TypeError: __init__() missing 1 required positional argument: 'dtype'

Pytorch version 1.9
2021-08-20 00:05:48 | ERROR | yolox.core.launch:68 - An error has been caught in function 'launch', process 'MainProcess' (18728), thread 'MainThread' (568):
Traceback (most recent call last):

File "tools\train.py", line 115, in
dist_url=dist_url, args=(exp, args)
│ │ └ Namespace(batch_size=16, ckpt='./YOLOX_outputs\yolox_coco_tiny\latest_ckpt.pth.tar', devices=1, dist_backend='nccl', dist_u...
│ └ ╒══════════════════╤═════════════════════════════════════════════════════════════════════════════════════════════════════════...
└ 'auto'

File "d:\work\demo\yolox-main\yolox\core\launch.py", line 68, in launch
main_func(*args)
│ └ (╒══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════...
└ <function main at 0x000001ED23056A68>

File "tools\train.py", line 101, in main
trainer.train()
│ └ <function Trainer.train at 0x000001ED47B264C8>
└ <yolox.core.trainer.Trainer object at 0x000001ED48997088>

File "d:\work\demo\yolox-main\yolox\core\trainer.py", line 70, in train
self.train_in_epoch()
│ └ <function Trainer.train_in_epoch at 0x000001ED48215D38>
└ <yolox.core.trainer.Trainer object at 0x000001ED48997088>

File "d:\work\demo\yolox-main\yolox\core\trainer.py", line 80, in train_in_epoch
self.after_epoch()
│ └ <function Trainer.after_epoch at 0x000001ED49023C18>
└ <yolox.core.trainer.Trainer object at 0x000001ED48997088>

File "d:\work\demo\yolox-main\yolox\core\trainer.py", line 210, in after_epoch
self.evaluate_and_save_model()
│ └ <function Trainer.evaluate_and_save_model at 0x000001ED49023EE8>
└ <yolox.core.trainer.Trainer object at 0x000001ED48997088>

File "d:\work\demo\yolox-main\yolox\core\trainer.py", line 293, in evaluate_and_save_model
ap50_95, ap50, summary = self.exp.eval(evalmodel, self.evaluator, self.is_distributed)
│ │ │ │ │ │ │ └ False
│ │ │ │ │ │ └ <yolox.core.trainer.Trainer object at 0x000001ED48997088>
│ │ │ │ │ └ <yolox.evaluators.coco_evaluator.COCOEvaluator object at 0x000001ED60BD4C48>
│ │ │ │ └ <yolox.core.trainer.Trainer object at 0x000001ED48997088>
│ │ │ └ YOLOX(
│ │ │ (backbone): YOLOPAFPN(
│ │ │ (backbone): CSPDarknet(
│ │ │ (stem): Focus(
│ │ │ (conv): BaseConv(
│ │ │ (conv): ...
│ │ └ <function Exp.eval at 0x000001ED49067798>
│ └ ╒══════════════════╤═════════════════════════════════════════════════════════════════════════════════════════════════════════...
└ <yolox.core.trainer.Trainer object at 0x000001ED48997088>

File "exps\example\yolox_voc\yolox_coco_tiny.py", line 240, in eval
return evaluator.evaluate(model, is_distributed, half)
│ │ │ │ └ False
│ │ │ └ False
│ │ └ YOLOX(
│ │ (backbone): YOLOPAFPN(
│ │ (backbone): CSPDarknet(
│ │ (stem): Focus(
│ │ (conv): BaseConv(
│ │ (conv): ...
│ └ <function COCOEvaluator.evaluate at 0x000001ED4901F4C8>
└ <yolox.evaluators.coco_evaluator.COCOEvaluator object at 0x000001ED60BD4C48>

File "d:\work\demo\yolox-main\yolox\evaluators\coco_evaluator.py", line 98, in evaluate
progress_bar(self.dataloader)
│ │ └ <torch.utils.data.dataloader.DataLoader object at 0x000001ED60BD4CC8>
│ └ <yolox.evaluators.coco_evaluator.COCOEvaluator object at 0x000001ED60BD4C48>
└ <class 'tqdm.std.tqdm'>

File "D:\Program Files (x86)\anaconda3\envs\yolox\lib\site-packages\tqdm\std.py", line 1185, in iter
for obj in iterable:
│ └ <torch.utils.data.dataloader.DataLoader object at 0x000001ED60BD4CC8>
└ [tensor([[[[-1.7069, -1.7069, -1.6898, ..., -1.6727, -1.5014, -1.0904],
[-1.7069, -1.7069, -1.7069, ..., -1.7069,...

File "D:\Program Files (x86)\anaconda3\envs\yolox\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next
data = self._next_data()
│ └ <function _MultiProcessingDataLoaderIter._next_data at 0x000001ED47049168>
└ <torch.utils.data.dataloader._MultiProcessingDataLoaderIter object at 0x000001ED536354C8>

File "D:\Program Files (x86)\anaconda3\envs\yolox\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data
return self._process_data(data)
│ │ └ <torch._utils.ExceptionWrapper object at 0x000001ED53664848>
│ └ <function _MultiProcessingDataLoaderIter._process_data at 0x000001ED47049288>
└ <torch.utils.data.dataloader._MultiProcessingDataLoaderIter object at 0x000001ED536354C8>

File "D:\Program Files (x86)\anaconda3\envs\yolox\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data
data.reraise()
│ └ <function ExceptionWrapper.reraise at 0x000001ED22F993A8>
└ <torch._utils.ExceptionWrapper object at 0x000001ED53664848>

File "D:\Program Files (x86)\anaconda3\envs\yolox\lib\site-packages\torch_utils.py", line 425, in reraise
raise self.exc_type(msg)
│ │ └ 'Caught MemoryError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File "D:\Program Files (...
│ └ <class 'numpy.core._exceptions._ArrayMemoryError'>
└ <torch._utils.ExceptionWrapper object at 0x000001ED53664848>

TypeError: init() missing 1 required positional argument: 'dtype'

Start with tools/train.py and follow the evaluation path into exps/example/yolox_voc/yolox_coco_tiny.py and yolox/evaluators/coco_evaluator.py. Reproduce evaluation with the reported PyTorch 1.9 environment, determine why the DataLoader worker raises the dtype-related TypeError, and verify that model evaluation completes without the exception.