def build_vocabs():
train, dev, test = load_boknilev()
samples = [
s for r in train + dev + test
for s in boknilev_record_to_hcpd_samples(r)
]
gold_pos_vocab = Vocabulary('GOLD_POS')
gold_pos_vocab.add_words(
set([hc.next_pos for s in samples for hc in s.x.head_cands]))
gold_pos_vocab.add_word(None)
words_vocab = Vocabulary('WORDS')
words_vocab.add_words(
set([hc.word for s in samples for hc in s.x.head_cands]))
words_vocab.add_words(set([s.x.pp.word for s in samples]))
words_vocab.add_words(set([s.x.child.word for s in samples]))
words_vocab.add_word(None)
words_to_lemmas = {}
words_to_lemmas.update({s.x.child.word: s.x.child.lemma for s in samples})
words_to_lemmas.update(
{hc.word: hc.lemma
for s in samples for hc in s.x.head_cands})
return [gold_pos_vocab, words_vocab, words_to_lemmas]
def build_vocabulary_from_dataset(self, data):
vocabulary = Vocabulary(custom_unk_word=' ')
for transcription in data['transcription_tokens']:
for word in transcription:
vocabulary.add_word(word)
dataset_info = {'vocabulary': vocabulary}
return dataset_info
def __init__(self,
data_file,
character_level=None,
phoneme_level=None,
vocabulary=None,
transform=None):
self.data_file = data_file
self.data = joblib.load(open(self.data_file, 'rb'))
self.character_level = character_level
self.phoneme_level = phoneme_level
self.transcription_processor = lambda words: words
if self.character_level:
characters = [chr(c) for c in range(ord('a'), ord('z') + 1)]
characters += [' ']
character_vocab = Vocabulary()
for character in characters:
character_vocab.add_word(character)
self.vocabulary = character_vocab
self.transcription_processor = self._character_level_transcription_processor
elif self.phoneme_level:
cmu_phones = list(map(lambda x: x[0], cmudict.phones()))
cmu_phones += [' ']
phones_vocab = Vocabulary(custom_unk_word=' ')
for phone in cmu_phones:
phones_vocab.add_word(phone)
self.vocabulary = phones_vocab
self.phones_dict = cmudict.dict()
self.transcription_processor = self._phone_level_transcription_processor
elif vocabulary is None:
data_file_dir = os.path.dirname(self.data_file)
data_file_prefix = os.path.splitext(self.data_file)[0]
pickle_file_name = f'{data_file_prefix}_SpeechDataset.pickle'
pickle_file_path = os.path.join(data_file_dir, pickle_file_name)
if not os.path.isfile(pickle_file_path):
dataset_info = self.build_vocabulary_from_dataset(self.data)
pickle.dump(dataset_info, open(pickle_file_path, 'wb'))
else:
dataset_info = pickle.load(open(pickle_file_path, 'rb'))
self.vocabulary = dataset_info['vocabulary']
else:
self.vocabulary = vocabulary
self.transform = transform
self.max_transcription_length = max([
len(transcription)
for transcription in self.data['transcription_tokens']
])
self.max_input_length = max([
spectrogram.shape[1]
for spectrogram in self.data['audio_spectrograms']
])
def read_instances_from_file(files, max_len=400, keep_case=False):
''' Collect instances and construct vocab '''
vocab = Vocabulary()
lb_vocab = Vocabulary(need_default=False)
sets = []
for file in files:
sents, labels = [], []
trimmed_sent = 0
with open(file) as f:
lines = f.readlines()
for l in lines:
l = l.strip().split('\t')
if len(l) < 2:
continue
label = l[0]
sent = l[1]
if not keep_case:
sent = sent.lower()
word_lst = sent.split()
if len(word_lst) > max_len:
word_lst = word_lst[:max_len]
trimmed_sent += 1
if word_lst:
sents.append(word_lst)
labels.append(label)
vocab.add_word_lst(word_lst)
lb_vocab.add_word(label)
assert len(sents) == len(labels)
sets.append({'sents': sents, 'labels': labels})
logger.info('Get {} instances from file {}'.format(len(sents), file))
if trimmed_sent:
logger.info(
'{} sentences are trimmed. Max sentence length: {}.'.format(
trimmed_sent, max_len))
logger.info('Building vocabulary...')
vocab.add_word_lst(['<cls>'] * 6)
vocab.build_vocab()
lb_vocab.build_vocab()
logger.info('Finished. Size of vocab: {}. # Class: {}.'.format(
len(vocab), len(lb_vocab)))
logger.info('<pad>: {}'.format(vocab.to_index('<pad>')))
logger.info('<unk>: {}'.format(vocab.to_index('<unk>')))
logger.info('<cls>: {}'.format(vocab.to_index('<cls>')))
return sets, vocab, lb_vocab
def build_vocab(cleaned_captions):
"""
Parses training set token file captions and builds a Vocabulary object
Args:
cleaned_captions (str list): cleaned list of human captions to build vocab with
Returns:
vocab (Vocabulary): Vocabulary object
"""
# QUESTION 1.1
# TODO collect words
word_count = {}
for caption in cleaned_captions:
for word in caption.split():
if word not in word_count:
word_count[word] = 0
word_count[word] += 1
# create a vocab instance
vocab = Vocabulary()
# add the token words
vocab.add_word('<pad>')
vocab.add_word('<start>')
vocab.add_word('<end>')
vocab.add_word('<unk>')
# TODO add the rest of the words from the cleaned captions here
# vocab.add_word('word')
for word, n in word_count.items():
if n > 3:
vocab.add_word(word)
return vocab
def build_vocab(annotation_path, threshold):
"""Build a simple vocabulary wrapper."""
df_annotation = pd.read_csv(annotation_path, keep_default_na=False)
counter = Counter()
for _, each_annotation in df_annotation.iterrows():
attribute_tags = each_annotation['attribute_tags']
tokens = list(re.split('[,]', attribute_tags))
if len(tokens) > 0:
tokens = [
token.strip() for token in tokens if not has_numbers(token)
]
counter.update(tokens)
# If the word frequency is less than 'threshold', then the word is discarded.
words = [word for word, cnt in counter.items() if cnt >= threshold]
# print(words)
# Create a vocab wrapper and add some special tokens.
vocab = Vocabulary()
vocab.add_word('<pad>')
vocab.add_word('<start>')
vocab.add_word('<end>')
vocab.add_word('<unk>')
# Add the words to the vocabulary.
for i, word in enumerate(words):
vocab.add_word(word)
return vocab
def build_vocab(caption, threshold):
"""Build a simple vocabulary wrapper."""
counter = Counter()
n = len(caption.keys())
for i, key in enumerate(caption.keys()):
for sentence in caption[key]:
tokens = nltk.tokenize.word_tokenize(sentence)
counter.update(tokens)
if i % 1000 == 0:
print("[%d/%d] Tokenized the captions." % (i, n))
# If the word frequency is less than 'threshold', then the word is discarded.
words = [word for word, cnt in counter.items() if cnt >= threshold]
# Creates a vocab wrapper and add some special tokens.
vocab = Vocabulary()
vocab.add_word('<pad>')
vocab.add_word('<start>')
vocab.add_word('<end>')
vocab.add_word('<unk>')
# Adds the words to the vocabulary.
for i, word in enumerate(words):
vocab.add_word(word)
return vocab
def process_dataset(self, fileobj):
vocabulary = Vocabulary()
sequence_delimiters = [0]
while True:
line = fileobj.readline()
if line is None or len(line) < 1:
break
sequence_delimiters.append(fileobj.tell())
words = line.strip().split(',')
for word in words:
vocabulary.add_word(word)
dataset_info = {
'sequence_delimiters': sequence_delimiters,
'vocabulary': vocabulary
}
return dataset_info
class EmbeddingLoader(Dataset):
"""Face Landmarks dataset."""
def __init__(self, n_views, emb_directory, label_directory):
self.n_views = n_views
self._read_embedding_dir(emb_directory) # Creates list of paths with all videos
self._read_label_dir(label_directory) # Creates list of paths with all videos
# The negative example has to be from outside the buffer window. Taken from both sides of
# ihe frame.
self.sequence_index = 0
self.vocab = Vocabulary()
words =['blue', 'orange', 'green', 'blue', 'red', 'yellow']
for i, word in enumerate(words):
self.vocab.add_word(word)
def __len__(self):
return len(self.emb_paths)
def __getitem__(self, idx):
emb = read_npy_file(self.emb_paths[idx])
seq_idx = self.emb_paths[idx].split('/')[-1].split('_')[0]
label = read_caption(os.path.join(self._label_directory, seq_idx + '_parsed.txt'))
label = nltk.tokenize.word_tokenize(str(label).lower())
active_label = self.vocab(label[-4])
passive_label = self.vocab(label[-2])
# Convert caption (string) to word ids.
target1 = torch.LongTensor([active_label])
target2 = torch.LongTensor([passive_label])
emb = np.mean(emb, axis=0)
emb = torch.FloatTensor(emb)
return emb, target1, target2
def _read_embedding_dir(self, emb_directory):
self._emb_directory = emb_directory
filenames = ls_npy(emb_directory)
self.emb_paths = [os.path.join(self._emb_directory, f) for f in filenames]
self.sequence_count = int(len(self.emb_paths) / self.n_views)
def _read_label_dir(self, label_directory):
self._label_directory = label_directory
filenames = ls_txt(label_directory)
self.label_paths = [os.path.join(self._label_directory, f) for f in filenames]
def glove(self):
embeddings = []
vocab = Vocabulary()
with open(self.file_name, encoding='UTF-8') as f:
for line in f:
values = line.split()
vocab.add_word(values[0])
embeddings.append(np.asarray(values[1:], dtype='float32'))
if "<UNK>" not in vocab.word2idx:
vocab.add_word("<UNK>")
embeddings.append(
np.random.uniform(low=-0.05,
high=0.05,
size=self.embedding_dim))
return vocab, len(embeddings), Embeddings(
vocab_size=len(embeddings),
embedding_dim=self.embedding_dim,
embeddings=np.array(embeddings, dtype='float32'),
trainable=False)
def build_vocab_question(imgs, params):
# build vocabulary for question and answers.
count_thr = params['word_count_threshold']
# count up the number of words
counts = {}
for img in imgs:
for w in img['processed_tokens']:
counts[w] = counts.get(w, 0) + 1
cw = sorted([(count, w) for w, count in counts.items()], reverse=True)
print('top words and their counts:')
print('\n'.join(map(str, cw[:20])))
# print some stats
total_words = sum(counts.values())
print('total words:', total_words)
bad_words = [w for w, n in counts.items() if n <= count_thr]
words = [w for w, n in counts.items() if n > count_thr]
bad_count = sum(counts[w] for w in bad_words)
print('number of bad words: %d/%d = %.2f%%' %
(len(bad_words), len(counts), len(bad_words) * 100.0 / len(counts)))
print('number of words in vocab would be %d' % (len(words), ))
print('number of UNKs: %d/%d = %.2f%%' %
(bad_count, total_words, bad_count * 100.0 / total_words))
vocab = Vocabulary()
vocab.add_word('<pad>')
vocab.add_word('<start>')
vocab.add_word('<end>')
vocab.add_word('<unk>')
for i, word in enumerate(words):
vocab.add_word(word)
for img in imgs:
txt = img['processed_tokens']
question = [
w if counts.get(w, 0) > count_thr else '<unk>' for w in txt
]
img['final_question'] = question
return imgs, vocab
def build_vocab(json_path: str, threshold: int) -> Vocabulary:
coco_cls = COCO(json_path)
countt_cls = Counter()
ids = coco_cls.anns.keys()
for i, idt in enumerate(ids):
caption = str(coco_cls.anns[idt]['caption'])
tokens = nltk.tokenize.word_tokenize(caption.lower())
countt_cls.update(tokens)
if (i + 1) % 1000 == 0:
print('%d/%d tokenize the captions' % (i + 1, len(ids)))
words = [word for word, cnt in countt_cls.items() if cnt >= threshold]
vocab = Vocabulary()
vocab.add_word('<pad>')
vocab.add_word('<start>')
vocab.add_word('<end>')
vocab.add_word('<unk>')
for word in words:
vocab.add_word(word)
return vocab
def read_instances_from_file(files, max_len, keep_case):
''' Collect instances and construct vocab '''
vocab = Vocabulary()
pos_vocab = Vocabulary(need_default=False)
ner_vocab = Vocabulary(need_default=False)
srl_vocab = Vocabulary(need_default=False)
chunk_vocab = Vocabulary(need_default=False)
sets = []
for file in files:
sents = []
pos_labels, ner_labels, srl_labels, chunk_labels = [], [], [], []
trimmed_sent = 0
with open(file) as f:
lines = f.readlines()
sent = []
pos_label, ner_label, srl_label, chunk_label = [], [], [], []
for l in lines:
l = l.strip()
if l == '':
if len(sent) > 0:
if len(sent) > max_len:
trimmed_sent += 1
pos_labels.append(pos_label[:max_len])
ner_labels.append(ner_label[:max_len])
srl_labels.append(srl_label[:max_len])
chunk_labels.append(chunk_label[:max_len])
sents.append(sent[:max_len])
else:
pos_labels.append(pos_label)
ner_labels.append(ner_label)
srl_labels.append(srl_label)
chunk_labels.append(chunk_label)
sents.append(sent)
sent = []
pos_label, ner_label, srl_label, chunk_label = [], [], [], []
else:
l = l.split()
word = l[0]
if not keep_case:
word = word.lower()
sent.append(word)
pos_label.append(l[2])
ner_label.append(l[3])
srl_label.append(l[4])
chunk_label.append(l[5])
vocab.add_word(word)
pos_vocab.add_word(l[2])
ner_vocab.add_word(l[3])
srl_vocab.add_word(l[4])
chunk_vocab.add_word(l[5])
sets.append({
'sents': sents,
'pos_labels': pos_labels,
'ner_labels': ner_labels,
'srl_labels': srl_labels,
'chunk_labels': chunk_labels
})
logger.info('Get {} instances from file {}'.format(len(sents), file))
if trimmed_sent:
logger.warning(
'{} sentences are trimmed. Max sentence length: {}.'.format(
trimmed_sent, max_len))
logger.info('Building vocabulary...')
vocab.build_vocab()
logger.info('Finished. Size of vocab: {}'.format(len(vocab)))
pos_vocab.build_vocab()
ner_vocab.build_vocab()
srl_vocab.build_vocab()
chunk_vocab.build_vocab()
logger.info('# class in POS Tagging: {}'.format(len(pos_vocab)))
logger.info('# class in NER Tagging: {}'.format(len(ner_vocab)))
logger.info('# class in SRL Tagging: {}'.format(len(srl_vocab)))
logger.info('# class in Chunking: {}'.format(len(chunk_vocab)))
return sets, vocab, [pos_vocab, ner_vocab, srl_vocab, chunk_vocab]
class MultiViewTripletLabelDataset(Dataset):
def __init__(self, n_views, video_directory, label_directory, image_size, sample_size=500):
self.frame_size = image_size
self.n_views = n_views
self._read_video_dir(video_directory)
self.vocab = Vocabulary()
words =['blue', 'orange', 'green', 'red', 'yellow']
for i, word in enumerate(words):
self.vocab.add_word(word)
self._read_label_dir(label_directory)
self._count_frames()
self.sample_size = sample_size
self.valid_sequence_indices = self._get_valid_sequence_indices(label_directory)
self.sequence_index = 0
self.negative_frame_margin = 30
assert len(self.label_paths) == int(len(self.video_paths) / self.n_views)
def __len__(self):
return len(self.valid_sequence_indices)
def __getitem__(self, idx):
# build image triplet item
self.sequence_index = int(idx)
triplets = []
triplets = torch.Tensor(self.sample_size, 3, 3, *self.frame_size)
label = read_caption(self.label_paths[self.sequence_index])
label = nltk.tokenize.word_tokenize(str(label).lower())
# print("index: {}, label: {}".format(self.valid_sequence_indices[idx], label))
for i in range(self.sample_size):
snaps = self.get_videos(self.sequence_index * self.n_views)
anchor_frame, positive_frame, negative_frame = self.sample_triplet(snaps)
triplets[i, 0, :, :, :] = anchor_frame
triplets[i, 1, :, :, :] = positive_frame
triplets[i, 2, :, :, :] = negative_frame
try:
active_label = self.vocab(label[-4])
passive_label = self.vocab(label[-2])
except:
print("Unknown label: ", label)
print("sequence: ", self.sequence_index)
seq_idx = torch.LongTensor([self.sequence_index] * self.sample_size)
# Convert caption (string) to word ids.
target = torch.LongTensor([[active_label, passive_label]] * self.sample_size) # Needs padded targets of same size as inputs
return triplets, target, seq_idx
def _read_video_dir(self, video_directory):
self._video_directory = video_directory
filenames = ls(video_directory)
self.video_paths = [os.path.join(self._video_directory, f) for f in filenames]
self.sequence_count = int(len(self.video_paths) / self.n_views)
def _count_frames(self):
frame_lengths = np.array([len(imageio.read(p)) for p in self.video_paths])
self.frame_lengths = frame_lengths - OFFSET
self.cumulative_lengths = np.zeros(len(self.frame_lengths), dtype=np.int32)
prev = 0
for i, frames in enumerate(self.frame_lengths):
prev = self.cumulative_lengths[i-1]
self.cumulative_lengths[i] = prev + frames
def _read_label_dir(self, label_directory):
self._label_directory = label_directory
filenames = ls_txt(label_directory)
self.label_paths = [os.path.join(self._label_directory, f) for f in filenames]
def _get_valid_sequence_indices(self, label_directory):
valid_sequence_indices = []
curr_seq_idx = 0
filenames = ls_txt(label_directory)
for filename in filenames:
label = read_caption(os.path.join(label_directory, filename))
label = nltk.tokenize.word_tokenize(str(label).lower())
if label[-4] is None or label[-2] is None:
curr_seq_idx += 1
continue
else:
valid_sequence_indices.append(int(filename.split('_')[0]))
curr_seq_idx += 1
return valid_sequence_indices
@functools.lru_cache(maxsize=1)
def get_videos(self, index):
views = []
for i in range(self.n_views):
views.append(read_video(self.video_paths[index + i], self.frame_size))
return views
def sample_triplet(self, snaps):
loaded_sample = False
while not loaded_sample:
try:
anchor_index = self.sample_anchor_frame_index()
positive_index = anchor_index
negative_index = self.sample_negative_frame_index(anchor_index)
loaded_sample = True
except:
print("Error loading video - sequence index: ", self.sequence_index)
print("video lengths: ", [len(snaps[i]) for i in range(0, len(snaps))])
print("Maybe margin too high")
# random sample anchor view,and positive view
view_set = set(range(self.n_views))
anchor_view = np.random.choice(np.array(list(view_set)))
view_set.remove(anchor_view)
positive_view = np.random.choice(np.array(list(view_set)))
negative_view = anchor_view # negative example comes from same view INQUIRE TODO
anchor_frame = snaps[anchor_view][anchor_index]
positive_frame = snaps[positive_view][positive_index]
negative_frame = snaps[negative_view][negative_index]
return (torch.Tensor(anchor_frame), torch.Tensor(positive_frame),
torch.Tensor(negative_frame))
def build_set(self):
triplets = []
triplets = torch.Tensor(self.sample_size, 3, 3, *self.frame_size)
for i in range(0, self.sample_size):
snaps = self.get_videos(self.sequence_index * self.n_views)
anchor_frame, positive_frame, negative_frame = self.sample_triplet(snaps)
triplets[i, 0, :, :, :] = anchor_frame
triplets[i, 1, :, :, :] = positive_frame
triplets[i, 2, :, :, :] = negative_frame
self.sequence_index = (self.sequence_index + 1) % self.sequence_count
# Second argument is labels. Not used.
return TensorDataset(triplets, torch.zeros(triplets.size()[0]))
def sample_anchor_frame_index(self):
arange = np.arange(0, self.frame_lengths[self.sequence_index * self.n_views])
return np.random.choice(arange)
# def sample_positive_frame_index(self, anchor_index):
# upper_bound = min(self.frame_lengths[self.sequence_index * self.n_views + 1], anchor_index)
# return upper_bound # in case video has less frames than anchor video
def negative_frame_indices(self, anchor_index):
video_length = self.frame_lengths[self.sequence_index * self.n_views]
lower_bound = 0
upper_bound = max(0, anchor_index - self.negative_frame_margin)
range1 = np.arange(lower_bound, upper_bound)
lower_bound = min(anchor_index + self.negative_frame_margin, video_length)
upper_bound = video_length
range2 = np.arange(lower_bound, upper_bound)
return np.concatenate([range1, range2])
def sample_negative_frame_index(self, anchor_index):
return np.random.choice(self.negative_frame_indices(anchor_index))
def build_vocabs():
tasks = [
'.'.join([id, syn]) for id in ['autoid', 'goldid']
for syn in ['autosyn', 'goldsyn']
]
stypes = ['train', 'dev', 'test']
loader = StreusleLoader()
STREUSLE_BASE = os.environ.get(
'STREUSLE_BASE'
) or '/cs/usr/aviramstern/nlp/datasets/streusle_v4/release'
all_files = [
STREUSLE_BASE + '/' + stype + '/streusle.ud_' + stype + '.' + task +
'.json' for task in tasks for stype in stypes
]
records = sum([loader.load(f, input_format='json') for f in all_files], [])
samples = [streusle_record_to_lstm_model_sample(r) for r in records]
pp_vocab = Vocabulary('PREPS')
pp_vocab.add_words(
set([
x.token for s in samples for x, y in zip(s.xs, s.ys)
if any([y.supersense_role, y.supersense_func])
]))
ner_vocab = Vocabulary('NERS')
ner_vocab.add_words(
set([x.ner for s in samples for x, y in zip(s.xs, s.ys)]))
ner_vocab.add_word(None)
lemmas_vocab = Vocabulary('LEMMAS')
lemmas_vocab.add_words(
set([x.lemma for s in samples for x, y in zip(s.xs, s.ys)]))
ud_dep_vocab = Vocabulary('UD_DEPS')
ud_dep_vocab.add_words(
set([x.ud_dep for s in samples for x, y in zip(s.xs, s.ys)]))
ud_dep_vocab.add_word(None)
ud_xpos_vocab = Vocabulary('UD_XPOS')
ud_xpos_vocab.add_words(
set([x.ud_xpos for s in samples for x, y in zip(s.xs, s.ys)]))
ud_xpos_vocab.add_word(None)
token_vocab = Vocabulary('TOKENS')
token_vocab.add_words(
set([x.token for s in samples for x, y in zip(s.xs, s.ys)]))
govobj_config_vocab = Vocabulary('GOVOBJ_CONFIGS')
govobj_config_vocab.add_words(
set([x.govobj_config for s in samples for x, y in zip(s.xs, s.ys)]))
pss_vocab = Vocabulary('PSS')
pss_vocab.add_words(supersense_repo.PREPOSITION_SUPERSENSES_SET)
pss_vocab.add_word(None)
pss_vocab = Vocabulary('LEXCAT')
pss_vocab.add_words(
set([x.lexcat for s in samples for x, y in zip(s.xs, s.ys)]))
return [
pp_vocab, ner_vocab, lemmas_vocab, ud_dep_vocab, ud_xpos_vocab,
token_vocab, pss_vocab, govobj_config_vocab
]