如何解决PyTorch:“类型错误:在 DataLoader 工作进程 0 中捕获到类型错误”
我正在尝试实施 RoBERTa 模型进行情感分析。首先,我声明了 GPReviewDataset 来创建 PyTorch 数据集。
MAX_LEN = 160
class GPReviewDataset(Dataset):
def __init__(self,reviews,targets,tokenizer,max_len):
self.reviews = reviews
self.targets = targets
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.reviews)
def __getitem__(self,item):
review = str(self.reviews[item])
target = self.targets[item]
encoding = self.tokenizer.encode_plus(
review,add_special_tokens=True,max_length=self.max_len,return_token_type_ids=False,pad_to_max_length=True,return_attention_mask=True,return_tensors='pt',)
return {
'review_text': review,'input_ids': encoding['input_ids'].flatten(),'attention_mask': encoding['attention_mask'].flatten(),'targets': torch.tensor(target,dtype=torch.long)
}
接下来,我实现 create_data_loader
来创建几个数据加载器。这是一个帮助函数来做到这一点:
def create_data_loader(df,max_len,batch_size):
ds = GPReviewDataset(
reviews=df.text.to_numpy(),targets=df.sentiment.to_numpy(),tokenizer=tokenizer,max_len=max_len
)
return DataLoader(
ds,batch_size=batch_size,num_workers=4
)
BATCH_SIZE = 16
train_data_loader = create_data_loader(df_train,MAX_LEN,BATCH_SIZE)
val_data_loader = create_data_loader(df_val,BATCH_SIZE)
test_data_loader = create_data_loader(df_test,BATCH_SIZE)
dt = next(iter(train_data_loader))
TypeError Traceback (most recent call last)
<ipython-input-35-a673c0794f60> in <module>()
----> 1 dt = next(iter(train_data_loader))
3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/DataLoader.py in __next__(self)
433 if self._sampler_iter is None:
434 self._reset()
--> 435 data = self._next_data()
436 self._num_yielded += 1
437 if self._dataset_kind == _DatasetKind.Iterable and \
/usr/local/lib/python3.6/dist-packages/torch/utils/data/DataLoader.py in _next_data(self)
1083 else:
1084 del self._task_info[idx]
-> 1085 return self._process_data(data)
1086
1087 def _try_put_index(self):
/usr/local/lib/python3.6/dist-packages/torch/utils/data/DataLoader.py in _process_data(self,data)
1109 self._try_put_index()
1110 if isinstance(data,ExceptionWrapper):
-> 1111 data.reraise()
1112 return data
1113
/usr/local/lib/python3.6/dist-packages/torch/_utils.py in reraise(self)
426 # have message field
427 raise self.exc_type(message=msg)
--> 428 raise self.exc_type(msg)
429
430
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py",line 198,in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py",line 44,in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py",in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "<ipython-input-18-1e537ce5a428>",line 25,in __getitem__
'targets': torch.tensor(target,dtype=torch.long)
TypeError: new(): invalid data type 'str'
我不明白为什么会这样,谁能帮我解释一下。
解决方法
您需要将类定义为整数。我假设您正在处理分类问题。但看起来您已将类定义为字符串。您需要将类从字符串转换为整数。比如df.sentiment对应正数,必须用0表示,df.sentiment对应负数,需要在新列中用1表示。
def to_int_sentiment(label):
if label == "positive":
return 0
elif label == "negative":
return 1
df['int_sentiment'] = df.sentiment.apply(to_int_sentiment)
那么您应该使用列 df.int_sentiment 而不是 df.sentiment。所以你必须改变 create_data_loader 函数如下。
def create_data_loader(df,tokenizer,max_len,batch_size):
ds = GPReviewDataset(
reviews=df.text.to_numpy(),targets=df.int_sentiment.to_numpy(),tokenizer=tokenizer,max_len=max_len
)
return DataLoader(
ds,batch_size=batch_size,num_workers=4
)
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