Open amelia808 opened 5 years ago
It actually fails for all onnx object detection models.
I got similar problem with my own .onnx.
I compiled the code with debug mode and tried to convert ONNX yolov3.onnx and I got this error
#0 __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50
#1 0x00007ffff7a4c899 in __GI_abort () at abort.c:79
#2 0x00007ffff7e1f5f6 in ?? () from /lib/x86_64-linux-gnu/libstdc++.so.6
#3 0x00007ffff7e2b9ec in ?? () from /lib/x86_64-linux-gnu/libstdc++.so.6
#4 0x00007ffff7e2ba47 in std::terminate() () from /lib/x86_64-linux-gnu/libstdc++.so.6
#5 0x00007ffff7e2bca9 in __cxa_throw () from /lib/x86_64-linux-gnu/libstdc++.so.6
#6 0x00007ffff7e21f04 in std::__throw_out_of_range(char const*) ()
from /lib/x86_64-linux-gnu/libstdc++.so.6
#7 0x0000555555686023 in std::__detail::_Map_base<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, onnx_daq::Value*>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, onnx_daq::Value*> >, std::__detail::_Select1st, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::__detail::_Mod_range_hashing, std::__detail::_Default_ranged_hash, std::__detail::_Prime_rehash_policy, std::__detail::_Hashtable_traits<true, false, true>, true>::at (this=0x7fffffffc600, __k="W74")
at /usr/include/c++/9/bits/hashtable_policy.h:750
#8 0x0000555555683999 in std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, onnx_daq::Value*, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, onnx_daq::Value*> > >::at (this=0x7fffffffc600, __k="W74")
at /usr/include/c++/9/bits/unordered_map.h:1002
#9 0x000055555567dcaf in onnx_daq::graphProtoToGraph (gp=..., nested=false)
at /home/ardiya/Workspace/DNNLibrary/third_party/onnx/onnx/common/ir_pb_converter.cc:287
#10 0x000055555567e52d in onnx_daq::ImportModelProto (mp=...)
at /home/ardiya/Workspace/DNNLibrary/third_party/onnx/onnx/common/ir_pb_converter.cc:331
#11 0x0000555555646fd5 in onnx_daq::optimization::Optimizer::optimize (this=0x7fffffffcb40, mp_in=...)
at /home/ardiya/Workspace/DNNLibrary/third_party/onnx/onnx/optimizer/optimize.h:26
#12 0x0000555555639d9a in onnx_daq::optimization::OptimizeFixed (mp_in=...,
names=std::vector of length 18, capacity 18 = {...})
at /home/ardiya/Workspace/DNNLibrary/third_party/onnx/onnx/optimizer/optimize.cc:38
#13 0x000055555557711d in dnn::OnnxConverter::Convert (this=0x7fffffffd880, model_proto=...,
table_file="") at /home/ardiya/Workspace/DNNLibrary/tools/onnx2daq/OnnxConverter.cpp:646
#14 0x000055555556bfc0 in main (argc=3, argv=0x7fffffffdbc8)
at /home/ardiya/Workspace/DNNLibrary/tools/onnx2daq/onnx2daq.cpp:34
from frame 9, file ir_pb_converter.cc line 287, which is n->addInput(value_by_name_of.at(input));
here are more info from the gdb
(gdb) print input
$1 = "W74"
(gdb) print value_by_name_of
$2 = std::unordered_map with 478 elements = {["yolonms_layer_1/ExpandDims_1:0"] = 0x55555b61a6e0,
["TFNodes/yolo_evaluation_layer_1/concat_7:0"] = 0x55555b619640,
["TFNodes/yolo_evaluation_layer_1/strided_slice_56:0"] = 0x55555b619140,
["TFNodes/yolo_evaluation_layer_1/add_5:0"] = 0x55555b618880,
["TFNodes/yolo_evaluation_layer_1/strided_slice_54:0"] = 0x55555b618380,
["TFNodes/yolo_evaluation_layer_1/mul_15:0"] = 0x55555b617650,
["TFNodes/yolo_evaluation_layer_1/strided_slice_52:0"] = 0x55555b616d00,
["TFNodes/yolo_evaluation_layer_1/add_4:0"] = 0x55555b616560,
["TFNodes/yolo_evaluation_layer_1/Sigmoid_6:0"] = 0x55555b616310,
["TFNodes/yolo_evaluation_layer_1/strided_slice_46:0"] = 0x55555b615cc0,
["TFNodes/yolo_evaluation_layer_1/truediv_23:0"] = 0x55555b6158a0,
["TFNodes/yolo_evaluation_layer_1/strided_slice_53:0"] = 0x55555b615040,
["TFNodes/yolo_evaluation_layer_1/mul_13:0"] = 0x55555b614a50,
["TFNodes/yolo_evaluation_layer_1/strided_slice_48:0"] = 0x55555b614240,
["yolo_evaluation_layer_1/concat_10:0_tx"] = 0x55555b613d20,
["TFNodes/yolo_evaluation_layer_1/Reshape_17:0"] = 0x55555b6134b0,
["TFNodes/yolo_evaluation_layer_1/mul_18:0"] = 0x55555b613240,
["TFNodes/yolo_evaluation_layer_1/Sigmoid_7:0"] = 0x55555b613050,
["TFNodes/yolo_evaluation_layer_1/Sigmoid_8:0"] = 0x55555b612840,
["TFNodes/yolo_evaluation_layer_1/strided_slice_51:0"] = 0x55555b612220,
["TFNodes/yolo_evaluation_layer_1/Reshape_15__87:0"] = 0x55555b611a60,
["TFNodes/yolo_evaluation_layer_1/Reshape_15/shape_Concat__36:0"] = 0x55555b6114b0,
["TFNodes/yolo_evaluation_layer_1/concat_6:0"] = 0x55555b610ea0,
["TFNodes/yolo_evaluation_layer_1/Tile_5/multiples_Concat__46:0"] = 0x55555b6105d0,
["TFNodes/yolo_evaluation_layer_1/Reshape_15/shape_Unsqueeze__32:0"] = 0x55555b60da10,
["TFNodes/yolo_evaluation_layer_1/arange_4__77_loop:1"] = 0x55555b60e520,
["TFNodes/yolo_evaluation_layer_1/arange_4__77_loop:0"] = 0x55555b60e450,
["TFNodes/yolo_evaluation_layer_1/arange_4__77_trip_cnt:0"] = 0x55555b60d780,
["TFNodes/yolo_evaluation_layer_1/arange_4__77_div:0"] = 0x55555b60cfc0,
["TFNodes/yolo_evaluation_layer_1/arange_4__77_cast_diff:0"] = 0x55555b60cd40,
["TFNodes/yolo_evaluation_layer_1/arange_4__77_diff__78:0"] = 0x55555b60ca50,
["TFNodes/yolo_evaluation_layer_1/strided_slice_44:0"] = 0x55555b5cb8a0,
["TFNodes/yolo_evaluation_layer_1/Reshape_14:0"] = 0x55555b5c8b10,
["TFNodes/yolo_evaluation_layer_1/arange_5__52_trip_cnt:0"] = 0x55555b5caa10,
["TFNodes/yolo_evaluation_layer_1/arange_5__52_ceil:0"] = 0x55555b5ca640,
["TFNodes/yolo_evaluation_layer_1/arange_5__52_cast_diff:0"] = 0x55555b5c9e90,
["TFNodes/yolo_evaluation_layer_1/arange_5__52_diff__53:0"] = 0x55555b5c9b50,
["TFNodes/yolo_evaluation_layer_1/strided_slice_45:0"] = 0x55555b604060,
["TFNodes/yolo_evaluation_layer_1/Reshape_3__306:0"] = 0x55555b5d8770,
["TFNodes/yolo_evaluation_layer_1/Reshape_15/shape_Unsqueeze__33:0"] = 0x55555b5c8930,
["TFNodes/yolo_evaluation_layer_1/strided_slice_13:0"] = 0x55555b5da1c0,
["TFNodes/yolo_evaluation_layer_1/Cast_8:0"] = 0x55555b611150,
["batch_norm_output_buffer64"] = 0x55555b59c670, ["model_1/add_23/add:0"] = 0x55555b5c44e0,
["model_1/leaky_re_lu_52/LeakyRelu:0"] = 0x55555b5c42c0,
["TFNodes/yolo_evaluation_layer_1/mul_7:0"] = 0x55555b5f7e80,
["convolution_output23"] = 0x55555b5c3a10, ["convolution_output57"] = 0x55555b5a4ed0,
["TFNodes/yolo_evaluation_layer_1/strided_slice_1:0"] = 0x55555b5cd130,
["model_1/leaky_re_lu_51/LeakyRelu:0"] = 0x55555b5c3630,
["TFNodes/yolo_evaluation_layer_1/Shape_2:0"] = 0x55555b5e9b70,
["TFNodes/yolo_evaluation_layer_1/strided_slice_7__219:0"] = 0x55555b5ce4a0,
["model_1/leaky_re_lu_68/LeakyRelu:0"] = 0x55555b5fec20, ["convolution_output25"] = 0x55555b5c1e80,
["model_1/leaky_re_lu_49/LeakyRelu:0"] = 0x55555b5c1aa0,
["TFNodes/yolo_evaluation_layer_1/Sigmoid_5:0"] = 0x55555b5f65f0,
["model_1/add_21/add:0"] = 0x55555b5c0dc0, ["model_1/leaky_re_lu_48/LeakyRelu:0"] = 0x55555b5c0ba0,
["convolution_output12"] = 0x55555b5e6930, ["batch_norm_output_buffer25"] = 0x55555b5bfb90,
["model_1/add_20/add:0"] = 0x55555b5bf220, ["model_1/leaky_re_lu_45/LeakyRelu:0"] = 0x55555b5be3d0,
["convolution_output30"] = 0x55555b5bdb20, ["model_1/add_6/add:0"] = 0x55555b5a3e10,
["model_1/leaky_re_lu_44/LeakyRelu:0"] = 0x55555b5bd7d0,
["batch_norm_output_buffer40"] = 0x55555b5b2e00, ["batch_norm_output_buffer28"] = 0x55555b5bd450,
["convolution_output34"] = 0x55555b5ba3a0,
["TFNodes/yolo_evaluation_layer_1/Reshape_9:0"] = 0x55555b5f5b40,
["convolution_output54"] = 0x55555b5a79a0,
["TFNodes/yolo_evaluation_layer_1/concat_10:0"] = 0x55555b613800,
["model_1/leaky_re_lu_43/LeakyRelu:0"] = 0x55555b5bc7e0,
["TFNodes/yolo_evaluation_layer_1/strided_slice_37:0"] = 0x55555b5fc2c0,
["model_1/leaky_re_lu_50/LeakyRelu:0"] = 0x55555b5c2730,
["model_1/leaky_re_lu_41/LeakyRelu:0"] = 0x55555b5bac50,
["batch_norm_output_buffer48"] = 0x55555b5aa6b0, ["batch_norm_output_buffer32"] = 0x55555b5b9c40,
["convolution_output35"] = 0x55555b5b9750, ["convolution_output28"] = 0x55555b5bf6a0,
["TFNodes/yolo_evaluation_layer_1/arange__271_diff__272:0"] = 0x55555b5d3900,
["TFNodes/yolo_evaluation_layer_1/Shape_3:0"] = 0x55555b602a50,
["model_1/add_17/add:0"] = 0x55555b5b92e0,
["TFNodes/yolo_evaluation_layer_1/arange_5__52_loop:1"] = 0x55555b609270,
["model_1/leaky_re_lu_5/LeakyRelu:0"] = 0x55555b599fb0,
["batch_norm_output_buffer15"] = 0x55555b5c7f60,
["model_1/leaky_re_lu_32/LeakyRelu:0"] = 0x55555b5b3180, ["model_1/add_19/add:0"] = 0x55555b5bca00,
["batch_norm_output_buffer34"] = 0x55555b5b80b0, ["model_1/add_16/add:0"] = 0x55555b5b7750,
["convolution_output38"] = 0x55555b5b6c80,
["TFNodes/yolo_evaluation_layer_1/mul_16:0"] = 0x55555b615660,
["convolution_output18"] = 0x55555b5c7a30,
["TFNodes/yolo_evaluation_layer_1/Squeeze:0"] = 0x55555b595170,
["convolution_output39"] = 0x55555b5b6030, ["convolution_output10"] = 0x55555b5fd750,
["TFNodes/yolo_evaluation_layer_1/Tile:0"] = 0x55555b5d48c0,
["batch_norm_output_buffer16"] = 0x55555b5c7330, ["batch_norm_output_buffer37"] = 0x55555b5b5620,
["TFNodes/yolo_evaluation_layer_1/mul_17:0"] = 0x55555b619750,
["batch_norm_output_buffer38"] = 0x55555b5b4990...}
@daquexian could you provide any guidance on how to fix this?
Any updates on this?
Having the same error while trying to convert a custom SSD object detection model. The original model was implemented in GluonCV, fine-tuned from one of the models in the ZOO, and converted to ONNX format. Any updates?
this is the error while trying to convert https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/yolov3