Add retire warning to legacy code

torchtext/data/batch.py

@@ -1,4 +1,5 @@
 import torch
+import warnings
 
 
 class Batch(object):
@@ -19,6 +20,7 @@ class Batch(object):
 
     def __init__(self, data=None, dataset=None, device=None):
         """Create a Batch from a list of examples."""
+        warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)
         if data is not None:
             self.batch_size = len(data)
             self.dataset = dataset

torchtext/data/example.py

@@ -1,5 +1,6 @@
 import json
 from functools import reduce
+import warnings
 
 
 class Example(object):
@@ -9,6 +10,7 @@ class Example(object):
     """
     @classmethod
     def fromJSON(cls, data, fields):
+        warnings.warn('Example class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.', UserWarning)
         ex = cls()
         obj = json.loads(data)
 
@@ -47,6 +49,7 @@ def reducer(obj, key):
 
     @classmethod
     def fromdict(cls, data, fields):
+        warnings.warn('Example class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.', UserWarning)
         ex = cls()
         for key, vals in fields.items():
             if key not in data:
@@ -62,6 +65,7 @@ def fromdict(cls, data, fields):
 
     @classmethod
     def fromCSV(cls, data, fields, field_to_index=None):
+        warnings.warn('Example class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.', UserWarning)
         if field_to_index is None:
             return cls.fromlist(data, fields)
         else:
@@ -71,6 +75,7 @@ def fromCSV(cls, data, fields, field_to_index=None):
 
     @classmethod
     def fromlist(cls, data, fields):
+        warnings.warn('Example class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.', UserWarning)
         ex = cls()
         for (name, field), val in zip(fields, data):
             if field is not None:
@@ -86,6 +91,7 @@ def fromlist(cls, data, fields):
 
     @classmethod
     def fromtree(cls, data, fields, subtrees=False):
+        warnings.warn('Example class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.', UserWarning)
         try:
             from nltk.tree import Tree
         except ImportError:

torchtext/data/field.py

@@ -3,7 +3,7 @@
 from itertools import chain
 import torch
 from tqdm import tqdm
-
+import warnings
 from .dataset import Dataset
 from .pipeline import Pipeline
 from .utils import get_tokenizer, dtype_to_attr, is_tokenizer_serializable
@@ -33,6 +33,7 @@ class RawField(object):
     """
 
     def __init__(self, preprocessing=None, postprocessing=None, is_target=False):
+        warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)
         self.preprocessing = preprocessing
         self.postprocessing = postprocessing
         self.is_target = is_target
@@ -146,6 +147,7 @@ def __init__(self, sequential=True, use_vocab=True, init_token=None,
                  batch_first=False, pad_token="<pad>", unk_token="<unk>",
                  pad_first=False, truncate_first=False, stop_words=None,
                  is_target=False):
+        warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)
         self.sequential = sequential
         self.use_vocab = use_vocab
         self.init_token = init_token
@@ -365,6 +367,7 @@ def numericalize(self, arr, device=None):
 
 class ReversibleField(Field):
     def __init__(self, **kwargs):
+        warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)
         if kwargs.get('tokenize') is list:
             self.use_revtok = False
         else:
@@ -411,6 +414,7 @@ class SubwordField(ReversibleField):
     vocab_cls = SubwordVocab
 
     def __init__(self, **kwargs):
+        warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)
         kwargs['tokenize'] = 'subword'
         if 'unk_token' not in kwargs:
             kwargs['unk_token'] = '�'
@@ -491,6 +495,7 @@ def __init__(self, nesting_field, use_vocab=True, init_token=None, eos_token=Non
                  postprocessing=None, tokenize=None, tokenizer_language='en',
                  include_lengths=False, pad_token='<pad>',
                  pad_first=False, truncate_first=False):
+        warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)
         if isinstance(nesting_field, NestedField):
             raise ValueError('nesting field must not be another NestedField')
         if nesting_field.include_lengths:

torchtext/data/iterator.py

@@ -2,7 +2,7 @@
 import random
 
 import logging
-
+import warnings
 import torch
 from .utils import RandomShuffler
 from .batch import Batch
@@ -45,6 +45,7 @@ def __init__(self, dataset, batch_size, sort_key=None, device=None,
                  batch_size_fn=None, train=True,
                  repeat=False, shuffle=None, sort=None,
                  sort_within_batch=None):
+        warnings.warn('{} class will be retired in the 0.8.0 release and moved to torchtext.legacy. Please see 0.7.0 release notes for further information.'.format(self.__class__.__name__), UserWarning)
         self.batch_size, self.train, self.dataset = batch_size, train, dataset
         self.batch_size_fn = batch_size_fn
         self.iterations = 0