telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4 | // |
| 5 | #include "OnnxParser.hpp" |
| 6 | |
Matthew Sloyan | ac001ee | 2021-02-03 10:43:04 +0000 | [diff] [blame] | 7 | #include "armnnOnnxParser/Version.hpp" |
| 8 | |
Matthew Bentham | 39ef3e5 | 2020-01-20 10:09:09 +0000 | [diff] [blame] | 9 | #include <armnn/Descriptors.hpp> |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 10 | #include <armnn/utility/Assert.hpp> |
Matthew Sloyan | 589e3e8 | 2020-09-11 16:17:48 +0100 | [diff] [blame] | 11 | #include <armnn/utility/NumericCast.hpp> |
Narumol Prangnawarat | bc3bb62 | 2021-09-24 16:08:34 +0100 | [diff] [blame^] | 12 | #include <ParserHelper.hpp> |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 13 | #include <VerificationHelpers.hpp> |
| 14 | |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 15 | #include <fmt/format.h> |
Aron Virginas-Tar | d4f0fea | 2019-04-09 14:08:06 +0100 | [diff] [blame] | 16 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 17 | #include <google/protobuf/text_format.h> |
| 18 | #include <google/protobuf/io/zero_copy_stream_impl.h> |
| 19 | |
Matthew Sloyan | ac001ee | 2021-02-03 10:43:04 +0000 | [diff] [blame] | 20 | #include <iostream> |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 21 | #include <numeric> |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 22 | #include <armnnUtils/Permute.hpp> |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 23 | |
| 24 | using namespace armnn; |
| 25 | |
| 26 | namespace armnnOnnxParser |
| 27 | { |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 28 | |
| 29 | IOnnxParser::IOnnxParser() : pOnnxParserImpl(new OnnxParserImpl()) {} |
| 30 | |
| 31 | IOnnxParser::~IOnnxParser() = default; |
| 32 | |
| 33 | IOnnxParser* IOnnxParser::CreateRaw() |
| 34 | { |
| 35 | return new IOnnxParser(); |
| 36 | } |
| 37 | |
| 38 | IOnnxParserPtr IOnnxParser::Create() |
| 39 | { |
| 40 | return IOnnxParserPtr(CreateRaw(), &IOnnxParser::Destroy); |
| 41 | } |
| 42 | |
| 43 | void IOnnxParser::Destroy(IOnnxParser* parser) |
| 44 | { |
| 45 | delete parser; |
| 46 | } |
| 47 | |
| 48 | armnn::INetworkPtr IOnnxParser::CreateNetworkFromBinaryFile(const char* graphFile) |
| 49 | { |
| 50 | return pOnnxParserImpl->CreateNetworkFromBinaryFile(graphFile); |
| 51 | } |
| 52 | |
| 53 | armnn::INetworkPtr IOnnxParser::CreateNetworkFromTextFile(const char* graphFile) |
| 54 | { |
| 55 | return pOnnxParserImpl->CreateNetworkFromTextFile(graphFile); |
| 56 | } |
| 57 | |
| 58 | armnn::INetworkPtr IOnnxParser::CreateNetworkFromString(const std::string& protoText) |
| 59 | { |
| 60 | return pOnnxParserImpl->CreateNetworkFromString(protoText); |
| 61 | } |
| 62 | |
| 63 | BindingPointInfo IOnnxParser::GetNetworkInputBindingInfo(const std::string& name) const |
| 64 | { |
| 65 | return pOnnxParserImpl->GetNetworkInputBindingInfo(name); |
| 66 | } |
| 67 | |
| 68 | BindingPointInfo IOnnxParser::GetNetworkOutputBindingInfo(const std::string& name) const |
| 69 | { |
| 70 | return pOnnxParserImpl->GetNetworkOutputBindingInfo(name); |
| 71 | } |
| 72 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 73 | namespace |
| 74 | { |
| 75 | void CheckValidDataType(std::initializer_list<onnx::TensorProto::DataType> validInputTypes, |
| 76 | const onnx::TensorProto::DataType actualValue, |
| 77 | const char* validExpr, |
| 78 | std::string nodeName, |
| 79 | std::string tensorName, |
| 80 | const armnn::CheckLocation& location) |
| 81 | { |
| 82 | bool isValid = std::any_of(validInputTypes.begin(), |
| 83 | validInputTypes.end(), |
| 84 | [&actualValue](onnx::TensorProto::DataType x) { return x == actualValue; } ); |
| 85 | if (!isValid) |
| 86 | { |
| 87 | throw ParseException( |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 88 | fmt::format("Datatype {} is not valid for tensor '{}' of node '{}', not in {{{}}}. {}", |
| 89 | onnx::TensorProto::DataType_Name(actualValue), |
| 90 | tensorName, |
| 91 | nodeName, |
| 92 | validExpr, |
| 93 | location.AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 94 | } |
| 95 | } |
| 96 | |
| 97 | #define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL, ...) \ |
| 98 | CheckValidDataType({__VA_ARGS__}, ACTUAL, #__VA_ARGS__, NODE, TENSOR, CHECK_LOCATION()) |
| 99 | |
| 100 | using StrTypeListPair = std::pair<const char*, std::initializer_list<onnx::TensorProto::DataType>>; |
| 101 | #define STR_LIST(...) StrTypeListPair(#__VA_ARGS__, {__VA_ARGS__}) |
| 102 | |
| 103 | template <typename Callable> |
| 104 | void ReadMandatoryNodeAttributeImpl(const onnx::NodeProto& node, |
| 105 | const std::string& attribName, |
| 106 | onnx::AttributeProto::AttributeType expectedType, |
| 107 | Callable callable) |
| 108 | { |
| 109 | auto attribs = node.attribute(); |
| 110 | int attriNum = 0; |
| 111 | while (attriNum < node.attribute_size()) |
| 112 | { |
| 113 | if (attribs.Get(attriNum).name() == attribName) |
| 114 | { |
| 115 | if (attribs.Get(attriNum).type() == expectedType) |
| 116 | { |
| 117 | callable(attribs.Get(attriNum)); |
| 118 | } |
| 119 | else |
| 120 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 121 | throw ParseException(fmt::format("Attribute {} of node {} expected to have {} as " |
| 122 | "onnx::AttributeProto::AttributeType, but found {} instead {}", |
| 123 | attribName, |
| 124 | node.name(), |
| 125 | onnx::AttributeProto::AttributeType_Name(expectedType), |
| 126 | onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type()), |
| 127 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 128 | } |
| 129 | break; |
| 130 | } |
| 131 | ++attriNum; |
| 132 | } |
| 133 | if (attriNum == node.attribute_size()) |
| 134 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 135 | throw ParseException(fmt::format("Could not find required attribute {} in node {} {}", |
| 136 | attribName, node.name(), CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 137 | } |
| 138 | } |
| 139 | |
| 140 | template <typename Callable> |
| 141 | void ReadOptionalNodeAttributeImpl(const onnx::NodeProto& node, |
| 142 | const std::string& attribName, |
| 143 | onnx::AttributeProto::AttributeType expectedType, |
| 144 | Callable callable) |
| 145 | { |
| 146 | auto attribs = node.attribute(); |
| 147 | for (int attriNum = 0; attriNum < node.attribute_size(); ++attriNum) |
| 148 | { |
| 149 | if (attribs.Get(attriNum).name() == attribName) |
| 150 | { |
| 151 | if (attribs.Get(attriNum).type() == expectedType) |
| 152 | { |
| 153 | callable(attribs.Get(attriNum)); |
| 154 | } |
| 155 | else |
| 156 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 157 | throw ParseException( |
| 158 | fmt::format("Attribute {} of node {} expected to have {} as onnx::AttributeProto::AttributeType, " |
| 159 | "but found {} instead {}", |
| 160 | attribName, |
| 161 | node.name(), |
| 162 | onnx::AttributeProto::AttributeType_Name(expectedType), |
| 163 | onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type()), |
| 164 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 165 | } |
| 166 | } |
| 167 | } |
| 168 | } |
| 169 | |
Narumol Prangnawarat | bc3bb62 | 2021-09-24 16:08:34 +0100 | [diff] [blame^] | 170 | int ReadMandatoryNodeIntAttribute(const onnx::NodeProto& node, |
| 171 | const std::string& name) |
| 172 | { |
| 173 | int attribValue = 0; |
| 174 | ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INT, |
| 175 | [&attribValue](const onnx::AttributeProto& attrValue) |
| 176 | { |
| 177 | attribValue = CHECKED_INT32(attrValue.i()); |
| 178 | }); |
| 179 | return attribValue; |
| 180 | } |
| 181 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 182 | int64_t ReadOptionalNodeInt64Attribute(const onnx::NodeProto& node, |
| 183 | const std::string& name, |
| 184 | const int64_t defaultValue = 0) |
| 185 | { |
| 186 | int64_t attribValue = defaultValue; |
| 187 | ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT, |
| 188 | [&attribValue](const onnx::AttributeProto& attrValue) |
| 189 | { |
| 190 | attribValue = attrValue.i(); |
| 191 | }); |
| 192 | return attribValue; |
| 193 | } |
| 194 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 195 | std::vector<uint32_t> ReadMandatoryNodeUint32ListAttribute(const onnx::NodeProto& node, |
| 196 | const std::string& name) |
| 197 | { |
| 198 | std::vector<uint32_t> attriList; |
| 199 | ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INTS, |
| 200 | [&attriList](const onnx::AttributeProto& attrValue) |
| 201 | { |
| 202 | for (int attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum) |
| 203 | { |
| 204 | attriList.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(attrValue.ints().Get(attriNum)))); |
| 205 | } |
| 206 | }); |
| 207 | return attriList; |
| 208 | } |
| 209 | |
| 210 | uint32_t ReadOptionalNodeUint32Attribute(const onnx::NodeProto& node, |
| 211 | const std::string& name, |
| 212 | const uint32_t defaultVal = 0u) |
| 213 | { |
| 214 | uint32_t attribValue = defaultVal; |
| 215 | ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT, |
| 216 | [&attribValue](const onnx::AttributeProto& attrValue) |
| 217 | { |
| 218 | attribValue = CHECKED_NON_NEGATIVE(CHECKED_INT32((attrValue.i()))); |
| 219 | }); |
| 220 | return attribValue; |
| 221 | } |
| 222 | |
| 223 | std::vector<uint32_t> ReadOptionalNodeUint32ListAttribute(const onnx::NodeProto& node, |
| 224 | const std::string& name) |
| 225 | { |
| 226 | std::vector<uint32_t> attriList; |
| 227 | ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INTS, |
| 228 | [&attriList](const onnx::AttributeProto& attrValue) |
| 229 | { |
| 230 | for (int attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum) |
| 231 | { |
| 232 | attriList.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(attrValue.ints().Get(attriNum)))); |
| 233 | } |
| 234 | }); |
| 235 | |
| 236 | return attriList; |
| 237 | } |
| 238 | |
| 239 | float ReadOptionalNodeFloatAttribute(const onnx::NodeProto& node, |
| 240 | const std::string& name, |
| 241 | const float defaultValue = 0.0f) |
| 242 | { |
| 243 | float attribValue = defaultValue; |
| 244 | ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::FLOAT, |
| 245 | [&attribValue](const onnx::AttributeProto& attrValue) |
| 246 | { |
| 247 | attribValue = attrValue.f(); |
| 248 | }); |
| 249 | return attribValue; |
| 250 | } |
| 251 | |
| 252 | std::string ReadOptionalNodeStringAttribute(const onnx::NodeProto& node, const std::string& name) |
| 253 | { |
| 254 | std::string attribValue = ""; |
| 255 | ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::STRING, |
| 256 | [&attribValue](const onnx::AttributeProto& attrValue) |
| 257 | { |
| 258 | attribValue = attrValue.s(); |
| 259 | }); |
| 260 | return attribValue; |
| 261 | } |
| 262 | |
Tee Jung | fcf6fd5 | 2019-11-01 05:27:28 +0000 | [diff] [blame] | 263 | armnn::TensorInfo ToTensorInfo(const std::string& name, std::vector<unsigned int>& shape, int data_type) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 264 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 265 | DataType type; |
Tee Jung | fcf6fd5 | 2019-11-01 05:27:28 +0000 | [diff] [blame] | 266 | switch(data_type) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 267 | { |
| 268 | case onnx::TensorProto::FLOAT: |
| 269 | { |
| 270 | type = DataType::Float32; |
| 271 | break; |
| 272 | } |
| 273 | case onnx::TensorProto::INT32: |
| 274 | case onnx::TensorProto::INT64: |
| 275 | { |
| 276 | type = DataType::Signed32; |
| 277 | break; |
| 278 | } |
| 279 | default: |
| 280 | { |
| 281 | throw ParseException( |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 282 | fmt::format("'{}' is not a currently supported datatype for tensor {}." |
| 283 | " Supported dataTypes are FLOAT, INT32 and INT64. {}", |
| 284 | onnx::TensorProto::DataType_Name(static_cast<onnx::TensorProto::DataType>(data_type)), |
| 285 | name, |
| 286 | CHECK_LOCATION().AsString() )); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 287 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 288 | } |
Tee Jung | caf2bdd | 2019-11-13 07:23:14 +0000 | [diff] [blame] | 289 | |
| 290 | // To avoid crashes by trivial tensors |
| 291 | if (shape.empty()) |
| 292 | { |
| 293 | return TensorInfo(TensorShape(), type); |
| 294 | } |
| 295 | |
Tee Jung | fcf6fd5 | 2019-11-01 05:27:28 +0000 | [diff] [blame] | 296 | return TensorInfo(TensorShape(static_cast<unsigned int>(shape.size()), shape.data()), type); |
| 297 | } |
| 298 | |
| 299 | armnn::TensorInfo ToTensorInfo(const onnx::ValueInfoProto& info) |
| 300 | { |
| 301 | const onnx::TensorShapeProto onnxShape = info.type().tensor_type().shape(); |
| 302 | std::vector<unsigned int> shapeDims; |
| 303 | for (int i = 0; i < onnxShape.dim_size(); ++i) |
| 304 | { |
| 305 | shapeDims.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(onnxShape.dim(i).dim_value()))); |
| 306 | } |
| 307 | |
Ryan OShea | 337c17f | 2020-02-21 12:33:17 +0000 | [diff] [blame] | 308 | if (shapeDims.empty()) |
| 309 | { |
| 310 | shapeDims.push_back(1); |
| 311 | } |
| 312 | |
Tee Jung | fcf6fd5 | 2019-11-01 05:27:28 +0000 | [diff] [blame] | 313 | return ToTensorInfo(info.name(), shapeDims, info.type().tensor_type().elem_type()); |
| 314 | } |
| 315 | |
| 316 | armnn::TensorInfo ToTensorInfo(const onnx::TensorProto& tensor) |
| 317 | { |
| 318 | std::vector<unsigned int> shapeDims; |
Ryan OShea | 337c17f | 2020-02-21 12:33:17 +0000 | [diff] [blame] | 319 | |
Tee Jung | fcf6fd5 | 2019-11-01 05:27:28 +0000 | [diff] [blame] | 320 | for (auto dim: tensor.dims()) |
| 321 | { |
| 322 | shapeDims.push_back(CHECKED_NON_NEGATIVE(CHECKED_INT32(dim))); |
| 323 | } |
| 324 | |
Ryan OShea | 337c17f | 2020-02-21 12:33:17 +0000 | [diff] [blame] | 325 | if (shapeDims.empty()) |
| 326 | { |
| 327 | shapeDims.push_back(1); |
| 328 | } |
| 329 | |
Tee Jung | fcf6fd5 | 2019-11-01 05:27:28 +0000 | [diff] [blame] | 330 | return ToTensorInfo(tensor.name(), shapeDims, tensor.data_type()); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 331 | } |
| 332 | |
| 333 | std::string TensorInfoAsString(const TensorInfo& info, |
| 334 | const std::string& name, |
| 335 | const onnx::TensorProto::DataType& type) |
| 336 | { |
| 337 | const TensorShape shape = info.GetShape(); |
| 338 | std::stringstream ss; |
| 339 | ss << "tensor '" << name << "' contains " |
| 340 | << onnx::TensorProto::DataType_Name(type) |
| 341 | << " and has shape ["; |
| 342 | |
| 343 | for (uint32_t i = 0; i < shape.GetNumDimensions() - 1; ++i) |
| 344 | { |
| 345 | ss << shape[i] << ", "; |
| 346 | } |
| 347 | ss << shape[shape.GetNumDimensions() - 1] << "]"; |
| 348 | return ss.str(); |
| 349 | } |
| 350 | |
Sadik Armagan | 60bb9d8 | 2021-01-11 15:15:01 +0000 | [diff] [blame] | 351 | void CalcPadding(uint32_t inputSize, |
| 352 | uint32_t filterSize, |
| 353 | uint32_t stride, |
| 354 | uint32_t dilation, |
| 355 | uint32_t* paddingFront, |
| 356 | uint32_t* paddingBack, |
| 357 | bool isUpper) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 358 | { |
| 359 | uint32_t outputSize = (inputSize + stride - 1) / stride; |
Sadik Armagan | 60bb9d8 | 2021-01-11 15:15:01 +0000 | [diff] [blame] | 360 | uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1); |
| 361 | uint32_t temp = (outputSize - 1) * stride + dilatedSize; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 362 | *paddingFront = (temp - inputSize) / 2; |
| 363 | *paddingBack = *paddingFront; |
| 364 | if((temp - inputSize) % 2 == 1) |
| 365 | { |
| 366 | if (isUpper) |
| 367 | { |
Sadik Armagan | 60bb9d8 | 2021-01-11 15:15:01 +0000 | [diff] [blame] | 368 | *paddingBack += 1; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 369 | } |
| 370 | else |
| 371 | { |
Sadik Armagan | 60bb9d8 | 2021-01-11 15:15:01 +0000 | [diff] [blame] | 372 | *paddingFront += 1; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 373 | } |
| 374 | } |
| 375 | } |
| 376 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 377 | TensorInfo ComputeReshapeInfo(const TensorShape& targetShapeTensor, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 378 | const TensorShape& inShape, |
| 379 | const std::string& outName) |
| 380 | { |
| 381 | std::vector<int> targetDims; |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 382 | for(uint i = 0; i < targetShapeTensor.GetNumDimensions(); ++i) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 383 | { |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 384 | int val = CHECKED_INT32(targetShapeTensor[i]); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 385 | if(val == 0) |
| 386 | { |
| 387 | targetDims.push_back(static_cast<int>(inShape[static_cast<uint>(i)])); |
| 388 | } |
| 389 | else |
| 390 | { |
| 391 | targetDims.push_back(val); |
| 392 | } |
| 393 | } |
| 394 | |
| 395 | std::vector<unsigned int> outDims(targetDims.begin(), targetDims.end()); |
| 396 | const auto stretchDim = std::find(targetDims.begin(), targetDims.end(), -1); |
| 397 | if (stretchDim != targetDims.end()) |
| 398 | { |
| 399 | if (std::find(std::next(stretchDim), targetDims.end(), -1) != targetDims.end()) |
| 400 | { |
| 401 | std::stringstream ss; |
| 402 | ss << "[ "; |
| 403 | for(uint i = 0; i < targetDims.size() - 1; ++i) |
| 404 | { |
| 405 | ss << targetDims[i] << ", "; |
| 406 | } |
| 407 | ss << targetDims[targetDims.size() - 1] << " ]"; |
| 408 | |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 409 | throw ParseException( |
| 410 | fmt::format("Error during creation of reshaped tensor '{}'. At most one component of shape can be " |
| 411 | " -1 and here, shape is {} {}", |
| 412 | outName, |
| 413 | ss.str(), |
| 414 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 415 | } |
| 416 | |
Matthew Sloyan | 589e3e8 | 2020-09-11 16:17:48 +0100 | [diff] [blame] | 417 | auto targetNumElements = armnn::numeric_cast<unsigned int>(std::accumulate(targetDims.begin(), targetDims.end(), |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 418 | -1, std::multiplies<int32_t>())); |
| 419 | auto stretchIndex = static_cast<size_t>(std::distance(targetDims.begin(), stretchDim)); |
| 420 | outDims[stretchIndex] = inShape.GetNumElements() / targetNumElements; |
| 421 | } |
| 422 | TensorShape outShape = TensorShape{static_cast<unsigned int>(outDims.size()), outDims.data()}; |
| 423 | return TensorInfo(outShape, DataType::Float32); |
| 424 | } |
| 425 | |
| 426 | } //namespace |
| 427 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 428 | const std::map<std::string, OnnxParserImpl::OperationParsingFunction> OnnxParserImpl::m_ParserFunctions = { |
| 429 | { "BatchNormalization", &OnnxParserImpl::ParseBatchNormalization}, |
| 430 | { "GlobalAveragePool", &OnnxParserImpl::ParseGlobalAveragePool}, |
| 431 | { "AveragePool", &OnnxParserImpl::ParseAveragePool }, |
| 432 | { "Clip", &OnnxParserImpl::ParseClip }, |
| 433 | { "Constant", &OnnxParserImpl::ParseConstant }, |
| 434 | { "MaxPool", &OnnxParserImpl::ParseMaxPool }, |
| 435 | { "Reshape", &OnnxParserImpl::ParseReshape }, |
| 436 | { "Sigmoid", &OnnxParserImpl::ParseSigmoid }, |
| 437 | { "Tanh", &OnnxParserImpl::ParseTanh }, |
| 438 | { "Relu", &OnnxParserImpl::ParseRelu }, |
| 439 | { "LeakyRelu", &OnnxParserImpl::ParseLeakyRelu }, |
| 440 | { "Conv", &OnnxParserImpl::ParseConv }, |
| 441 | { "Add", &OnnxParserImpl::ParseAdd }, |
Narumol Prangnawarat | cdc495e | 2021-09-16 18:13:39 +0100 | [diff] [blame] | 442 | { "Flatten", &OnnxParserImpl::ParseFlatten }, |
Narumol Prangnawarat | f10b15a | 2021-09-17 21:08:57 +0100 | [diff] [blame] | 443 | { "Shape", &OnnxParserImpl::ParseShape }, |
| 444 | { "Gather", &OnnxParserImpl::ParseGather }, |
Narumol Prangnawarat | bc3bb62 | 2021-09-24 16:08:34 +0100 | [diff] [blame^] | 445 | { "Unsqueeze", &OnnxParserImpl::ParseUnsqueeze }, |
| 446 | { "Concat", &OnnxParserImpl::ParseConcat } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 447 | }; |
| 448 | |
| 449 | template<typename TypePair, typename Location> |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 450 | void OnnxParserImpl::ValidateInputs(const onnx::NodeProto& node, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 451 | TypePair validInputs, |
| 452 | const Location& location) |
| 453 | { |
| 454 | for(auto input : node.input()) |
| 455 | { |
| 456 | CheckValidDataType(validInputs.second, |
| 457 | m_TensorsInfo[input].m_dtype, |
| 458 | validInputs.first, |
| 459 | node.name(), |
| 460 | input, |
| 461 | location); |
| 462 | } |
| 463 | } |
| 464 | |
| 465 | #define VALID_INPUTS(NODE, VALID_INPUTS) \ |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 466 | OnnxParserImpl::ValidateInputs(NODE, \ |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 467 | VALID_INPUTS, \ |
| 468 | CHECK_LOCATION()) |
| 469 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 470 | std::vector<TensorInfo> OnnxParserImpl::ComputeOutputInfo(std::vector<std::string> outNames, |
| 471 | const IConnectableLayer* layer, |
| 472 | std::vector<TensorShape> inputShapes) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 473 | { |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 474 | ARMNN_ASSERT(! outNames.empty()); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 475 | bool needCompute = std::any_of(outNames.begin(), |
| 476 | outNames.end(), |
| 477 | [this](std::string name) |
| 478 | { |
| 479 | return (m_TensorsInfo.count(name) == 0 || m_TensorsInfo[name].m_info == nullptr); |
| 480 | }); |
| 481 | std::vector<TensorInfo> outInfo; |
| 482 | //if the output info(s) are not here, we need to compute them |
| 483 | std::vector<TensorShape> inferredShapes; |
| 484 | if(needCompute) |
| 485 | { |
| 486 | inferredShapes = layer->InferOutputShapes(inputShapes); |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 487 | ARMNN_ASSERT(inferredShapes.size() == outNames.size()); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 488 | } |
| 489 | for (uint i = 0; i < outNames.size(); ++i) |
| 490 | { |
| 491 | if(needCompute) |
| 492 | { |
| 493 | m_TensorsInfo[outNames[i]] = OnnxTensor(); |
| 494 | m_TensorsInfo[outNames[i]].m_info = std::make_unique<TensorInfo>( |
| 495 | TensorInfo(inferredShapes[i], DataType::Float32)); |
| 496 | } |
| 497 | outInfo.push_back(*m_TensorsInfo[outNames[i]].m_info); |
| 498 | } |
| 499 | return outInfo; |
| 500 | } |
| 501 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 502 | OnnxParserImpl::OnnxParserImpl() |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 503 | : m_Network(nullptr, nullptr) |
| 504 | { |
| 505 | } |
| 506 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 507 | void OnnxParserImpl::ResetParser() |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 508 | { |
| 509 | m_Network = armnn::INetworkPtr(nullptr, nullptr); |
| 510 | m_Graph = nullptr; |
| 511 | } |
| 512 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 513 | void OnnxParserImpl::Cleanup() |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 514 | { |
| 515 | m_TensorConnections.clear(); |
| 516 | m_TensorsInfo.clear(); |
| 517 | m_OutputsMap.clear(); |
| 518 | m_OutputsFusedAndUsed.clear(); |
| 519 | } |
| 520 | |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 521 | template<typename T> |
| 522 | std::pair<armnn::ConstTensor, std::unique_ptr<T[]>> |
| 523 | CreateConstTensorImpl(const T* bufferPtr, |
| 524 | armnn::TensorInfo& tensorInfo, |
| 525 | const armnn::Optional<armnn::PermutationVector&> permutationVector) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 526 | { |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 527 | ARMNN_ASSERT_MSG(bufferPtr != nullptr, fmt::format("Buffer for permutation is null").c_str()); |
| 528 | |
| 529 | std::unique_ptr<T[]> data(new T[tensorInfo.GetNumElements()]); |
| 530 | |
| 531 | if (permutationVector.has_value() && permutationVector.value().GetSize() > 0) |
| 532 | { |
| 533 | tensorInfo = armnnUtils::Permuted(tensorInfo, permutationVector.value()); |
| 534 | armnnUtils::Permute(tensorInfo.GetShape(), permutationVector.value(), |
| 535 | reinterpret_cast<const T*>(bufferPtr), data.get(), sizeof(T)); |
| 536 | } |
| 537 | else |
| 538 | { |
| 539 | ::memcpy(data.get(), bufferPtr, tensorInfo.GetNumBytes()); |
| 540 | } |
| 541 | |
| 542 | return std::make_pair(ConstTensor(tensorInfo, data.get()), std::move(data)); |
| 543 | } |
| 544 | |
| 545 | std::pair<ConstTensor, std::unique_ptr<float[]>> |
| 546 | OnnxParserImpl::CreateConstTensor(const std::string name, |
| 547 | armnn::Optional<armnn::PermutationVector&> permutationVector) |
| 548 | { |
| 549 | TensorInfo tensorInfo = *m_TensorsInfo[name].m_info; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 550 | onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor; |
| 551 | |
Narumol Prangnawarat | f10b15a | 2021-09-17 21:08:57 +0100 | [diff] [blame] | 552 | //ONNX can have Float16 and double constant nodes but ArmNN only supports float32 |
| 553 | CHECK_VALID_DATATYPE(name, onnxTensor.name(), |
| 554 | static_cast<onnx::TensorProto::DataType>(onnxTensor.data_type()), onnx::TensorProto::FLOAT); |
| 555 | |
Matthew Sloyan | 81beae3 | 2021-07-13 19:46:11 +0100 | [diff] [blame] | 556 | // Makes sure IsConstant flag is set. |
| 557 | tensorInfo.SetConstant(); |
| 558 | |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 559 | // Const tensors requires at least a list of values |
| 560 | if (tensorInfo.GetNumElements() == 0) |
| 561 | { |
| 562 | throw ParseException(fmt::format("No tensor data found for Const tensor '{}' {}", |
| 563 | name, |
| 564 | CHECK_LOCATION().AsString())); |
| 565 | } |
| 566 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 567 | auto srcData = onnxTensor.float_data().data(); |
Pablo Tello | 3dcc1c6 | 2019-04-24 14:20:21 +0100 | [diff] [blame] | 568 | // Copy the value list entries into the destination |
| 569 | if (!onnxTensor.has_raw_data()) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 570 | { |
Pablo Tello | 3dcc1c6 | 2019-04-24 14:20:21 +0100 | [diff] [blame] | 571 | if(tensorInfo.GetNumElements() != static_cast<uint>(onnxTensor.float_data_size())) |
| 572 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 573 | throw ParseException( |
| 574 | fmt::format("The number of data provided ({}) does not match the tensor '{}' number of " |
| 575 | "elements ({}) {}", |
| 576 | onnxTensor.float_data_size(), |
| 577 | name, |
| 578 | tensorInfo.GetNumElements(), |
| 579 | CHECK_LOCATION().AsString())); |
Pablo Tello | 3dcc1c6 | 2019-04-24 14:20:21 +0100 | [diff] [blame] | 580 | } |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 581 | return CreateConstTensorImpl<float>(srcData, tensorInfo, permutationVector); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 582 | } |
Pablo Tello | 3dcc1c6 | 2019-04-24 14:20:21 +0100 | [diff] [blame] | 583 | else |
| 584 | { |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 585 | return CreateConstTensorImpl<float>(reinterpret_cast<const float*>(onnxTensor.raw_data().c_str()), |
| 586 | tensorInfo, |
| 587 | permutationVector); |
Pablo Tello | 3dcc1c6 | 2019-04-24 14:20:21 +0100 | [diff] [blame] | 588 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 589 | } |
| 590 | |
Narumol Prangnawarat | f10b15a | 2021-09-17 21:08:57 +0100 | [diff] [blame] | 591 | std::pair<ConstTensor, std::unique_ptr<int32_t[]>> |
| 592 | OnnxParserImpl::CreateInt64ConstTensor(const std::string name, |
| 593 | armnn::Optional<armnn::PermutationVector&> permutationVector) |
| 594 | { |
| 595 | TensorInfo tensorInfo = *m_TensorsInfo[name].m_info; |
| 596 | onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor; |
| 597 | |
| 598 | CHECK_VALID_DATATYPE(name, onnxTensor.name(), |
| 599 | static_cast<onnx::TensorProto::DataType>(onnxTensor.data_type()), onnx::TensorProto::INT64); |
| 600 | |
| 601 | // Makes sure IsConstant flag is set. |
| 602 | tensorInfo.SetConstant(); |
| 603 | uint numElements = tensorInfo.GetNumElements(); |
| 604 | |
| 605 | // Const tensors requires at least a list of values |
| 606 | if (numElements == 0) |
| 607 | { |
| 608 | throw ParseException(fmt::format("No tensor data found for Const tensor '{}' {}", |
| 609 | name, |
| 610 | CHECK_LOCATION().AsString())); |
| 611 | } |
| 612 | |
| 613 | // Copy the value list entries into the destination |
| 614 | if (!onnxTensor.has_raw_data()) |
| 615 | { |
| 616 | auto srcData = onnxTensor.int64_data().data(); |
| 617 | if(numElements != static_cast<uint>(onnxTensor.int64_data_size())) |
| 618 | { |
| 619 | throw ParseException( |
| 620 | fmt::format("The number of data provided ({}) does not match the tensor '{}' number of " |
| 621 | "elements ({}) {}", |
| 622 | onnxTensor.int64_data_size(), |
| 623 | name, |
| 624 | tensorInfo.GetNumElements(), |
| 625 | CHECK_LOCATION().AsString())); |
| 626 | } |
| 627 | |
| 628 | std::vector<int32_t> int32Data; |
| 629 | for(uint i = 0; i < numElements; i++) |
| 630 | { |
| 631 | int32_t int32Value = CHECKED_INT32(srcData[i]); |
| 632 | int32Data.push_back(int32Value); |
| 633 | } |
| 634 | |
| 635 | return CreateConstTensorImpl<int32_t>(int32Data.data(), tensorInfo, permutationVector); |
| 636 | } |
| 637 | else |
| 638 | { |
| 639 | auto srcData = reinterpret_cast<const int64_t*>(onnxTensor.raw_data().c_str()); |
| 640 | std::vector<int32_t> int32Data; |
| 641 | for(uint i = 0; i < numElements; i++) |
| 642 | { |
| 643 | int32_t int32Value = CHECKED_INT32(srcData[i]); |
| 644 | int32Data.push_back(int32Value); |
| 645 | } |
| 646 | return CreateConstTensorImpl<int32_t>(int32Data.data(), tensorInfo, permutationVector); |
| 647 | } |
| 648 | } |
| 649 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 650 | ModelPtr OnnxParserImpl::LoadModelFromTextFile(const char* graphFile) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 651 | { |
| 652 | FILE* fd = fopen(graphFile, "r"); |
| 653 | |
| 654 | if (fd == nullptr) |
| 655 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 656 | throw FileNotFoundException(fmt::format("Invalid (null) filename {}", CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 657 | } |
| 658 | |
| 659 | // Parse the file into a message |
| 660 | ModelPtr modelProto = std::make_unique<onnx::ModelProto>(); |
| 661 | using google::protobuf::io::FileInputStream; |
| 662 | std::unique_ptr<FileInputStream> input = std::make_unique<FileInputStream>(fileno(fd)); |
| 663 | bool success = google::protobuf::TextFormat::Parse(input.get(), modelProto.get()); |
| 664 | fclose(fd); |
| 665 | |
| 666 | if (!success) |
| 667 | { |
| 668 | std::stringstream error; |
| 669 | error << "Failed to parse graph file"; |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 670 | throw ParseException(fmt::format("{} {}", error.str(), CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 671 | } |
| 672 | return modelProto; |
| 673 | } |
| 674 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 675 | INetworkPtr OnnxParserImpl::CreateNetworkFromTextFile(const char* graphFile) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 676 | { |
| 677 | ResetParser(); |
| 678 | ModelPtr modelProto = LoadModelFromTextFile(graphFile); |
| 679 | return CreateNetworkFromModel(*modelProto); |
| 680 | } |
| 681 | |
| 682 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 683 | ModelPtr OnnxParserImpl::LoadModelFromBinaryFile(const char* graphFile) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 684 | { |
| 685 | FILE* fd = fopen(graphFile, "rb"); |
| 686 | |
| 687 | if (fd == nullptr) |
| 688 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 689 | throw FileNotFoundException(fmt::format("Invalid (null) filename {}", CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 690 | } |
| 691 | |
| 692 | // Parse the file into a message |
| 693 | ModelPtr modelProto = std::make_unique<onnx::ModelProto>(); |
| 694 | |
| 695 | google::protobuf::io::FileInputStream inStream(fileno(fd)); |
| 696 | google::protobuf::io::CodedInputStream codedStream(&inStream); |
Nikhil Raj | e518153 | 2020-10-09 14:52:25 +0100 | [diff] [blame] | 697 | codedStream.SetTotalBytesLimit(INT_MAX); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 698 | bool success = modelProto.get()->ParseFromCodedStream(&codedStream); |
| 699 | fclose(fd); |
| 700 | |
| 701 | if (!success) |
| 702 | { |
| 703 | std::stringstream error; |
| 704 | error << "Failed to parse graph file"; |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 705 | throw ParseException(fmt::format("{} {}", error.str(), CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 706 | } |
| 707 | return modelProto; |
| 708 | |
| 709 | } |
| 710 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 711 | INetworkPtr OnnxParserImpl::CreateNetworkFromBinaryFile(const char* graphFile) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 712 | { |
| 713 | ResetParser(); |
| 714 | ModelPtr modelProto = LoadModelFromBinaryFile(graphFile); |
| 715 | return CreateNetworkFromModel(*modelProto); |
| 716 | } |
| 717 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 718 | ModelPtr OnnxParserImpl::LoadModelFromString(const std::string& protoText) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 719 | { |
| 720 | if (protoText == "") |
| 721 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 722 | throw InvalidArgumentException(fmt::format("Invalid (empty) string for model parameter {}", |
| 723 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 724 | } |
| 725 | // Parse the string into a message |
| 726 | ModelPtr modelProto = std::make_unique<onnx::ModelProto>(); |
| 727 | bool success = google::protobuf::TextFormat::ParseFromString(protoText, modelProto.get()); |
| 728 | if (!success) |
| 729 | { |
| 730 | std::stringstream error; |
| 731 | error << "Failed to parse graph file"; |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 732 | throw ParseException(fmt::format("{} {}", error.str(), CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 733 | } |
| 734 | return modelProto; |
| 735 | } |
| 736 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 737 | INetworkPtr OnnxParserImpl::CreateNetworkFromString(const std::string& protoText) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 738 | { |
| 739 | ResetParser(); |
| 740 | ModelPtr modelProto = LoadModelFromString(protoText); |
| 741 | return CreateNetworkFromModel(*modelProto); |
| 742 | } |
| 743 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 744 | INetworkPtr OnnxParserImpl::CreateNetworkFromModel(onnx::ModelProto& model) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 745 | { |
| 746 | m_Network = INetwork::Create(); |
| 747 | try |
| 748 | { |
| 749 | m_Graph = std::make_unique<onnx::GraphProto>(*model.mutable_graph()); |
| 750 | LoadGraph(); |
| 751 | } |
| 752 | catch (const ParseException& e) |
| 753 | { |
| 754 | Cleanup(); |
| 755 | throw e; |
| 756 | } |
| 757 | Cleanup(); |
| 758 | return std::move(m_Network); |
| 759 | } |
| 760 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 761 | void OnnxParserImpl::LoadGraph() |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 762 | { |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 763 | ARMNN_ASSERT(m_Graph.get() != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 764 | |
| 765 | //Fill m_TensorsInfo with the shapes and value of every tensor |
| 766 | SetupInfo(m_Graph->mutable_output()); |
| 767 | SetupInfo(m_Graph->mutable_input()); |
| 768 | SetupInfo(m_Graph->mutable_value_info()); |
| 769 | |
| 770 | for (auto tensor : m_Graph->initializer()) |
| 771 | { |
| 772 | m_TensorsInfo[tensor.name()].m_tensor = std::make_unique<const onnx::TensorProto>(tensor); |
Tee Jung | fcf6fd5 | 2019-11-01 05:27:28 +0000 | [diff] [blame] | 773 | m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(ToTensorInfo(tensor)); |
| 774 | m_TensorsInfo[tensor.name()].m_dtype = |
| 775 | static_cast<onnx::TensorProto::DataType>(tensor.data_type()); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 776 | } |
| 777 | |
| 778 | SetupInputLayers(); |
| 779 | SetupOutputLayers(); |
| 780 | |
| 781 | //Detect FullyConnected layers with bias and update the FusedAndUsed map acccordingly |
| 782 | DetectFullyConnected(); |
| 783 | |
| 784 | //Parsing the graph |
| 785 | for(size_t nodeIndex = 0; nodeIndex < static_cast<size_t>(m_Graph->node_size()); nodeIndex++) |
| 786 | { |
| 787 | auto node = m_Graph->node(static_cast<int>(nodeIndex)); |
| 788 | const std::string& operation = node.op_type(); |
| 789 | |
| 790 | // check which layers we handled already (add and matmul fused as FC) |
Ryan OShea | 337c17f | 2020-02-21 12:33:17 +0000 | [diff] [blame] | 791 | if (operation == "MatMul" ) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 792 | { |
| 793 | if(m_OutputsFusedAndUsed[nodeIndex].inputForNodes != m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.size()) |
| 794 | { |
| 795 | //Node which can not be fused as a FullyConnected layer (used in layers as a simple matmul output) |
| 796 | AddFullyConnected(node); |
| 797 | } |
| 798 | } |
| 799 | else if (!(m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) && operation == "Add") |
| 800 | { |
| 801 | int matmulIndex = static_cast<int> (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes[0]); |
| 802 | AddFullyConnected(m_Graph->node(matmulIndex), &node); |
| 803 | } |
| 804 | else if (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) //node is not part of a fused layer |
| 805 | { |
| 806 | auto it = m_ParserFunctions.find(operation); |
| 807 | if (it != m_ParserFunctions.end()) |
| 808 | { |
| 809 | auto func = it->second; |
| 810 | (this->*func)(node); |
| 811 | } |
| 812 | else |
| 813 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 814 | throw ParseException(fmt::format("Unsupported operation {} for node '{}' {}", |
| 815 | operation, |
| 816 | node.name(), |
| 817 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 818 | } |
| 819 | } |
| 820 | } |
| 821 | |
| 822 | //Making the connections between outputs and inputs of each layers |
| 823 | for (const auto& tensorCon : m_TensorConnections) |
| 824 | { |
| 825 | if (tensorCon.second.outputSlot != nullptr) |
| 826 | { |
| 827 | for (size_t inputSlotIdx = 0; inputSlotIdx < tensorCon.second.inputSlots.size(); ++inputSlotIdx) |
| 828 | { |
| 829 | tensorCon.second.outputSlot->Connect(*(tensorCon.second.inputSlots[inputSlotIdx])); |
| 830 | } |
| 831 | } |
| 832 | } |
| 833 | } |
| 834 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 835 | void OnnxParserImpl::SetupInfo(const google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 836 | { |
| 837 | for (auto tensor : *list) |
| 838 | { |
| 839 | m_TensorsInfo[tensor.name()] = OnnxTensor(); |
| 840 | m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(ToTensorInfo(tensor)); |
Matteo Martincigh | e355dc2 | 2018-12-10 13:45:27 +0000 | [diff] [blame] | 841 | m_TensorsInfo[tensor.name()].m_dtype = |
| 842 | static_cast<onnx::TensorProto::DataType>(tensor.type().tensor_type().elem_type()); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 843 | } |
| 844 | } |
| 845 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 846 | void OnnxParserImpl::DetectFullyConnected() |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 847 | { |
| 848 | m_OutputsFusedAndUsed = std::vector<UsageSummary> (static_cast<size_t>(m_Graph->node_size()), UsageSummary()); |
| 849 | auto matmulAndConstant = [&](const std::string& constInput, |
| 850 | const std::string& matmulInput, |
| 851 | int& nodeIndex) |
| 852 | { |
| 853 | auto matmulIt = m_OutputsMap.find(matmulInput); |
| 854 | if(matmulIt != m_OutputsMap.end() && matmulIt->second.first->op_type() == "MatMul" |
| 855 | && m_TensorsInfo[constInput].isConstant()) |
| 856 | { |
| 857 | nodeIndex = matmulIt->second.second; |
| 858 | return true; |
| 859 | } |
| 860 | return false; |
| 861 | }; |
| 862 | |
| 863 | for(int nodeIndex = 0; nodeIndex < m_Graph->node_size(); nodeIndex++) |
| 864 | { |
| 865 | const onnx::NodeProto* node = &m_Graph->node(nodeIndex); |
| 866 | for (const std::string& output : node->output()) |
| 867 | { |
| 868 | m_OutputsMap[output] = std::make_pair(node, nodeIndex); |
| 869 | } |
| 870 | |
| 871 | for (const std::string& input : node->input()) //count how many time a node is used as input |
| 872 | { |
| 873 | auto matmulIt = m_OutputsMap.find(input); |
| 874 | if(matmulIt != m_OutputsMap.end()){ |
| 875 | ++m_OutputsFusedAndUsed[static_cast<size_t>(matmulIt->second.second)].inputForNodes; //node used |
| 876 | } |
| 877 | } |
| 878 | |
| 879 | if (node->op_type() == "Add") |
| 880 | { |
| 881 | int matmulIndex = 0; |
| 882 | if (matmulAndConstant(node->input(0), node->input(1), matmulIndex) || |
| 883 | matmulAndConstant(node->input(1), node->input(0), matmulIndex)) |
| 884 | { |
| 885 | //matmul and add were fused |
| 886 | m_OutputsFusedAndUsed[static_cast<size_t>(matmulIndex)].fusedWithNodes |
| 887 | .push_back(static_cast<size_t>(nodeIndex)); |
| 888 | |
| 889 | m_OutputsFusedAndUsed[static_cast<size_t>(nodeIndex)].fusedWithNodes |
| 890 | .push_back(static_cast<size_t>(matmulIndex)); |
| 891 | } |
| 892 | } |
| 893 | } |
| 894 | |
| 895 | for (auto output: m_Graph->output()) { //Add usages as output of the graph in count of usages |
| 896 | auto matmulIt = m_OutputsMap.find(output.name()); |
| 897 | if(matmulIt != m_OutputsMap.end()){ |
| 898 | ++m_OutputsFusedAndUsed[static_cast<size_t>(matmulIt->second.second)].inputForNodes; |
| 899 | } |
| 900 | } |
| 901 | } |
| 902 | |
| 903 | template<typename Location> |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 904 | void OnnxParserImpl::GetInputAndParam(const onnx::NodeProto& node, |
| 905 | std::string* inputName, |
| 906 | std::string* constName, |
| 907 | const Location& location) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 908 | { |
| 909 | int cstIndex; |
| 910 | if (m_TensorsInfo[node.input(0)].isConstant()) |
| 911 | { |
| 912 | cstIndex = 0; |
| 913 | } |
| 914 | else if (m_TensorsInfo[node.input(1)].isConstant()) |
| 915 | { |
| 916 | cstIndex = 1; |
| 917 | } |
| 918 | else |
| 919 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 920 | throw ParseException(fmt::format("One of the input tensors ('{}' or '{}') should be constant in node '{}' {}", |
| 921 | node.input(0), |
| 922 | node.input(1), |
| 923 | node.name(), |
| 924 | location.AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 925 | } |
| 926 | if(constName) |
| 927 | { |
| 928 | *constName = node.input(cstIndex); |
| 929 | } |
| 930 | if(inputName) |
| 931 | { |
| 932 | *inputName = node.input(!cstIndex); |
| 933 | } |
| 934 | } |
| 935 | |
| 936 | template<typename Location> |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 937 | void OnnxParserImpl::To1DTensor(const std::string& name, const Location& location) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 938 | { |
| 939 | TensorShape shape = m_TensorsInfo[name].m_info->GetShape(); |
| 940 | std::vector<uint32_t> newShape; |
| 941 | for(uint i = 0; i < shape.GetNumDimensions() - 1; ++i) |
| 942 | { |
| 943 | if(shape[i] != 1) |
| 944 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 945 | throw ParseException( |
| 946 | fmt::format("Only tensors with shape [1, ..., 1, X] can be converted to 1D and {} {}", |
| 947 | TensorInfoAsString(*m_TensorsInfo[name].m_info, name, m_TensorsInfo[name].m_dtype), |
| 948 | location.AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 949 | } |
| 950 | } |
| 951 | newShape.push_back(shape[shape.GetNumDimensions() - 1]); |
| 952 | |
| 953 | m_TensorsInfo[name].m_info->SetShape(TensorShape(static_cast<unsigned int>(newShape.size()), newShape.data())); |
| 954 | } |
| 955 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 956 | void OnnxParserImpl::AddConvLayerWithDepthwiseConv(const onnx::NodeProto& node, const Convolution2dDescriptor& convDesc) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 957 | { |
| 958 | ARMNN_ASSERT(node.op_type() == "Conv"); |
| 959 | |
| 960 | DepthwiseConvolution2dDescriptor desc; |
| 961 | desc.m_PadLeft = convDesc.m_PadLeft; |
| 962 | desc.m_PadRight = convDesc.m_PadRight; |
| 963 | desc.m_PadTop = convDesc.m_PadTop; |
| 964 | desc.m_PadBottom = convDesc.m_PadBottom; |
| 965 | desc.m_StrideX = convDesc.m_StrideX; |
| 966 | desc.m_StrideY = convDesc.m_StrideY; |
| 967 | desc.m_BiasEnabled = convDesc.m_BiasEnabled; |
| 968 | |
| 969 | armnn::IConnectableLayer* layer; |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 970 | |
| 971 | // weights come in as [O,1,H,W] from ONNX and need to be converted to ArmNNs dephtwise weights layout [1,H,W,O] |
| 972 | armnn::PermutationVector perVec {3,0,1,2}; |
| 973 | auto weightTensor = CreateConstTensor(node.input(1), perVec); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 974 | |
| 975 | if (node.input_size() == 3) |
| 976 | { |
| 977 | if(!m_TensorsInfo[node.input(2)].isConstant()) |
| 978 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 979 | throw ParseException(fmt::format("Bias '{}' should be constant in Conv layer '{}' {}", |
| 980 | node.input(2), |
| 981 | node.name(), |
| 982 | CHECK_LOCATION().AsString())); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 983 | } |
| 984 | desc.m_BiasEnabled = true; |
| 985 | auto biasTensor = CreateConstTensor(node.input(2)); |
| 986 | layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| 987 | weightTensor.first, |
| 988 | Optional<ConstTensor>(biasTensor.first), |
| 989 | node.name().c_str()); |
| 990 | } |
| 991 | else |
| 992 | { |
| 993 | layer = m_Network->AddDepthwiseConvolution2dLayer(desc, |
| 994 | weightTensor.first, |
| 995 | EmptyOptional(), |
| 996 | node.name().c_str()); |
| 997 | } |
| 998 | ARMNN_ASSERT(layer != nullptr); |
| 999 | |
| 1000 | auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer, |
| 1001 | { m_TensorsInfo[node.input(0)].m_info->GetShape(), |
Jan Eilers | 53ef795 | 2021-06-02 12:01:25 +0100 | [diff] [blame] | 1002 | weightTensor.first.GetInfo().GetShape() }); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1003 | |
| 1004 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1005 | |
| 1006 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1007 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1008 | RegisterInputSlots(layer, {node.input(0)}); |
| 1009 | |
| 1010 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1011 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1012 | } |
| 1013 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1014 | void OnnxParserImpl::AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1015 | { |
| 1016 | |
| 1017 | // find matmul inputs |
| 1018 | std::string weightName; |
| 1019 | std::string inputName; |
| 1020 | CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.input_size()), 2); |
| 1021 | CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.output_size()), 1); |
| 1022 | VALID_INPUTS(matmulNode, STR_LIST(onnx::TensorProto::FLOAT)); |
| 1023 | |
| 1024 | GetInputAndParam(matmulNode, &inputName, &weightName, CHECK_LOCATION()); |
| 1025 | |
| 1026 | FullyConnectedDescriptor desc; |
| 1027 | desc.m_BiasEnabled = addNode != nullptr; |
| 1028 | |
| 1029 | IConnectableLayer* layer = nullptr; |
| 1030 | if(desc.m_BiasEnabled) |
| 1031 | { |
| 1032 | // find bias const |
| 1033 | std::string biasName; |
| 1034 | CHECK_VALID_SIZE(static_cast<size_t>(addNode->input_size()), 2); |
| 1035 | CHECK_VALID_SIZE(static_cast<size_t>(addNode->output_size()), 1); |
| 1036 | VALID_INPUTS(*addNode, STR_LIST(onnx::TensorProto::FLOAT)); |
| 1037 | |
| 1038 | GetInputAndParam(*addNode, nullptr, &biasName, CHECK_LOCATION()); |
| 1039 | |
| 1040 | //Output shape is [1, weights[1]] and 1d vec in ONNX can be [1,X] so we convert biases to "armnn" 1D |
| 1041 | To1DTensor(biasName, CHECK_LOCATION()); |
| 1042 | TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info; |
| 1043 | TensorInfo biasInfo = *m_TensorsInfo[biasName].m_info; |
| 1044 | |
| 1045 | if (weightInfo.GetShape()[1] != biasInfo.GetShape()[0]) |
| 1046 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1047 | throw ParseException( |
| 1048 | fmt::format("Shape of weights '{}' and bias of following Add node '{}' do not match : {}" |
| 1049 | " and {} ( /!\\ bias should be a 1D tensor) {}", |
| 1050 | weightName, |
| 1051 | addNode->name(), |
| 1052 | TensorInfoAsString(*m_TensorsInfo[weightName].m_info, weightName, |
| 1053 | m_TensorsInfo[weightName].m_dtype), |
| 1054 | TensorInfoAsString(*m_TensorsInfo[biasName].m_info, biasName, |
| 1055 | m_TensorsInfo[biasName].m_dtype ), |
| 1056 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1057 | } |
Matthew Sloyan | 81beae3 | 2021-07-13 19:46:11 +0100 | [diff] [blame] | 1058 | |
| 1059 | // Just add a FullyConnected layer, weights and biases are handled as inputs now. |
| 1060 | layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str()); |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 1061 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1062 | |
| 1063 | auto outputInfo = ComputeOutputInfo({addNode->output(0)}, layer, |
| 1064 | {m_TensorsInfo[inputName].m_info->GetShape(), |
| 1065 | m_TensorsInfo[weightName].m_info->GetShape()}); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1066 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1067 | |
Matthew Sloyan | 81beae3 | 2021-07-13 19:46:11 +0100 | [diff] [blame] | 1068 | // Add constant layer to store weights/biases and connect to FullyConnected layer.. |
| 1069 | if(m_TensorsInfo[weightName].isConstant()) |
| 1070 | { |
| 1071 | IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first); |
| 1072 | |
| 1073 | weightInfo.SetConstant(); |
| 1074 | weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo); |
| 1075 | weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| 1076 | } |
| 1077 | |
| 1078 | if(m_TensorsInfo[biasName].isConstant()) |
| 1079 | { |
| 1080 | IConnectableLayer* biasLayer = m_Network->AddConstantLayer(CreateConstTensor(biasName).first); |
| 1081 | |
| 1082 | biasInfo.SetConstant(); |
| 1083 | biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo); |
| 1084 | biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| 1085 | } |
| 1086 | |
| 1087 | RegisterInputSlots(layer, {inputName, weightName, biasName}); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1088 | RegisterOutputSlots(layer, {addNode->output(0)}); |
| 1089 | } |
| 1090 | else |
| 1091 | { |
Matthew Sloyan | 81beae3 | 2021-07-13 19:46:11 +0100 | [diff] [blame] | 1092 | layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str()); |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 1093 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1094 | |
| 1095 | auto outputInfo = ComputeOutputInfo({matmulNode.output(0)}, layer, |
| 1096 | {m_TensorsInfo[inputName].m_info->GetShape(), |
| 1097 | m_TensorsInfo[weightName].m_info->GetShape()}); |
| 1098 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1099 | |
Matthew Sloyan | 81beae3 | 2021-07-13 19:46:11 +0100 | [diff] [blame] | 1100 | // Add constant layer to store weights and connect to FullyConnected layer. |
| 1101 | if(m_TensorsInfo[weightName].isConstant()) |
| 1102 | { |
| 1103 | TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info; |
| 1104 | IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first); |
| 1105 | |
| 1106 | weightInfo.SetConstant(); |
| 1107 | weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| 1108 | weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo); |
| 1109 | } |
| 1110 | |
| 1111 | RegisterInputSlots(layer, {inputName, weightName}); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1112 | RegisterOutputSlots(layer, {matmulNode.output(0)}); |
| 1113 | } |
| 1114 | } |
| 1115 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1116 | void OnnxParserImpl::AddPoolingLayer(const onnx::NodeProto& node, Pooling2dDescriptor& desc) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1117 | { |
| 1118 | |
| 1119 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1); |
| 1120 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1121 | |
| 1122 | VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| 1123 | |
| 1124 | std::vector<uint32_t> kernel_shape = ReadMandatoryNodeUint32ListAttribute(node, "kernel_shape"); //size of pool win |
| 1125 | std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, "strides"); |
| 1126 | std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, "pads"); |
| 1127 | |
| 1128 | desc.m_OutputShapeRounding = OutputShapeRounding::Floor; |
| 1129 | desc.m_PoolWidth = kernel_shape[1]; |
| 1130 | desc.m_PoolHeight = kernel_shape[0]; |
| 1131 | |
| 1132 | if(strides.empty()) |
| 1133 | { |
| 1134 | desc.m_StrideX = 1; |
| 1135 | desc.m_StrideY = 1; |
| 1136 | } |
| 1137 | else |
| 1138 | { |
| 1139 | desc.m_StrideX = strides[1]; |
| 1140 | desc.m_StrideY = strides[0]; |
| 1141 | } |
| 1142 | |
| 1143 | //Check new padding version first |
| 1144 | if(pads.empty()) |
| 1145 | { |
| 1146 | //Check deprecated version |
| 1147 | std::string paddingString = ReadOptionalNodeStringAttribute(node, "auto_pad"); |
| 1148 | if(paddingString != "VALID" && paddingString != "" && paddingString != "NOTSET") |
| 1149 | { |
| 1150 | bool isUpper; |
| 1151 | if( paddingString == "SAME_LOWER") |
| 1152 | { |
| 1153 | isUpper = false; |
| 1154 | } |
| 1155 | else if (paddingString == "SAME_UPPER") |
| 1156 | { |
| 1157 | isUpper = true; |
| 1158 | } |
| 1159 | else |
| 1160 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1161 | throw ParseException(fmt::format("Invalid auto_pad attribute for node {}. " |
| 1162 | "Only SAME_UPPER, SAME_LOWER or VALID supported and found {} {}", |
| 1163 | node.name(), |
| 1164 | paddingString, |
| 1165 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1166 | } |
| 1167 | auto inputInfo = *m_TensorsInfo[node.input(0)].m_info; |
| 1168 | uint32_t inputHeight = inputInfo.GetShape()[2]; |
| 1169 | uint32_t inputWidth = inputInfo.GetShape()[3]; |
Sadik Armagan | 60bb9d8 | 2021-01-11 15:15:01 +0000 | [diff] [blame] | 1170 | CalcPadding(inputHeight, |
| 1171 | desc.m_PoolHeight, |
| 1172 | desc.m_StrideY, |
| 1173 | 1u, |
| 1174 | &desc.m_PadTop, |
| 1175 | &desc.m_PadBottom, |
| 1176 | isUpper); |
| 1177 | CalcPadding(inputWidth, |
| 1178 | desc.m_PoolWidth, |
| 1179 | desc.m_StrideX, |
| 1180 | 1u, |
| 1181 | &desc.m_PadLeft, |
| 1182 | &desc.m_PadRight, |
| 1183 | isUpper); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1184 | } |
| 1185 | } |
| 1186 | else |
| 1187 | { |
| 1188 | desc.m_PadTop = pads[0]; |
| 1189 | desc.m_PadLeft = pads[1]; |
| 1190 | desc.m_PadBottom = pads[2]; |
| 1191 | desc.m_PadRight = pads[3]; |
| 1192 | } |
| 1193 | |
| 1194 | IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str()); |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 1195 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1196 | |
| 1197 | auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()}); |
| 1198 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1199 | |
| 1200 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1201 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1202 | RegisterInputSlots(layer, {node.input(0)}); |
| 1203 | |
| 1204 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1205 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1206 | } |
| 1207 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1208 | std::pair<std::string, std::string> OnnxParserImpl::AddPrepareBroadcast(const std::string& input0, |
| 1209 | const std::string& input1) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1210 | { |
| 1211 | std::pair<std::string, std::string> inputs = std::make_pair(input0, input1); |
| 1212 | |
| 1213 | TensorShape input0Shape = m_TensorsInfo[input0].m_info->GetShape(); |
| 1214 | TensorShape input1Shape = m_TensorsInfo[input1].m_info->GetShape(); |
| 1215 | |
| 1216 | if(input1Shape.GetNumDimensions() < input0Shape.GetNumDimensions()) |
| 1217 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1218 | auto outputName = fmt::format("reshape_output_{}", input1); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1219 | PrependForBroadcast(outputName, input1, input0); |
| 1220 | inputs.second = outputName; |
| 1221 | } |
| 1222 | else if(input0Shape.GetNumDimensions() < input1Shape.GetNumDimensions()) |
| 1223 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1224 | auto outputName = fmt::format("reshape_output_{}", input0); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1225 | PrependForBroadcast(outputName, input0, input1); |
| 1226 | inputs.first = outputName; |
| 1227 | } |
| 1228 | return inputs; |
| 1229 | } |
| 1230 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1231 | void OnnxParserImpl::CreateConstantLayer(const std::string& tensorName, const std::string& layerName) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1232 | { |
| 1233 | auto armnnTensor = CreateConstTensor(tensorName); |
Narumol Prangnawarat | f10b15a | 2021-09-17 21:08:57 +0100 | [diff] [blame] | 1234 | IConnectableLayer* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str()); |
| 1235 | layer->GetOutputSlot(0).SetTensorInfo(armnnTensor.first.GetInfo()); |
| 1236 | RegisterOutputSlots(layer, {tensorName}); |
| 1237 | } |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1238 | |
Narumol Prangnawarat | f10b15a | 2021-09-17 21:08:57 +0100 | [diff] [blame] | 1239 | void OnnxParserImpl::CreateInt64ConstantLayer(const std::string& tensorName, const std::string& layerName) |
| 1240 | { |
| 1241 | auto armnnTensor = CreateInt64ConstTensor(tensorName); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1242 | IConnectableLayer* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str()); |
| 1243 | layer->GetOutputSlot(0).SetTensorInfo(armnnTensor.first.GetInfo()); |
| 1244 | RegisterOutputSlots(layer, {tensorName}); |
| 1245 | } |
| 1246 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1247 | void OnnxParserImpl::CreateReshapeLayer(const std::string& inputName, |
| 1248 | const std::string& outputName, |
| 1249 | const std::string& layerName) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1250 | { |
| 1251 | const TensorInfo outputTensorInfo = *m_TensorsInfo[outputName].m_info; |
| 1252 | ReshapeDescriptor reshapeDesc; |
| 1253 | reshapeDesc.m_TargetShape = outputTensorInfo.GetShape(); |
| 1254 | |
| 1255 | IConnectableLayer* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 1256 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1257 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 1258 | |
| 1259 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1260 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1261 | RegisterInputSlots(layer, {inputName}); |
| 1262 | |
| 1263 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1264 | RegisterOutputSlots(layer, {outputName}); |
| 1265 | } |
| 1266 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1267 | void OnnxParserImpl::ParseActivation(const onnx::NodeProto& node, const armnn::ActivationFunction func) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1268 | { |
Finn Williams | 7ee5d2c | 2020-03-27 11:11:50 +0000 | [diff] [blame] | 1269 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1, 3); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1270 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1271 | |
| 1272 | VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| 1273 | |
| 1274 | ActivationDescriptor desc; |
Tee Jung | 7ff9a60 | 2019-11-01 07:04:42 +0000 | [diff] [blame] | 1275 | desc.m_Function = func; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1276 | |
Finn Williams | 7ee5d2c | 2020-03-27 11:11:50 +0000 | [diff] [blame] | 1277 | if (func == ActivationFunction::BoundedReLu) |
| 1278 | { |
Narumol Prangnawarat | f106ab7 | 2021-09-15 17:30:37 +0100 | [diff] [blame] | 1279 | if (node.input_size() == 1 && node.attribute_size() > 0) |
| 1280 | { |
| 1281 | desc.m_A = ReadOptionalNodeFloatAttribute(node, "max", std::numeric_limits<float>::max()); |
| 1282 | desc.m_B = ReadOptionalNodeFloatAttribute(node, "min", std::numeric_limits<float>::lowest()); |
| 1283 | } |
| 1284 | else |
| 1285 | { |
| 1286 | desc.m_A = node.input(2).empty() ? std::numeric_limits<float>::max() : std::stof(node.input(2)); |
| 1287 | desc.m_B = node.input(1).empty() ? std::numeric_limits<float>::lowest() : std::stof(node.input(1)); |
| 1288 | } |
Finn Williams | 7ee5d2c | 2020-03-27 11:11:50 +0000 | [diff] [blame] | 1289 | } |
| 1290 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1291 | IConnectableLayer* const layer = m_Network->AddActivationLayer(desc, node.name().c_str()); |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 1292 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1293 | |
| 1294 | auto outputInfo = ComputeOutputInfo({ node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()}); |
| 1295 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1296 | |
| 1297 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1298 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1299 | RegisterInputSlots(layer, {node.input(0)}); |
| 1300 | |
| 1301 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1302 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1303 | } |
| 1304 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1305 | void OnnxParserImpl::ParseClip(const onnx::NodeProto& node) |
Finn Williams | 7ee5d2c | 2020-03-27 11:11:50 +0000 | [diff] [blame] | 1306 | { |
| 1307 | ParseActivation(node, ActivationFunction::BoundedReLu); |
| 1308 | } |
| 1309 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1310 | void OnnxParserImpl::ParseSigmoid(const onnx::NodeProto& node) |
Tee Jung | 7ff9a60 | 2019-11-01 07:04:42 +0000 | [diff] [blame] | 1311 | { |
| 1312 | ParseActivation(node, ActivationFunction::Sigmoid); |
| 1313 | } |
| 1314 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1315 | void OnnxParserImpl::ParseTanh(const onnx::NodeProto& node) |
Tee Jung | 7ff9a60 | 2019-11-01 07:04:42 +0000 | [diff] [blame] | 1316 | { |
| 1317 | ParseActivation(node, ActivationFunction::TanH); |
| 1318 | } |
| 1319 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1320 | void OnnxParserImpl::ParseRelu(const onnx::NodeProto& node) |
Tee Jung | 7ff9a60 | 2019-11-01 07:04:42 +0000 | [diff] [blame] | 1321 | { |
| 1322 | ParseActivation(node, ActivationFunction::ReLu); |
| 1323 | } |
| 1324 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1325 | void OnnxParserImpl::ParseLeakyRelu(const onnx::NodeProto& node) |
Tee Jung | 7ff9a60 | 2019-11-01 07:04:42 +0000 | [diff] [blame] | 1326 | { |
| 1327 | ParseActivation(node, ActivationFunction::LeakyReLu); |
| 1328 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1329 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1330 | void OnnxParserImpl::ParseAdd(const onnx::NodeProto& node) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1331 | { |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1332 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2); |
| 1333 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1334 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1335 | VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1336 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1337 | // TODO: unify broadcast validation code across layers |
| 1338 | // tracked by: IVGCVSW-1576 |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1339 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1340 | // Checking broadcast compatibility : only scalar or 1D tensors |
| 1341 | auto inputs = AddPrepareBroadcast(node.input(0), node.input(1)); |
| 1342 | auto input0 = *m_TensorsInfo[inputs.first].m_info; |
| 1343 | auto input1 = *m_TensorsInfo[inputs.second].m_info; |
| 1344 | ARMNN_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions()); |
| 1345 | |
| 1346 | unsigned int numDims = input0.GetNumDimensions(); |
| 1347 | for (unsigned int i = 0; i < numDims; i++) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1348 | { |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1349 | unsigned int dim0 = input0.GetShape()[i]; |
| 1350 | unsigned int dim1 = input1.GetShape()[i]; |
| 1351 | if (dim0 != dim1 && dim0 != 1 && dim1 != 1) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1352 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1353 | throw ParseException( |
| 1354 | fmt::format("Broadcast is only supported for scalar or 1D tensors in Add node '{}'. " |
| 1355 | "Input dimensions should either match or one should be of size 1 and here, " |
| 1356 | "{} and {} {}", |
| 1357 | node.name(), |
| 1358 | TensorInfoAsString(*m_TensorsInfo[inputs.first].m_info, inputs.first, |
| 1359 | m_TensorsInfo[inputs.first].m_dtype), |
| 1360 | TensorInfoAsString(*m_TensorsInfo[inputs.second].m_info, inputs.second, |
| 1361 | m_TensorsInfo[inputs.second].m_dtype), |
| 1362 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1363 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1364 | } |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1365 | |
| 1366 | |
| 1367 | IConnectableLayer* layer = m_Network->AddAdditionLayer(node.name().c_str()); |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 1368 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1369 | |
| 1370 | auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer, |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1371 | { m_TensorsInfo[inputs.first].m_info->GetShape(), |
| 1372 | m_TensorsInfo[inputs.second].m_info->GetShape() }); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1373 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1374 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1375 | // register the input connection -> for constant inputs, we need to make a newDim constant layer |
| 1376 | if(m_TensorsInfo[inputs.first].isConstant()) { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1377 | CreateConstantLayer(inputs.first, fmt::format("Add:constant_of_{}", node.input(0))); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1378 | } |
| 1379 | if(m_TensorsInfo[inputs.second].isConstant()) { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1380 | CreateConstantLayer(inputs.second, fmt::format("Add:constant_of_{}", node.input(1))); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1381 | } |
| 1382 | RegisterInputSlots(layer, {inputs.first, inputs.second}); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1383 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1384 | // register the output connection |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1385 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1386 | } |
| 1387 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1388 | void OnnxParserImpl::ParseAveragePool(const onnx::NodeProto& node) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1389 | { |
| 1390 | Pooling2dDescriptor desc; |
| 1391 | desc.m_PoolType = PoolingAlgorithm::Average; |
| 1392 | |
| 1393 | uint32_t count_include_pad = 0; |
| 1394 | count_include_pad = ReadOptionalNodeUint32Attribute(node, "count_include_pad"); |
| 1395 | if(count_include_pad) { |
| 1396 | desc.m_PaddingMethod = PaddingMethod::IgnoreValue; |
| 1397 | } |
| 1398 | AddPoolingLayer(node, desc); |
| 1399 | } |
| 1400 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1401 | void OnnxParserImpl::ParseBatchNormalization(const onnx::NodeProto& node) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1402 | { |
| 1403 | //IGNORE momentum parameter and spatial parameters |
| 1404 | |
| 1405 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 5); |
| 1406 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1407 | |
| 1408 | VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| 1409 | for(int ind = 1; ind < node.input_size(); ++ind) |
| 1410 | { |
| 1411 | auto tensor = node.input(ind); |
| 1412 | if(! m_TensorsInfo[tensor].isConstant()) |
| 1413 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1414 | throw ParseException( |
| 1415 | fmt::format("Input tensor '{}' should be constant in BatchNormalization node '{}' {}", |
| 1416 | tensor, |
| 1417 | node.name(), |
| 1418 | CHECK_LOCATION().AsString())); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1419 | } |
| 1420 | } |
| 1421 | |
| 1422 | float epsilon = ReadOptionalNodeFloatAttribute(node, "epsilon", 1e-5f); |
| 1423 | BatchNormalizationDescriptor desc; |
| 1424 | desc.m_Eps = epsilon; |
| 1425 | |
| 1426 | auto scaleTensor = CreateConstTensor(node.input(1)); |
| 1427 | auto biasTensor = CreateConstTensor(node.input(2)); |
| 1428 | auto meanTensor = CreateConstTensor(node.input(3)); |
| 1429 | auto varTensor = CreateConstTensor(node.input(4)); |
| 1430 | |
| 1431 | IConnectableLayer* layer = m_Network->AddBatchNormalizationLayer(desc, |
| 1432 | meanTensor.first, |
| 1433 | varTensor.first, |
| 1434 | biasTensor.first, |
| 1435 | scaleTensor.first, |
| 1436 | node.name().c_str()); |
| 1437 | ARMNN_ASSERT(layer != nullptr); |
| 1438 | |
| 1439 | auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()}); |
| 1440 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1441 | |
| 1442 | RegisterInputSlots(layer, {node.input(0)}); //don't register constant inputs |
| 1443 | |
| 1444 | // register the output connection |
| 1445 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1446 | } |
| 1447 | |
Narumol Prangnawarat | bc3bb62 | 2021-09-24 16:08:34 +0100 | [diff] [blame^] | 1448 | void OnnxParserImpl::ParseConcat(const onnx::NodeProto& node) |
| 1449 | { |
| 1450 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1451 | |
| 1452 | uint32_t numConcatView = static_cast<uint32_t>(node.input_size()); |
| 1453 | uint32_t inputRank = m_TensorsInfo[node.input(0)].m_info->GetNumDimensions(); |
| 1454 | |
| 1455 | int axisInt = ReadMandatoryNodeIntAttribute(node, "axis"); |
| 1456 | |
| 1457 | unsigned int concatDimInput = static_cast<unsigned int>( |
| 1458 | (static_cast<int>(inputRank) + axisInt) % static_cast<int>(inputRank)); |
| 1459 | |
| 1460 | OriginsDescriptor concatDescriptor(numConcatView, inputRank); |
| 1461 | concatDescriptor.SetConcatAxis(concatDimInput); |
| 1462 | |
| 1463 | unsigned int mergeDimOrigin = 0; |
| 1464 | |
| 1465 | std::vector<TensorShape> inputShapes; |
| 1466 | std::vector<std::string> tensorIds; |
| 1467 | |
| 1468 | for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) |
| 1469 | { |
| 1470 | std::string nodeName = node.input(static_cast<int>(viewIndex)); |
| 1471 | auto inputTensorInfo = *m_TensorsInfo[nodeName].m_info; |
| 1472 | inputShapes.push_back(inputTensorInfo.GetShape()); |
| 1473 | tensorIds.push_back(nodeName); |
| 1474 | |
| 1475 | // Set up concatDescriptor view origin |
| 1476 | armnnUtils::ProcessConcatInputTensorInfo( |
| 1477 | inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); |
| 1478 | } |
| 1479 | |
| 1480 | IConnectableLayer* layer = m_Network->AddConcatLayer(concatDescriptor, node.name().c_str()); |
| 1481 | ARMNN_ASSERT(layer != nullptr); |
| 1482 | |
| 1483 | auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, inputShapes); |
| 1484 | |
| 1485 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1486 | |
| 1487 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1488 | RegisterInputSlots(layer, tensorIds); |
| 1489 | |
| 1490 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1491 | RegisterOutputSlots(layer, { node.output(0) }); |
| 1492 | } |
| 1493 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1494 | void OnnxParserImpl::ParseConstant(const onnx::NodeProto& node) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1495 | { |
| 1496 | CHECK_VALID_SIZE(static_cast<size_t>(node.attribute_size()), 1); |
| 1497 | if (!node.attribute(0).has_t()) |
| 1498 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1499 | throw ParseException(fmt::format("Value not found for Constant node '{}' {}", |
| 1500 | node.name(), |
| 1501 | CHECK_LOCATION().AsString())); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1502 | } |
| 1503 | const onnx::TensorProto& onnxTensor = node.attribute(0).t(); |
| 1504 | |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1505 | //Register this as a m_ConstParam so we know we can use it as a constant param in future layers. |
| 1506 | m_TensorsInfo[node.output(0)].m_tensor = std::make_unique<const onnx::TensorProto>(onnxTensor); |
| 1507 | m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(ToTensorInfo(onnxTensor)); |
| 1508 | m_TensorsInfo[node.output(0)].m_dtype = static_cast<onnx::TensorProto::DataType>(onnxTensor.data_type()); |
| 1509 | |
Narumol Prangnawarat | f10b15a | 2021-09-17 21:08:57 +0100 | [diff] [blame] | 1510 | if (m_TensorsInfo[node.output(0)].m_dtype == onnx::TensorProto_DataType_FLOAT) |
| 1511 | { |
| 1512 | CreateConstantLayer(node.output(0), node.name()); |
| 1513 | } |
| 1514 | else if (m_TensorsInfo[node.output(0)].m_dtype == onnx::TensorProto_DataType_INT64) |
| 1515 | { |
| 1516 | CreateInt64ConstantLayer(node.output(0), node.name()); |
| 1517 | } |
| 1518 | else |
| 1519 | { |
| 1520 | throw ParseException(fmt::format("Data type not support for Constant node '{}' {}", |
| 1521 | node.name(), |
| 1522 | CHECK_LOCATION().AsString())); |
| 1523 | } |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1524 | } |
| 1525 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1526 | void OnnxParserImpl::ParseConv(const onnx::NodeProto& node) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1527 | { |
| 1528 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2, 3); //input, weight, (bias) |
| 1529 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1530 | |
| 1531 | VALID_INPUTS(node, STR_LIST(onnx::TensorProto::FLOAT)); |
| 1532 | |
| 1533 | if(m_TensorsInfo[node.input(0)].m_info->GetNumDimensions() != 4) |
| 1534 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1535 | throw ParseException( |
| 1536 | fmt::format("ArmNN only supports 2D convolution and Conv layer '{}' input {} {}", |
| 1537 | node.name(), |
| 1538 | TensorInfoAsString(*m_TensorsInfo[node.input(0)].m_info, node.input(0), |
| 1539 | m_TensorsInfo[node.input(0)].m_dtype), |
| 1540 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1541 | } |
| 1542 | |
| 1543 | if(!m_TensorsInfo[node.input(1)].isConstant()) |
| 1544 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1545 | throw ParseException( |
| 1546 | fmt::format("Weights '{}' should be constant in Conv layer '{}' {}", |
| 1547 | node.input(1), |
| 1548 | node.name(), |
| 1549 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1550 | } |
| 1551 | |
| 1552 | auto inputInfo = *m_TensorsInfo[node.input(0)].m_info; |
| 1553 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1554 | Convolution2dDescriptor desc; |
| 1555 | desc.m_BiasEnabled = false; |
| 1556 | |
| 1557 | std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, "strides"); |
| 1558 | if(strides.empty()) |
| 1559 | { |
| 1560 | desc.m_StrideX = 1; |
| 1561 | desc.m_StrideY = 1; |
| 1562 | } |
| 1563 | else |
| 1564 | { |
| 1565 | desc.m_StrideX = strides[1]; |
| 1566 | desc.m_StrideY = strides[0]; |
| 1567 | } |
| 1568 | |
Sadik Armagan | 60bb9d8 | 2021-01-11 15:15:01 +0000 | [diff] [blame] | 1569 | std::vector<uint32_t> dilations = ReadOptionalNodeUint32ListAttribute(node, "dilations"); |
| 1570 | if(!dilations.empty()) |
| 1571 | { |
| 1572 | desc.m_DilationX = dilations[1]; |
| 1573 | desc.m_DilationY = dilations[0]; |
| 1574 | } |
| 1575 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1576 | std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, "pads"); |
| 1577 | //Check new padding version first |
| 1578 | if(pads.empty()) |
| 1579 | { |
| 1580 | //Check deprecated version |
| 1581 | std::string paddingString = ReadOptionalNodeStringAttribute(node, "auto_pad"); |
| 1582 | if(paddingString != "VALID" && paddingString != "" && paddingString != "NOTSET") |
| 1583 | { |
| 1584 | bool isUpper; |
| 1585 | if( paddingString == "SAME_LOWER") |
| 1586 | { |
| 1587 | isUpper = false; |
| 1588 | } |
| 1589 | else if (paddingString == "SAME_UPPER") |
| 1590 | { |
| 1591 | isUpper = true; |
| 1592 | } |
| 1593 | else |
| 1594 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1595 | throw ParseException( |
| 1596 | fmt::format("Invalid auto_pad attribute for node {}. Only SAME_UPPER, SAME_LOWER or VALID " |
| 1597 | "supported and found {} {}", |
| 1598 | node.name(), |
| 1599 | paddingString, |
| 1600 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1601 | } |
| 1602 | uint32_t inputHeight = inputInfo.GetShape()[2]; |
| 1603 | uint32_t inputWidth = inputInfo.GetShape()[3]; |
| 1604 | |
| 1605 | uint32_t weightHeight; |
| 1606 | uint32_t weightWidth; |
| 1607 | std::vector<uint32_t> kernel_shape = ReadOptionalNodeUint32ListAttribute(node, "kernel_shape"); |
| 1608 | if (kernel_shape.empty()) |
| 1609 | { |
| 1610 | const TensorInfo weightTensorInfo = *m_TensorsInfo[node.input(1)].m_info; |
| 1611 | weightHeight = weightTensorInfo.GetShape()[2]; |
| 1612 | weightWidth = weightTensorInfo.GetShape()[3]; |
| 1613 | } |
| 1614 | else |
| 1615 | { |
| 1616 | weightHeight = kernel_shape[0]; |
| 1617 | weightWidth = kernel_shape[1]; |
| 1618 | } |
Sadik Armagan | 60bb9d8 | 2021-01-11 15:15:01 +0000 | [diff] [blame] | 1619 | CalcPadding(inputHeight, |
| 1620 | weightHeight, |
| 1621 | desc.m_StrideY, |
| 1622 | desc.m_DilationY, |
| 1623 | &desc.m_PadTop, |
| 1624 | &desc.m_PadBottom, |
| 1625 | isUpper); |
| 1626 | CalcPadding(inputWidth, |
| 1627 | weightWidth, |
| 1628 | desc.m_StrideX, |
| 1629 | desc.m_DilationX, |
| 1630 | &desc.m_PadLeft, |
| 1631 | &desc.m_PadRight, |
| 1632 | isUpper); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1633 | } |
| 1634 | } |
| 1635 | else |
| 1636 | { |
| 1637 | desc.m_PadTop = pads[0]; |
| 1638 | desc.m_PadLeft = pads[1]; |
| 1639 | desc.m_PadBottom = pads[2]; |
| 1640 | desc.m_PadRight = pads[3]; |
| 1641 | } |
| 1642 | |
| 1643 | uint32_t group = ReadOptionalNodeUint32Attribute(node, "group", 1); |
| 1644 | if(group > 1) |
| 1645 | { |
| 1646 | if (group > inputInfo.GetShape()[1]) |
| 1647 | { |
| 1648 | throw ParseException( |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1649 | fmt::format("Error parsing Convolution node: {}. " |
| 1650 | "The 'group'={} parameter cannot be larger than the " |
| 1651 | "channel of the input shape={} (in NCHW format). {}", |
| 1652 | node.name(), |
| 1653 | group, |
| 1654 | inputInfo.GetShape()[1], |
| 1655 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1656 | } |
| 1657 | else if (group == inputInfo.GetShape()[1]) |
| 1658 | { |
| 1659 | // we use a depthwise convolution here, because the number of groups equals to the |
| 1660 | // input channels |
| 1661 | AddConvLayerWithDepthwiseConv(node, desc); |
| 1662 | return; |
| 1663 | } |
| 1664 | else |
| 1665 | { |
| 1666 | // TODO: split the input by channels into channels/groups separate convolutions |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 1667 | // and concatenate the results afterwards |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1668 | throw ParseException(fmt::format("Error parsing Convolution node: {}. " |
| 1669 | "The 'group'={} parameter should be 1 or be equal to the " |
| 1670 | "channel of the input shape={} (in NCHW format). {}", |
| 1671 | node.name(), |
| 1672 | group, |
| 1673 | inputInfo.GetShape()[1], |
| 1674 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1675 | } |
| 1676 | } |
| 1677 | |
| 1678 | armnn::IConnectableLayer* layer; |
| 1679 | auto weightTensor = CreateConstTensor(node.input(1)); |
| 1680 | |
| 1681 | if (node.input_size() == 3) |
| 1682 | { |
| 1683 | if(!m_TensorsInfo[node.input(2)].isConstant()) |
| 1684 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1685 | throw ParseException(fmt::format("Bias '{}' should be constant in Conv layer '{}' {}", |
| 1686 | node.input(2), |
| 1687 | node.name(), |
| 1688 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1689 | } |
| 1690 | desc.m_BiasEnabled = true; |
| 1691 | auto biasTensor = CreateConstTensor(node.input(2)); |
| 1692 | layer = m_Network->AddConvolution2dLayer(desc, |
| 1693 | weightTensor.first, |
Matteo Martincigh | fc598e1 | 2019-05-14 10:36:13 +0100 | [diff] [blame] | 1694 | Optional<ConstTensor>(biasTensor.first), |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1695 | node.name().c_str()); |
| 1696 | } |
| 1697 | else |
| 1698 | { |
| 1699 | layer = m_Network->AddConvolution2dLayer(desc, |
| 1700 | weightTensor.first, |
Matteo Martincigh | fc598e1 | 2019-05-14 10:36:13 +0100 | [diff] [blame] | 1701 | EmptyOptional(), |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1702 | node.name().c_str()); |
| 1703 | } |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 1704 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1705 | |
| 1706 | auto outputInfo = ComputeOutputInfo({ node.output(0) }, layer, |
| 1707 | { m_TensorsInfo[node.input(0)].m_info->GetShape(), |
| 1708 | m_TensorsInfo[node.input(1)].m_info->GetShape() }); |
| 1709 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1710 | |
| 1711 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1712 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1713 | RegisterInputSlots(layer, {node.input(0)}); |
| 1714 | |
| 1715 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1716 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1717 | } |
| 1718 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1719 | void OnnxParserImpl::ParseFlatten(const onnx::NodeProto& node) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1720 | { |
| 1721 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1); |
| 1722 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1723 | |
| 1724 | CHECK_VALID_DATATYPE(node.name(), node.input(0), |
| 1725 | m_TensorsInfo[node.input(0)].m_dtype, |
| 1726 | onnx::TensorProto::FLOAT); |
| 1727 | |
| 1728 | int64_t axis = ReadOptionalNodeInt64Attribute(node, "axis", 1); |
| 1729 | TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| 1730 | |
| 1731 | /// Negative axis conversion |
| 1732 | if (axis < 0) |
| 1733 | { |
| 1734 | axis += inputShape.GetNumDimensions(); |
| 1735 | } |
| 1736 | |
| 1737 | /// Check Axis is within dimensions |
| 1738 | if (axis < 0 || axis >= inputShape.GetNumDimensions()) |
| 1739 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1740 | throw ParseException(fmt::format("Axis '{}' invalid. Tensor has '{}' dimensions in FlattenLayer '{}'", |
| 1741 | axis, inputShape.GetNumDimensions(), node.name())); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1742 | } |
| 1743 | |
| 1744 | /// If axis chosen is 0 dimension1 will always be 1 in output , default dimension2 to 1 because 0 is invalid |
| 1745 | uint dimension1{1}; |
| 1746 | uint dimension2{1}; |
| 1747 | uint i{0}; |
| 1748 | |
| 1749 | /// dimension1 = (d_0 * d_1 ... d_(axis-1)) |
| 1750 | for (i = 0; i < axis; i++){ |
| 1751 | dimension1 *= inputShape[i]; |
| 1752 | } |
| 1753 | |
| 1754 | /// dimension2 = (d_axis * d_(axis+1) ... d_n) |
| 1755 | for (i = static_cast<uint>(axis); i < inputShape.GetNumDimensions(); i++){ |
| 1756 | dimension2 *= inputShape[i]; |
| 1757 | } |
| 1758 | |
| 1759 | TensorShape outputShape{dimension1, dimension2}; |
| 1760 | |
| 1761 | auto outInfo = ComputeReshapeInfo(outputShape, inputShape, node.output(0)); |
| 1762 | m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo); |
| 1763 | CreateReshapeLayer(node.input(0), node.output(0), node.name()); |
| 1764 | } |
| 1765 | |
Narumol Prangnawarat | f10b15a | 2021-09-17 21:08:57 +0100 | [diff] [blame] | 1766 | void OnnxParserImpl::ParseGather(const onnx::NodeProto& node) |
| 1767 | { |
| 1768 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2); |
| 1769 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1770 | |
| 1771 | armnn::GatherDescriptor gatherDescriptor; |
| 1772 | gatherDescriptor.m_Axis = static_cast<int>(ReadOptionalNodeInt64Attribute(node, "axis", 0)); |
| 1773 | |
| 1774 | IConnectableLayer* layer = m_Network->AddGatherLayer(gatherDescriptor, node.name().c_str()); |
| 1775 | ARMNN_ASSERT(layer != nullptr); |
| 1776 | |
| 1777 | TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| 1778 | TensorShape indicesShape = m_TensorsInfo[node.input(1)].m_info->GetShape(); |
| 1779 | auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, { inputShape, indicesShape }); |
| 1780 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1781 | |
| 1782 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1783 | RegisterInputSlots(layer, { node.input(0), node.input(1) }); |
| 1784 | |
| 1785 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1786 | RegisterOutputSlots(layer, { node.output(0) }); |
| 1787 | } |
| 1788 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1789 | void OnnxParserImpl::ParseGlobalAveragePool(const onnx::NodeProto& node) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1790 | { |
| 1791 | Pooling2dDescriptor desc = Pooling2dDescriptor(); |
| 1792 | desc.m_PoolType = PoolingAlgorithm::Average; |
| 1793 | |
| 1794 | //kernel size is the same as input |
| 1795 | TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| 1796 | desc.m_PoolWidth = inputShape[3]; |
| 1797 | desc.m_PoolHeight = inputShape[2]; |
| 1798 | |
| 1799 | IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str()); |
| 1800 | ARMNN_ASSERT(layer != nullptr); |
| 1801 | |
| 1802 | auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape}); |
| 1803 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1804 | |
| 1805 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1806 | // only the tensors for the inputs are relevant, exclude the const tensors |
| 1807 | RegisterInputSlots(layer, {node.input(0)}); |
| 1808 | |
| 1809 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1810 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1811 | } |
| 1812 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1813 | void OnnxParserImpl::ParseMaxPool(const onnx::NodeProto& node) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1814 | { |
| 1815 | Pooling2dDescriptor desc; |
| 1816 | desc.m_PoolType = PoolingAlgorithm::Max; |
| 1817 | desc.m_PaddingMethod = PaddingMethod::Exclude; |
| 1818 | AddPoolingLayer(node, desc); |
| 1819 | } |
| 1820 | |
Narumol Prangnawarat | cdc495e | 2021-09-16 18:13:39 +0100 | [diff] [blame] | 1821 | void OnnxParserImpl::ParseShape(const onnx::NodeProto& node) |
| 1822 | { |
| 1823 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 1); |
| 1824 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1825 | |
| 1826 | // Output must be INT64 |
| 1827 | CHECK_VALID_DATATYPE(node.name(), node.output(0), |
| 1828 | m_TensorsInfo[node.output(0)].m_dtype, |
| 1829 | onnx::TensorProto::INT64); |
| 1830 | |
| 1831 | IConnectableLayer* layer = m_Network->AddShapeLayer(node.name().c_str()); |
| 1832 | ARMNN_ASSERT(layer != nullptr); |
| 1833 | |
| 1834 | TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| 1835 | auto outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape}); |
| 1836 | layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]); |
| 1837 | |
| 1838 | // register the input connection slots for the layer, connections are made after all layers have been created |
| 1839 | RegisterInputSlots(layer, {node.input(0)}); |
| 1840 | |
| 1841 | // register the output connection slots for the layer, connections are made after all layers have been created |
| 1842 | RegisterOutputSlots(layer, {node.output(0)}); |
| 1843 | } |
| 1844 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1845 | void OnnxParserImpl::ParseReshape(const onnx::NodeProto& node) |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1846 | { |
| 1847 | CHECK_VALID_SIZE(static_cast<size_t>(node.input_size()), 2); |
| 1848 | CHECK_VALID_SIZE(static_cast<size_t>(node.output_size()), 1); |
| 1849 | |
| 1850 | CHECK_VALID_DATATYPE(node.name(), node.input(0), |
| 1851 | m_TensorsInfo[node.input(0)].m_dtype, |
| 1852 | onnx::TensorProto::FLOAT); //input |
| 1853 | CHECK_VALID_DATATYPE(node.name(), node.input(1), |
| 1854 | m_TensorsInfo[node.input(1)].m_dtype, |
| 1855 | onnx::TensorProto::INT64); //shape |
| 1856 | |
| 1857 | if(!m_TensorsInfo[node.input(1)].isConstant()) |
| 1858 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1859 | throw ParseException(fmt::format("Shape '{}' should be constant in Reshape layer '{}' {}", |
| 1860 | node.input(1), |
| 1861 | node.name(), |
| 1862 | CHECK_LOCATION().AsString())); |
Ryan OShea | ed27ee7 | 2020-04-22 16:37:29 +0100 | [diff] [blame] | 1863 | } |
| 1864 | |
| 1865 | if(m_TensorsInfo[node.input(0)].isConstant()) |
| 1866 | { |
| 1867 | //make a new cst tensor -> move the data to the output tensor (the shape is already good in the output tensor) |
| 1868 | if(m_TensorsInfo.count(node.output(0)) == 0) |
| 1869 | { |
| 1870 | m_TensorsInfo[node.output(0)] = OnnxTensor(); |
| 1871 | } |
| 1872 | m_TensorsInfo[node.output(0)].m_tensor = |
| 1873 | std::make_unique<onnx::TensorProto>(*m_TensorsInfo[node.input(0)].m_tensor); |
| 1874 | } |
| 1875 | else |
| 1876 | { |
| 1877 | TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| 1878 | |
| 1879 | if(m_TensorsInfo.count(node.output(0)) == 0 || m_TensorsInfo[node.output(0)].m_info == nullptr) |
| 1880 | { |
| 1881 | uint64_t dims = static_cast<uint64_t>(m_TensorsInfo[node.input(1)].m_tensor->int64_data_size()); |
| 1882 | TensorShape targetShape{static_cast<unsigned int>(dims), 1}; |
| 1883 | |
| 1884 | for(uint i = 0; i < dims; i++) |
| 1885 | { |
| 1886 | int val = CHECKED_INT32(m_TensorsInfo[node.input(1)].m_tensor->int64_data(static_cast<int>(i))); |
| 1887 | targetShape[i]= static_cast<unsigned int>(val); |
| 1888 | } |
| 1889 | |
| 1890 | auto outInfo = ComputeReshapeInfo(targetShape, inputShape, node.output(0)); |
| 1891 | m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo); |
| 1892 | } |
| 1893 | |
| 1894 | CreateReshapeLayer(node.input(0), node.output(0), node.name()); |
| 1895 | } |
| 1896 | } |
| 1897 | |
Narumol Prangnawarat | fe6aa2f | 2021-09-23 16:11:17 +0100 | [diff] [blame] | 1898 | void OnnxParserImpl::ParseUnsqueeze(const onnx::NodeProto& node) |
| 1899 | { |
| 1900 | CHECK_VALID_SIZE(armnn::numeric_cast<size_t>(node.input_size()), 1, 2); |
| 1901 | CHECK_VALID_SIZE(armnn::numeric_cast<size_t>(node.output_size()), 1); |
| 1902 | |
| 1903 | CHECK_VALID_DATATYPE(node.name(), node.input(0), |
| 1904 | m_TensorsInfo[node.input(0)].m_dtype, |
| 1905 | onnx::TensorProto::FLOAT); //input |
| 1906 | |
| 1907 | TensorShape inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape(); |
| 1908 | std::vector<uint32_t> dims; |
| 1909 | if (node.input_size() == 1 && node.attribute_size() > 0) |
| 1910 | { |
| 1911 | dims = ReadMandatoryNodeUint32ListAttribute(node, "axes"); |
| 1912 | } |
| 1913 | else |
| 1914 | { |
| 1915 | CHECK_VALID_DATATYPE(node.name(), node.input(1), |
| 1916 | m_TensorsInfo[node.input(1)].m_dtype, |
| 1917 | onnx::TensorProto::INT64); //axes |
| 1918 | |
| 1919 | auto int64Axes = m_TensorsInfo[node.input(1)].m_tensor->int64_data().data(); |
| 1920 | uint numDim = armnn::numeric_cast<uint>(m_TensorsInfo[node.input(1)].m_tensor->int64_data_size()); |
| 1921 | |
| 1922 | for(uint i = 0; i < numDim; i++) |
| 1923 | { |
| 1924 | uint32_t uint32Value = CHECKED_NON_NEGATIVE(CHECKED_INT32(int64Axes[i])); |
| 1925 | dims.push_back(uint32Value); |
| 1926 | } |
| 1927 | } |
| 1928 | |
| 1929 | // Ensure that the axes are sorted |
| 1930 | std::sort(dims.begin(), dims.end()); |
| 1931 | |
| 1932 | std::vector<unsigned int> targetShape; |
| 1933 | |
| 1934 | for(uint i = 0; i < inputShape.GetNumDimensions(); i++) |
| 1935 | { |
| 1936 | targetShape.push_back(inputShape[i]); |
| 1937 | } |
| 1938 | |
| 1939 | for(uint i = 0; i < dims.size(); i++) |
| 1940 | { |
| 1941 | targetShape.insert(targetShape.begin() + armnn::numeric_cast<int>(dims[i]), 1); |
| 1942 | } |
| 1943 | |
| 1944 | auto outInfo = ComputeReshapeInfo(TensorShape(armnn::numeric_cast<unsigned int>(targetShape.size()), |
| 1945 | targetShape.data()), inputShape, node.output(0)); |
| 1946 | m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo); |
| 1947 | |
| 1948 | CreateReshapeLayer(node.input(0), node.output(0), node.name()); |
| 1949 | } |
| 1950 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1951 | void OnnxParserImpl::PrependForBroadcast(const std::string& outputName, |
| 1952 | const std::string& input0, |
| 1953 | const std::string& input1) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1954 | { |
| 1955 | //input0 should be reshaped to have same number of dim as input1 |
| 1956 | TensorInfo outputTensorInfo = TensorInfo(*m_TensorsInfo[input0].m_info); |
| 1957 | |
| 1958 | TensorShape input0Shape = m_TensorsInfo[input0].m_info->GetShape(); |
| 1959 | TensorShape input1Shape = m_TensorsInfo[input1].m_info->GetShape(); |
| 1960 | |
| 1961 | uint32_t diff = input1Shape.GetNumDimensions() - input0Shape.GetNumDimensions(); |
| 1962 | std::vector<uint32_t> newShape; |
| 1963 | while(diff > 0) |
| 1964 | { |
| 1965 | newShape.push_back(1); |
| 1966 | diff--; |
| 1967 | } |
| 1968 | for (uint dim = 0; dim < input0Shape.GetNumDimensions(); ++dim) |
| 1969 | { |
| 1970 | newShape.push_back(input0Shape[dim]); |
| 1971 | } |
| 1972 | outputTensorInfo.SetShape(TensorShape(static_cast<unsigned int>(newShape.size()), newShape.data())); |
| 1973 | |
| 1974 | //add the new tensor to m_TensorsInfo |
| 1975 | m_TensorsInfo[outputName] = OnnxTensor(); |
| 1976 | m_TensorsInfo[outputName].m_info = std::make_unique<TensorInfo>(outputTensorInfo); |
| 1977 | |
| 1978 | //add reshape layer if the parent was not constant... |
| 1979 | if( ! m_TensorsInfo[input0].isConstant()) |
| 1980 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 1981 | CreateReshapeLayer(input0, outputName, fmt::format("Add:reshapeOf{}", input0)); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1982 | } |
| 1983 | else //make it constant and it will be create in Add |
| 1984 | { |
| 1985 | m_TensorsInfo[outputName].m_tensor = std::make_unique<onnx::TensorProto>(*m_TensorsInfo[input0].m_tensor); |
| 1986 | |
| 1987 | } |
| 1988 | } |
| 1989 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 1990 | void OnnxParserImpl::SetupInputLayers() |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1991 | { |
| 1992 | //Find user input and add their layers |
| 1993 | for(int inputIndex = 0; inputIndex < m_Graph->input_size(); ++inputIndex) |
| 1994 | { |
| 1995 | auto input = m_Graph->input(inputIndex); |
| 1996 | if (! m_TensorsInfo[input.name()].isConstant()) |
| 1997 | { |
| 1998 | IConnectableLayer* layer = |
| 1999 | m_Network->AddInputLayer(static_cast<armnn::LayerBindingId>(inputIndex), input.name().c_str()); |
| 2000 | auto tensorInfo = ToTensorInfo(input); |
| 2001 | layer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 2002 | |
| 2003 | RegisterOutputSlots(layer,{ input.name() }); |
| 2004 | } |
| 2005 | } |
| 2006 | } |
| 2007 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 2008 | void OnnxParserImpl::SetupOutputLayers() |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2009 | { |
| 2010 | if(m_Graph->output_size() == 0) |
| 2011 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2012 | throw ParseException(fmt::format("The given model does not have any outputs {}", CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2013 | } |
| 2014 | |
| 2015 | for(int outputIndex = 0; outputIndex < m_Graph->output_size(); ++outputIndex) |
| 2016 | { |
| 2017 | IConnectableLayer* layer = |
| 2018 | m_Network->AddOutputLayer(static_cast<armnn::LayerBindingId>(outputIndex), |
| 2019 | m_Graph->output(outputIndex).name().c_str()); |
| 2020 | |
| 2021 | RegisterInputSlots(layer, { m_Graph->output(outputIndex).name() }); |
| 2022 | } |
| 2023 | } |
| 2024 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 2025 | void OnnxParserImpl::RegisterInputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2026 | { |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 2027 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2028 | if (tensorIds.size() != layer->GetNumInputSlots()) |
| 2029 | { |
| 2030 | throw ParseException( |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2031 | fmt::format("The number of tensor inputs ({}) does not match the number expected ({}) {}", |
| 2032 | tensorIds.size(), |
| 2033 | layer->GetNumInputSlots(), |
| 2034 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2035 | } |
Matthew Sloyan | 81beae3 | 2021-07-13 19:46:11 +0100 | [diff] [blame] | 2036 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2037 | for (unsigned int slotIndex = 0; slotIndex < layer->GetNumInputSlots(); ++slotIndex) |
| 2038 | { |
| 2039 | std::string tensorId = tensorIds[slotIndex]; |
| 2040 | armnn::IInputSlot* slot = &(layer->GetInputSlot(slotIndex)); |
| 2041 | |
| 2042 | auto it = m_TensorConnections.find(tensorId); |
| 2043 | |
| 2044 | if (it == m_TensorConnections.end()) |
| 2045 | { |
| 2046 | //First time seing this tensor, we need to map it |
| 2047 | m_TensorConnections[tensorId] = TensorSlots(); |
| 2048 | } |
| 2049 | m_TensorConnections[tensorId].inputSlots.push_back(slot); |
| 2050 | } |
| 2051 | } |
| 2052 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 2053 | void OnnxParserImpl::RegisterOutputSlots(IConnectableLayer* layer, const std::vector<std::string>& tensorIds) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2054 | { |
Narumol Prangnawarat | ac2770a | 2020-04-01 16:51:23 +0100 | [diff] [blame] | 2055 | ARMNN_ASSERT(layer != nullptr); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2056 | if (tensorIds.size() != layer->GetNumOutputSlots()) |
| 2057 | { |
| 2058 | throw ParseException( |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2059 | fmt::format("The number of tensor outputs ({}) does not match the number expected ({}) {} ", |
| 2060 | tensorIds.size(), |
| 2061 | layer->GetNumOutputSlots(), |
| 2062 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2063 | } |
| 2064 | |
| 2065 | for (unsigned int slotIndex = 0; slotIndex < layer->GetNumOutputSlots(); ++slotIndex) |
| 2066 | { |
| 2067 | std::string tensorId = tensorIds[slotIndex]; |
| 2068 | armnn::IOutputSlot* slot = &(layer->GetOutputSlot(slotIndex)); |
| 2069 | |
| 2070 | auto it = m_TensorConnections.find(tensorId); |
| 2071 | |
| 2072 | if (it == m_TensorConnections.end()) |
| 2073 | { |
| 2074 | //First time seing this tensor, we need to map it |
| 2075 | m_TensorConnections[tensorId] = TensorSlots(); |
| 2076 | } |
| 2077 | |
Ryan OShea | 337c17f | 2020-02-21 12:33:17 +0000 | [diff] [blame] | 2078 | TensorSlots& tensorSlots = m_TensorConnections[tensorId]; |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2079 | |
| 2080 | // assuming there is only one producer for that tensor |
| 2081 | if (tensorSlots.outputSlot != nullptr) |
| 2082 | { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2083 | throw ParseException(fmt::format("Another layer has already registered itself as the producer of " |
| 2084 | "tensor:{} {}", |
| 2085 | tensorId, |
| 2086 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2087 | } |
| 2088 | tensorSlots.outputSlot = slot; |
| 2089 | } |
| 2090 | } |
| 2091 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 2092 | BindingPointInfo OnnxParserImpl::GetNetworkInputBindingInfo(const std::string& name) const |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2093 | { |
| 2094 | for(int i = 0; i < m_Graph->input_size(); ++i) |
| 2095 | { |
| 2096 | auto input = m_Graph->input(i); |
| 2097 | if(input.name() == name) |
| 2098 | { |
| 2099 | return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(input)); |
| 2100 | } |
| 2101 | } |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2102 | throw InvalidArgumentException(fmt::format("The input layer '{}' does not exist {}", |
| 2103 | name, CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2104 | } |
| 2105 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 2106 | BindingPointInfo OnnxParserImpl::GetNetworkOutputBindingInfo(const std::string& name) const |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2107 | { |
| 2108 | for(int i = 0; i < m_Graph->output_size(); ++i) |
| 2109 | { |
| 2110 | auto output = m_Graph->output(i); |
| 2111 | if(output.name() == name) |
| 2112 | { |
| 2113 | return std::make_pair(static_cast<armnn::LayerBindingId>(i), ToTensorInfo(output)); |
| 2114 | } |
| 2115 | } |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2116 | throw InvalidArgumentException(fmt::format("The output layer '{}' does not exist {}", |
| 2117 | name, CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2118 | } |
| 2119 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 2120 | std::vector<std::string> OnnxParserImpl::GetInputs(ModelPtr& model) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2121 | { |
| 2122 | if(model == nullptr) { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2123 | throw InvalidArgumentException(fmt::format("The given model cannot be null {}", |
| 2124 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2125 | } |
| 2126 | |
| 2127 | std::vector<std::string> inputNames; |
| 2128 | std::map<std::string, bool> isConstant; |
| 2129 | for(auto tensor : model->graph().initializer()) |
| 2130 | { |
| 2131 | isConstant[tensor.name()] = true; |
| 2132 | } |
| 2133 | for(auto input : model->graph().input()) |
| 2134 | { |
| 2135 | auto it = isConstant.find(input.name()); |
| 2136 | if(it == isConstant.end()) |
| 2137 | { |
| 2138 | inputNames.push_back(input.name()); |
| 2139 | } |
| 2140 | } |
| 2141 | return inputNames; |
| 2142 | } |
| 2143 | |
Kevin May | ef33cb1 | 2021-01-29 14:24:57 +0000 | [diff] [blame] | 2144 | std::vector<std::string> OnnxParserImpl::GetOutputs(ModelPtr& model) |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2145 | { |
| 2146 | if(model == nullptr) { |
James Ward | 58dec6b | 2020-09-11 17:32:44 +0100 | [diff] [blame] | 2147 | throw InvalidArgumentException(fmt::format("The given model cannot be null {}", |
| 2148 | CHECK_LOCATION().AsString())); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2149 | } |
| 2150 | |
| 2151 | std::vector<std::string> outputNames; |
| 2152 | for(auto output : model->graph().output()) |
| 2153 | { |
| 2154 | outputNames.push_back(output.name()); |
| 2155 | } |
| 2156 | return outputNames; |
| 2157 | } |
| 2158 | |
Matthew Sloyan | ac001ee | 2021-02-03 10:43:04 +0000 | [diff] [blame] | 2159 | const std::string OnnxParserImpl::GetVersion() |
| 2160 | { |
| 2161 | return ONNX_PARSER_VERSION; |
| 2162 | } |
| 2163 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2164 | } // namespace armnnOnnxParser |