使用pnnx 把pytorch模型转为ncnn模型报错

[DHY-2020]

error log | 日志或报错信息 | ログ

model | 模型 | モデル

1. original model
   通过torch.jit.trace序列化后保存的pytorch模型
   

how to reproduce | 复现步骤 | 再現方法

vm-ubuntu@ubuntu:~/work_space/DHY/ncnn-master/tools/pnnx/build/install/bin$ ./pnnx track_track_pnnx.pt inputshape=[1,256,6,6]f32 inputshape2=[1,3,287,287]i8
pnnxparam = track_track_pnnx.pnnx.param
pnnxbin = track_track_pnnx.pnnx.bin
pnnxpy = track_track_pnnx_pnnx.py
ncnnparam = track_track_pnnx.ncnn.param
ncnnbin = track_track_pnnx.ncnn.bin
ncnnpy = track_track_pnnx_ncnn.py
optlevel = 2
device = cpu
inputshape = [1,256,6,6]f32
inputshape2 = [1,3,287,287]i8
customop =
moduleop =
############# pass_level0
inline module = pysot.models.backbone.alexnet.AlexNetLegacy
inline module = pysot.models.head.rpn.DepthwiseRPN
inline module = pysot.models.head.rpn.DepthwiseXCorr
inline module = pysot.models.backbone.alexnet.AlexNetLegacy
inline module = pysot.models.head.rpn.DepthwiseRPN
inline module = pysot.models.head.rpn.DepthwiseXCorr
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 75 76 77 81 82 83 batch.2 channel.2 k_2.2 k_3.2 x_2.2 x_3.2 102 x.2 106 kernel.2 out.2 out_2.2 out_3.2 input0.2 127 128 129 130 145 146 147 151 152 153 batch.1 channel.1 k_2.1 k_3.1 x_2.1 x_3.1 172 x.1 176 kernel.1 out.1 out_2.1 out_3.1 input0.1 197 198 199 200 12 13

terminate called after throwing an instance of 'c10::Error'
what(): forward() is missing value for argument 'input0'. Declaration: forward(torch.pysot.models.model_builder.ModelBuilder self, Tensor input, Tensor input0) -> ((Tensor, Tensor))
Exception raised from checkAndNormalizeInputs at ../aten/src/ATen/core/function_schema_inl.h:239 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) + 0x69 (0x7fd0bc7f4b29 in /home/vm-ubuntu/work_space/DHY/ncnn-master/tools/pnnx/build/install/bin/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&) + 0xd2 (0x7fd0bc7f1ab2 in /home/vm-ubuntu/work_space/DHY/ncnn-master/tools/pnnx/build/install/bin/libc10.so)
frame #2: + 0xd0f7e5 (0x7fd0ab5997e5 in /home/vm-ubuntu/work_space/DHY/ncnn-master/tools/pnnx/build/install/bin/libtorch_cpu.so)
frame #3: torch::jit::GraphFunction::operator()(std::vector<c10::IValue, std::allocatorc10::IValue >, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, c10::IValue, std::hash<std::__cxx11::basic_string<char, std::char_traits, std::allocator > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits, std::allocator > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator > const, c10::IValue> > > const&) + 0x2d (0x7fd0adc01bdd in /home/vm-ubuntu/work_space/DHY/ncnn-master/tools/pnnx/build/install/bin/libtorch_cpu.so)
frame #4: torch::jit::Method::operator()(std::vector<c10::IValue, std::allocatorc10::IValue >, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, c10::IValue, std::hash<std::__cxx11::basic_string<char, std::char_traits, std::allocator > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits, std::allocator > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator > const, c10::IValue> > > const&) + 0x16a (0x7fd0adc1109a in /home/vm-ubuntu/work_space/DHY/ncnn-master/tools/pnnx/build/install/bin/libtorch_cpu.so)
frame #5: ./pnnx() [0x449294]
frame #6: ./pnnx() [0x445c74]
frame #7: ./pnnx() [0x40b149]
frame #8: __libc_start_main + 0xf0 (0x7fd0a99d9840 in /lib/x86_64-linux-gnu/libc.so.6)
frame #9: ./pnnx() [0x414039]

已放弃 (核心已转储)