def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField(batch_first=True)
tgt = TargetField(batch_first=True)
self.dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'),
format='tsv',
fields=[('src', src), ('tgt', tgt)],
)
src.build_vocab(self.dataset)
tgt.build_vocab(self.dataset)
self.data_iterator = torchtext.data.BucketIterator(
dataset=self.dataset,
batch_size=64,
sort=False,
sort_within_batch=True,
sort_key=lambda x: len(x.src),
repeat=False)
encoder = EncoderRNN(len(src.vocab), 10, 10, 10, rnn_cell='lstm')
decoder = DecoderRNN(len(tgt.vocab),
10,
10,
tgt.sos_id,
tgt.eos_id,
rnn_cell='lstm')
self.seq2seq = Seq2seq(encoder, decoder)
for param in self.seq2seq.parameters():
param.data.uniform_(-0.08, 0.08)
def get_train_valid_tests(train_path,
valid_path,
test_paths,
max_len,
src_vocab=50000,
tgt_vocab=50000):
"""Gets the formatted train, valid, and test data."""
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
src = SourceField()
tgt = TargetField(include_eos=True)
fields = [('src', src), ('tgt', tgt)]
train = torchtext.data.TabularDataset(path=train_path,
format='tsv',
fields=fields,
filter_pred=len_filter)
valid = torchtext.data.TabularDataset(path=valid_path,
format='tsv',
fields=fields,
filter_pred=len_filter)
tests = []
for t in test_paths:
tests.append(
torchtext.data.TabularDataset(path=t,
format='tsv',
fields=fields,
filter_pred=len_filter))
src.build_vocab(train, max_size=src_vocab)
tgt.build_vocab(train, max_size=tgt_vocab)
return train, valid, tests, src, tgt
def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField()
tgt = TargetField()
self.dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'), format='tsv',
fields=[('src', src), ('tgt', tgt)],
)
src.build_vocab(self.dataset)
tgt.build_vocab(self.dataset)
def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField(batch_first=True)
tgt = TargetField(batch_first=True)
self.dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'), format='tsv',
fields=[('src', src), ('tgt', tgt)],
)
src.build_vocab(self.dataset)
tgt.build_vocab(self.dataset)
self.data_iterator = torchtext.data.BucketIterator(
dataset=self.dataset, batch_size=4,
sort=False, sort_within_batch=True,
sort_key=lambda x: len(x.src),
repeat=False)
def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField()
tgt = TargetField()
self.dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'), format='tsv',
fields=[('src', src), ('tgt', tgt)],
)
src.build_vocab(self.dataset)
tgt.build_vocab(self.dataset)
encoder = EncoderRNN(len(src.vocab), 10, 10, 10, rnn_cell='lstm')
decoder = DecoderRNN(len(tgt.vocab), 10, 10, tgt.sos_id, tgt.eos_id, rnn_cell='lstm')
self.seq2seq = Seq2seq(encoder, decoder)
for param in self.seq2seq.parameters():
param.data.uniform_(-0.08, 0.08)
def setUpClass(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField(batch_first=True)
trg = TargetField(batch_first=True)
dataset = torchtext.data.TabularDataset(
path=os.path.join(test_path, 'data/eng-fra.txt'),
format='tsv',
fields=[('src', src), ('trg', trg)],
)
src.build_vocab(dataset)
trg.build_vocab(dataset)
encoder = EncoderRNN(len(src.vocab), 5, 10, 10, rnn_cell='lstm')
decoder = DecoderRNN(len(trg.vocab),
10,
10,
trg.sos_id,
trg.eos_id,
rnn_cell='lstm')
seq2seq = Seq2seq(encoder, decoder)
self.predictor = Predictor(seq2seq, src.vocab, trg.vocab)
os.path.join(opt.output_dir, opt.load_checkpoint)))
checkpoint_path = os.path.join(opt.output_dir, opt.load_checkpoint)
checkpoint = Checkpoint.load(checkpoint_path)
seq2seq = checkpoint.model
input_vocab = checkpoint.input_vocab
src.vocab = input_vocab
output_vocab = checkpoint.output_vocab
tgt.vocab = output_vocab
tgt.eos_id = tgt.vocab.stoi[tgt.SYM_EOS]
tgt.sos_id = tgt.vocab.stoi[tgt.SYM_SOS]
else:
# build vocabulary
src.build_vocab(train, max_size=opt.src_vocab)
tgt.build_vocab(train, max_size=opt.tgt_vocab)
input_vocab = src.vocab
output_vocab = tgt.vocab
# Initialize model
hidden_size = opt.hidden_size
decoder_hidden_size = hidden_size * 2 if opt.bidirectional else hidden_size
encoder = EncoderRNN(len(src.vocab),
max_len,
hidden_size,
opt.embedding_size,
dropout_p=opt.dropout_p_encoder,
n_layers=opt.n_layers,
bidirectional=opt.bidirectional,
rnn_cell=opt.rnn_cell,
os.path.join(opt.output_dir, opt.load_checkpoint)))
checkpoint_path = os.path.join(opt.output_dir, opt.load_checkpoint)
checkpoint = Checkpoint.load(checkpoint_path)
seq2seq = checkpoint.model
input_vocab = checkpoint.input_vocab
src.vocab = input_vocab
output_vocab = checkpoint.output_vocab
tgt.vocab = output_vocab
tgt.eos_id = tgt.vocab.stoi[tgt.SYM_EOS]
tgt.sos_id = tgt.vocab.stoi[tgt.SYM_SOS]
else:
# build vocabulary
src.build_vocab(train.dataset, max_size=opt.src_vocab)
tgt.build_vocab(train.dataset, max_size=opt.tgt_vocab)
input_vocab = src.vocab
output_vocab = tgt.vocab
# Initialize model
if opt.model == 'seq2attn':
hidden_size = opt.hidden_size
decoder_hidden_size = hidden_size * 2 if opt.bidirectional else hidden_size
seq2attn_encoder = EncoderRNN(
len(src.vocab),
max_len,
hidden_size,
opt.embedding_size,
dropout_p=opt.dropout_p_encoder,
n_layers=opt.n_layers,