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PaddlePaddle / Paddle-Lite

PaddlePaddle High Performance Deep Learning Inference Engine for Mobile and Edge (飞桨高性能深度学习端侧推理引擎）
https://www.paddlepaddle.org.cn/lite
Apache License 2.0
6.94k stars 1.61k forks source link

opt.run()及编译opt时报错 #10393

Open dicklim opened 1 year ago

dicklim commented 1 year ago 
model_root = 'Round-5_Epo058_acc0.9788.mdl'
model = torchvision.models.mobilenet_v3_large(weights=torchvision.models.MobileNet_V3_Large_Weights.DEFAULT)
model.classifier = nn.Sequential(
    nn.Linear(960, 1280),
    nn.Hardswish(),
    nn.Dropout(0.2),
    nn.Linear(1280, 256),
    nn.Hardswish(),
    nn.Dropout(0.2),
    nn.Linear(256, 2)
)
checkpoint = torch.load(model_root)
model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint.items()})
model.eval()

image = Image.open('./demo.jpg')
image = np.array(image)
image = np.transpose(image)
image = np.expand_dims(image, 0)
# print(image.size())
image = torch.from_numpy(image.astype(np.float32))

pytorch2paddle(module=model,
               save_dir="./pd_model",
               jit_type="trace",
               input_examples=[image])

跑出的文件为：

pd_model
├── inference_model
│   ├── model.pdiparams
│   ├── model.pdiparams.info
│   └── model.pdmodel
├── model.pdparams
├── __pycache__
│   └── x2paddle_code.cpython-38.pyc
└── x2paddle_code.py

但是我后来想跑opt的时候就会报错，代码如下：

opt=Opt()
opt.set_model_dir("/home/dd/Code/Demo4/Paddle/pd_model_trace/inference_model")
opt.set_valid_places("arm")
opt.set_model_type("naive_buffer")
opt.set_optimize_out("./test_opt")
opt.run()

跑了报错139 Process finished with exit code 139 换用命令行跑结果一样

dd@dd-Super-Server:~/Code/Demo4/Paddle$ paddle_lite_opt --model_dir=/home/dd/Code/Demo4/Paddle/pd_model/inference_model --valid_targets=arm --optimize_out=./mobilenetv3_opt
Loading topology data from /home/dd/Code/Demo4/Paddle/pd_model/inference_model/model.pdmodel
Loading params data from /home/dd/Code/Demo4/Paddle/pd_model/inference_model/model.pdiparams
1. Model is successfully loaded!
Segmentation fault (core dumped)

然后我怀疑会不会是没编译好，就去编译了一下 ./lite/tools/build.sh build_optimize_tool

结果还是不行，报错如下

(NewDL) dd@dd-Super-Server:/home/ssd/Software/Paddle-Lite$ vim ./lite/tools/build.sh
(NewDL) dd@dd-Super-Server:/home/ssd/Software/Paddle-Lite$ ./lite/tools/build.sh build_optimize_tool
-- Found Paddle host system: ubuntu, version: 22.04.1
-- Found Paddle host system's CPU: 128 cores
-- CXX compiler: /usr/bin/c++, version: GNU 11.4.0
-- C compiler: /usr/bin/cc, version: GNU 11.4.0
-- AR tools: /usr/bin/ar
-- CMAKE_BUILD_TYPE: RelWithDebInfo
-- Do not have AVX2 intrinsics and disabled MKL-DNN
-- Protobuf protoc executable: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/protobuf/bin/protoc
-- Protobuf-lite library: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/protobuf/lib/libprotobuf-lite.a
-- Protobuf library: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/protobuf/lib/libprotobuf.a
-- Protoc library: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/protobuf/lib/libprotoc.a
-- Protobuf version: 3.3.0
-- BLAS library: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/openblas/lib/libopenblas.a
-- BLAS Include: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/openblas/include
-- Paddle version is 0.0.0
-- LIGHT_FRAMEWORK:     OFF
-- LITE_WITH_CUDA:      OFF
-- LITE_WITH_X86:       ON
-- LITE_WITH_ARM:       OFF
-- LITE_WITH_SW:        OFF
-- LITE_WITH_OPENCL:    OFF
-- LITE_WITH_METAL:     OFF
-- LITE_WITH_NPU:       OFF
-- LITE_WITH_RKNPU:     OFF
-- LITE_WITH_XPU:       OFF
-- LITE_WITH_APU:       OFF
-- LITE_WITH_XTCL:      
-- LITE_WITH_FPGA:      OFF
-- LITE_WITH_INTEL_FPGA:        OFF
-- LITE_WITH_MLU:       OFF
-- LITE_WITH_HUAWEI_ASCEND_NPU: OFF
-- LITE_WITH_BM:        OFF
-- LITE_WITH_IMAGINATION_NNA:   OFF
-- LITE_WITH_NNADAPTER: OFF
-- LITE_WITH_PROFILE:   OFF
-- LITE_WITH_CV:        OFF
-- add lite kernels
-- compile with lite host kernels
-- compile with lite ARM kernels
-- compile with lite CUDA kernels
-- compile with lite Metal kernels
fatal: no tag exactly matches '30fe065bcc6cbd0d9cfd4f0497cf4b4f59913749'
-- tag: 
-- branch: release/v2.10
-- commit: 30fe065bc
-- bundled_tgt_full_name:  /home/ssd/Software/Paddle-Lite/build.opt/libpaddle_api_full_bundled.a
-- bundled_tgt_full_name:  /home/ssd/Software/Paddle-Lite/build.opt/libpaddle_api_light_bundled.a
-- Compiling opt
-- publish inference lib to /home/ssd/Software/Paddle-Lite/build.opt/inference_lite_lib
-- Configuring done
-- Generating done
-- Build files have been written to: /home/ssd/Software/Paddle-Lite/build.opt
make[1]: Entering directory '/home/ssd/Software/Paddle-Lite/build.opt'
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make[3]: Entering directory '/home/ssd/Software/Paddle-Lite/build.opt'
[  0%] Creating directories for 'extern_protobuf'
[  1%] Built target extern_gflags
[  1%] Built target extern_eigen3
make[3]: Entering directory '/home/ssd/Software/Paddle-Lite/build.opt'
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[  1%] Performing configure step for 'extern_protobuf'
-- extern_protobuf configure command succeeded.  See also /home/ssd/Software/Paddle-Lite/third-party/protobuf-host/src/extern_protobuf-stamp/extern_protobuf-configure-*.log
[  1%] Performing build step for 'extern_protobuf'
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Scanning dependencies of target libprotobuf-lite
Scanning dependencies of target libprotobuf
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In file included from /usr/include/c++/11/set:60,
                 from /home/ssd/Software/Paddle-Lite/third-party/protobuf-host/src/google/protobuf/compiler/java/java_file.cc:41:
/usr/include/c++/11/bits/stl_tree.h: In instantiation of ‘static const _Key& std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::_S_key(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::_Const_Link_type) [with _Key = const google::protobuf::FieldDescriptor*; _Val = const google::protobuf::FieldDescriptor*; _KeyOfValue = std::_Identity<const google::protobuf::FieldDescriptor*>; _Compare = google::protobuf::compiler::java::{anonymous}::FieldDescriptorCompare; _Alloc = std::allocator<const google::protobuf::FieldDescriptor*>; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::_Const_Link_type = const std::_Rb_tree_node<const google::protobuf::FieldDescriptor*>*]’:
/usr/include/c++/11/bits/stl_tree.h:2071:47:   required from ‘std::pair<std::_Rb_tree_node_base*, std::_Rb_tree_node_base*> std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::_M_get_insert_unique_pos(const key_type&) [with _Key = const google::protobuf::FieldDescriptor*; _Val = const google::protobuf::FieldDescriptor*; _KeyOfValue = std::_Identity<const google::protobuf::FieldDescriptor*>; _Compare = google::protobuf::compiler::java::{anonymous}::FieldDescriptorCompare; _Alloc = std::allocator<const google::protobuf::FieldDescriptor*>; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::key_type = const google::protobuf::FieldDescriptor*]’
/usr/include/c++/11/bits/stl_tree.h:2124:4:   required from ‘std::pair<std::_Rb_tree_iterator<_Val>, bool> std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::_M_insert_unique(_Arg&&) [with _Arg = const google::protobuf::FieldDescriptor* const&; _Key = const google::protobuf::FieldDescriptor*; _Val = const google::protobuf::FieldDescriptor*; _KeyOfValue = std::_Identity<const google::protobuf::FieldDescriptor*>; _Compare = google::protobuf::compiler::java::{anonymous}::FieldDescriptorCompare; _Alloc = std::allocator<const google::protobuf::FieldDescriptor*>]’
/usr/include/c++/11/bits/stl_set.h:512:25:   required from ‘std::pair<typename std::_Rb_tree<_Key, _Key, std::_Identity<_Tp>, _Compare, typename __gnu_cxx::__alloc_traits<_Allocator>::rebind<_Key>::other>::const_iterator, bool> std::set<_Key, _Compare, _Alloc>::insert(const value_type&) [with _Key = const google::protobuf::FieldDescriptor*; _Compare = google::protobuf::compiler::java::{anonymous}::FieldDescriptorCompare; _Alloc = std::allocator<const google::protobuf::FieldDescriptor*>; typename std::_Rb_tree<_Key, _Key, std::_Identity<_Tp>, _Compare, typename __gnu_cxx::__alloc_traits<_Allocator>::rebind<_Key>::other>::const_iterator = std::_Rb_tree<const google::protobuf::FieldDescriptor*, const google::protobuf::FieldDescriptor*, std::_Identity<const google::protobuf::FieldDescriptor*>, google::protobuf::compiler::java::{anonymous}::FieldDescriptorCompare, std::allocator<const google::protobuf::FieldDescriptor*> >::const_iterator; typename __gnu_cxx::__alloc_traits<_Allocator>::rebind<_Key>::other = std::allocator<const google::protobuf::FieldDescriptor*>; typename __gnu_cxx::__alloc_traits<_Allocator>::rebind<_Key> = __gnu_cxx::__alloc_traits<std::allocator<const google::protobuf::FieldDescriptor*>, const google::protobuf::FieldDescriptor*>::rebind<const google::protobuf::FieldDescriptor*>; typename _Allocator::value_type = const google::protobuf::FieldDescriptor*; std::set<_Key, _Compare, _Alloc>::value_type = const google::protobuf::FieldDescriptor*]’
/home/ssd/Software/Paddle-Lite/third-party/protobuf-host/src/google/protobuf/compiler/java/java_file.cc:97:54:   required from here
/usr/include/c++/11/bits/stl_tree.h:770:15: error: static assertion failed: comparison object must be invocable as const
  770 |               is_invocable_v<const _Compare&, const _Key&, const _Key&>,
      |               ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/usr/include/c++/11/bits/stl_tree.h:770:15: note: ‘std::is_invocable_v<const google::protobuf::compiler::java::{anonymous}::FieldDescriptorCompare&, const google::protobuf::FieldDescriptor* const&, const google::protobuf::FieldDescriptor* const&>’ evaluates to false
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make[6]: *** Waiting for unfinished jobs....
-- extern_glog configure command succeeded.  See also /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/glog-stamp/extern_glog-configure-*.log
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Scanning dependencies of target glog
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Install the project...
-- Install configuration: ""
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/lib/libglog.a
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/include/glog/config.h
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/include/glog/logging.h
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/include/glog/raw_logging.h
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/include/glog/stl_logging.h
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/include/glog/vlog_is_on.h
-- Up-to-date: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/include/glog/log_severity.h
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/lib/cmake/glog/glog-config.cmake
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/lib/cmake/glog/glog-config-version.cmake
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/lib/cmake/glog/glog-targets.cmake
-- Installing: /home/ssd/Software/Paddle-Lite/build.opt/third_party/install/glog/lib/cmake/glog/glog-targets-noconfig.cmake
make[4]: Leaving directory '/home/ssd/Software/Paddle-Lite/third-party/glog/src/extern_glog-build'
[  2%] Completed 'extern_glog'
make[3]: Leaving directory '/home/ssd/Software/Paddle-Lite/build.opt'
[  2%] Built target extern_glog
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make: *** [Makefile:521: opt] Error 2

想问一下这个原因是什么？要如何解决？我的内存内存肯定是够的，所以不知道为啥会这样？extern_glog-configure-err.log也没有任何结果

是否是我编译有什么问题？我是按照文档的protocol做的，换了pytorch的官方模型也出不来结果？求解

FLamefiREz commented 10 months ago

@zhupengyang 我也遇到了同样的问题 系统Ubuntu 22.04 LTS gdb debug： Program received signal SIGSEGV, Segmentation fault. 0x00000000011924df in paddle::lite::operators::FlattenContiguousRangeOp::InferType() ()

模型为：PP-OCRv4-mobile-rec

Qengineering commented 9 months ago

In file build/third_party/protobuf/src/extern_protobuf/src/google/protobuf/compiler/java/java_file.cc:68 add const behind the operator like bool operator ()(const FieldDescriptor* f1, const FieldDescriptor* f2){ bool operator ()(const FieldDescriptor* f1, const FieldDescriptor* f2)const { it seems like a feature in cpp17

hong19860320 commented 7 months ago

可以尝试使用最新版 Paddle Lite opt 工具 https://github.com/PaddlePaddle/Paddle-Lite/releases https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.13-rc/opt_linux_x86 进行模型转换。 如果仍然报错，运行前请 export GLOG_v=5 打开详细 log，然后把详细 log 上传一下。