def save_imdb_to_tsv():
TEXT = data.Field(lower=True, include_lengths=True, batch_first=True)
LABEL = data.Field(sequential=False, unk_token=False)
train, val = IMDB.splits(TEXT, LABEL)
str2label = {
'negative': '0',
'positive': '1',
}
test = val.examples
dev = train.examples[-len(test):]
train = train.examples[:-len(test)]
def save_to_tsv(examples, fname):
with open(fname, 'w') as f:
f.write('sentence\tlabel\n')
for e in examples:
t, l = e.text, e.label
t = ' '.join(t)
l = str2label[l]
f.write(f'{t}\t{l}\n') # tsv format
tsv_dir = 'data/imdb/fine-tune'
save_to_tsv(train, os.path.join(tsv_dir, 'train.tsv'))
save_to_tsv(dev, os.path.join(tsv_dir, 'dev.tsv'))
save_to_tsv(test, os.path.join(tsv_dir, 'test.tsv'))
def imdb(embedding=None):
# make splits for data
train, test = IMDB.splits(TEXT, LABEL)
train, valid = train.split(random_state=random.seed(SEED))
TEXT.build_vocab(
train,
vectors=embedding,
specials=["<pad>", "<null>"],
unk_init=torch.Tensor.normal_,
max_size=25000,
)
TEXT.null_token = "<null>"
# Need to build the vocab for the labels because they are `pos` and `neg`
# This will convert them to numerical values
LABEL.build_vocab(train)
# make iterator for splits
train_iter, valid_iter, test_iter = BucketIterator.splits(
(train, valid, test),
batch_size=64,
sort_within_batch=True,
)
return train_iter, valid_iter, test_iter
def test(config):
device = 'cuda' if config['cuda'] else 'cpu'
model = TextCNN.load(config['model_path']).to(device)
with open(f"{config['text_vocab']}", "rb") as f:
TEXT = dill.load(f)
with open(f"{config['label_vocab']}", "rb") as f:
LABEL = dill.load(f)
_, test_data = IMDB.splits(TEXT, LABEL, root=config['data_path'])
test_iter = torchtext.data.Iterator(test_data,
batch_size=config['batch_size'],
device=device)
loss_fn = nn.CrossEntropyLoss(
weight=torch.tensor(config['class_weight'], device=device))
val_loss, accuracy = evaluate(model, test_iter, loss_fn)
print(f"val_loss:{val_loss} - accuracy:{accuracy}")
def full_split(cls,
root_dir,
val_size=1000,
load_processed=True,
save_processed=True):
'''Generates the full train/val/test split'''
spd = os.path.join(root_dir, 'imdb', 'processed/')
train_path = os.path.join(spd, 'train.pkl')
val_path = os.path.join(spd, 'val.pkl')
test_path = os.path.join(spd, 'test.pkl')
if (load_processed and os.path.exists(train_path)
and os.path.exists(val_path) and os.path.exists(test_path)):
print(" [*] Loading pre-processed IMDB objects.")
with open(train_path,
'rb') as train_f, open(val_path, 'rb') as val_f, open(
test_path, 'rb') as test_f:
return pickle.load(train_f), pickle.load(val_f), pickle.load(
test_f)
# This means we're not loading from pickle
itrain, itest = IMDB.splits(RawField(), RawField(), root=root_dir)
vocab = Vocabulary([x.text for x in itrain] + [x.text for x in itest],
f_min=100)
# For val we take middle val_size values as this is where pos/neg switch occurs
mid = len(itrain) // 2
grab = val_size // 2
train = cls([[x.text, x.label] for x in itrain[:mid - grab]] +
[[x.text, x.label] for x in itrain[mid + grab:]], vocab)
val = cls([[x.text, x.label] for x in itrain[mid - grab:mid + grab]],
vocab)
test = cls([[x.text, x.label] for x in itest], vocab)
if save_processed:
if not os.path.exists(spd):
os.makedirs(spd)
with open(train_path, 'wb') as f:
pickle.dump(train, f)
with open(val_path, 'wb') as f:
pickle.dump(val, f)
with open(test_path, 'wb') as f:
pickle.dump(test, f)
return train, val, test
def prepare_data(self):
self.text_field = Field(sequential=True,
fix_length=200,
include_lengths=True)
self.label_field = LabelField()
train_val, test = IMDB.splits(self.text_field, self.label_field)
random.seed(42)
train, val = train_val.split(random_state=random.getstate())
self.text_field.build_vocab(
train, vectors=GloVe()) #vectors=FastText('simple'))
self.label_field.build_vocab(train)
self.train_iter, self.test_iter, self.val_iter = BucketIterator.splits(
(train, test, val), batch_size=self.batch_size)
self.train_iter.sort_within_batch = True
self.val_iter.sort_within_batch = True
def process_sents():
def insert_index(dataset: data.Dataset):
examples = dataset.examples
fields = dataset.fields
for i, e in enumerate(examples):
setattr(e, 'index', i)
fields['index'] = data.Field(sequential=False, use_vocab=False)
dataset.examples = examples
dataset.fields = fields
return dataset
text = data.Field(lower=True, include_lengths=True)
label = data.Field(sequential=False, is_target=True, use_vocab=False)
train_data, test_data = IMDB.splits(text, label)
train_data = insert_index(train_data)
test_data = insert_index(test_data)
# save data
torch.save(train_data.examples, 'data/imdb/train.data')
torch.save(test_data.examples, 'data/imdb/test.data')
torch.save(train_data.fields, 'data/imdb/fields')
def load(self, split_ratio=None, random_state=None, verbose=False):
if split_ratio is None:
split_ratio = self.split_ratio
assert (split_ratio <= 1)
if random_state is None:
random_state = self.random_state
## create field - tokenize text & create label classes
self.TEXT = data.Field(tokenize='spacy')
self.LABEL = data.LabelField(dtype=torch.float)
# load dataset
self.train_data, self.test_data = IMDB.splits(self.TEXT, self.LABEL)
# split training into train & validation
self.train_data, self.valid_data = self.train_data.split(
split_ratio=split_ratio, random_state=random_state)
if verbose:
print('Training data size: ', len(self.train_data))
print('Validation data size: ', len(self.valid_data))
print('Test data size: ', len(self.test_data))
def get_data_loader(
doc_processor:DocumentDataPreprocessor,\
batch_size=3,\
dataset_path ='data/IMDB/aclImdb/train',
MAX_WORD_COUNT=1000,\
MIN_DOC_THRESHOLD=300,\
MIN_WORD_COUNT=0,
num_samples=None
):
text_preprocessing = None #lambda x:mdl.model_processor(x)
label_preprocessing = None # lambda x:1 if 'pos' else 0
TEXT = torchtext.data.RawField(preprocessing=text_preprocessing)
LABEL = torchtext.data.RawField(is_target=True,
preprocessing=label_preprocessing)
dataset = IMDB(dataset_path, text_field=TEXT, label_field=LABEL)
data_objects = [{
'text': i.text,
'label': i.label
} for i in dataset.examples]
df = pandas.DataFrame(data_objects)
df['training_content'] = df.apply(
lambda row: doc_processor.formatter(row['text']), axis=1)
df = df[df['training_content'].str.split().str.len() <= MAX_WORD_COUNT]
# Filtering post cleanup.
df = df[df['training_content'].str.split().str.len() >= MIN_WORD_COUNT]
if num_samples is not None:
df = df.sample(n=num_samples)
labels = df['label']
training_content_df = df['training_content']
tensor_dataset, column_split_order = doc_processor.prepare_dataset(
training_content_df, labels, max_length=1024)
dataloader = DataLoader(
tensor_dataset, # The training samples.
sampler=RandomSampler(tensor_dataset), # Select batches randomly
batch_size=batch_size # Trains with this batch size.
)
return dataloader, column_split_order
from torchtext.vocab import GloVe,FastText,CharNGram
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch
from torchtext.datasets import IMDB
import sys
import time
from apex import amp
is_cuda = torch.cuda.is_available()
TEXT = data.Field(lower=True, fix_length=200, batch_first=False)
LABEL = data.Field(sequential=False,)
train, test = IMDB.splits(TEXT, LABEL)
TEXT.build_vocab(train, vectors=GloVe(name='6B', dim=300), max_size=10000, min_freq=10)
LABEL.build_vocab(train,)
train_iter, test_iter = data.BucketIterator.splits((train, test), batch_size=64)
train_iter.repeat = False
test_iter.repeat = False
class IMDBrnn(nn.Module):
def __init__(self, vocab, hidden_size, n_cat, bs=1, nl=2):
super().__init__()
self.hidden_size = hidden_size
self.bs = bs
output = F.softmax(output, dim=-1)
print(output)
if __name__ == "__main__":
text_field = Field(use_vocab=False,
tokenize=tokenize_and_trunc,
preprocessing=tokenizer.convert_tokens_to_ids,
batch_first=True,
init_token=init_token_idx,
eos_token=eos_token_idx,
pad_token=pad_token_idx,
unk_token=unk_token_idx)
label_field = LabelField()
train_data, test_data = IMDB.splits(text_field, label_field)
train_data, valid_data = train_data.split()
label_field.build_vocab(train_data)
n_epochs = 5
batch_size = 128
rnn_hidden_size = 256
dropout_p = 0.2
num_classes = len(label_field.vocab)
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
model = BertGRU(bert.config.to_dict()['dim'],
rnn_hidden_size, num_classes=num_classes,
dropout_p=dropout_p)
for name, params in model.named_parameters():
from torchtext.datasets import IMDB
from os.path import join, exists
from os import mkdir
from tqdm import tqdm
import pandas as pd
import torch
import torch.nn as nn
print("loading dataset...")
train_iter, test_iter = IMDB("datasets", split=('train', 'test'))
# tokenize
def tokenize(text):
return [t.lower() for t in text.split()]
train_set = [(label, tokenize(line)) for label, line in tqdm(train_iter, desc="tokenizing trainset...")]
test_set = [(label, tokenize(line)) for label, line in tqdm(test_iter, desc="tokenizing testset...")]
# vocab
vocab = sorted(list(set(t for (_, tokens) in train_set for t in tokens)))
PADDING_IDX = 0
vocab.insert(PADDING_IDX, "<padding>")
UNKNOWN_IDX = 1
vocab.insert(UNKNOWN_IDX, "<unknown>")
token2idx = {token: idx for idx, token in enumerate(vocab)}
def main():
args = parse_arguments()
use_cuda = torch.cuda.is_available()
# visdom for plotting
vis = Visdom()
win_g, win_d, win_w = None, None, None
assert vis.check_connection()
# load datasets
print("[!] preparing dataset...")
TEXT = Field(lower=True, fix_length=args.seq_len,
tokenize=list, batch_first=True)
LABEL = Field(sequential=False)
train_data, test_data = IMDB.splits(TEXT, LABEL)
TEXT.build_vocab(train_data)
LABEL.build_vocab(train_data)
train_iter, test_iter = BucketIterator.splits(
(train_data, test_data), batch_size=args.batch_size, repeat=True)
vocab_size = len(TEXT.vocab)
print("[TRAIN]:%d (dataset:%d)\t[TEST]:%d (dataset:%d)\t[VOCAB]:%d"
% (len(train_iter), len(train_iter.dataset),
len(test_iter), len(test_iter.dataset), vocab_size))
# instantiate models
G = Generator(dim=512, seq_len=args.seq_len, vocab_size=vocab_size)
D = Discriminator(dim=512, seq_len=args.seq_len, vocab_size=vocab_size)
optim_G = optim.Adam(G.parameters(), lr=args.lr, betas=(0.5, 0.9))
optim_D = optim.Adam(D.parameters(), lr=args.lr, betas=(0.5, 0.9))
global one, mone
one = torch.FloatTensor([1])
mone = one * -1
if use_cuda:
G, D = G.cuda(), D.cuda()
one, mone = one.cuda(), mone.cuda()
train_iter = iter(train_iter)
batch_size = args.batch_size
for b in range(1, args.batchs+1):
# (1) Update D network
for p in D.parameters(): # reset requires_grad
p.requires_grad = True
for iter_d in range(args.critic_iters): # CRITIC_ITERS
batch = next(train_iter)
text, label = batch.text, batch.label
text = to_onehot(text, vocab_size)
if use_cuda:
text = text.cuda()
real = Variable(text)
d_loss, wasserstein = train_discriminator(
D, G, optim_D, real, args.lamb, batch_size, use_cuda)
# (2) Update G network
for p in D.parameters():
p.requires_grad = False # to avoid computation
g_loss = train_generator(D, G, optim_G, batch_size, use_cuda)
# plot losses on visdom
win_d = plot('Discriminator Loss', vis,
x=b, y=d_loss.data[0], win=win_d)
win_g = plot('Generator Loss', vis,
x=b, y=g_loss.data[0], win=win_g)
win_w = plot('Wasserstein Distance', vis,
x=b, y=wasserstein.data[0], win=win_w)
if b % 500 == 0 and b > 1:
samples = sample(G, TEXT, 1, args.seq_len, vocab_size, use_cuda)
print("[%d] D:%5.2f G:%5.2f W:%5.2f \nsample:%s \t [%d]" %
(b, d_loss.data[0], g_loss.data[0], wasserstein.data[0],
samples[0], label.data[0]))
log_sample("Sample %d" % b, vis, samples)
if b % 5000 == 0 and b > 1:
print("[!] saving model")
if not os.path.isdir(".save"):
os.makedirs(".save")
torch.save(G.state_dict(), './.save/wgan_g_%d.pt' % (b))
torch.save(D.state_dict(), './.save/wgan_d_%d.pt' % (b))
from nntoolbox.sequence.utils import extract_last
from nntoolbox.components import MLP, ConcatPool
from functools import partial
MAX_VOCAB_SIZE = 25000
BATCH_SIZE = 16
TEXT = data.Field(tokenize='spacy', include_lengths=True, fix_length=500)
LABEL = data.LabelField(dtype=torch.float)
# train_data, val_data, test_data = SST.splits(
# text_field=TEXT,
# label_field=LABEL
# )
train_val_data, test_data = IMDB.splits(TEXT, LABEL)
train_data, val_data = train_val_data.split(split_ratio=0.8)
train_iterator, val_iterator, test_iterator = data.BucketIterator.splits(
(train_data, val_data, test_data),
batch_size=BATCH_SIZE,
sort_within_batch=True,
device=get_device()
)
TEXT.build_vocab(train_data, max_size=MAX_VOCAB_SIZE, vectors="glove.6B.100d")
LABEL.build_vocab(train_data)
# max_length = 0
# for batch in train_iterator:
# texts, text_lengths = batch.text
def train(config):
try:
split = config["split"]
data_path = config["data_path"]
pretrained_model_dir = config["pretrained_model_dir"]
pretrained_model_file = config["pretrained_model_file"]
last_model_path = config["last_model_path"]
save_to = config["save_to"]
min_freq = config["min_freq"]
batch_size = config["batch_size"]
max_sent_length = config["max_sent_length"]
embed_dim = config["embed_dim"]
filter_num = config["filter_num"]
filter_widths = config["filter_widths"]
learning_rate = config["learning_rate"]
patience = config["patience"]
lr_decay = config["lr_decay"]
max_num_trial = config["max_num_trial"]
max_epoch = config["max_epoch"]
save_every = config["save_every"]
cuda = config["cuda"]
debug = config["debug"]
except KeyError:
print("Input Parameter Error")
exit(1)
if not Path(save_to).exists():
Path(save_to).mkdir()
device = torch.device("cuda:0" if (
torch.cuda.is_available() and cuda) else "cpu")
# build torchtext field
TEXT = torchtext.data.Field(tokenize='spacy', lower=True)
LABEL = torchtext.data.Field(dtype=torch.long)
train_data, test_data = IMDB.splits(TEXT, LABEL, root=data_path)
if debug:
train_data, val_data = train_data.split(split_ratio=0.1)
train_data, val_data = train_data.split(split_ratio=0.7)
train_iter, val_iter = torchtext.data.Iterator.splits(
(train_data, val_data), batch_size=batch_size, device=device)
if (pretrained_model_file is not None) and (pretrained_model_dir
is not None):
pretrained_vector = Vectors(name=pretrained_model_file,
cache=pretrained_model_dir)
TEXT.build_vocab(train_data, min_freq=min_freq, vectors=pretrained_vector)
LABEL.build_vocab(train_data)
logging.info("saving TEXT/LABEL vocabulary...")
with open(f"{save_to}/TEXT_vocab.bin", "wb") as f:
dill.dump(TEXT, f)
with open(f"{save_to}/LABEL_vocab.bin", "wb") as f:
dill.dump(LABEL, f)
assert embed_dim == TEXT.vocab.vectors.shape[
-1], "incompatiable embeddings"
embed_num, class_num = len(TEXT.vocab), len(LABEL.vocab)
model = TextCNN(embed_num,
embed_dim,
class_num,
filter_num,
filter_widths,
from_pretrained=TEXT.vocab.vectors).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
cross_entropy = nn.CrossEntropyLoss(weight=torch.tensor(
[0, 0, 1.0, 1.0], device=device)) # class [<unk>,<pad>,'pos','neg']
if last_model_path is not None:
# load model
logging.info(f'load model from {last_model_path}')
params = torch.load(last_model_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
logging.info('restore parameters of the optimizers')
optimizer.load_state_dict(torch.load(last_model_path + '.optim'))
model.train()
epoch = 0
cur_trial = 0
hist_valid_scores = []
train_time = begin_time = time.time()
logging.info("begin training!")
while True:
epoch += 1
train_loss = 0
cum_cnt = 0
step = 0
for batch in iter(train_iter):
feature, target = batch.text.T, batch.label.squeeze(0)
step += 1
optimizer.zero_grad()
res = model(feature)
loss = cross_entropy(res, target)
train_loss += loss
loss.backward()
optimizer.step()
train_loss = train_loss / step
val_loss, accuracy = evaluate(model, val_iter, cross_entropy)
logging.info(
f'epoch {epoch}\t train_loss: {train_loss}\t val_loss:{val_loss}\t val_accuracy:{accuracy} speed:{time.time()-train_time:.2f}s/epoch\t time elapsed {time.time()-begin_time:.2f}s'
)
train_time = time.time()
is_better = len(
hist_valid_scores) == 0 or val_loss < min(hist_valid_scores)
hist_valid_scores.append(val_loss)
if epoch % save_every == 0:
model.save(f"{save_to}/model_step_{epoch}")
torch.save(optimizer.state_dict(),
f"{save_to}/model_step_{epoch}.optim")
if is_better:
cur_patience = 0
model_save_path = f"{save_to}/model_best"
print(f'save currently the best model to [{model_save_path}]')
model.save(model_save_path)
# also save the optimizers' state
torch.save(optimizer.state_dict(), model_save_path + '.optim')
elif cur_patience < patience:
cur_patience += 1
print('hit patience %d' % cur_patience)
if cur_patience == patience:
cur_trial += 1
print(f'hit #{cur_trial} trial')
if cur_trial == max_num_trial:
print('early stop!')
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * lr_decay
logging.info(
f'load previously best model and decay learning rate to {lr}'
)
# load model
params = torch.load(model_save_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
model = model.to(device)
logging.info('restore parameters of the optimizers')
optimizer.load_state_dict(
torch.load(model_save_path + '.optim'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
cur_patience = 0
if epoch == max_epoch:
print('reached maximum number of epochs!')
exit(0)