TypeError: argument of type 'GeneralizedRCNN' is not iterable

I trained a custom model for leukocytes detection. To save the model I use the following command"

##Full runnable code or full changes you made:

torch.save(trainer.model,path+"leukocytes.pth")
f = open(path+'leukocytes.yaml', 'w')
f.write(cfg.dump())
f.close()

Once the mode is saved I open a new notebook to run inference as follows:

from detectron2.utils.logger import setup_logger
setup_logger()
import cv2
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog

config_file_path = path+'leukocytes.yaml'

weights_path = path+'leukocytes.pth'

image_path = path+"testImages/data/small_lymphocytes_Image_22-02-22_at_10.36_AM.jpeg"

model = config_file_path
im = cv2.imread(image_path)
cfg = get_cfg()
cfg.merge_from_file(config_file_path)

cfg.MODEL.WEIGHTS = weights_path
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8
predictor = DefaultPredictor(cfg)
outputs = predictor(im)
MetadataCatalog.get(cfg.DATASETS.TRAIN[0]).thing_classes = ['Leukocytes']
v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2.imshow('',v.get_image()[:, :, ::-1])
cv2.waitKey(0)



## __Full logs__ or other relevant observations:

---

TypeError Traceback (most recent call last) <ipython-input-52-26a8f28254df> in <module>() 20 cfg.MODEL.WEIGHTS = weights_path 21 cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8 —> 22 predictor = DefaultPredictor(cfg) 23 outputs = predictor(im) 24 MetadataCatalog.get(cfg.DATASETS.TRAIN[0]).thing_classes = [‘Leukocytes’]

2 frames /usr/local/lib/python3.7/dist-packages/detectron2/checkpoint/detection_checkpoint.py in _load_file(self, filename) 41 42 loaded = super()._load_file(filename) # load native pth checkpoint —> 43 if “model” not in loaded: 44 loaded = {“model”: loaded} 45 return loaded

TypeError: argument of type ‘GeneralizedRCNN’ is not iterable

Environment:

sys.platform              linux
Python                    3.7.12 (default, Jan 15 2022, 18:48:18) [GCC 7.5.0]
numpy                     1.21.5
detectron2                0.1.3 @/usr/local/lib/python3.7/dist-packages/detectron2
detectron2 compiler       GCC 7.3
detectron2 CUDA compiler  10.1
detectron2 arch flags     sm_35, sm_37, sm_50, sm_52, sm_60, sm_61, sm_70, sm_75
DETECTRON2_ENV_MODULE     <not set>
PyTorch                   1.5.0+cu101 @/usr/local/lib/python3.7/dist-packages/torch
PyTorch debug build       False
CUDA available            True
GPU 0                     Tesla P100-PCIE-16GB
CUDA_HOME                 /usr/local/cuda
NVCC                      Build cuda_11.1.TC455_06.29190527_0
Pillow                    7.1.2
torchvision               0.6.0+cu101 @/usr/local/lib/python3.7/dist-packages/torchvision
torchvision arch flags    sm_35, sm_50, sm_60, sm_70, sm_75
fvcore                    0.1.5.post20220212
cv2                       4.1.2
------------------------  ---------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - 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 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -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-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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, USE_STATIC_DISPATCH=OFF, 

[ishank-juneja]

I think we just need,

torch.save(trainer.model.state_dict(), "models/detectron/tmp.pt")

instead.

[shrutikapoyrekar]

So I used the model which is saved in cfg.OUTPUT_DIR and it works fine