123 lines
3.3 KiB
Python
123 lines
3.3 KiB
Python
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import os
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import torch
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from collections import Counter
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class Dictionary(object):
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def __init__(self):
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self.word2idx = {}
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self.idx2word = []
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self.counter = Counter()
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self.total = 0
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def add_word(self, word):
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if word not in self.word2idx:
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self.idx2word.append(word)
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self.word2idx[word] = len(self.idx2word) - 1
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token_id = self.word2idx[word]
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self.counter[token_id] += 1
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self.total += 1
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return self.word2idx[word]
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def __len__(self):
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return len(self.idx2word)
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class Corpus(object):
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def __init__(self, path):
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self.dictionary = Dictionary()
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self.train = self.tokenize(os.path.join(path, 'train.txt'))
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self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
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self.test = self.tokenize(os.path.join(path, 'test.txt'))
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def tokenize(self, path):
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"""Tokenizes a text file."""
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assert os.path.exists(path)
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# Add words to the dictionary
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with open(path, 'r', encoding='utf-8') as f:
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tokens = 0
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for line in f:
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words = line.split() + ['<eos>']
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tokens += len(words)
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for word in words:
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self.dictionary.add_word(word)
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# Tokenize file content
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with open(path, 'r', encoding='utf-8') as f:
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ids = torch.LongTensor(tokens)
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token = 0
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for line in f:
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words = line.split() + ['<eos>']
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for word in words:
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ids[token] = self.dictionary.word2idx[word]
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token += 1
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return ids
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class SentCorpus(object):
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def __init__(self, path):
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self.dictionary = Dictionary()
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self.train = self.tokenize(os.path.join(path, 'train.txt'))
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self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
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self.test = self.tokenize(os.path.join(path, 'test.txt'))
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def tokenize(self, path):
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"""Tokenizes a text file."""
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assert os.path.exists(path)
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# Add words to the dictionary
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with open(path, 'r', encoding='utf-8') as f:
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tokens = 0
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for line in f:
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words = line.split() + ['<eos>']
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tokens += len(words)
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for word in words:
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self.dictionary.add_word(word)
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# Tokenize file content
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sents = []
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with open(path, 'r', encoding='utf-8') as f:
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for line in f:
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if not line:
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continue
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words = line.split() + ['<eos>']
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sent = torch.LongTensor(len(words))
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for i, word in enumerate(words):
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sent[i] = self.dictionary.word2idx[word]
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sents.append(sent)
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return sents
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class BatchSentLoader(object):
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def __init__(self, sents, batch_size, pad_id=0, cuda=False, volatile=False):
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self.sents = sents
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self.batch_size = batch_size
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self.sort_sents = sorted(sents, key=lambda x: x.size(0))
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self.cuda = cuda
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self.volatile = volatile
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self.pad_id = pad_id
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def __next__(self):
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if self.idx >= len(self.sort_sents):
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raise StopIteration
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batch_size = min(self.batch_size, len(self.sort_sents)-self.idx)
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batch = self.sort_sents[self.idx:self.idx+batch_size]
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max_len = max([s.size(0) for s in batch])
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tensor = torch.LongTensor(max_len, batch_size).fill_(self.pad_id)
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for i in range(len(batch)):
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s = batch[i]
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tensor[:s.size(0),i].copy_(s)
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if self.cuda:
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tensor = tensor.cuda()
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self.idx += batch_size
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return tensor
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next = __next__
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def __iter__(self):
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self.idx = 0
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return self
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