100 lines
2.7 KiB
Python
100 lines
2.7 KiB
Python
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# -----------------------------------------------------------
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# Stacked Cross Attention Network implementation based on
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# https://arxiv.org/abs/1803.08024.
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# "Stacked Cross Attention for Image-Text Matching"
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# Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, Xiaodong He
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#
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# Writen by Kuang-Huei Lee, 2018
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# ---------------------------------------------------------------
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"""Vocabulary wrapper"""
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import nltk
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from collections import Counter
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import argparse
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import os
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import json
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annotations = {
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'coco_precomp': ['train_caps.txt', 'dev_caps.txt'],
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'f30k_precomp': ['train_caps.txt', 'dev_caps.txt'],
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}
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class Vocabulary(object):
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"""Simple vocabulary wrapper."""
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def __init__(self):
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self.word2idx = {}
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self.idx2word = {}
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self.idx = 0
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def add_word(self, word):
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if word not in self.word2idx:
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self.word2idx[word] = self.idx
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self.idx2word[self.idx] = word
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self.idx += 1
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def __call__(self, word):
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if word not in self.word2idx:
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return self.word2idx['<unk>']
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return self.word2idx[word]
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def __len__(self):
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return len(self.word2idx)
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def serialize_vocab(vocab, dest):
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d = {}
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d['word2idx'] = vocab.word2idx
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d['idx2word'] = vocab.idx2word
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d['idx'] = vocab.idx
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with open(dest, "w") as f:
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json.dump(d, f)
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def deserialize_vocab(src):
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with open(src) as f:
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d = json.load(f)
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vocab = Vocabulary()
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vocab.word2idx = d['word2idx']
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vocab.idx2word = d['idx2word']
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vocab.idx = d['idx']
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return vocab
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def from_txt(txt):
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captions = []
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with open(txt, 'rb') as f:
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for line in f:
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captions.append(line.strip())
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return captions
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def build_vocab(data_path, data_name, caption_file, threshold):
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"""Build a simple vocabulary wrapper."""
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counter = Counter()
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for path in caption_file[data_name]:
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full_path = os.path.join(os.path.join(data_path, data_name), path)
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captions = from_txt(full_path)
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for i, caption in enumerate(captions):
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tokens = nltk.tokenize.word_tokenize(
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caption.lower().decode('utf-8'))
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counter.update(tokens)
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if i % 1000 == 0:
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print("[%d/%d] tokenized the captions." % (i, len(captions)))
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# Discard if the occurrence of the word is less than min_word_cnt.
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words = [word for word, cnt in counter.items() if cnt >= threshold]
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# Create a vocab wrapper and add some special tokens.
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vocab = Vocabulary()
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vocab.add_word('<pad>')
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vocab.add_word('<start>')
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vocab.add_word('<end>')
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vocab.add_word('<unk>')
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# Add words to the vocabulary.
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for i, word in enumerate(words):
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vocab.add_word(word)
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return vocab
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