Issue in loading pre-trained model

[hapyty]

Hello! The pre-trained model I load was trained by "epoch", so detectron2 can't recognize it. Then I met following error:

ValueError: Unsupported type found in checkpoint! epoch: <class 'int'>

Anyone met this error? Enviroment info:

---

sys.platform linux Python 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] numpy 1.21.5 detectron2 0.5 @/data/wrz/transfiner/detectron2 Compiler GCC 7.3 CUDA compiler CUDA 10.2 detectron2 arch flags 7.0 DETECTRON2_ENV_MODULE PyTorch 1.7.1 @/home/asc005/anaconda3/envs/transfiner/lib/python3.7/site-packages/torch PyTorch debug build False GPU available Yes GPU 0,1 Tesla V100-PCIE-32GB (arch=7.0) Driver version 440.64.00 CUDA_HOME /usr/local/cuda-10.2 Pillow 9.0.1 torchvision 0.8.2 @/home/asc005/anaconda3/envs/transfiner/lib/python3.7/site-packages/torchvision torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5 fvcore 0.1.5.post20221221 iopath 0.1.9 cv2 4.4.0

---

PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.2
• 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
• CuDNN 7.6.5
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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-variable -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

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[Prayag22]

Looks like you have a problem loading pre-trained models firstly load the correct yaml file depending upon which pretrained model to use you cannot have mask_rcnn_R_50_FPN_3x.yaml and use different models weight both should be same you can either define like this or give direct path to pretrained weights using cfg.MODEL.WEIGHTS = ("pretrained_model_path")

cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")) cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")

[FurkanGozukara]

Looks like you have a problem loading pre-trained models firstly load the correct yaml file depending upon which pretrained model to use you cannot have mask_rcnn_R_50_FPN_3x.yaml and use different models weight both should be same you can either define like this or give direct path to pretrained weights using cfg.MODEL.WEIGHTS = ("pretrained_model_path")

cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")) cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")

thank you for reply

can you also checkout this? https://github.com/facebookresearch/detectron2/issues/5170