[12/17 21:15:06 d2.data.build]: Using training sampler TrainingSampler
[12/17 21:15:06 d2.data.common]: Serializing 5199 elements to byte tensors and concatenating them all ...
[12/17 21:15:06 d2.data.common]: Serialized dataset takes 16.94 MiB
[12/17 21:15:06 fvcore.common.checkpoint]: [Checkpointer] Loading from catalog://ImageNetPretrained/FAIR/X-101-64x4d ...
[12/17 21:15:06 d2.checkpoint.catalog]: Catalog entry catalog://ImageNetPretrained/FAIR/X-101-64x4d points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl
Traceback (most recent call last):
File "train_Rtinanet.py", line 175, in
args=(args,),
File "D:\paddle\detectron2\detectron2\engine\launch.py", line 82, in launch
main_func(args)
File "train_Rtinanet.py", line 158, in main
trainer.resume_or_load(resume=args.resume)
File "D:\paddle\detectron2\detectron2\engine\defaults.py", line 412, in resume_or_load
self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)
File "D:\Anaconda\envs\detectron2\lib\site-packages\fvcore\common\checkpoint.py", line 229, in resume_or_load
return self.load(path, checkpointables=[])
File "D:\paddle\detectron2\detectron2\checkpoint\detection_checkpoint.py", line 52, in load
ret = super().load(path, args, **kwargs)
File "D:\Anaconda\envs\detectron2\lib\site-packages\fvcore\common\checkpoint.py", line 156, in load
incompatible = self._load_model(checkpoint)
File "D:\paddle\detectron2\detectron2\checkpoint\detection_checkpoint.py", line 95, in _load_model
self._convert_ndarray_to_tensor(checkpoint["model"])
File "D:\Anaconda\envs\detectron2\lib\site-packages\fvcore\common\checkpoint.py", line 372, in _convert_ndarray_to_tensor
"Unsupported type found in checkpoint! {}: {}".format(k, type(v))
ValueError: Unsupported type found in checkpoint! weight_order: <class 'str'>
训练时使用X-101-64x4d作为主干网络，下载的预训练权重无法使用，权重文件的下载地址在detectron文件夹中。 但是其他主干网络的预训练权重的下载地址都在detectron2文件夹中，是因为X-101-64x4d的预训练权重和detectron2的环境不匹配吗，请问作者在训练时有遇到过这样的问题吗？
[12/17 21:15:06 d2.data.build]: Using training sampler TrainingSampler [12/17 21:15:06 d2.data.common]: Serializing 5199 elements to byte tensors and concatenating them all ... [12/17 21:15:06 d2.data.common]: Serialized dataset takes 16.94 MiB [12/17 21:15:06 fvcore.common.checkpoint]: [Checkpointer] Loading from catalog://ImageNetPretrained/FAIR/X-101-64x4d ... [12/17 21:15:06 d2.checkpoint.catalog]: Catalog entry catalog://ImageNetPretrained/FAIR/X-101-64x4d points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl Traceback (most recent call last): File "train_Rtinanet.py", line 175, in
args=(args,),
File "D:\paddle\detectron2\detectron2\engine\launch.py", line 82, in launch
main_func(args)
File "train_Rtinanet.py", line 158, in main
trainer.resume_or_load(resume=args.resume)
File "D:\paddle\detectron2\detectron2\engine\defaults.py", line 412, in resume_or_load
self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)
File "D:\Anaconda\envs\detectron2\lib\site-packages\fvcore\common\checkpoint.py", line 229, in resume_or_load
return self.load(path, checkpointables=[])
File "D:\paddle\detectron2\detectron2\checkpoint\detection_checkpoint.py", line 52, in load
ret = super().load(path, args, **kwargs)
File "D:\Anaconda\envs\detectron2\lib\site-packages\fvcore\common\checkpoint.py", line 156, in load
incompatible = self._load_model(checkpoint)
File "D:\paddle\detectron2\detectron2\checkpoint\detection_checkpoint.py", line 95, in _load_model
self._convert_ndarray_to_tensor(checkpoint["model"])
File "D:\Anaconda\envs\detectron2\lib\site-packages\fvcore\common\checkpoint.py", line 372, in _convert_ndarray_to_tensor
"Unsupported type found in checkpoint! {}: {}".format(k, type(v))
ValueError: Unsupported type found in checkpoint! weight_order: <class 'str'>