环境:
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Jetpack 4.4.1
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TensorRT 7.1
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python 3.6.8
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CMake 3.14.4
一、升级 tensorRT 的 protobuf
1 2 3 4 5 | git clone https://github.com/google/protobuf -b '3.8.x' apt remove libprotobuf-dev # uninstall old version cd protobuf && ./autogen.sh && ./configure --prefix=/usr/ && make -j4 && make install -j4 |
二、升级 python protobuf 模块
1 2 3 | pip3 uninstall protobuf pip3 install protobuf==3.8.0 |
三、下载 onnx-tensorrt 地址
1 | git clone --recursive -b 7.1 https://github.com/onnx/onnx-tensorrt.git onnx_tensorrt |
ps:jetpack 4.4.1 兼容的 7.1 版本(其他版本未试)
四、编译安装 onnx-tensorrt
1 2 3 4 5 6 7 8 9 10 11 12 13 | cd onnx-tensorrt mkdir build && cd build cmake .. -DTENSORRT_ROOT=/usr/src/tensorrt -DCMAKE_INSTALL_PREFIX=/usr/ make -j8 sudo make install ######如果不行再试这个####### cmake .. -DCUDA_INCLUDE_DIRS=/usr/local/cuda/include -DTENSORRT_ROOT=/usr/src/tensorrt -DCMAKE_INSTALL_PREFIX=/usr/ ############################ |
五、onnx-tensorrt 测试
1 | onnx2trt my_model.onnx -o my_engine.trt |
六、安装 python 模块
1 2 3 4 5 6 7 | cd onnx-tensorrt ## 安装 python模块依赖 sudo apt install swig ## 安装 python模块 sudo python3 setup.py install |
七、错误处理
修正方法:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | --- a/NvOnnxParser.h +++ b/NvOnnxParser.h @@ -31,6 +31,10 @@ #define NV_ONNX_PARSER_MINOR 1 #define NV_ONNX_PARSER_PATCH 0 +#ifndef TENSORRTAPI +#define TENSORRTAPI +#endif // TENSORRTAPI + static const int NV_ONNX_PARSER_VERSION = ((NV_ONNX_PARSER_MAJOR * 10000) + (NV_ONNX_PARSER_MINOR * 100) + NV_ONNX_PARSER_PATCH); //! \typedef SubGraph_t diff --git a/setup.py b/setup.py index 8ffa543..d6244a3 100644 --- a/setup.py +++ b/setup.py @@ -59,10 +59,11 @@ EXTRA_COMPILE_ARGS = [ '-std=c++11', '-DUNIX', '-D__UNIX', - '-m64', '-fPIC', '-O2', '-w', + '-march=armv8-a+crypto', + '-mcpu=cortex-a57+crypto', '-fmessage-length=0', '-fno-strict-aliasing', '-D_FORTIFY_SOURCE=2', |
ps:解决方法针对 jetson:小白直接替换 -》两份文件
NvOnnxParser.h
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | /* * Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. * * Permission is hereby granted, free of charge, to any person obtaining a * copy of this software and associated documentation files (the "Software"), * to deal in the Software without restriction, including without limitation * the rights to use, copy, modify, merge, publish, distribute, sublicense, * and/or sell copies of the Software, and to permit persons to whom the * Software is furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER * DEALINGS IN THE SOFTWARE. */ #ifndef NV_ONNX_PARSER_H #define NV_ONNX_PARSER_H #include "NvInfer.h" #include <stddef.h> #include <vector> #define NV_ONNX_PARSER_MAJOR 0 #define NV_ONNX_PARSER_MINOR 1 #define NV_ONNX_PARSER_PATCH 0 / #ifndef TENSORRTAPI #define TENSORRTAPI #endif // TENSORRTAPI / static const int NV_ONNX_PARSER_VERSION = ((NV_ONNX_PARSER_MAJOR * 10000) + (NV_ONNX_PARSER_MINOR * 100) + NV_ONNX_PARSER_PATCH); //! \typedef SubGraph_t //! //! \brief The data structure containing the parsing capability of //! a set of nodes in an ONNX graph. //! typedef std::pair<std::vector<size_t>, bool> SubGraph_t; //! \typedef SubGraphCollection_t //! //! \brief The data structure containing all SubGraph_t partitioned //! out of an ONNX graph. //! typedef std::vector<SubGraph_t> SubGraphCollection_t; class onnxTensorDescriptorV1; //! //! \namespace nvonnxparser //! //! \brief The TensorRT ONNX parser API namespace //! namespace nvonnxparser { template <typename T> inline int EnumMax(); /** \enum ErrorCode * * \brief the type of parser error */ enum class ErrorCode : int { kSUCCESS = 0, kINTERNAL_ERROR = 1, kMEM_ALLOC_FAILED = 2, kMODEL_DESERIALIZE_FAILED = 3, kINVALID_VALUE = 4, kINVALID_GRAPH = 5, kINVALID_NODE = 6, kUNSUPPORTED_GRAPH = 7, kUNSUPPORTED_NODE = 8 }; template <> inline int EnumMax<ErrorCode>() { return 9; } /** \class IParserError * * \brief an object containing information about an error */ class IParserError { public: /** \brief the error code */ virtual ErrorCode code() const = 0; /** \brief description of the error */ virtual const char* desc() const = 0; /** \brief source file in which the error occurred */ virtual const char* file() const = 0; /** \brief source line at which the error occurred */ virtual int line() const = 0; /** \brief source function in which the error occurred */ virtual const char* func() const = 0; /** \brief index of the ONNX model node in which the error occurred */ virtual int node() const = 0; protected: virtual ~IParserError() {} }; /** \class IParser * * \brief an object for parsing ONNX models into a TensorRT network definition */ class IParser { public: /** \brief Parse a serialized ONNX model into the TensorRT network. * This method has very limited diagnostic. If parsing the serialized model * fails for any reason (e.g. unsupported IR version, unsupported opset, etc.) * it the user responsibility to intercept and report the error. * To obtain a better diagnostic, use the parseFromFile method below. * * \param serialized_onnx_model Pointer to the serialized ONNX model * \param serialized_onnx_model_size Size of the serialized ONNX model * in bytes * \return true if the model was parsed successfully * \see getNbErrors() getError() */ virtual bool parse(void const* serialized_onnx_model, size_t serialized_onnx_model_size) = 0; /** \brief Parse an onnx model file, can be a binary protobuf or a text onnx model * calls parse method inside. * * \param File name * \param Verbosity Level * * \return true if the model was parsed successfully * */ virtual bool parseFromFile(const char* onnxModelFile, int verbosity) = 0; /** \brief Check whether TensorRT supports a particular ONNX model * * \param serialized_onnx_model Pointer to the serialized ONNX model * \param serialized_onnx_model_size Size of the serialized ONNX model * in bytes * \param sub_graph_collection Container to hold supported subgraphs * \return true if the model is supported */ virtual bool supportsModel(void const* serialized_onnx_model, size_t serialized_onnx_model_size, SubGraphCollection_t& sub_graph_collection) = 0; /** \brief Parse a serialized ONNX model into the TensorRT network * with consideration of user provided weights * * \param serialized_onnx_model Pointer to the serialized ONNX model * \param serialized_onnx_model_size Size of the serialized ONNX model * in bytes * \param weight_count number of user provided weights * \param weight_descriptors pointer to user provided weight array * \return true if the model was parsed successfully * \see getNbErrors() getError() */ virtual bool parseWithWeightDescriptors( void const* serialized_onnx_model, size_t serialized_onnx_model_size, uint32_t weight_count, onnxTensorDescriptorV1 const* weight_descriptors) = 0; /** \brief Returns whether the specified operator may be supported by the * parser. * * Note that a result of true does not guarantee that the operator will be * supported in all cases (i.e., this function may return false-positives). * * \param op_name The name of the ONNX operator to check for support */ virtual bool supportsOperator(const char* op_name) const = 0; /** \brief destroy this object */ virtual void destroy() = 0; /** \brief Get the number of errors that occurred during prior calls to * \p parse * * \see getError() clearErrors() IParserError */ virtual int getNbErrors() const = 0; /** \brief Get an error that occurred during prior calls to \p parse * * \see getNbErrors() clearErrors() IParserError */ virtual IParserError const* getError(int index) const = 0; /** \brief Clear errors from prior calls to \p parse * * \see getNbErrors() getError() IParserError */ virtual void clearErrors() = 0; protected: virtual ~IParser() {} }; } // namespace nvonnxparser extern "C" TENSORRTAPI void* createNvOnnxParser_INTERNAL(void* network, void* logger, int version); extern "C" TENSORRTAPI int getNvOnnxParserVersion(); namespace nvonnxparser { #ifdef SWIG inline IParser* createParser(nvinfer1::INetworkDefinition* network, nvinfer1::ILogger* logger) { return static_cast<IParser*>( createNvOnnxParser_INTERNAL(network, logger, NV_ONNX_PARSER_VERSION)); } #endif // SWIG namespace { /** \brief Create a new parser object * * \param network The network definition that the parser will write to * \param logger The logger to use * \return a new parser object or NULL if an error occurred * \see IParser */ #ifdef _MSC_VER TENSORRTAPI IParser* createParser(nvinfer1::INetworkDefinition& network, nvinfer1::ILogger& logger) #else inline IParser* createParser(nvinfer1::INetworkDefinition& network, nvinfer1::ILogger& logger) #endif { return static_cast<IParser*>( createNvOnnxParser_INTERNAL(&network, &logger, NV_ONNX_PARSER_VERSION)); } } // namespace } // namespace nvonnxparser #endif // NV_ONNX_PARSER_H |
setup.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. import os import argparse from setuptools import setup, find_packages, Extension __version__ = '0.1.0' parser = argparse.ArgumentParser(description='Setup to build ONNX TensorRT parser') parser.add_argument('action', nargs='*') parser.add_argument('--build-lib', type=str, help='A location of the build directory') parser.add_argument('--include-dirs', type=str, help='A location of the include directories, semicolon separated') args = parser.parse_args() print(args) if args.build_lib == None: args.build_lib = 'build' TRT_ROOT = os.getenv('TRT_ROOT') if TRT_ROOT == None: INC_DIRS = [] else: INC_DIRS = [TRT_ROOT + '/include'] SWIG_OPTS = [ '-c++', '-modern', '-builtin', ] EXTRA_COMPILE_ARGS = [ '-std=c++11', '-DUNIX', '-D__UNIX', '-fPIC', '-O2', '-w', '-march=armv8-a+crypto', '-mcpu=cortex-a57+crypto', '-fmessage-length=0', '-fno-strict-aliasing', '-D_FORTIFY_SOURCE=2', '-fstack-protector', '--param=ssp-buffer-size=4', '-Wformat', '-Werror=format-security', '-DNDEBUG', '-g', '-fwrapv', '-Wall', '-DSWIG', ] EXTRA_LINK_ARGS = [ ] nv_onnx_parser_module = Extension( 'onnx_tensorrt.parser._nv_onnx_parser_bindings', sources=['nv_onnx_parser_bindings.i'], swig_opts=SWIG_OPTS, extra_objects=[ args.build_lib + '/libnvonnxparser.so', ], include_dirs=INC_DIRS, extra_compile_args=EXTRA_COMPILE_ARGS, extra_link_args=EXTRA_LINK_ARGS) setup(name='onnx_tensorrt', version=__version__, description='TensorRT backend for ONNX', author='NVIDIA Corporation', author_email='[email protected]', url='https://github.com/onnx/onnx-tensorrt', packages=find_packages(), ext_modules=[nv_onnx_parser_module], install_requires=[ "numpy>=1.8.1", "tensorrt>=3.0.0", "onnx>=1.0.1", "pycuda", ]) |
八、测试 python onnx-tensorrt
1 2 | # python3 import onnx_tensorrt.backend as backend |