C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\python.exe C:\Users\wsco\Desktop\HREM-main\train.py
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
GraphLoss(
(base_loss): TripletLoss()
(gnn_loss): TripletLoss()
(gnn): TransformerEncoder(
(layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True)
)
(linear1): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=1024, out_features=1024, bias=True)
(norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
)
)
(adj_model): AdjacencyModel(
(adj_learning): AdjacencyLearning(
(mlp_t2i): Sequential(
(0): Linear(in_features=10, out_features=10, bias=True)
(1): ReLU(inplace=True)
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=10, out_features=1, bias=True)
)
(mlp_i2t): Sequential(
(0): Linear(in_features=10, out_features=10, bias=True)
(1): ReLU(inplace=True)
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=10, out_features=1, bias=True)
)
)
)
)
Traceback (most recent call last):
File "C:\Users\wsco\Desktop\HREM-main\train.py", line 271, in <module>
main()
File "C:\Users\wsco\Desktop\HREM-main\train.py", line 83, in main
train(opt, train_loader, model, epoch)
File "C:\Users\wsco\Desktop\HREM-main\train.py", line 143, in train
for i, train_data in enumerate(train_loader):
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\site-packages\torch\utils\data\dataloader.py", line 368, in __iter__
return self._get_iterator()
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\site-packages\torch\utils\data\dataloader.py", line 314, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\site-packages\torch\utils\data\dataloader.py", line 927, in __init__
w.start()
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
reduction.dump(process_obj, to_child)
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
OSError: [Errno 22] Invalid argument
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\Users\wsco\anaconda3\envs\py3.9_torch11.3.1_cuda11.3\lib\multiprocessing\spawn.py", line 126, in _main
self = reduction.pickle.load(from_parent)
_pickle.UnpicklingError: pickle data was truncated
进程已结束,退出代码1
如何解决这个报错’
本文解析了在操作ListView组件时出现的常见错误原因，并提供了具体的解决办法。例如，当ListView中的item数量不足时尝试对其进行操作会导致程序异常。文章还分享了一个实例，展示了如何确保足够的item数量以避免此类问题。
扫一扫
2536
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
