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You should compile onnx2tnn first !!!
1. 使用环境（environment）
- OS: Mac
- OS Version: Monterey 12.4
2. Github版本
- branch：origin/master
- commit(optional): commit a690339
3. 详细描述bug 情况 (Describe the bug) A clear and concise description of what the bug is.
- issue type: 模型转换失败（converter failed）
- original model：ONNX
4. 错误日志（Log）
$ python onnx2tnn.py -h
Segmentation fault: 11
5. 其他相关日志（Log）
(base) ~/Documents/TNN/tools/convert2tnn
$ ./build.sh
/Users/oliverxu/Documents/TNN/tools/convert2tnn
/Users/oliverxu/Documents/TNN
/Users/oliverxu/Documents/TNN
-- >>>>>>>>>>>>>
-- TNN BUILD INFO:
-- System: Darwin
-- Processor: x86_64
-- Cpu: ON
-- X86: OFF
-- Arm: OFF
-- Arm82: OFF
-- Metal: OFF
-- OpenCL: OFF
-- CUDA: OFF
-- DSP: OFF
-- Atlas: OFF
-- TensorRT: OFF
-- AppleNPU: OFF
-- HuaweiNPU: OFF
-- RKNPU: OFF
-- OpenVINO: OFF
-- OpenMP: OFF
-- TEST: OFF
-- --Unit Test: OFF
-- Quantization: OFF
-- ModelCheck: ON
-- DEBUG: ON
-- PROFILE: OFF
-- BENCHMARK: OFF
-- BENCHMARK Layer: OFF
-- Model Converter: ON
-- ONNX2TNN Converter: ON
-- TNN2MEM: OFF
-- BENCHMARK Test Lib: OFF
-- Dynamic Range Quantization: ON
/Users/oliverxu/Documents/TNN/tools/model_check/flags.cc/Users/oliverxu/Documents/TNN/tools/model_check/main.cc/Users/oliverxu/Documents/TNN/tools/model_check/model_checker.cc
target link libraries -force_load
-- CMAKE_CXX_FLAGS: -Wno-pass-failed -Wno-deprecated-declarations -Wno-ignored-attributes -DTNN_COREML_FULL_PRECISION=1 -DTNN_METAL_FULL_PRECISION=1 -DTNN_METAL_BENCHMARK=1 -DTNN_METAL_DEBUG=1 -pthread -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++11 -stdlib=libc++ -Wall -pedantic -Werror -Wextra -Wno-unused-parameter
-- Proceeding with version: 0.1.0.2308
-- pybind11 v2.4.dev4
/Users/oliverxu/Documents/TNN/tools/dynamic_range_quantization/dynamic_range_quantization.cc/Users/oliverxu/Documents/TNN/tools/dynamic_range_quantization/dynamic_range_quantization.h/Users/oliverxu/Documents/TNN/tools/dynamic_range_quantization/flags.cc/Users/oliverxu/Documents/TNN/tools/dynamic_range_quantization/flags.h/Users/oliverxu/Documents/TNN/tools/dynamic_range_quantization/main.cc
target link libraries -force_load
-- Configuring done
-- Generating done
-- Build files have been written to: /Users/oliverxu/Documents/TNN/tools/convert2tnn/build
Consolidate compiler generated dependencies of target gflags_nothreads_static
Consolidate compiler generated dependencies of target flatbuffers
Consolidate compiler generated dependencies of target flatc
[ 1%] Built target gflags_nothreads_static
[ 2%] Built target flatbuffers
Consolidate compiler generated dependencies of target flathash
[ 2%] Built target flathash
[ 6%] Built target flatc
Consolidate compiler generated dependencies of target TNNCpu
[ 23%] Built target TNNCpu
Consolidate compiler generated dependencies of target TNN
[ 67%] Built target TNN
Consolidate compiler generated dependencies of target model_check
Consolidate compiler generated dependencies of target dynamic_range_quantization
[ 67%] Built target model_check
[ 67%] Built target dynamic_range_quantization
Consolidate compiler generated dependencies of target TnnConverter
Consolidate compiler generated dependencies of target onnx2tnn
[ 78%] Built target TnnConverter
[ 79%] Linking CXX shared module onnx2tnn.cpython-38-darwin.so
[100%] Built target onnx2tnn
Compiled model_check successfully !
Compiled dynamic_range_quantization successfully !
Compiled TNNConverter successfully !
Compiled onnx2tnn successfully !
通过代码观察到： tools/convert2tnn/converter.py 识别 subcommand onnx2tnn 后指向 tools/convert2tnn/onnx_converter/onnx2tnn.py，然后：
command = "python3 onnx2tnn.py " + onnx_path
...
work_dir = "../onnx2tnn/onnx-converter/"
result = cmd.run(command, work_dir=work_dir)
实际上是 tools/onnx2tnn/onnx-converter/onnx2tnn.py 起作用。
build.sh成功，目录中含onnx2tnn.cpython-38-darwin.so：
(base) ~/Documents/TNN/tools/onnx2tnn/onnx-converter
$ l
total 98384
drwxr-xr-x 5 oliverxu staff 160B Mar 29 2021 3rdparty/
-rwxr-xr-x 1 oliverxu staff 2.8K May 17 17:57 CMakeLists.txt*
-rw-r--r-- 1 oliverxu staff 23B Nov 20 2020 __init__.py
drwxr-xr-x 3 oliverxu staff 96B May 20 16:02 __pycache__/
drwxr-xr-x 3 oliverxu staff 96B May 20 15:31 backup_so/
drwxr-xr-x 2 oliverxu staff 64B May 20 16:17 bin/
drwxr-xr-x 9 oliverxu staff 288B May 20 16:24 build/
-rwxr-xr-x 1 oliverxu staff 1.9K May 17 17:57 build.sh*
-rwxr-xr-x 1 oliverxu staff 48M Jun 1 15:57 onnx2tnn.cpython-38-darwin.so*
-rw-r--r-- 1 oliverxu staff 4.9K May 20 16:02 onnx2tnn.py
-rw-r--r-- 1 oliverxu staff 8.7K May 17 17:57 onnx2tnn_convert.cc
-rw-r--r-- 1 oliverxu staff 519B Nov 20 2020 onnx_model_cheker.py
drwxr-xr-x 6 oliverxu staff 192B May 17 17:57 onnx_optimizer/
drwxr-xr-x 15 oliverxu staff 480B Nov 20 2020 pybind11/
drwxr-xr-x 6 oliverxu staff 192B Nov 20 2020 script/
-rw-r--r-- 1 oliverxu staff 1.1K Nov 20 2020 version.py
但是报错：
(base) ~/Documents/TNN/tools/onnx2tnn/onnx-converter
$ python onnx2tnn.py -h
Segmentation fault: 11
由于我本地环境的问题，常用的环境的protobuf版本与 brew 安装在 conda base的版本有冲突，导致我不方便直接执行python converter.py onnx2tnn -h
** 附录
$ nm onnx2tnn.cpython-38-darwin.so | grep convert
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000000000004ecf0 t __GLOBAL__sub_I_onnx_converter_ceil.cc
0000000000050a90 t __GLOBAL__sub_I_onnx_converter_clip.cc
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0000000000054c50 t __GLOBAL__sub_I_onnx_converter_const.cc
0000000000056400 t __GLOBAL__sub_I_onnx_converter_constantofshape.cc
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0000000000064720 t __GLOBAL__sub_I_onnx_converter_dropout.cc
0000000000066770 t __GLOBAL__sub_I_onnx_converter_einsum.cc
0000000000068380 t __GLOBAL__sub_I_onnx_converter_elu.cc
0000000000069720 t __GLOBAL__sub_I_onnx_converter_equal.cc
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0000000000092770 t __GLOBAL__sub_I_onnx_converter_leakyrelu.cc
0000000000093b00 t __GLOBAL__sub_I_onnx_converter_less.cc
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请问当 执行 $ python onnx2tnn.py -h 时，除了报错 "Segmentation fault: 11" 之外，还有其他的报错信息吗？我在你给的 commit 上测试了，没有发现你说的问题。
建议你检查下，你编译用的 Python 和 你执行 onnx2tnn.py 是否是同一个版本？
请问当 执行
$ python onnx2tnn.py -h时，除了报错 "Segmentation fault: 11" 之外，还有其他的报错信息吗？
没有其他信息了
建议你检查下，你编译用的 Python 和 你执行 onnx2tnn.py 是否是同一个版本？
是同一个版本。log里面都是anaconda base 的环境，Python 3.8.3
谢谢回复，那我再看看。我确认一下：直接执行 $ python onnx2tnn.py -h 也应当是正常work的对吧？
