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| import random | |
| import nltk | |
| nltk.data.path.append('/mnt/data/nltk_data') | |
| import numpy as np | |
| from utils.constants import IMAGENET_DEFAULT_TEMPLATES | |
| def get_tag(tokenized, tags): | |
| if not isinstance(tags, (list, tuple)): | |
| tags = [tags] | |
| ret = [] | |
| for (word, pos) in nltk.pos_tag(tokenized): | |
| for tag in tags: | |
| if pos == tag: | |
| ret.append(word) | |
| return ret | |
| def get_noun_phrase(tokenized): | |
| # Taken from Su Nam Kim Paper... | |
| grammar = r""" | |
| NBAR: | |
| {<NN.*|JJ>*<NN.*>} # Nouns and Adjectives, terminated with Nouns | |
| NP: | |
| {<NBAR>} | |
| {<NBAR><IN><NBAR>} # Above, connected with in/of/etc... | |
| """ | |
| chunker = nltk.RegexpParser(grammar) | |
| chunked = chunker.parse(nltk.pos_tag(tokenized)) | |
| continuous_chunk = [] | |
| current_chunk = [] | |
| for subtree in chunked: | |
| if isinstance(subtree, nltk.Tree): | |
| current_chunk.append(' '.join([token for token, pos in subtree.leaves()])) | |
| elif current_chunk: | |
| named_entity = ' '.join(current_chunk) | |
| if named_entity not in continuous_chunk: | |
| continuous_chunk.append(named_entity) | |
| current_chunk = [] | |
| else: | |
| continue | |
| return continuous_chunk | |
| def text_noun_with_prompt_all(text, phrase_prob=0.0, append_text=True): | |
| tokenized = nltk.word_tokenize(text) | |
| if random.random() >= phrase_prob: | |
| nouns = get_tag(tokenized, ['NN', 'NNS', 'NNP']) | |
| else: | |
| nouns = get_noun_phrase(tokenized) | |
| prompt_texts = [np.random.choice(IMAGENET_DEFAULT_TEMPLATES).format(noun) for noun in nouns] | |
| if append_text: | |
| prompt_texts += [text] | |
| nouns += [text] | |
| return prompt_texts, nouns |