def __getitem__(self, key):
""" Get a batch with index. """
if not isinstance(key, int):
raise TypeError
if key < 0 or key >= len(self.data):
raise IndexError
batch = self.data[key]
batch_size = len(batch)
batch = list(zip(*batch))
assert len(batch) == 6
# sort sentences by lens for easy RNN operations
lens = [len(x) for x in batch[0]]
batch, orig_idx = sort_all(batch, lens)
# sort words by lens for easy char-RNN operations
batch_words = [w for sent in batch[1] for w in sent]
word_lens = [len(x) for x in batch_words]
batch_words, word_orig_idx = sort_all([batch_words], word_lens)
batch_words = batch_words[0]
word_lens = [len(x) for x in batch_words]
# convert to tensors
words = batch[0]
words = get_long_tensor(words, batch_size)
words_mask = torch.eq(words, PAD_ID)
wordchars = get_long_tensor(batch_words, len(word_lens))
wordchars_mask = torch.eq(wordchars, PAD_ID)
upos = get_long_tensor(batch[2], batch_size)
xpos = get_long_tensor(batch[3], batch_size)
ufeats = get_long_tensor(batch[4], batch_size)
pretrained = get_long_tensor(batch[5], batch_size)
sentlens = [len(x) for x in batch[0]]
return words, words_mask, wordchars, wordchars_mask, upos, xpos, ufeats, pretrained, orig_idx, word_orig_idx, sentlens, word_lens
def __getitem__(self, key):
""" Get a batch with index. """
if not isinstance(key, int):
raise TypeError
if key < 0 or key >= len(self.data):
raise IndexError
batch = self.data[key]
batch_size = len(batch)
batch = list(zip(*batch))
assert len(
batch
) == 3 # words: List[List[int]], chars: List[List[List[int]]], tags: List[List[int]]
# sort sentences by lens for easy RNN operations
sentlens = [len(x) for x in batch[0]]
batch, orig_idx = sort_all(batch, sentlens)
sentlens = [len(x) for x in batch[0]]
# sort chars by lens for easy char-LM operations
chars_forward, chars_backward, charoffsets_forward, charoffsets_backward, charlens = self.process_chars(
batch[1])
chars_sorted, char_orig_idx = sort_all([
chars_forward, chars_backward, charoffsets_forward,
charoffsets_backward
], charlens)
chars_forward, chars_backward, charoffsets_forward, charoffsets_backward = chars_sorted
charlens = [len(sent) for sent in chars_forward]
# sort words by lens for easy char-RNN operations
batch_words = [w for sent in batch[1] for w in sent]
wordlens = [len(x) for x in batch_words]
batch_words, word_orig_idx = sort_all([batch_words], wordlens)
batch_words = batch_words[0]
wordlens = [len(x) for x in batch_words]
# convert to tensors
words = get_long_tensor(batch[0], batch_size)
words_mask = torch.eq(words, PAD_ID)
wordchars = get_long_tensor(batch_words, len(wordlens))
wordchars_mask = torch.eq(wordchars, PAD_ID)
chars_forward = get_long_tensor(chars_forward,
batch_size,
pad_id=self.vocab['char'].unit2id(' '))
chars_backward = get_long_tensor(
chars_backward, batch_size, pad_id=self.vocab['char'].unit2id(' '))
chars = torch.cat([
chars_forward.unsqueeze(0),
chars_backward.unsqueeze(0)
]) # padded forward and backward char idx
charoffsets = [
charoffsets_forward, charoffsets_backward
] # idx for forward and backward lm to get word representation
tags = get_long_tensor(batch[2], batch_size)
return words, words_mask, wordchars, wordchars_mask, chars, tags, orig_idx, word_orig_idx, char_orig_idx, sentlens, wordlens, charlens, charoffsets
def chunk_batches(self, data):
res = []
if not self.eval:
# sort sentences (roughly) by length for better memory utilization
data = sorted(data,
key=lambda x: len(x[0]),
reverse=random.random() > .5)
elif self.sort_during_eval:
(data, ), self.data_orig_idx = sort_all([data],
[len(x[0]) for x in data])
current = []
currentlen = 0
for x in data:
if len(x[0]) + currentlen > self.batch_size and currentlen > 0:
res.append(current)
current = []
currentlen = 0
current.append(x)
currentlen += len(x[0])
if currentlen > 0:
res.append(current)
return res
def __getitem__(self, key):
""" Get a batch with index. """
if not isinstance(key, int):
raise TypeError
if key < 0 or key >= len(self.data):
raise IndexError
batch = self.data[key]
batch_size = len(batch)
batch = list(zip(*batch))
assert len(batch) == 5
# sort all fields by lens for easy RNN operations
lens = [len(x) for x in batch[0]]
batch, orig_idx = sort_all(batch, lens)
# convert to tensors
src = batch[0]
src = get_long_tensor(src, batch_size)
src_mask = torch.eq(src, constant.PAD_ID)
tgt_in = get_long_tensor(batch[1], batch_size)
tgt_out = get_long_tensor(batch[2], batch_size)
pos = torch.LongTensor(batch[3])
edits = torch.LongTensor(batch[4])
assert tgt_in.size(1) == tgt_out.size(
1), "Target input and output sequence sizes do not match."
return src, src_mask, tgt_in, tgt_out, pos, edits, orig_idx
def data_to_batches(data, batch_size, eval_mode, sort_during_eval, max_sentence_size):
"""
Given a list of lists, where the first element of each sublist
represents the sentence, group the sentences into batches.
During training mode (not eval_mode) the sentences are sorted by
length with a bit of random shuffling. During eval mode, the
sentences are sorted by length if sort_during_eval is true.
Refactored from the data structure in case other models could use
it and for ease of testing.
Returns (batches, original_order), where original_order is None
when in train mode or when unsorted and represents the original
location of each sentence in the sort
"""
res = []
if not eval_mode:
# sort sentences (roughly) by length for better memory utilization
data = sorted(data, key = lambda x: len(x[0]), reverse=random.random() > .5)
data_orig_idx = None
elif sort_during_eval:
(data, ), data_orig_idx = sort_all([data], [len(x[0]) for x in data])
else:
data_orig_idx = None
current = []
currentlen = 0
for x in data:
if max_sentence_size is not None and len(x[0]) > max_sentence_size:
if currentlen > 0:
res.append(current)
current = []
currentlen = 0
res.append([x])
else:
if len(x[0]) + currentlen > batch_size and currentlen > 0:
res.append(current)
current = []
currentlen = 0
current.append(x)
currentlen += len(x[0])
if currentlen > 0:
res.append(current)
return res, data_orig_idx
