利用MNNConvert工具转换ONNX模型报Tensor.hpp:247 247，return mBuffer.dim[0].extent;

[wuqingshan2010]

利用MNNConvert工具转换ONNX模型，自己已经实现Tanh，ReduceMean，ReduceMax，L2_Norm等自定义算子， 在onnx2MNNNet步能够成功，但是在writeFb步骤中报Tensor.hpp，return mBuffer.dim[0].extent;
通过该出错误定位到生成onnx中源码中有torch.cat([avg_out, max_out], dim=1)操作；在转onnx源码中有多次torch.cat操作，若不添加该操作或者只使用一次能够成功，否则不能成功。

[wuqingshan2010]

Program received signal SIGSEGV, Segmentation fault.
0x00007ffff5fff23a in MNN::Tensor::batch (this=0x583cc1e0) at /home/work/sdk/MNN-0.2.1.5/include/Tensor.hpp:247
247 return mBuffer.dim[0].extent;
(gdb) where
#0 0x00007ffff5fff23a in MNN::Tensor::batch (this=0x583cc1e0) at /home/work/sdk/MNN-0.2.1.5/include/Tensor.hpp:247
#1 0x00007ffff60d37d7 in MNN::ConvolutionTiledExecutorBasic::<lambda(int)>::operator()(int) const (__closure=0x555559c67eb0, tId=0)
at /home/work/sdk/MNN-0.2.1.5/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp:137
#2 0x00007ffff60d60c0 in std::_Function_handler<void(int), MNN::ConvolutionTiledExecutorBasic::onResize(const std::vectorMNN::Tensor*&, const std::vectorMNN::Tensor*&)::<lambda(int)> >::_M_invoke(const std::_Any_data &, <unknown type in libMNN.so, CU 0x1c69a43, DIE 0x1cac00a>) (__functor=...,
__args#0=<unknown type in libMNN.so, CU 0x1c69a43, DIE 0x1cac00a>) at /usr/include/c++/7/bits/std_function.h:316
#3 0x00007ffff602b0a6 in std::function<void (int)>::operator()(int) const (this=0x555559c67f58, __args#0=0) at /usr/include/c++/7/bits/std_function.h:706
#4 0x00007ffff60d5c19 in MNN::ConvolutionTiledExecutorBasic::<lambda(int)>::operator()(int) const (__closure=0x7fffffffb780, tId=0)
at /home/work/sdk/MNN-0.2.1.5/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp:283
#5 0x00007ffff60d6d13 in std::_Function_handler<void(int), MNN::ConvolutionTiledExecutorBasic::onExecute(const std::vectorMNN::Tensor*&, const std::vectorMNN::Tensor*&)::<lambda(int)> >::_M_invoke(const std::_Any_data &, <unknown type in libMNN.so, CU 0x1c69a43, DIE 0x1caa599>) (__functor=...,
__args#0=<unknown type in libMNN.so, CU 0x1c69a43, DIE 0x1caa599>) at /usr/include/c++/7/bits/std_function.h:316
#6 0x00007ffff602b0a6 in std::function<void (int)>::operator()(int) const (this=0x7fffffffb780, __args#0=0) at /usr/include/c++/7/bits/std_function.h:706
#7 0x00007ffff60afe81 in MNN::ThreadPool::enqueue(std::pair<std::function<void (int)>, int>&&, int) (task=<unknown type in libMNN.so, CU 0x1a32f0e, DIE 0x1a4a4a3>, index=-1)
at /home/work/sdk/MNN-0.2.1.5/source/backend/cpu/ThreadPool.cpp:248
#8 0x00007ffff60d5d04 in MNN::ConvolutionTiledExecutorBasic::onExecute (this=0x555559c67d90, inputs=std::vector of length 1, capacity 1 = {...},
outputs=std::vector of length 1, capacity 1 = {...}) at /home/work/sdk/MNN-0.2.1.5/source/backend/cpu/compute/ConvolutionTiledExecutor.cpp:285
#9 0x00007ffff60d266b in MNN::ConvolutionTiledExecutor::onExecute (this=0x555559c30980, inputs=std::vector of length 1, capacity 1 = {...},
outputs=std::vector of length 1, capacity 1 = {...}) at /home/work/sdk/MNN-0.2.1.5/source/backend/cpu/compute/ConvolutionTiledExecutor.hpp:56
#10 0x00007ffff654aae6 in MNN::Express::InsideExpr::onComputeContent (this=0x5555583c4320, inputs=std::vector of length 1, capacity 1 = {...},
outputs=std::vector of length 1, capacity 1 = {...}) at /home/work/sdk/MNN-0.2.1.5/express/source/InsideExpr.cpp:408
#11 0x00007ffff6515857 in MNN::Express::Expr::requireCompute (this=0x555555a947f0) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:228
#12 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x555555aa2990) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#13 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555559ac910) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#14 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555559a8f60) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#15 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x555555aa2da0) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#16 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555559bff30) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#17 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x555555a2a730) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#18 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x555555a54290) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#19 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x555555a3f7d0) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#20 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555557de5f0) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#21 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555557fc610) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#22 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555557f27a0) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#23 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x555555994300) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#24 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555557f34a0) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#25 0x00007ffff651577c in MNN::Express::Expr::requireCompute (this=0x5555557ea740) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:214
#26 0x00007ffff65172de in MNN::Express::Variable::readInternal (this=0x5555557f3d50) at /home/work/sdk/MNN-0.2.1.5/express/source/Expr.cpp:517
#27 0x00007ffff6eb290a in std::_Function_handler<bool (std::shared_ptrMNN::Express::Variable), MNN::Express::{lambda()#1}::operator()() const::{lambda(std::shared_ptrMNN::Express::Variable)#1}>::_M_invoke(std::_Any_data const&, std::shared_ptrMNN::Express::Variable&&) ()
from /home/work/sdk/MNN-0.2.1.5/build/tools/converter/source/optimizer/liboptimizer.so
#28 0x00007ffff6eb0015 in MNN::Express::TemplateMerge::onExecute(std::vector<std::shared_ptrMNN::Express::Variable, std::allocator<std::shared_ptrMNN::Express::Variable > > const&, std::shared_ptrMNN::Express::Optimizer::Parameters) () from /home/work/sdk/MNN-0.2.1.5/build/tools/converter/source/optimizer/liboptimizer.so
---Type to continue, or q to quit---
#29 0x00007ffff6e9b0a1 in optimizeNet(std::unique_ptr<MNN::NetT, std::default_deleteMNN::NetT >&) ()
from /home/work/sdk/MNN-0.2.1.5/build/tools/converter/source/optimizer/liboptimizer.so

[wuqingshan2010]

inputParam->dformat = MNN::MNN_DATA_FORMAT_NCHW;更改为inputParam->dformat = MNN::MNN_DATA_FORMAT_NC4HW4;即可解决问题