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 load_test_data(opt):
src = SourceField()
tgt = TargetField()
tabular_data_fields = [('src', src), ('tgt', tgt)]
max_len = opt.max_len
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
# generate training and testing data
test = torchtext.data.TabularDataset(path=opt.test,
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter)
return test, src, tgt
def prepare_iters(parameters,
train_path,
test_paths,
valid_path,
batch_size,
eval_batch_size=512):
src = SourceField(batch_first=True)
tgt = TargetField(include_eos=False, batch_first=True)
tabular_data_fields = [('src', src), ('tgt', tgt)]
max_len = parameters['max_len']
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
# generate training and testing data
train = get_standard_iter(torchtext.data.TabularDataset(
path=train_path,
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=batch_size)
dev = get_standard_iter(torchtext.data.TabularDataset(
path=valid_path,
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=eval_batch_size)
monitor_data = OrderedDict()
for dataset in test_paths:
m = get_standard_iter(torchtext.data.TabularDataset(
path=dataset,
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=eval_batch_size)
monitor_data[dataset] = m
return src, tgt, train, dev, monitor_data
def prepare_iters(opt):
use_output_eos = not opt.ignore_output_eos
src = SourceField(batch_first=True)
tgt = TargetField(include_eos=use_output_eos, batch_first=True)
tabular_data_fields = [('src', src), ('tgt', tgt)]
max_len = opt.max_len
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
# generate training and testing data
train = get_standard_iter(torchtext.data.TabularDataset(
path=opt.train,
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=opt.batch_size)
if opt.dev:
dev = get_standard_iter(torchtext.data.TabularDataset(
path=opt.dev,
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=opt.eval_batch_size)
else:
dev = None
monitor_data = OrderedDict()
for dataset in opt.monitor:
m = get_standard_iter(torchtext.data.TabularDataset(
path=dataset,
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=opt.eval_batch_size)
monitor_data[dataset] = m
return src, tgt, train, dev, monitor_data
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)
def prepare_iters(opt):
src = SourceField(batch_first=True)
tgt = TargetField(batch_first=True, include_eos=True)
tabular_data_fields = [('src', src), ('tgt', tgt)]
max_len = opt.max_len
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
ds = '100K'
if opt.mini:
ds = '10K'
# generate training and testing data
train = get_standard_iter(torchtext.data.TabularDataset(
path='data/pcfg_set/{}/train.tsv'.format(ds),
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=opt.batch_size)
dev = get_standard_iter(torchtext.data.TabularDataset(
path='data/pcfg_set/{}/dev.tsv'.format(ds),
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=opt.eval_batch_size)
monitor_data = OrderedDict()
m = get_standard_iter(torchtext.data.TabularDataset(
path='data/pcfg_set/{}/test.tsv'.format(ds),
format='tsv',
fields=tabular_data_fields,
filter_pred=len_filter),
batch_size=opt.eval_batch_size)
monitor_data['Test'] = m
return src, tgt, train, dev, monitor_data
logging.info("Cuda device set to %i" % opt.cuda_device)
torch.cuda.set_device(opt.cuda_device)
#################################################################################
# load model
logging.info("loading checkpoint from {}".format(
os.path.join(opt.checkpoint_path)))
checkpoint = Checkpoint.load(opt.checkpoint_path)
seq2seq = checkpoint.model
input_vocab = checkpoint.input_vocab
output_vocab = checkpoint.output_vocab
############################################################################
# Prepare dataset and loss
src = SourceField()
tgt = TargetField(output_eos_used)
tabular_data_fields = [('src', src), ('tgt', tgt)]
if opt.use_attention_loss or opt.attention_method == 'hard':
attn = AttentionField(use_vocab=False, ignore_index=IGNORE_INDEX)
tabular_data_fields.append(('attn', attn))
src.vocab = input_vocab
tgt.vocab = output_vocab
tgt.eos_id = tgt.vocab.stoi[tgt.SYM_EOS]
tgt.sos_id = tgt.vocab.stoi[tgt.SYM_SOS]
max_len = opt.max_len
def test_sourcefield(self):
field = SourceField()
self.assertTrue(isinstance(field, torchtext.data.Field))
self.assertTrue(field.batch_first)
self.assertTrue(field.include_lengths)
def test_sourcefield_with_wrong_setting(self):
field = SourceField(batch_first=False, include_lengths=False)
self.assertTrue(isinstance(field, torchtext.data.Field))
self.assertTrue(field.batch_first)
self.assertTrue(field.include_lengths)
logging.info("Cuda device set to %i" % opt.cuda_device)
torch.cuda.set_device(opt.cuda_device)
##########################################################################
# load model
logging.info("loading checkpoint from {}".format(
os.path.join(opt.checkpoint_path)))
checkpoint = Checkpoint.load(opt.checkpoint_path)
seq2seq = checkpoint.model
input_vocab = checkpoint.input_vocab
output_vocab = checkpoint.output_vocab
############################################################################
# Prepare dataset and loss
src = SourceField(batch_first=True)
tgt = TargetField(output_eos_used, batch_first=True)
tabular_data_fields = [('src', src), ('tgt', tgt)]
src.vocab = input_vocab
tgt.vocab = output_vocab
tgt.eos_id = tgt.vocab.stoi[tgt.SYM_EOS]
tgt.sos_id = tgt.vocab.stoi[tgt.SYM_SOS]
max_len = opt.max_len
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
if opt.attention:
if not opt.attention_method:
logging.info("No attention method provided. Using DOT method.")
opt.attention_method = 'dot'
# Set random seed
if opt.random_seed:
random.seed(opt.random_seed)
np.random.seed(opt.random_seed)
torch.manual_seed(opt.random_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(opt.random_seed)
############################################################################
# Prepare dataset
src = SourceField(lower=opt.lower, preprocessing=lambda seq: seq + ['<eos>'])
tgt = TargetField(include_eos=use_output_eos, lower=opt.lower)
tabular_data_fields = [('src', src), ('tgt', tgt)]
max_len = opt.max_len
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
# generate training and testing data
if opt.augmented_input:
dataset_class = TabularAugmentedDataset
else:
if opt.attention and not opt.attention_method:
parser.error("Attention turned on, but no attention method provided")
if torch.cuda.is_available():
logging.info("Cuda device set to %i" % opt.cuda_device)
torch.cuda.set_device(opt.cuda_device)
if opt.attention:
if not opt.attention_method:
logging.info("No attention method provided. Using DOT method.")
opt.attention_method = 'dot'
############################################################################
# Prepare dataset
src = SourceField(lower=opt.lower)
tgt = TargetField(include_eos=use_output_eos, lower=opt.lower)
tabular_data_fields = [('src', src), ('tgt', tgt)]
max_len = opt.max_len
def len_filter(example):
return len(example.src) <= max_len and len(example.tgt) <= max_len
# generate training and testing data
train = torchtext.data.TabularDataset(path=opt.train,
format='tsv',
fields=tabular_data_fields,