Python torchtext.data() Examples
The following are 30
code examples of torchtext.data().
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and go to the original project or source file by following the links above each example.
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torchtext
, or try the search function
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.
Example #1
| Source File: IO.py From graph-2-text with MIT License | 6 votes |
|
def get_morph(batch):
#Not very nice but we do not have access to value comming from opt.gpuid command line parameter here.
use_cuda = batch.src[0].is_cuda
# morph_index = batch.morph.data.transpose(0, 1) # [ seqLen x batch_size ] ==> [ batch_size x seqLen ]
# morph_voc = batch.dataset.fields['morph'].vocab.stoi
morph_index = batch.morph.view((batch.src[0].data.size()[0], 6, batch.src[0].data.size()[1]))
morph_index = morph_index.permute(2, 0, 1).contiguous()
# morph_index = torch.LongTensor(morph_index)
morph_mask = torch.lt(torch.eq(morph_index, 1), 1).float()
# morph_index = autograd.Variable(morph_index)
# morph_mask = autograd.Variable(torch.FloatTensor(morph_mask), requires_grad=False)
if use_cuda:
morph_index = morph_index.cuda()
morph_mask = morph_mask.cuda()
return morph_index, morph_mask
Example #2
| Source File: IO.py From QG-Net with MIT License | 6 votes |
|
def make_features(batch, side, data_type='text'):
"""
Args:
batch (Variable): a batch of source or target data.
side (str): for source or for target.
data_type (str): type of the source input. Options are [text|img].
Returns:
A sequence of src/tgt tensors with optional feature tensors
of size (len x batch).
"""
assert side in ['src', 'tgt']
if isinstance(batch.__dict__[side], tuple):
data = batch.__dict__[side][0]
else:
data = batch.__dict__[side]
feat_start = side + "_feat_"
keys = sorted([k for k in batch.__dict__ if feat_start in k])
features = [batch.__dict__[k] for k in keys]
levels = [data] + features
if data_type == 'text':
return torch.cat([level.unsqueeze(2) for level in levels], 2)
else:
return levels[0]
Example #3
| Source File: IO.py From video-caption-openNMT.pytorch with MIT License | 6 votes |
|
def get_fields(data_type, n_src_features, n_tgt_features):
"""
Args:
data_type: type of the source input. Options are [text|img|audio].
n_src_features: the number of source features to
create `torchtext.data.Field` for.
n_tgt_features: the number of target features to
create `torchtext.data.Field` for.
Returns:
A dictionary whose keys are strings and whose values are the
corresponding Field objects.
"""
if data_type == 'text':
return TextDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'img':
return ImageDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'video':
return VideoDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'audio':
return AudioDataset.get_fields(n_src_features, n_tgt_features)
Example #4
| Source File: inputter.py From ITDD with MIT License | 6 votes |
|
def _merge_field_vocabs(knl_field, src_field, tgt_field, vocab_size, min_freq):
# in the long run, shouldn't it be possible to do this by calling
# build_vocab with both the src and tgt data?
specials = [tgt_field.unk_token, tgt_field.pad_token,
tgt_field.init_token, tgt_field.eos_token]
merged = sum(
[knl_field.vocab.freqs, src_field.vocab.freqs, tgt_field.vocab.freqs], Counter()
)
merged_vocab = Vocab(
merged, specials=specials,
max_size=vocab_size, min_freq=min_freq
)
knl_field.vocab = merged_vocab
src_field.vocab = merged_vocab
tgt_field.vocab = merged_vocab
assert len(src_field.vocab) == len(tgt_field.vocab) == len(knl_field.vocab)
Example #5
| Source File: IO.py From QG-Net with MIT License | 6 votes |
|
def coalesce_datasets(datasets):
"""Coalesce all dataset instances. """
final = datasets[0]
for d in datasets[1:]:
# `src_vocabs` is a list of `torchtext.vocab.Vocab`.
# Each sentence transforms into on Vocab.
# Coalesce them into one big list.
final.src_vocabs += d.src_vocabs
# All datasets have same number of features.
aeq(final.n_src_feats, d.n_src_feats)
aeq(final.n_tgt_feats, d.n_tgt_feats)
# `examples` is a list of `torchtext.data.Example`.
# Coalesce them into one big list.
final.examples += d.examples
# All datasets have same fields, no need to update.
return final
Example #6
| Source File: inputter.py From ITDD with MIT License | 6 votes |
|
def create_batches(self):
""" Create batches """
if self.train:
def _pool(data, random_shuffler):
for p in torchtext.data.batch(data, self.batch_size * 100):
p_batch = torchtext.data.batch(
sorted(p, key=self.sort_key),
self.batch_size, self.batch_size_fn)
for b in random_shuffler(list(p_batch)):
yield b
self.batches = _pool(self.data(), self.random_shuffler)
else:
self.batches = []
for b in torchtext.data.batch(self.data(), self.batch_size,
self.batch_size_fn):
self.batches.append(sorted(b, key=self.sort_key))
Example #7
| Source File: preprocessing_funcs.py From NLP_Toolkit with Apache License 2.0 | 6 votes |
|
def load_dataloaders(args):
logger.info("Preparing dataloaders...")
FR = torchtext.data.Field(tokenize=dum_tokenizer, lower=True, init_token="<sos>", eos_token="<eos>",\
batch_first=True)
EN = torchtext.data.Field(tokenize=dum_tokenizer, lower=True, batch_first=True)
train_path = os.path.join("./data/", "df.csv")
if not os.path.isfile(train_path):
tokenize_data(args)
train = torchtext.data.TabularDataset(train_path, format="csv", \
fields=[("EN", EN), ("FR", FR)])
FR.build_vocab(train)
EN.build_vocab(train)
train_iter = BucketIterator(train, batch_size=args.batch_size, repeat=False, sort_key=lambda x: (len(x["EN"]), len(x["FR"])),\
shuffle=True, train=True)
train_length = len(train)
logger.info("Loaded dataloaders.")
return train_iter, FR, EN, train_length
Example #8
| Source File: inputter.py From OpenNMT-py with MIT License | 6 votes |
|
def create_batches(self):
if self.train:
if self.yield_raw_example:
self.batches = batch_iter(
self.data(),
1,
batch_size_fn=None,
batch_size_multiple=1)
else:
self.batches = _pool(
self.data(),
self.batch_size,
self.batch_size_fn,
self.batch_size_multiple,
self.sort_key,
self.random_shuffler,
self.pool_factor)
else:
self.batches = []
for b in batch_iter(
self.data(),
self.batch_size,
batch_size_fn=self.batch_size_fn,
batch_size_multiple=self.batch_size_multiple):
self.batches.append(sorted(b, key=self.sort_key))
Example #9
| Source File: IO.py From DC-NeuralConversation with MIT License | 6 votes |
|
def get_fields(data_type, n_src_features, n_tgt_features):
"""
Args:
data_type: type of the source input. Options are [text|img|audio].
n_src_features: the number of source features to
create `torchtext.data.Field` for.
n_tgt_features: the number of target features to
create `torchtext.data.Field` for.
Returns:
A dictionary whose keys are strings and whose values are the
corresponding Field objects.
"""
if data_type == 'text':
return TextDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'img':
return ImageDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'audio':
return AudioDataset.get_fields(n_src_features, n_tgt_features)
Example #10
| Source File: inputter.py From OpenNMT-py with MIT License | 6 votes |
|
def _old_style_vocab(vocab):
"""Detect old-style vocabs (``List[Tuple[str, torchtext.data.Vocab]]``).
Args:
vocab: some object loaded from a *.vocab.pt file
Returns:
Whether ``vocab`` is a list of pairs where the second object
is a :class:`torchtext.vocab.Vocab` object.
This exists because previously only the vocab objects from the fields
were saved directly, not the fields themselves, and the fields needed to
be reconstructed at training and translation time.
"""
return isinstance(vocab, list) and \
any(isinstance(v[1], Vocab) for v in vocab)
Example #11
| Source File: IO.py From quantized_distillation with MIT License | 6 votes |
|
def collapse_copy_scores(self, scores, batch, tgt_vocab):
"""Given scores from an expanded dictionary
corresponeding to a batch, sums together copies,
with a dictionary word when it is ambigious.
"""
offset = len(tgt_vocab)
for b in range(batch.batch_size):
index = batch.indices.data[b]
src_vocab = self.src_vocabs[index]
for i in range(1, len(src_vocab)):
sw = src_vocab.itos[i]
ti = tgt_vocab.stoi[sw]
if ti != 0:
scores[:, b, ti] += scores[:, b, offset + i]
scores[:, b, offset + i].fill_(1e-20)
return scores
Example #12
| Source File: IO.py From quantized_distillation with MIT License | 6 votes |
|
def make_features(batch, side):
"""
Args:
batch (Variable): a batch of source or target data.
side (str): for source or for target.
Returns:
A sequence of src/tgt tensors with optional feature tensors
of size (len x batch).
"""
assert side in ['src', 'tgt']
if isinstance(batch.__dict__[side], tuple):
data = batch.__dict__[side][0]
else:
data = batch.__dict__[side]
feat_start = side + "_feat_"
features = sorted(batch.__dict__[k]
for k in batch.__dict__ if feat_start in k)
levels = [data] + features
return torch.cat([level.unsqueeze(2) for level in levels], 2)
Example #13
| Source File: inputter.py From encoder-agnostic-adaptation with MIT License | 6 votes |
|
def _old_style_vocab(vocab):
"""Detect old-style vocabs (``List[Tuple[str, torchtext.data.Vocab]]``).
Args:
vocab: some object loaded from a *.vocab.pt file
Returns:
Whether ``vocab`` is a list of pairs where the second object
is a :class:`torchtext.vocab.Vocab` object.
This exists because previously only the vocab objects from the fields
were saved directly, not the fields themselves, and the fields needed to
be reconstructed at training and translation time.
"""
return isinstance(vocab, list) and \
any(isinstance(v[1], Vocab) for v in vocab)
Example #14
| Source File: inputter.py From encoder-agnostic-adaptation with MIT License | 6 votes |
|
def _merge_field_vocabs(src_field, tgt_field, vocab_size, min_freq,
vocab_size_multiple):
# in the long run, shouldn't it be possible to do this by calling
# build_vocab with both the src and tgt data?
specials = [tgt_field.unk_token, tgt_field.pad_token,
tgt_field.init_token, tgt_field.eos_token]
merged = sum(
[src_field.vocab.freqs, tgt_field.vocab.freqs], Counter()
)
merged_vocab = Vocab(
merged, specials=specials,
max_size=vocab_size, min_freq=min_freq
)
if vocab_size_multiple > 1:
_pad_vocab_to_multiple(merged_vocab, vocab_size_multiple)
src_field.vocab = merged_vocab
tgt_field.vocab = merged_vocab
assert len(src_field.vocab) == len(tgt_field.vocab)
Example #15
| Source File: inputter.py From encoder-agnostic-adaptation with MIT License | 6 votes |
|
def create_batches(self):
if self.train:
def _pool(data, random_shuffler):
for p in torchtext.data.batch(data, self.batch_size * 100):
p_batch = batch_iter(
sorted(p, key=self.sort_key),
self.batch_size,
batch_size_fn=self.batch_size_fn,
batch_size_multiple=self.batch_size_multiple)
for b in random_shuffler(list(p_batch)):
yield b
self.batches = _pool(self.data(), self.random_shuffler)
else:
self.batches = []
for b in batch_iter(
self.data(),
self.batch_size,
batch_size_fn=self.batch_size_fn,
batch_size_multiple=self.batch_size_multiple):
self.batches.append(sorted(b, key=self.sort_key))
Example #16
| Source File: IO.py From data2text-entity-py with MIT License | 6 votes |
|
def get_fields(data_type, n_src_features, n_tgt_features):
"""
Args:
data_type: type of the source input. Options are [text|img|audio].
n_src_features: the number of source features to
create `torchtext.data.Field` for.
n_tgt_features: the number of target features to
create `torchtext.data.Field` for.
Returns:
A dictionary whose keys are strings and whose values are the
corresponding Field objects.
"""
if data_type == 'text':
return TextDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'img':
return ImageDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'audio':
return AudioDataset.get_fields(n_src_features, n_tgt_features)
Example #17
| Source File: IO.py From var-attn with MIT License | 6 votes |
|
def get_fields(data_type, n_src_features, n_tgt_features):
"""
Args:
data_type: type of the source input. Options are [text|img|audio].
n_src_features: the number of source features to
create `torchtext.data.Field` for.
n_tgt_features: the number of target features to
create `torchtext.data.Field` for.
Returns:
A dictionary whose keys are strings and whose values are the
corresponding Field objects.
"""
if data_type == 'text':
return TextDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'img':
return ImageDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'audio':
return AudioDataset.get_fields(n_src_features, n_tgt_features)
Example #18
| Source File: main.py From castor with Apache License 2.0 | 6 votes |
|
def predict(test_mode, dataset_iter):
model.eval()
dataset_iter.init_epoch()
qids = []
predictions = []
labels = []
for dev_batch_idx, dev_batch in enumerate(dataset_iter):
qid_array = np.transpose(dev_batch.id.cpu().data.numpy())
true_label_array = np.transpose(dev_batch.label.cpu().data.numpy())
output = model.convModel(dev_batch)
scores = model.linearLayer(output)
score_array = scores.cpu().data.numpy().reshape(-1)
qids.extend(qid_array.tolist())
predictions.extend(score_array.tolist())
labels.extend(true_label_array.tolist())
dev_map, dev_mrr = get_map_mrr(qids, predictions, labels)
logger.info("{} {}".format(dev_map, dev_mrr))
# Run the model on the dev set
Example #19
| Source File: IO.py From graph-2-text with MIT License | 6 votes |
|
def get_fields(data_type, n_src_features, n_tgt_features):
"""
Args:
data_type: type of the source input. Options are [text|img|audio].
n_src_features: the number of source features to
create `torchtext.data.Field` for.
n_tgt_features: the number of target features to
create `torchtext.data.Field` for.
Returns:
A dictionary whose keys are strings and whose values are the
corresponding Field objects.
"""
if data_type == 'text':
return TextDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'img':
return ImageDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'audio':
return AudioDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'gcn':
return GCNDataset.get_fields(n_src_features, n_tgt_features)
Example #20
| Source File: inputter.py From OpenNMT-py with MIT License | 6 votes |
|
def _merge_field_vocabs(src_field, tgt_field, vocab_size, min_freq,
vocab_size_multiple):
# in the long run, shouldn't it be possible to do this by calling
# build_vocab with both the src and tgt data?
specials = [tgt_field.unk_token, tgt_field.pad_token,
tgt_field.init_token, tgt_field.eos_token]
merged = sum(
[src_field.vocab.freqs, tgt_field.vocab.freqs], Counter()
)
merged_vocab = Vocab(
merged, specials=specials,
max_size=vocab_size, min_freq=min_freq
)
if vocab_size_multiple > 1:
_pad_vocab_to_multiple(merged_vocab, vocab_size_multiple)
src_field.vocab = merged_vocab
tgt_field.vocab = merged_vocab
assert len(src_field.vocab) == len(tgt_field.vocab)
Example #21
| Source File: IO.py From coarse2fine with MIT License | 5 votes |
|
def get_fields():
fields = {}
fields["src"] = torchtext.data.Field(
pad_token=PAD_WORD, include_lengths=True)
fields["ent"] = torchtext.data.Field(
pad_token=PAD_WORD, include_lengths=False)
fields["agg"] = torchtext.data.Field(
sequential=False, use_vocab=False, batch_first=True)
fields["sel"] = torchtext.data.Field(
sequential=False, use_vocab=False, batch_first=True)
fields["tbl"] = torchtext.data.Field(
pad_token=PAD_WORD, include_lengths=True)
fields["tbl_split"] = torchtext.data.Field(
use_vocab=False, pad_token=0)
fields["tbl_mask"] = torchtext.data.Field(
use_vocab=False, tensor_type=torch.ByteTensor, batch_first=True, pad_token=1)
fields["lay"] = torchtext.data.Field(
sequential=False, batch_first=True)
fields["cond_op"] = torchtext.data.Field(
include_lengths=True, pad_token=PAD_WORD)
fields["cond_col"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=0)
fields["cond_span_l"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=0)
fields["cond_span_r"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=0)
fields["cond_col_loss"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=-1)
fields["cond_span_l_loss"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=-1)
fields["cond_span_r_loss"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=-1)
fields["indices"] = torchtext.data.Field(
use_vocab=False, sequential=False)
return fields
Example #22
| Source File: inputter.py From OpenNMT-kpg-release with MIT License | 5 votes |
|
def make_src(data, vocab):
src_size = max([t.size(0) for t in data])
src_vocab_size = max([t.max() for t in data]) + 1
alignment = torch.zeros(src_size, len(data), src_vocab_size)
for i, sent in enumerate(data):
for j, t in enumerate(sent):
alignment[j, i, t] = 1
return alignment
Example #23
| Source File: IO.py From nl2sql with MIT License | 5 votes |
|
def get_fields():
fields = {}
fields["src"] = torchtext.data.Field(
pad_token=PAD_WORD, include_lengths=True)
fields["ent"] = torchtext.data.Field(
pad_token=PAD_WORD, include_lengths=False)
fields["agg"] = torchtext.data.Field(
sequential=False, use_vocab=False, batch_first=True)
fields["sel"] = torchtext.data.Field(
sequential=False, use_vocab=False, batch_first=True)
fields["tbl"] = torchtext.data.Field(
pad_token=PAD_WORD, include_lengths=True)
fields["tbl_split"] = torchtext.data.Field(
use_vocab=False, pad_token=0)
fields["tbl_mask"] = torchtext.data.Field(
use_vocab=False, tensor_type=torch.ByteTensor, batch_first=True, pad_token=1)
fields["lay"] = torchtext.data.Field(
sequential=False, batch_first=True)
fields["cond_op"] = torchtext.data.Field(
include_lengths=True, pad_token=PAD_WORD)
fields["cond_col"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=0)
fields["cond_span_l"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=0)
fields["cond_span_r"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=0)
fields["cond_col_loss"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=-1)
fields["cond_span_l_loss"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=-1)
fields["cond_span_r_loss"] = torchtext.data.Field(
use_vocab=False, include_lengths=False, pad_token=-1)
fields["indices"] = torchtext.data.Field(
use_vocab=False, sequential=False)
return fields
Example #24
| Source File: IO.py From nl2sql with MIT License | 5 votes |
|
def create_batches(self):
if self.train:
self.batches = torchtext.data.pool(
self.data(), self.batch_size,
self.sort_key, self.batch_size_fn,
random_shuffler=self.random_shuffler)
else:
self.batches = []
for b in torchtext.data.batch(self.data(), self.batch_size,
self.batch_size_fn):
self.batches.append(sorted(b, key=self.sort_key))
Example #25
| Source File: IO.py From coarse2fine with MIT License | 5 votes |
|
def create_batches(self):
if self.train:
self.batches = torchtext.data.pool(
self.data(), self.batch_size,
self.sort_key, self.batch_size_fn,
random_shuffler=self.random_shuffler)
else:
self.batches = []
for b in torchtext.data.batch(self.data(), self.batch_size,
self.batch_size_fn):
self.batches.append(sorted(b, key=self.sort_key))
Example #26
| Source File: IO.py From coarse2fine with MIT License | 5 votes |
|
def create_batches(self):
if self.train:
self.batches = torchtext.data.pool(
self.data(), self.batch_size,
self.sort_key, self.batch_size_fn,
random_shuffler=self.random_shuffler)
else:
self.batches = []
for b in torchtext.data.batch(self.data(), self.batch_size,
self.batch_size_fn):
self.batches.append(sorted(b, key=self.sort_key))
Example #27
| Source File: IO.py From var-attn with MIT License | 5 votes |
|
def create_batches(self):
if self.train:
def pool(data, random_shuffler):
for p in torchtext.data.batch(data, self.batch_size * 100):
p_batch = torchtext.data.batch(
sorted(p, key=self.sort_key),
self.batch_size, self.batch_size_fn)
for b in random_shuffler(list(p_batch)):
yield b
self.batches = pool(self.data(), self.random_shuffler)
else:
self.batches = []
for b in torchtext.data.batch(self.data(), self.batch_size,
self.batch_size_fn):
self.batches.append(sorted(b, key=self.sort_key))
Example #28
| Source File: IO.py From coarse2fine with MIT License | 5 votes |
|
def get_fields():
fields = {}
fields["src"] = torchtext.data.Field(
pad_token=PAD_WORD, include_lengths=True)
fields["lay"] = torchtext.data.Field(
init_token=BOS_WORD, include_lengths=True, eos_token=EOS_WORD, pad_token=PAD_WORD)
fields["lay_e"] = torchtext.data.Field(
include_lengths=False, pad_token=PAD_WORD)
fields["lay_index"] = torchtext.data.Field(
use_vocab=False, pad_token=0)
fields["lay_parent_index"] = torchtext.data.Field(
use_vocab=False, pad_token=0)
fields["copy_to_tgt"] = torchtext.data.Field(pad_token=UNK_WORD)
fields["copy_to_ext"] = torchtext.data.Field(pad_token=UNK_WORD)
fields["tgt_mask"] = torchtext.data.Field(
use_vocab=False, tensor_type=torch.FloatTensor, pad_token=1)
fields["tgt"] = torchtext.data.Field(
init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD)
fields["tgt_copy_ext"] = torchtext.data.Field(
init_token=UNK_WORD, eos_token=UNK_WORD, pad_token=UNK_WORD)
fields["tgt_parent_index"] = torchtext.data.Field(
use_vocab=False, pad_token=0)
fields["tgt_loss"] = torchtext.data.Field(
init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD)
fields["indices"] = torchtext.data.Field(
use_vocab=False, sequential=False)
return fields
Example #29
| Source File: IO.py From coarse2fine with MIT License | 5 votes |
|
def create_batches(self):
if self.train:
self.batches = torchtext.data.pool(
self.data(), self.batch_size,
self.sort_key, self.batch_size_fn,
random_shuffler=self.random_shuffler)
else:
self.batches = []
for b in torchtext.data.batch(self.data(), self.batch_size,
self.batch_size_fn):
self.batches.append(sorted(b, key=self.sort_key))
Example #30
| Source File: IO.py From var-attn with MIT License | 5 votes |
|
def make_features(batch, side, data_type='text'):
"""
Args:
batch (Variable): a batch of source or target data.
side (str): for source or for target.
data_type (str): type of the source input.
Options are [text|img|audio].
Returns:
A sequence of src/tgt tensors with optional feature tensors
of size (len x batch).
"""
assert side in ['src', 'tgt']
if isinstance(batch.__dict__[side], tuple):
data = batch.__dict__[side][0]
else:
data = batch.__dict__[side]
feat_start = side + "_feat_"
keys = sorted([k for k in batch.__dict__ if feat_start in k])
features = [batch.__dict__[k] for k in keys]
levels = [data] + features
if data_type == 'text':
return torch.cat([level.unsqueeze(2) for level in levels], 2)
else:
return levels[0]
