Error while loading pretrained model weights from `detectron2://ImageNetPretrained/MSRA/R-101.pkl` for training with custom dataset

[hnanacc]

Hello authors, thanks for code.

I was trying to adapt the model to our custom dataset and faced the following issue.

Traceback (most recent call last):
  File "models/adaptive_teacher/train_net.py", line 84, in <module>
    args=(args,),
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/detectron2/engine/launch.py", line 59, in launch
    daemon=False,
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 199, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 157, in start_processes
    while not context.join():
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 118, in join
    raise Exception(msg)
Exception: 

-- Process 0 terminated with the following error:
Traceback (most recent call last):
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
    fn(i, *args)
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/detectron2/engine/launch.py", line 94, in _distributed_worker
    main_func(*args)
  File "/misc/no_backups/s1437/DA-Multimodal-OD/models/adaptive_teacher/train_net.py", line 69, in main
    trainer.resume_or_load(resume=args.resume)
  File "/misc/no_backups/s1437/DA-Multimodal-OD/models/adaptive_teacher/adapteacher/engine/trainer.py", line 377, in resume_or_load
    self.cfg.MODEL.WEIGHTS, resume=resume
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/fvcore/common/checkpoint.py", line 229, in resume_or_load
    return self.load(path, checkpointables=[])
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/fvcore/common/checkpoint.py", line 158, in load
    incompatible = self._load_model(checkpoint)
  File "/misc/no_backups/s1437/DA-Multimodal-OD/models/adaptive_teacher/adapteacher/checkpoint/detection_checkpoint.py", line 28, in _load_model
    incompatible = self._load_student_model(checkpoint)
  File "/misc/no_backups/s1437/DA-Multimodal-OD/models/adaptive_teacher/adapteacher/checkpoint/detection_checkpoint.py", line 70, in _load_student_model
    self._convert_ndarray_to_tensor(checkpoint_state_dict)
  File "/no_backups/s1437/.pyenv/versions/adaptiveteacher/lib/python3.7/site-packages/fvcore/common/checkpoint.py", line 368, in _convert_ndarray_to_tensor
    for k in list(state_dict.keys()):
AttributeError: 'NoneType' object has no attribute 'keys'

Configuration used:

_BASE_: "./Base-RCNN-C4.yaml"
MODEL:
  META_ARCHITECTURE: "DAobjTwoStagePseudoLabGeneralizedRCNN"
  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
  MASK_ON: False
  RESNETS:
    DEPTH: 101
  PROPOSAL_GENERATOR:
    NAME: "PseudoLabRPN"
  PIXEL_MEAN: [87.0, 91.0, 95.0]
  # RPN:
  #   POSITIVE_FRACTION: 0.25
  ROI_HEADS:
    NAME: "StandardROIHeadsPseudoLab"
    LOSS: "CrossEntropy" # variant: "CrossEntropy"
    NUM_CLASSES: 4 # this doesn't include background.
  ROI_BOX_HEAD:
    NAME: "FastRCNNConvFCHead"
    NUM_FC: 2
    POOLER_RESOLUTION: 7
SOLVER:
  LR_SCHEDULER_NAME: "WarmupTwoStageMultiStepLR"
  STEPS: (60000, 80000, 90000, 360000)
  FACTOR_LIST: (1, 1, 1, 1, 1)
  MAX_ITER: 100000
  IMG_PER_BATCH_LABEL: 4
  IMG_PER_BATCH_UNLABEL: 4
  BASE_LR: 0.04
  CHECKPOINT_PERIOD: 1000
DATALOADER:
  SUP_PERCENT: 100.0
DATASETS:
  CROSS_DATASET: True
  TRAIN_LABEL: ("train_clear_day",) #voc_2012_train
  TRAIN_UNLABEL: ("train_dense_fog_day",) #Clipart1k_train
  TEST: ("test_clear_day",) #Clipart1k_test
SEMISUPNET:
  Trainer: "ateacher"
  BBOX_THRESHOLD: 0.8
  TEACHER_UPDATE_ITER: 1
  BURN_UP_STEP: 20000
  EMA_KEEP_RATE: 0.9996
  UNSUP_LOSS_WEIGHT: 1.0
  SUP_LOSS_WEIGHT: 1.0
  DIS_TYPE: "res4" #["concate","p2","multi"]
TEST:
  EVAL_PERIOD: 1000

Please have look into it.