detectron2: Panoptic Segmentation Can Not be Exported to ONNX

Instructions To Reproduce the 🐛 Bug:

1. Full runnable code or full changes you made:

I use the original repo and do not change anything.

1. What exact command do you run:

export DETECTRON2_DATASETS=/data/datasets/

python3 export_model.py \
    --config-file ../../configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml \
    --output /data/output/ \
    --export-method caffe2_tracing \
    --format onnx \
    MODEL.WEIGHTS /data/model/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/model_final_dbfeb4.pkl \
    MODEL.DEVICE cpu

I have already prepared the COCO dataset in /data/datasets/.

1. Full logs or other relevant observations:

[06/23 06:22:33 detectron2]: Command line arguments: Namespace(format='onnx', export_method='caffe2_tracing', config_file='../../configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml', sample_image=None, run_eval=False, output='/data/output/', opts=['MODEL.WEIGHTS', '/data/model/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/model_final_dbfeb4.pkl', 'MODEL.DEVICE', 'cpu'])
[W init.cpp:759] Warning: Use _jit_set_fusion_strategy, bailout depth is deprecated. Setting to (STATIC, 1) (function operator())
[06/23 06:22:35 d2.data.datasets.coco]: Loaded 5000 images in COCO format from /data/datasets/coco/annotations/instances_val2017.json
[06/23 06:22:35 d2.data.datasets.coco]: Loaded 5000 images with semantic segmentation from /data/datasets/coco/val2017
[06/23 06:22:35 d2.data.build]: Distribution of instances among all 80 categories:
|   category    | #instances   |   category   | #instances   |   category    | #instances   |
|:-------------:|:-------------|:------------:|:-------------|:-------------:|:-------------|
|    person     | 10777        |   bicycle    | 314          |      car      | 1918         |
|  motorcycle   | 367          |   airplane   | 143          |      bus      | 283          |
|     train     | 190          |    truck     | 414          |     boat      | 424          |
| traffic light | 634          | fire hydrant | 101          |   stop sign   | 75           |
| parking meter | 60           |    bench     | 411          |     bird      | 427          |
|      cat      | 202          |     dog      | 218          |     horse     | 272          |
|     sheep     | 354          |     cow      | 372          |   elephant    | 252          |
|     bear      | 71           |    zebra     | 266          |    giraffe    | 232          |
|   backpack    | 371          |   umbrella   | 407          |    handbag    | 540          |
|      tie      | 252          |   suitcase   | 299          |    frisbee    | 115          |
|     skis      | 241          |  snowboard   | 69           |  sports ball  | 260          |
|     kite      | 327          | baseball bat | 145          | baseball gl.. | 148          |
|  skateboard   | 179          |  surfboard   | 267          | tennis racket | 225          |
|    bottle     | 1013         |  wine glass  | 341          |      cup      | 895          |
|     fork      | 215          |    knife     | 325          |     spoon     | 253          |
|     bowl      | 623          |    banana    | 370          |     apple     | 236          |
|   sandwich    | 177          |    orange    | 285          |   broccoli    | 312          |
|    carrot     | 365          |   hot dog    | 125          |     pizza     | 284          |
|     donut     | 328          |     cake     | 310          |     chair     | 1771         |
|     couch     | 261          | potted plant | 342          |      bed      | 163          |
| dining table  | 695          |    toilet    | 179          |      tv       | 288          |
|    laptop     | 231          |    mouse     | 106          |    remote     | 283          |
|   keyboard    | 153          |  cell phone  | 262          |   microwave   | 55           |
|     oven      | 143          |   toaster    | 9            |     sink      | 225          |
| refrigerator  | 126          |     book     | 1129         |     clock     | 267          |
|     vase      | 274          |   scissors   | 36           |  teddy bear   | 190          |
|  hair drier   | 11           |  toothbrush  | 57           |               |              |
|     total     | 36335        |              |              |               |              |
[06/23 06:22:35 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[06/23 06:22:35 d2.data.common]: Serializing 5000 elements to byte tensors and concatenating them all ...
[06/23 06:22:35 d2.data.common]: Serialized dataset takes 19.52 MiB
Traceback (most recent call last):
  File "/data/detectron2/tools/deploy/export_model.py", line 217, in <module>
    exported_model = export_caffe2_tracing(cfg, torch_model, sample_inputs)
  File "/data/detectron2/tools/deploy/export_model.py", line 36, in export_caffe2_tracing
    from detectron2.export import Caffe2Tracer
ImportError: cannot import name 'Caffe2Tracer' from 'detectron2.export' (/usr/local/lib/python3.10/dist-packages/detectron2-0.6-py3.10-linux-x86_64.egg/detectron2/export/__init__.py)

1. please simplify the steps as much as possible so they do not require additional resources to run, such as a private dataset.

Expected behavior:

I expected that the official code export_model.py can help me to export onnx model.

Environment:

Provide your environment information using the following command:

---------------------  ----------------------------------------------------------------------------------------------
sys.platform           linux
Python                 3.10.4 (main, Apr  2 2022, 09:04:19) [GCC 11.2.0]
numpy                  1.22.4
detectron2             0.6 @/usr/local/lib/python3.10/dist-packages/detectron2-0.6-py3.10-linux-x86_64.egg/detectron2
Compiler               GCC 11.2
CUDA compiler          not available
DETECTRON2_ENV_MODULE  <not set>
PyTorch                1.11.0+cpu @/usr/local/lib/python3.10/dist-packages/torch
PyTorch debug build    False
GPU available          No: torch.cuda.is_available() == False
Pillow                 9.1.1
torchvision            0.12.0+cpu @/usr/local/lib/python3.10/dist-packages/torchvision
fvcore                 0.1.5.post20220512
iopath                 0.1.9
cv2                    4.6.0
---------------------  ----------------------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=0, USE_CUDNN=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

If your issue looks like an installation issue / environment issue, please first try to solve it yourself with the instructions in https://detectron2.readthedocs.io/tutorials/install.html#common-installation-issues

Finally, thanks to your effects that contribute such excellent repo!