BucketIterator throws 'Field' object has no attribute 'vocab'
It's not a new question, references I found without any solution working for me first and second.
I'm a newbie to PyTorch, facing AttributeError: 'Field' object has no attribute 'vocab' while creating batches of the text data in PyTorch using torchtext.

Following up the book Deep Learning with PyTorch I wrote the same example as explained in the book.

Here's the snippet:

from torchtext import data
from torchtext import datasets
from torchtext.vocab import GloVe

TEXT = data.Field(lower=True, batch_first=True, fix_length=20)
LABEL = data.Field(sequential=False)
train, test = datasets.IMDB.splits(TEXT, LABEL)

print("train.fields:", train.fields)
print()
print(vars(train[0]))  # prints the object



TEXT.build_vocab(train, vectors=GloVe(name="6B", dim=300),
                 max_size=10000, min_freq=10)

# VOCABULARY
# print(TEXT.vocab.freqs)  # freq
# print(TEXT.vocab.vectors)  # vectors
# print(TEXT.vocab.stoi)  # Index

train_iter, test_iter = data.BucketIterator.splits(
    (train, test), batch_size=128, device=-1, shuffle=True, repeat=False)  # -1 for cpu, None for gpu

# Not working (FROM BOOK)
# batch = next(iter(train_iter))

# print(batch.text)
# print()
# print(batch.label)

# This also not working (FROM Second solution)
for i in train_iter:
    print (i.text)
    print (i.label)


Here's the stacktrace:

AttributeError                            Traceback (most recent call last)
&lt;ipython-input-33-433ec3a2ca3c&gt; in &lt;module&gt;()
      7 
      8 
----&gt; 9 for i in train_iter:
     10     print (i.text)
     11     print (i.label)

/anaconda3/lib/python3.6/site-packages/torchtext/data/iterator.py in __iter__(self)
    155                     else:
    156                         minibatch.sort(key=self.sort_key, reverse=True)
--&gt; 157                 yield Batch(minibatch, self.dataset, self.device)
    158             if not self.repeat:
    159                 return

/anaconda3/lib/python3.6/site-packages/torchtext/data/batch.py in __init__(self, data, dataset, device)
     32                 if field is not None:
     33                     batch = [getattr(x, name) for x in data]
---&gt; 34                     setattr(self, name, field.process(batch, device=device))
     35 
     36     @classmethod

/anaconda3/lib/python3.6/site-packages/torchtext/data/field.py in process(self, batch, device)
    199         """
    200         padded = self.pad(batch)
--&gt; 201         tensor = self.numericalize(padded, device=device)
    202         return tensor
    203 

/anaconda3/lib/python3.6/site-packages/torchtext/data/field.py in numericalize(self, arr, device)
    300                 arr = [[self.vocab.stoi[x] for x in ex] for ex in arr]
    301             else:
--&gt; 302                 arr = [self.vocab.stoi[x] for x in arr]
    303 
    304             if self.postprocessing is not None:

/anaconda3/lib/python3.6/site-packages/torchtext/data/field.py in &lt;listcomp&gt;(.0)
    300                 arr = [[self.vocab.stoi[x] for x in ex] for ex in arr]
    301             else:
--&gt; 302                 arr = [self.vocab.stoi[x] for x in arr]
    303 
    304             if self.postprocessing is not None:

AttributeError: 'Field' object has no attribute 'vocab'



  If not using BucketIterator, what else I can use to get a similar
  output?


You haven't built the vocab for the LABEL field.
After TEXT.build_vocab(train, ...), run LABEL.build_vocab(train), and the rest will run.
