def build_iters(args):
TXT = data.Field(lower=args.lower, include_lengths=True, batch_first=True)
LBL = data.Field(sequential=False, unk_token=None)
TREE = data.Field(sequential=True, use_vocab=False, pad_token=0)
ftrain = 'data/sst/sst/trees/train.txt'
fvalid = 'data/sst/sst/trees/dev.txt'
ftest = 'data/sst/sst/trees/test.txt'
examples_train, len_ave = load_examples(ftrain, subtrees=True)
examples_valid, _ = load_examples(fvalid, subtrees=False)
examples_test, _ = load_examples(ftest, subtrees=False)
train = Dataset(examples_train,
fields=[('txt', TXT), ('tree', TREE), ('lbl', LBL)])
TXT.build_vocab(train, vectors=args.pretrained)
LBL.build_vocab(train)
valid = Dataset(examples_valid,
fields=[('txt', TXT), ('tree', TREE), ('lbl', LBL)])
test = Dataset(examples_test,
fields=[('txt', TXT), ('tree', TREE), ('lbl', LBL)])
def batch_size_fn(new_example, current_count, ebsz):
return ebsz + (len(new_example.txt) / len_ave)**0.3
device = torch.device(args.gpu if args.gpu != -1 else 'cpu')
train_iter = basic.BucketIterator(train,
batch_size=args.batch_size,
sort=True,
shuffle=True,
repeat=False,
sort_key=lambda x: len(x.txt),
batch_size_fn=batch_size_fn,
device=device)
valid_iter = basic.BucketIterator(valid,
batch_size=args.batch_size,
sort=True,
shuffle=True,
repeat=False,
sort_key=lambda x: len(x.txt),
batch_size_fn=batch_size_fn,
device=device)
test_iter = basic.BucketIterator(test,
batch_size=args.batch_size,
sort=True,
shuffle=True,
repeat=False,
sort_key=lambda x: len(x.txt),
batch_size_fn=batch_size_fn,
device=device)
return train_iter, valid_iter, test_iter, (TXT, TREE, LBL)
def build_iters(args):
EXPR = torchtext.data.Field(sequential=True,
use_vocab=True,
batch_first=True,
include_lengths=True,
pad_token=PAD,
eos_token=None)
VAL = torchtext.data.Field(sequential=False)
ftrain = 'data/train_d20s.tsv'
fvalid = 'data/test_d20s.tsv'
# ftest = 'data/test_d20s.tsv'
examples_train, len_ave = load_examples(ftrain)
examples_valid, _ = load_examples(fvalid)
train = Dataset(examples_train, fields=[('expr', EXPR),
('val', VAL)])
EXPR.build_vocab(train)
VAL.build_vocab(train)
valid = Dataset(examples_valid, fields=[('expr', EXPR),
('val', VAL)])
device = torch.device(args.gpu if args.gpu != -1 else 'cpu')
def batch_size_fn(new_example, current_count, ebsz):
return ebsz + (len(new_example.expr) / len_ave) ** 0.3
splits, split_avels = split_examples(examples_train)
train_iters = {srange:None for srange in splits}
for srange, split in splits.items():
train_split = Dataset(split, fields=[('expr', EXPR),
('val', VAL)])
data_iter = basic.BucketIterator(train_split,
batch_size=args.bsz,
sort=True,
shuffle=True,
repeat=False,
sort_key=lambda x: len(x.expr),
batch_size_fn=batch_size_fn,
device=device)
train_iters[srange] = data_iter
valid_iter = basic.BucketIterator(valid,
batch_size=args.bsz,
sort=True,
shuffle=True,
repeat=False,
sort_key=lambda x: len(x.expr),
batch_size_fn=batch_size_fn,
device=device)
return train_iters, valid_iter, EXPR, VAL
def train(args):
text_field = data.Field(lower=args.lower,
include_lengths=True,
batch_first=True)
label_field = data.Field(sequential=False)
filter_pred = None
if not args.fine_grained:
filter_pred = lambda ex: ex.label != 'neutral'
dataset_splits = datasets.SST.splits(root='./data/sst',
text_field=text_field,
label_field=label_field,
fine_grained=args.fine_grained,
train_subtrees=True,
filter_pred=filter_pred)
lens = []
for e in dataset_splits[0].examples:
lens.append(len(e.text))
len_ave = np.mean(lens)
text_field.build_vocab(*dataset_splits, vectors=args.pretrained)
label_field.build_vocab(*dataset_splits)
logging.info(f'Initialize with pretrained vectors: {args.pretrained}')
logging.info(f'Number of classes: {len(label_field.vocab)}')
def batch_size_fn(new_example, current_count, ebsz):
return ebsz + (len(new_example.text) / len_ave)**0.3
device = torch.device(args.gpu if args.gpu != -1 else 'cpu')
train_loader = basic.BucketIterator(dataset_splits[0],
batch_size=args.batch_size,
sort=True,
shuffle=True,
repeat=False,
sort_key=lambda x: len(x.text),
batch_size_fn=batch_size_fn,
device=device)
valid_loader = basic.BucketIterator(dataset_splits[1],
batch_size=args.batch_size,
sort=True,
shuffle=True,
repeat=False,
sort_key=lambda x: len(x.text),
batch_size_fn=batch_size_fn,
device=device)
num_classes = len(label_field.vocab)
model = SSTModel(num_classes=num_classes,
num_words=len(text_field.vocab),
word_dim=args.word_dim,
hidden_dim=args.hidden_dim,
clf_hidden_dim=args.clf_hidden_dim,
clf_num_layers=args.clf_num_layers,
use_leaf_rnn=args.leaf_rnn,
bidirectional=args.bidirectional,
intra_attention=args.intra_attention,
use_batchnorm=args.batchnorm,
dropout_prob=args.dropout)
if args.pretrained:
model.word_embedding.weight.data.set_(text_field.vocab.vectors)
if args.fix_word_embedding:
logging.info('Will not update word embeddings')
model.word_embedding.weight.requires_grad = False
logging.info(f'Using device {args.gpu}')
model.to(device)
params = [p for p in model.parameters() if p.requires_grad]
if args.optimizer == 'adam':
optimizer_class = optim.Adam
elif args.optimizer == 'adagrad':
optimizer_class = optim.Adagrad
elif args.optimizer == 'adadelta':
optimizer_class = optim.Adadelta
else:
raise NotImplementedError
optimizer = optimizer_class(params=params, weight_decay=args.l2reg)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
mode='max',
factor=0.5,
patience=20 *
args.halve_lr_every,
verbose=True)
criterion = nn.CrossEntropyLoss()
train_summary_writer = SummaryWriter(
log_dir=os.path.join(args.save_dir, 'log', 'train'))
valid_summary_writer = SummaryWriter(
log_dir=os.path.join(args.save_dir, 'log', 'valid'))
def run_iter(batch, is_training):
model.train(is_training)
words, length = batch.text
label = batch.label
logits = model(words=words, length=length)
label_pred = logits.max(1)[1]
accuracy = torch.eq(label, label_pred).float().mean()
loss = criterion(input=logits, target=label)
if is_training:
optimizer.zero_grad()
loss.backward()
clip_grad_norm_(parameters=params, max_norm=5)
optimizer.step()
return loss, accuracy
def add_scalar_summary(summary_writer, name, value, step):
if torch.is_tensor(value):
value = value.item()
summary_writer.add_scalar(tag=name,
scalar_value=value,
global_step=step)
num_train_batches = len(train_loader)
validate_every = num_train_batches // 20
best_vaild_accuacy = 0
iter_count = 0
for epoch in range(args.max_epoch):
for batch_iter, train_batch in enumerate(tqdm.tqdm(train_loader)):
train_loss, train_accuracy = run_iter(batch=train_batch,
is_training=True)
iter_count += 1
add_scalar_summary(summary_writer=train_summary_writer,
name='loss',
value=train_loss,
step=iter_count)
add_scalar_summary(summary_writer=train_summary_writer,
name='accuracy',
value=train_accuracy,
step=iter_count)
if (batch_iter + 1) % validate_every == 0:
valid_loss_sum = valid_accuracy_sum = 0
num_valid_batches = len(valid_loader)
for valid_batch in valid_loader:
valid_loss, valid_accuracy = run_iter(batch=valid_batch,
is_training=False)
valid_loss_sum += valid_loss.item()
valid_accuracy_sum += valid_accuracy.item()
valid_loss = valid_loss_sum / num_valid_batches
valid_accuracy = valid_accuracy_sum / num_valid_batches
add_scalar_summary(summary_writer=valid_summary_writer,
name='loss',
value=valid_loss,
step=iter_count)
add_scalar_summary(summary_writer=valid_summary_writer,
name='accuracy',
value=valid_accuracy,
step=iter_count)
scheduler.step(valid_accuracy)
progress = train_loader.iterations / len(train_loader)
logging.info(f'Epoch {epoch}: '
f'valid loss = {valid_loss:.4f}, '
f'valid accuracy = {valid_accuracy:.4f}')
if valid_accuracy > best_vaild_accuacy:
best_vaild_accuacy = valid_accuracy
model_filename = (f'nets-{progress:.2f}'
f'-{valid_loss:.4f}'
f'-{valid_accuracy:.4f}.pkl')
model_path = os.path.join(args.save_dir, model_filename)
torch.save(model.state_dict(), model_path)
print(f'Saved the new best nets to {model_path}')
if progress > args.max_epoch:
break
