def get_datasets(dataset, dataset_dir, bptt, bs, lang, max_vocab, ds_pct, lm_type):
tmp_dir = dataset_dir / 'tmp'
tmp_dir.mkdir(exist_ok=True)
vocab_file = tmp_dir / f'vocab_{lang}.pkl'
if not (tmp_dir / f'{TRN}_{lang}_ids.npy').exists():
print('Reading the data...')
toks, lbls = read_clas_data(dataset_dir, dataset, lang)
# create the vocabulary
counter = Counter(word for example in toks[TRN]+toks[TST]+toks[VAL] for word in example)
itos = [word for word, count in counter.most_common(n=max_vocab)]
itos.insert(0, PAD)
itos.insert(0, UNK)
vocab = Vocab(itos)
stoi = vocab.stoi
with open(vocab_file, 'wb') as f:
pickle.dump(vocab, f)
ids = {}
for split in [TRN, VAL, TST]:
ids[split] = np.array([([stoi.get(w, stoi[UNK]) for w in s])
for s in toks[split]])
np.save(tmp_dir / f'{split}_{lang}_ids.npy', ids[split])
np.save(tmp_dir / f'{split}_{lang}_lbl.npy', lbls[split])
else:
print('Loading the pickled data...')
ids, lbls = {}, {}
for split in [TRN, VAL, TST]:
ids[split] = np.load(tmp_dir / f'{split}_{lang}_ids.npy')
lbls[split] = np.load(tmp_dir / f'{split}_{lang}_lbl.npy')
with open(vocab_file, 'rb') as f:
vocab = pickle.load(f)
print(f'Train size: {len(ids[TRN])}. Valid size: {len(ids[VAL])}. '
f'Test size: {len(ids[TST])}.')
if ds_pct < 1.0:
print(f"Making the dataset smaller {ds_pct}")
for split in [TRN, VAL, TST]:
ids[split] = np.array([np.array(e, dtype=np.int) for e in ids[split]])
#print([lbl for lbl in lbls[split] if not int(lbl) in [0,1,2]]) # debug by ak
#print(f'First 10 lbls[split] labels: {lbls[split][:11]}')
if split == TRN: print("processing TRN labels ... ")
lbls[split] = np.array([np.array(e, dtype=np.int) for e in lbls[split]])
if split == TRN: print("Info: Passed the train labels lbls[split] to np.array sucessfully .....")
data_lm = TextLMDataBunch.from_ids(path=tmp_dir, vocab=vocab, train_ids=np.concatenate([ids[TRN],ids[TST]]),
valid_ids=ids[VAL], bs=bs, bptt=bptt, lm_type=lm_type)
# TODO TextClasDataBunch allows tst_ids as input, but not tst_lbls?
data_clas = TextClasDataBunch.from_ids(
path=tmp_dir, vocab=vocab, train_ids=ids[TRN], valid_ids=ids[VAL],
train_lbls=lbls[TRN], valid_lbls=lbls[VAL], bs=bs, classes={l:l for l in lbls[TRN]})
print(f"Sizes of train_ds {len(data_clas.train_ds)}, valid_ds {len(data_clas.valid_ds)}")
return data_clas, data_lm
def create_lm_learner(self, data_lm, dps=None, **kwargs):
learner = super().create_lm_learner(data_lm, dps, **kwargs)
if self.parallel_data_path is not None:
src_trn_df = pd.read_csv(self.parallel_data_path / self.src_lang /
'train.csv',
header=None)
tgt_trn_df = pd.read_csv(self.parallel_data_path / self.tgt_lang /
'train.csv',
header=None)
bs = self.parallel_data_bs
data_src = TextClasDataBunch.from_df(path=self.cache_dir,
train_df=src_trn_df,
lm_type=self.lm_type,
bs=bs)
data_tgt = TextClasDataBunch.from_df(path=self.cache_dir,
train_df=tgt_trn_df,
lm_type=self.lm_type,
bs=bs)
learner.callback_fns = [
partial(ParallelAlignmentCallback,
data_src=data_src,
data_tgt=data_tgt)
] + learner.callback_fns
def _fit_class(self, df_train, df_val, data_lm):
n_data = min(len(df_train), len(df_val))
# Classifier model data
data_class = TextClasDataBunch.from_df(
path="",
train_df=df_train,
valid_df=df_val,
vocab=data_lm.train_ds.vocab,
bs=self.batch_size if self.batch_size < n_data else n_data // 2,
)
# train the learner object
class_learner = text_classifier_learner(
data_class, self.arch, drop_mult=self.dropout_lm
)
class_learner.load_encoder(self.path_lm.name)
class_learner.fit_one_cycle(1, self.lr_class)
class_learner.export(self.path_class)
def prepare_clas_dataset(input_path,
output_dir=None,
valid_split=0.2,
tokenizer_lang="xx",
min_freq=2,
seed=42):
"""
Reads a CSV file with texts and labels, splits it into training and validation sets,
tokenizes texts and saves datasets for fine-tuning and for classification.
Attributes:
input_path (str): Path to CSV file with texts in the first and labels in second column.
output_dir (str): Folder where to store the processed dataset.
valid_split (float): A fraction of data used for validation.
tokenizer_lang (str): Language setting for tokenizer.
min_freq (int): Minimal number of occurrences of a word to be conidered for adding to
vocabulary.
seed (int): Random seed that determines the training-validation split.
"""
input_path = Path(input_path)
output_dir = Path(output_dir or input_path.parent)
output_dir.mkdir(parents=True, exist_ok=True)
train_df, valid_df = csv_to_train_valid_df(input_path, valid_split, seed)
data_finetune_lm = TextLMDataBunch.from_df(
output_dir,
train_df,
valid_df,
tokenizer=Tokenizer(lang=tokenizer_lang),
text_cols=0,
min_freq=min_freq)
data_clas = TextClasDataBunch.from_df(
output_dir,
train_df,
valid_df,
tokenizer=Tokenizer(lang=tokenizer_lang),
text_cols=0,
label_cols=1,
vocab=data_finetune_lm.train_ds.vocab,
bs=32,
min_freq=min_freq)
data_finetune_lm.save("data_finetune_lm.pkl")
data_clas.save("data_clas.pkl")
def validate_cls(self, save_name='cls_last', bs=40):
args = self.tokenzier_to_fastai_args(
trn_data_loading_func=lambda: trn_df[1], add_moses=True)
data_clas, data_lm = self.load_cls_data(bs,
use_test_for_validation=True)
data_eval = [
TextClasDataBunch.from_csv(path=Path(tgt_path),
csv_name=self.csv_name,
**args)
for tgt_path in self.target_paths
]
for data in [data_clas] + data_eval:
learn = self.create_cls_learner(data, drop_mult=0.1)
learn.load(save_name)
print(
f"Loss and accuracy using ({save_name}) for dataset at {data.path}:",
learn.validate())
def load_cls_data_old_for_xnli(self, bs):
tmp_dir = self.cache_dir
tmp_dir.mkdir(exist_ok=True)
vocab_file = tmp_dir / f'vocab_{self.lang}.pkl'
if not (tmp_dir / f'{TRN}_{self.lang}_ids.npy').exists():
print('Reading the data...')
toks, lbls = read_clas_data(self.dataset_dir,
self.dataset_dir.name, self.lang)
# create the vocabulary
counter = Counter(word
for example in toks[TRN] + toks[TST] + toks[VAL]
for word in example)
itos = [
word for word, count in counter.most_common(n=self.max_vocab)
]
itos.insert(0, PAD)
itos.insert(0, UNK)
vocab = Vocab(itos)
stoi = vocab.stoi
with open(vocab_file, 'wb') as f:
pickle.dump(vocab, f)
ids = {}
for split in [TRN, VAL, TST]:
ids[split] = np.array([([stoi.get(w, stoi[UNK]) for w in s])
for s in toks[split]])
np.save(tmp_dir / f'{split}_{self.lang}_ids.npy', ids[split])
np.save(tmp_dir / f'{split}_{self.lang}_lbl.npy', lbls[split])
else:
print('Loading the pickled data...')
ids, lbls = {}, {}
for split in [TRN, VAL, TST]:
ids[split] = np.load(tmp_dir / f'{split}_{self.lang}_ids.npy')
lbls[split] = np.load(tmp_dir / f'{split}_{self.lang}_lbl.npy')
with open(vocab_file, 'rb') as f:
vocab = pickle.load(f)
print(f'Train size: {len(ids[TRN])}. Valid size: {len(ids[VAL])}. '
f'Test size: {len(ids[TST])}.')
for split in [TRN, VAL, TST]:
ids[split] = np.array(
[np.array(e, dtype=np.int) for e in ids[split]])
lbls[split] = np.array(
[np.array(e, dtype=np.int) for e in lbls[split]])
data_lm = TextLMDataBunch.from_ids(path=tmp_dir,
vocab=vocab,
train_ids=np.concatenate(
[ids[TRN], ids[TST]]),
valid_ids=ids[VAL],
bs=bs,
bptt=self.bptt,
lm_type=self.lm_type)
# TODO TextClasDataBunch allows tst_ids as input, but not tst_lbls?
data_clas = TextClasDataBunch.from_ids(
path=tmp_dir,
vocab=vocab,
train_ids=ids[TRN],
valid_ids=ids[VAL],
train_lbls=lbls[TRN],
valid_lbls=lbls[VAL],
bs=bs,
classes={l: l
for l in lbls[TRN]})
print(
f"Sizes of train_ds {len(data_clas.train_ds)}, valid_ds {len(data_clas.valid_ds)}"
)
return data_clas, data_lm
def load_cls_data_imdb(self,
bs,
force=False,
use_test_for_validation=False):
trn_df = pd.read_csv(self.dataset_path / 'train.csv', header=None)
tst_df = pd.read_csv(self.dataset_path / 'test.csv', header=None)
unsp_df = pd.read_csv(self.dataset_path / 'unsup.csv', header=None)
lm_trn_df = pd.concat([unsp_df, trn_df, tst_df])
val_len = max(int(len(lm_trn_df) * 0.1), 2)
lm_trn_df = lm_trn_df[val_len:]
lm_val_df = lm_trn_df[:val_len]
if use_test_for_validation:
val_df = tst_df
cls_cache = 'notst'
else:
val_len = max(int(len(trn_df) * 0.1), 2)
trn_len = len(trn_df) - val_len
trn_df, val_df = trn_df[:trn_len], trn_df[trn_len:]
cls_cache = '.'
if self.tokenizer is Tokenizers.SUBWORD:
args = get_sentencepiece(self.dataset_path,
self.dataset_path / 'train.csv',
self.name,
vocab_size=self.max_vocab,
pre_rules=[],
post_rules=[])
if self.tokenizer is Tokenizers.SUBWORD:
args = get_sentencepiece(self.dataset_path,
self.dataset_path / 'train.csv',
self.name,
vocab_size=self.max_vocab,
pre_rules=[],
post_rules=[])
elif self.tokenizer is Tokenizers.MOSES:
args = dict(tokenizer=Tokenizer(tok_func=MosesTokenizerFunc,
lang='en',
pre_rules=[],
post_rules=[]))
elif self.tokenizer is Tokenizers.MOSES_FA:
args = dict(
tokenizer=Tokenizer(tok_func=MosesTokenizerFunc,
lang='en')) # use default pre/post rules
elif self.tokenizer is Tokenizers.FASTAI:
args = dict()
else:
raise ValueError(
f"self.tokenizer has wrong value {self.tokenizer}, Allowed values are taken from {Tokenizers}"
)
try:
if force: raise FileNotFoundError("Forcing reloading of caches")
data_lm = TextLMDataBunch.load(self.cache_dir,
'lm',
lm_type=self.lm_type,
bs=bs)
print(
f"Tokenized data loaded, lm.trn {len(data_lm.train_ds)}, lm.val {len(data_lm.valid_ds)}"
)
except FileNotFoundError:
print(f"Running tokenization...")
data_lm = TextLMDataBunch.from_df(path=self.cache_dir,
train_df=lm_trn_df,
valid_df=lm_val_df,
max_vocab=self.max_vocab,
bs=bs,
lm_type=self.lm_type,
**args)
print(
f"Saving tokenized: cls.trn {len(data_lm.train_ds)}, cls.val {len(data_lm.valid_ds)}"
)
data_lm.save('lm')
try:
if force: raise FileNotFoundError("Forcing reloading of caches")
data_cls = TextClasDataBunch.load(self.cache_dir, cls_cache, bs=bs)
print(
f"Tokenized data loaded, cls.trn {len(data_cls.train_ds)}, cls.val {len(data_cls.valid_ds)}"
)
except FileNotFoundError:
args[
'vocab'] = data_lm.vocab # make sure we use the same vocab for classifcation
print(f"Running tokenization...")
data_cls = TextClasDataBunch.from_df(path=self.cache_dir,
train_df=trn_df,
valid_df=val_df,
test_df=tst_df,
max_vocab=self.max_vocab,
bs=bs,
**args)
print(
f"Saving tokenized: cls.trn {len(data_cls.train_ds)}, cls.val {len(data_cls.valid_ds)}"
)
data_cls.save(cls_cache)
print('Size of vocabulary:', len(data_lm.vocab.itos))
print('First 20 words in vocab:', data_lm.vocab.itos[:20])
return data_cls, data_lm
def load_cls_data(self,
bs,
force=False,
use_test_for_validation=False,
**kwargs):
args = self.tokenzier_to_fastai_args(
trn_data_loading_func=lambda: trn_df[1], add_moses=True)
src_path = self.dataset_path
csv_name = self.csv_name
tgt_paths = [Path(tgt_path) for tgt_path in self.target_paths]
mixed_csv = pd.read_csv(src_path / csv_name, header=None)
for tgt_path in tgt_paths:
mixed_csv = pd.concat(
[mixed_csv,
pd.read_csv(tgt_path / csv_name, header=None)])
xcvs_name = ('x_' + csv_name)
mixed_csv.to_csv(src_path / xcvs_name, header=None, index=False)
try:
if force: raise FileNotFoundError("Forcing reloading of caches")
data_lm = TextLMDataBunch.load(src_path,
'xlm',
lm_type=self.lm_type,
bs=bs)
print(
f"Tokenized data loaded, xlm.trn {len(data_lm.train_ds)}, xlm.val {len(data_lm.valid_ds)}"
)
except FileNotFoundError:
print(f"Running tokenization...")
data_lm = TextLMDataBunch.from_csv(path=src_path,
csv_name=xcvs_name,
bs=bs,
lm_type=self.lm_type,
**kwargs,
**args)
print(
f"Saving tokenized: cls.trn {len(data_lm.train_ds)}, cls.val {len(data_lm.valid_ds)}"
)
data_lm.save('xlm')
try:
if force: raise FileNotFoundError("Forcing reloading of caches")
data_cls = TextClasDataBunch.load(src_path, 'cls', bs=bs)
print(
f"Tokenized data loaded, cls.trn {len(data_cls.train_ds)}, cls.val {len(data_cls.valid_ds)}"
)
except FileNotFoundError:
args[
'vocab'] = data_lm.vocab # make sure we use the same vocab for classifcation
print(f"Running tokenization...")
data_cls = TextClasDataBunch.from_csv(path=src_path,
csv_name=csv_name,
bs=bs,
**kwargs,
**args)
print(
f"Saving tokenized: cls.trn {len(data_cls.train_ds)}, cls.val {len(data_cls.valid_ds)}"
)
data_cls.save('cls')
print('Size of vocabulary:', len(data_lm.vocab.itos))
print('First 20 words in vocab:', data_lm.vocab.itos[:20])
return data_cls, data_lm
SAMPLES_PER_CLASS = 12500
print('loading data')
texts = []
target = []
for class_index, classname in enumerate(CLASS_NAMES):
for n, line in enumerate(open(DATA_FOLDER+classname+'.txt')):
texts.append(preprocess_string(line,False))
target.append(class_index)
if n > SAMPLES_PER_CLASS:
break
df = DataFrame({'label':target,'text':texts})
df_train, df_val = train_test_split(df, stratify = df['label'], test_size = 0.4, random_state = 12)
data_lm = TextLMDataBunch.from_df(train_df = df_train, valid_df = df_val, path = "")
data_clas = TextClasDataBunch.from_df(path = "", train_df = df_train, valid_df = df_val, vocab=data_lm.train_ds.vocab, bs=32)
learn = language_model_learner(data_lm, pretrained_model=URLs.WT103, drop_mult=0.7)
learn.fit_one_cycle(1, 1e-2)
learn.save_encoder('ft_enc')
learn = text_classifier_learner(data_clas, drop_mult=0.7)
learn.load_encoder('ft_enc')
learn.fit_one_cycle(1, 1e-2)
lm_learner = language_model_learner(
text_lm,
arch=AWD_LSTM,
drop_mult=0.2,
)
lm_learner.lr_find()
lm_learner.recorder.plot(suggestion=True)
lm_learner.fit_one_cycle(1, lm_learner.recorder.min_grad_lr)
lm_learner.save_encoder(model)
text_clas = TextClasDataBunch.from_df(
train_df=train_df,
valid_df=valid_df,
vocab=text_lm.train_ds.vocab,
path="",
)
clf = text_classifier_learner(
text_clas,
arch=AWD_LSTM,
drop_mult=0.2,
)
clf.load_encoder(model)
clf.lr_find()
clf.recorder.plot(suggestion=True)
clf.fit_one_cycle(1, clf.recorder.min_grad_lr)
def new_train_clas(data_dir,
lang='en',
cuda_id=0,
pretrain_name='wt103',
model_dir='models',
qrnn=False,
fine_tune=True,
max_vocab=30000,
bs=20,
bptt=70,
name='imdb-clas',
dataset='imdb',
ds_pct=1.0):
"""
:param data_dir: The path to the `data` directory
:param lang: the language unicode
:param cuda_id: The id of the GPU. Uses GPU 0 by default or no GPU when
run on CPU.
:param pretrain_name: name of the pretrained model
:param model_dir: The path to the directory where the pretrained model is saved
:param qrrn: Use a QRNN. Requires installing cupy.
:param fine_tune: Fine-tune the pretrained language model
:param max_vocab: The maximum size of the vocabulary.
:param bs: The batch size.
:param bptt: The back-propagation-through-time sequence length.
:param name: The name used for both the model and the vocabulary.
:param dataset: The dataset used for evaluation. Currently only IMDb and
XNLI are implemented. Assumes dataset is located in `data`
folder and that name of folder is the same as dataset name.
"""
results = {}
if not torch.cuda.is_available():
print('CUDA not available. Setting device=-1.')
cuda_id = -1
torch.cuda.set_device(cuda_id)
print(f'Dataset: {dataset}. Language: {lang}.')
assert dataset in DATASETS, f'Error: {dataset} processing is not implemented.'
assert (dataset == 'imdb' and lang == 'en') or not dataset == 'imdb',\
'Error: IMDb is only available in English.'
data_dir = Path(data_dir)
assert data_dir.name == 'data',\
f'Error: Name of data directory should be data, not {data_dir.name}.'
dataset_dir = data_dir / dataset
model_dir = Path(model_dir)
if qrnn:
print('Using QRNNs...')
model_name = 'qrnn' if qrnn else 'lstm'
lm_name = f'{model_name}_{pretrain_name}'
pretrained_fname = (lm_name, f'itos_{pretrain_name}')
ensure_paths_exists(data_dir, dataset_dir, model_dir,
model_dir / f"{pretrained_fname[0]}.pth",
model_dir / f"{pretrained_fname[1]}.pkl")
tmp_dir = dataset_dir / 'tmp'
tmp_dir.mkdir(exist_ok=True)
vocab_file = tmp_dir / f'vocab_{lang}.pkl'
if not (tmp_dir / f'{TRN}_{lang}_ids.npy').exists():
print('Reading the data...')
toks, lbls = read_clas_data(dataset_dir, dataset, lang)
# create the vocabulary
counter = Counter(word for example in toks[TRN] for word in example)
itos = [word for word, count in counter.most_common(n=max_vocab)]
itos.insert(0, PAD)
itos.insert(0, UNK)
vocab = Vocab(itos)
stoi = vocab.stoi
with open(vocab_file, 'wb') as f:
pickle.dump(vocab, f)
ids = {}
for split in [TRN, VAL, TST]:
ids[split] = np.array([([stoi.get(w, stoi[UNK]) for w in s])
for s in toks[split]])
np.save(tmp_dir / f'{split}_{lang}_ids.npy', ids[split])
np.save(tmp_dir / f'{split}_{lang}_lbl.npy', lbls[split])
else:
print('Loading the pickled data...')
ids, lbls = {}, {}
for split in [TRN, VAL, TST]:
ids[split] = np.load(tmp_dir / f'{split}_{lang}_ids.npy')
lbls[split] = np.load(tmp_dir / f'{split}_{lang}_lbl.npy')
with open(vocab_file, 'rb') as f:
vocab = pickle.load(f)
print(f'Train size: {len(ids[TRN])}. Valid size: {len(ids[VAL])}. '
f'Test size: {len(ids[TST])}.')
if ds_pct < 1.0:
print(f"Makeing the dataset smaller {ds_pct}")
for split in [TRN, VAL, TST]:
ids[split] = ids[split][:int(len(ids[split]) * ds_pct)]
data_lm = TextLMDataBunch.from_ids(path=tmp_dir,
vocab=vocab,
train_ids=ids[TRN],
valid_ids=ids[VAL],
bs=bs,
bptt=bptt)
# TODO TextClasDataBunch allows tst_ids as input, but not tst_lbls?
data_clas = TextClasDataBunch.from_ids(path=tmp_dir,
vocab=vocab,
train_ids=ids[TRN],
valid_ids=ids[VAL],
train_lbls=lbls[TRN],
valid_lbls=lbls[VAL],
bs=bs)
if qrnn:
emb_sz, nh, nl = 400, 1550, 3
else:
emb_sz, nh, nl = 400, 1150, 3
learn = language_model_learner(data_lm,
bptt=bptt,
emb_sz=emb_sz,
nh=nh,
nl=nl,
qrnn=qrnn,
pad_token=PAD_TOKEN_ID,
pretrained_fnames=pretrained_fname,
path=model_dir.parent,
model_dir=model_dir.name)
lm_enc_finetuned = f"{lm_name}_{dataset}_enc"
if fine_tune and not (model_dir / f"lm_enc_finetuned.pth").exists():
print('Fine-tuning the language model...')
learn.unfreeze()
learn.fit(2, slice(1e-4, 1e-2))
# save encoder
learn.save_encoder(lm_enc_finetuned)
print("Starting classifier training")
learn = text_classifier_learner(data_clas,
bptt=bptt,
pad_token=PAD_TOKEN_ID,
path=model_dir.parent,
model_dir=model_dir.name,
qrnn=qrnn,
emb_sz=emb_sz,
nh=nh,
nl=nl)
learn.load_encoder(lm_enc_finetuned)
learn.fit_one_cycle(1, 2e-2, moms=(0.8, 0.7), wd=1e-7)
learn.freeze_to(-2)
learn.fit_one_cycle(1,
slice(1e-2 / (2.6**4), 1e-2),
moms=(0.8, 0.7),
wd=1e-7)
learn.freeze_to(-3)
learn.fit_one_cycle(1,
slice(5e-3 / (2.6**4), 5e-3),
moms=(0.8, 0.7),
wd=1e-7)
learn.unfreeze()
learn.fit_one_cycle(2,
slice(1e-3 / (2.6**4), 1e-3),
moms=(0.8, 0.7),
wd=1e-7)
results['accuracy'] = learn.validate()[1]
print(f"Saving models at {learn.path / learn.model_dir}")
learn.save(f'{model_name}_{name}')
return results