Pickling error while loading dump file generated by DensePose

[maheboobp]

Instructions To Reproduce the Issue:

I am trying to use DensePose on Google Colab. Specifically i am trying to run below command and load resulting dump file with pickle.

bwlo command works fine

python apply_net.py dump configs/densepose_rcnn_R_50_FPN_s1x.yaml \ https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl \ images --output dump.pkl -v

command i am using to load file is

import pickle f = open('dump.pk', 'rb') pickle.load(f)

i m getting below error

UnpicklingError: A load persistent id instruction was encountered, but no persistent_load function was specified.

Expected behavior:

Expecting pickle to load file without error.

Environment:

Paste the output of the following command:

wget -nc -nv https://github.com/facebookresearch/detectron2/raw/main/detectron2/utils/collect_env.py && python collect_env.py

---

sys.platform linux Python 3.9.16 (main, Dec 7 2022, 01:11:51) [GCC 9.4.0] numpy 1.22.4 detectron2 0.6 @/usr/local/lib/python3.9/dist-packages/detectron2 Compiler GCC 9.4 CUDA compiler CUDA 11.8 detectron2 arch flags 7.5 DETECTRON2_ENV_MODULE PyTorch 2.0.0+cu117 @/usr/local/lib/python3.9/dist-packages/torch PyTorch debug build False torch._C._GLIBCXX_USE_CXX11_ABI False GPU available Yes GPU 0 Tesla T4 (arch=7.5) Driver version 525.85.12 CUDA_HOME /usr/local/cuda Pillow 8.4.0 torchvision 0.15.1+cu117 @/usr/local/lib/python3.9/dist-packages/torchvision torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6 fvcore 0.1.5.post20221221 iopath 0.1.9 cv2 4.7.0

---

PyTorch built with:

• GCC 9.3
• C++ Version: 201703
• Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 11.7
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
• CuDNN 8.5
• Magma 2.6.1
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

[maheboobp]

I believe i have an idea on how this is broken , and potential fix. Please let me know if you want me to fix it.

Thnx Maheboob

[Reschivon]

I have the same issue. Any luck?

[Reschivon]

I made the simple mistake of using pickle.load() instead of torch.load()

[skthtu]

Hi @maheboobp ,

There is already a pull request addressing this issue. Please refer to pull request #4841 . Also, it would be greatly appreciated if you could review it.

Thanks. skthtu

[maheboobp]

Hi @maheboobp ,

There is already a pull request addressing this issue. Please refer to pull request #4841 . Also, it would be greatly appreciated if you could review it.

Thanks. skthtu

Thanks. looks good to me.