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dataloader.py
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dataloader.py
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import cPickle as pickle
import os, random,pdb, torch
from itertools import groupby
from tqdm import tqdm
import pdb
import numpy as np
class dataloader():
def __init__(self, batchSize, epochs, vocab, train_path, test_path, max_article_size=400, max_abstract_size=140, test_mode=False):
self.maxEpochs = epochs
self.epoch = 1
self.batchSize = batchSize
self.iterInd = 0
self.globalInd = 1
#self.vocab = vocab #list of all vocabulary words
self.word2id, self.id2word = self.getVocabMap(vocab)
self.vocabSize = len(vocab)
self.max_article_size = max_article_size
self.max_abstract_size = max_abstract_size
self.test_mode = test_mode
assert os.path.isfile(train_path) and os.path.isfile(test_path), 'Invalid paths to train/test datafiles'
self.train_path = train_path
self.test_path = test_path
if not self.test_mode:
print 'Loading training data from disk...will take a minute...'
with open(self.train_path,'rb') as f:
self.train_data = pickle.load(f)
self.trainSamples = len(self.train_data)
else:
print 'Initializing Dataloader in test mode with only eval-dataset...'
#Load eval set
print 'Loading eval data from disk...'
with open(self.test_path,'rb') as f:
self.test_data = pickle.load(f)
self.testSamples = len(self.test_data)
# self.loadEvalBatch()
if not self.test_mode:
self.stopFlag = False
self.pbar = tqdm(total=self.trainSamples * self.maxEpochs)
self.pbar.set_description('Epoch : %d/%d' % (self.epoch, self.maxEpochs))
def getVocabMap(self, vocab):
word2id, id2word = {}, {}
for i, word in enumerate(vocab):
word2id[word] = i+1 # reserve 0 for pad
id2word[i+1] = word
id2word[0] = ''
# word2id['<go>'] = len(word2id) + 1
# id2word[len(word2id)] = '<go>'
# word2id['<end>'] = len(word2id) + 1
# id2word[len(word2id)] = '<end>'
return word2id, id2word
def makeEncoderInput(self, article):
# tokenize article
# list of OOV words in article
self.encUnkCount = 1
_intArticle, extIntArticle = [], []
article_oov = []
art_len = min(self.max_article_size, len(article))
for word_ind, word in enumerate(article[:art_len]):
try:
_intArticle.append(self.word2id[word.lower().strip()])
extIntArticle.append(self.word2id[word.lower().strip()])
except KeyError:
_intArticle.append(self.word2id['<unk>'])
extIntArticle.append(self.vocabSize + self.encUnkCount)
article_oov.append(word)
#article_oov_ind.append(word_ind)
self.encUnkCount += 1
return _intArticle, extIntArticle, article_oov, art_len
def makeDecoderInput(self, abstract, article_oov):
_intAbstract, extIntAbstract = [], []
abs_len = min(self.max_abstract_size, len(abstract))
# tokenize abstract
self.decUnkCount = 0
for word in abstract[:abs_len]:
try:
_intAbstract.append(self.word2id[word.lower().strip()])
extIntAbstract.append(self.word2id[word.lower().strip()])
except KeyError:
_intAbstract.append(self.word2id['<unk>'])
#check if OOV word present in article
if word in article_oov:
extIntAbstract.append(self.vocabSize + article_oov.index(word) + 1)
else:
extIntAbstract.append(self.word2id['<unk>'])
self.decUnkCount += 1
return _intAbstract, extIntAbstract, abs_len
def preproc(self, samples):
# batchArticles --> tensor batch of articles with ids
# batchRevArticles --> tensor batch of reversed articles with ids
# batchExtArticles --> tensor batch of articles with ids and <unk> replaced by temp OOV ids
# batchAbstracts --> tensor batch of abstract (input for decoder) with ids
# batchTargets --> tensor batch of target abstracts
# max_article_oov --> max number of OOV tokens in article batch
# limit max article size to 400 tokens
extIntArticles, intRevArticles, intAbstract, intTargets, extIntAbstracts = [], [], [], [], []
art_lens, abs_lens= [], []
maxLen = 0
max_article_oov = 0
for sampl in samples:
article = sampl['article'].split(' ')
abstract = sampl['abstract'].split(' ')
# get article and abstract int-tokenized
_intArticle, _extIntArticle, article_oov, art_len = self.makeEncoderInput(article)
if max_article_oov < len(article_oov):
max_article_oov = len(article_oov)
_intRevArticle = list(reversed(_intArticle))
_intAbstract, _extIntAbstract, abs_len = self.makeDecoderInput(abstract, article_oov)
# append stopping/start tokens and increment length by 1
intAbstract.append([self.word2id['<go>']] + _intAbstract)
# append end token
intTargets.append(_extIntAbstract + [self.word2id['<end>']])
abs_len += 1
extIntArticles.append(_extIntArticle)
intRevArticles.append(_intRevArticle)
art_lens.append(art_len)
abs_lens.append(abs_len)
padExtArticles = [torch.LongTensor(item + [0] * (max(art_lens) - len(item))) for item in extIntArticles]
padRevArticles = [torch.LongTensor(item + [0] * (max(art_lens) - len(item))) for item in intRevArticles]
padAbstracts = [torch.LongTensor(item + [0] * (max(abs_lens) - len(item))) for item in intAbstract]
padTargets = [torch.LongTensor(item + [0] * (max(abs_lens) - len(item))) for item in intTargets]
batchExtArticles = torch.stack(padExtArticles, 0)
# replace temp ids with unk token id for enc input
batchArticles = batchExtArticles.clone().masked_fill_((batchExtArticles > self.vocabSize), self.word2id['<unk>'])
batchRevArticles = torch.stack(padRevArticles, 0)
batchAbstracts = torch.stack(padAbstracts, 0)
batchTargets = torch.stack(padTargets, 0)
art_lens = torch.LongTensor(art_lens)
abs_lens = torch.LongTensor(abs_lens)
return batchArticles, batchExtArticles, batchRevArticles, batchAbstracts, batchTargets, art_lens, abs_lens, max_article_oov, article_oov
def getBatch(self, num_samples=None):
if num_samples is None:
num_samples = self.batchSize
if self.epoch > self.maxEpochs:
print 'Maximum Epoch Limit reached'
self.stopFlag = True
return None
if self.iterInd + num_samples > self.trainSamples:
data = [self.train_data[i] for i in xrange(self.iterInd, self.trainSamples)]
else:
data = [self.train_data[i] for i in xrange(self.iterInd, self.iterInd + num_samples)]
batchData = self.preproc(data)
self.globalInd += 1
self.iterInd += num_samples
if self.iterInd > self.trainSamples:
self.iterInd = 0
self.epoch += 1
self.globalInd = 1
self.pbar.set_description('Epoch : %d/%d' % (self.epoch, self.maxEpochs))
return batchData
def getEvalBatch(self, num_samples=1):
# select first sample for eval
data = [self.test_data[i] for i in range(num_samples)]
batchData = self.evalPreproc(data[0])
return batchData
def evalPreproc(self, sample):
# sample length = 1
# limit max article size to 400 tokens
extIntArticles, intRevArticles = [], []
max_article_oov = 0
article = sample['article'].split(' ')
# get article int-tokenized
_intArticle, _extIntArticle, article_oov, _ = self.makeEncoderInput(article)
if max_article_oov < len(article_oov):
max_article_oov = len(article_oov)
_intRevArticle = list(reversed(_intArticle))
# _intAbstract, _extIntAbstract, abs_len = self.makeDecoderInput(abstract, article_oov)
extIntArticles.append(_extIntArticle)
intRevArticles.append(_intRevArticle)
padExtArticles = [torch.LongTensor(item) for item in extIntArticles]
padRevArticles = [torch.LongTensor(item) for item in intRevArticles]
batchExtArticles = torch.stack(padExtArticles, 0)
# replace temp ids with unk token id for enc input
batchArticles = batchExtArticles.clone().masked_fill_((batchExtArticles > self.vocabSize), self.word2id['<unk>'])
batchRevArticles = torch.stack(padRevArticles, 0)
return batchArticles, batchRevArticles, batchExtArticles, max_article_oov, article_oov, sample['article'], sample['abstract']
def getEvalSample(self, index=None):
if index is None:
rand_index = np.random.randint(0, self.testSamples-1)
data = self.test_data[rand_index]
return self.evalPreproc(data)
elif isinstance(index, int) and (index>=0 and index < self.testSamples):
data = self.test_data[index]
return self.evalPreproc(data)
def getInputTextSample(self, tokenized_text):
extIntArticles, intRevArticles = [], []
max_article_oov = 0
# get article int-tokenized
_intArticle, _extIntArticle, article_oov, _ = self.makeEncoderInput(tokenized_text)
if max_article_oov < len(article_oov):
max_article_oov = len(article_oov)
_intRevArticle = list(reversed(_intArticle))
extIntArticles.append(_extIntArticle)
intRevArticles.append(_intRevArticle)
padExtArticles = [torch.LongTensor(item) for item in extIntArticles]
padRevArticles = [torch.LongTensor(item) for item in intRevArticles]
batchExtArticles = torch.stack(padExtArticles, 0)
# replace temp ids with unk token id for enc input
batchArticles = batchExtArticles.clone().masked_fill_((batchExtArticles > self.vocabSize), self.word2id['<unk>'])
batchRevArticles = torch.stack(padRevArticles, 0)
return batchArticles, batchRevArticles, batchExtArticles, max_article_oov, article_oov