 3313 | void TfParser::TrackInputBinding(IConnectableLayer* layer, LayerBindingId id, const TensorInfo& tensorInfo) |
| 3314 | { |
| 3315 | return TrackBindingPoint(layer, id, tensorInfo, "input", m_NetworkInputsBindingInfo); |
| 3316 | } |
| 3317 | |
| 3318 | void TfParser::TrackOutputBinding(IConnectableLayer* layer, LayerBindingId id, const TensorInfo& tensorInfo) |
| 3319 | { |
| 3320 | return TrackBindingPoint(layer, id, tensorInfo, "output", m_NetworkOutputsBindingInfo); |
| 3321 | } |
| 3322 | |
| 3323 | void TfParser::TrackBindingPoint(IConnectableLayer* layer, |
| 3324 | LayerBindingId id, |
| 3325 | const TensorInfo& tensorInfo, |
| 3326 | const char* bindingPointDesc, |
| 3327 | std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo) |
| 3328 | { |
| 3329 | const std::string layerName = layer->GetName(); |
| 3330 | auto it = nameToBindingInfo.find(layerName); |
| 3331 | if (it == nameToBindingInfo.end()) |
| 3332 | { |
| 3333 | nameToBindingInfo[layerName] = std::make_pair(id, tensorInfo); |
| 3334 | } |
| 3335 | else |
| 3336 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 3337 | throw ParseException( |
| 3338 | boost::str( |
| 3339 | boost::format( |
| 3340 | "Id %1% used by more than one %2% layer %3%") |
| 3341 | % id |
| 3342 | % bindingPointDesc |
| 3343 | % CHECK_LOCATION().AsString())); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 3344 | } |
| 3345 | } |
| 3346 | |
| 3347 | } // namespace armnnTfParser |