Torchtext 0.7 显示 Field 正在被弃用。有什么选择？

Question

看起来像以前声明字段、示例和使用 BucketIterator 的范例已被弃用，并将在 0.8 中移至旧版。但是，我似乎无法找到不使用 Field 的自定义数据集的新范例示例（例如，不是 torch.datasets 中包含的那些）。谁能给我指出一个最新的例子？

弃用参考：

https://github.com/pytorch/text/releases

Answer 1

我自己花了一点时间才找到解决方案。对于预建数据集，新范例是这样的：

from torchtext.experimental.datasets import AG_NEWS
train, test = AG_NEWS(ngrams=3)

或自定义构建的数据集：

from torch.utils.data import DataLoader
def collate_fn(batch):
    texts, labels = [], []
    for label, txt in batch:
        texts.append(txt)
        labels.append(label)
    return texts, labels
dataloader = DataLoader(train, batch_size=8, collate_fn=collate_fn)
for idx, (texts, labels) in enumerate(dataloader):
    print(idx, texts, labels)

我已经从 Source

中复制了示例

Answer 2

正在浏览 torchtext 的 GitHub repo I stumbled over the README in the legacy directory, which is not documented in the official docs. The README links a GitHub issue that explains the rationale behind the change as well as a migration guide。

如果您只想保留现有代码运行 torchtext 0.9.0，其中弃用的类已移至 legacy 模块，您有调整您的导入：

# from torchtext.data import Field, TabularDataset
from torchtext.legacy.data import Field, TabularDataset

或者，您可以按照 README 的建议将整个 torchtext.legacy 模块导入为 torchtext：

import torchtext.legacy as torchtext

Answer 3

有一个关于此的 post。它没有使用已弃用的 Field 和 BucketIterator 类，而是使用 TextClassificationDataset 以及整理器和其他预处理。它读取一个 txt 文件并构建一个数据集，然后是一个模型。在 post 里面，有一个 link 到一个完整的工作笔记本。 post 位于：https://mmg10.github.io/pytorch/2021/02/16/text_torch.html。但是你需要 PyTorch 的 'dev'（或每晚构建）才能工作。

来自上面的link：

分词和构建词汇表后，您可以按如下方式构建数据集

def data_to_dataset(data, tokenizer, vocab):
    
    data = [(text, label) for (text, label) in data]
    
    text_transform = sequential_transforms(tokenizer.tokenize,
                                                  vocab_func(vocab),
                                                  totensor(dtype=torch.long)
                                          )
    label_transform = sequential_transforms(lambda x: 1 if x =='1' else (0 if x =='0' else x),
                                                  totensor(dtype=torch.long)
                                          )
    
    
    transforms = (text_transform, label_transform)
    
    dataset = TextClassificationDataset(data, vocab, transforms)
    
    return dataset

整理如下：

    def __init__(self, pad_idx):
        
        self.pad_idx = pad_idx
        
    def collate(self, batch):
        text, labels = zip(*batch)
        labels = torch.LongTensor(labels)
        text = nn.utils.rnn.pad_sequence(text, padding_value=self.pad_idx, batch_first=True)
        return text, labels

然后，您可以使用 collate_fn 参数构建具有典型 torch.utils.data.DataLoader 的数据加载器。

Answer 4

嗯，看起来管道可能是这样的：

    import torchtext as TT
    import torch
    from collections import Counter
    from torchtext.vocab import Vocab

    # read the data

    with open('text_data.txt','r') as f:
        data = f.readlines()
    with open('labels.txt', 'r') as f:
        labels = f.readlines()

    
    tokenizer = TT.data.utils.get_tokenizer('spacy', 'en') # can remove 'spacy' and use a simple built-in tokenizer
    train_iter = zip(labels, data)
    counter = Counter()
    
    for (label, line) in train_iter:
        counter.update(tokenizer(line))
        
    vocab = TT.vocab.Vocab(counter, min_freq=1)

    text_pipeline = lambda x: [vocab[token] for token in tokenizer(x)]
    # this is data-specific - adapt for your data
    label_pipeline = lambda x: 1 if x == 'positive\n' else 0
    
    class TextData(torch.utils.data.Dataset):
        '''
        very basic dataset for processing text data
        '''
        def __init__(self, labels, text):
            super(TextData, self).__init__()
            self.labels = labels
            self.text = text
            
        def __getitem__(self, index):
            return self.labels[index], self.text[index]
        
        def __len__(self):
            return len(self.labels)
    
    
    def tokenize_batch(batch, max_len=200):
        '''
        tokenizer to use in DataLoader
        takes a text batch of text dataset and produces a tensor batch, converting text and labels though tokenizer, labeler
        tokenizer is a global function text_pipeline
        labeler is a global function label_pipeline
        max_len is a fixed len size, if text is less than max_len it is padded with ones (pad number)
        if text is larger that max_len it is truncated but from the end of the string
        '''
        labels_list, text_list = [], []
        for _label, _text in batch:
            labels_list.append(label_pipeline(_label))
            text_holder = torch.ones(max_len, dtype=torch.int32) # fixed size tensor of max_len
            processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int32)
            pos = min(200, len(processed_text))
            text_holder[-pos:] = processed_text[-pos:]
            text_list.append(text_holder.unsqueeze(dim=0))
        return torch.FloatTensor(labels_list), torch.cat(text_list, dim=0)
    
    train_dataset = TextData(labels, data)
    
    train_loader = DataLoader(train_dataset, batch_size=2, shuffle=False, collate_fn=tokenize_batch)
    
    lbl, txt = iter(train_loader).next()

Torchtext 0.7 显示 Field 正在被弃用。有什么选择？

Torchtext 0.7 shows Field is being deprecated. What is the alternative?

pytorch

torchtext