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WZM/vocab.py

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+import nltk
+from collections import Counter
+import argparse
+import os
+import json
+
+annotations = {
+    'coco_splits': ['train_caps.txt', 'val_caps.txt', 'test_caps.txt'],
+    'flickr30k_splits': ['train_caps.txt', 'val_caps.txt', 'test_caps.txt'],
+    'rsicd_precomp': ['train_caps.txt', 'test_caps.txt'],
+    'rsitmd_precomp': ['train_caps.txt', 'test_caps.txt'],
+    'ucm_precomp': ['train_caps.txt', 'val_caps.txt'],
+    'sydney_precomp': ['train_caps.txt', 'val_caps.txt'],
+    }
+
+
+class Vocabulary(object):
+    """Simple vocabulary wrapper."""
+
+    def __init__(self):
+        self.word2idx = {}
+        self.idx2word = {}
+        self.idx = 0
+
+    def add_word(self, word):
+        if word not in self.word2idx:
+            self.word2idx[word] = self.idx
+            self.idx2word[self.idx] = word
+            self.idx += 1
+
+    def __call__(self, word):
+        if word not in self.word2idx:
+            return self.word2idx['<unk>']
+        return self.word2idx[word]
+
+    def __len__(self):
+        return len(self.word2idx)
+
+
+def serialize_vocab(vocab, dest):
+    d = {}
+    d['word2idx'] = vocab.word2idx
+    d['idx2word'] = vocab.idx2word
+    d['idx'] = vocab.idx
+    with open(dest, "w") as f:
+        json.dump(d, f)
+
+
+def deserialize_vocab(src):
+    with open(src) as f:
+        d = json.load(f)
+    vocab = Vocabulary()
+    vocab.word2idx = d['word2idx']
+    vocab.idx2word = d['idx2word']
+    vocab.idx = d['idx']
+    return vocab
+
+
+def from_txt(txt):
+    captions = []
+    with open(txt, 'rb') as f:
+        for line in f:
+            captions.append(line.strip())
+    return captions
+
+
+def build_vocab(data_path, data_name, caption_file, threshold):
+    """Build a simple vocabulary wrapper."""
+
+    stopword_list = list(set(nltk.corpus.stopwords.words('english')))
+    counter = Counter()
+    for path in caption_file[data_name]:
+        full_path = os.path.join(os.path.join(data_path, data_name), path)
+        captions = from_txt(full_path)
+
+        for i, caption in enumerate(captions):
+            tokens = nltk.tokenize.word_tokenize(
+                caption.lower().decode('utf-8'))
+            punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']
+            tokens = [k for k in tokens if k not in punctuations]
+            tokens = [k for k in tokens if k not in stopword_list]
+            counter.update(tokens)
+
+            if i % 1000 == 0:
+                print("[%d/%d] tokenized the captions." % (i, len(captions)))
+
+    # Discard if the occurrence of the word is less than min_word_cnt.
+    words = [word for word, cnt in counter.items() if cnt >= threshold]
+
+    # 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 words to the vocabulary.
+    for i, word in enumerate(words):
+        vocab.add_word(word)
+    # vocab.add_word('<unk>')
+    return vocab
+
+
+def main(data_path, data_name):
+    vocab = build_vocab(data_path, data_name, caption_file=annotations, threshold=4)
+    serialize_vocab(vocab, 'vocab/%s_vocab.json' % data_name)
+    print("Saved vocabulary file to ", 'vocab/%s_vocab.json' %(data_name))
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_path', default='data')
+    parser.add_argument('--data_name', default='rsitmd_precomp',
+                        help='{coco,f30k}')
+    opt = parser.parse_args()
+    main(opt.data_path, opt.data_name)

WZM/vocab_glove.py

@@ -0,0 +1,119 @@
+import numpy as np
+import nltk
+from collections import Counter
+import argparse
+import os
+import json
+
+annotations = {
+    'coco_splits': ['train_caps.txt', 'val_caps.txt', 'test_caps.txt'],
+    'flickr30k_splits': ['train_caps.txt', 'val_caps.txt', 'test_caps.txt'],
+    'rsicd_precomp': ['train_caps.txt', 'test_caps.txt'],
+    'rsitmd_precomp': ['train_caps.txt', 'test_caps.txt'],
+    'ucm_precomp': ['train_caps.txt', 'val_caps.txt'],
+    'sydney_precomp': ['train_caps.txt', 'val_caps.txt'],
+    }
+
+
+class Vocabulary(object):
+    """Simple vocabulary wrapper."""
+
+    def __init__(self):
+        self.word2idx = {}
+        self.idx2word = {}
+        self.idx = 0
+
+    def add_word(self, word):
+        if word not in self.word2idx:
+            self.word2idx[word] = self.idx
+            self.idx2word[self.idx] = word
+            self.idx += 1
+
+    def __call__(self, word):
+        if word not in self.word2idx:
+            return self.word2idx['<unk>']
+        return self.word2idx[word]
+
+    def __len__(self):
+        return len(self.word2idx)
+
+
+def serialize_vocab(vocab, dest):
+    d = {}
+    d['word2idx'] = vocab.word2idx
+    d['idx2word'] = vocab.idx2word
+    d['idx'] = vocab.idx
+    with open(dest, "w") as f:
+        json.dump(d, f)
+
+
+def deserialize_vocab(src):
+    with open(src) as f:
+        d = json.load(f)
+    vocab = Vocabulary()
+    vocab.word2idx = d['word2idx']
+    vocab.idx2word = d['idx2word']
+    vocab.idx = d['idx']
+    return vocab
+
+
+def from_txt(txt):
+    captions = []
+    with open(txt, 'rb') as f:
+        for line in f:
+            captions.append(line.strip())
+    return captions
+
+
+def build_vocab(data_path, data_name, caption_file, threshold):
+    """Build a simple vocabulary wrapper."""
+
+    # stopword_list = list(set(nltk.corpus.stopwords.words('english')))
+    # counter = Counter()
+    # for path in caption_file[data_name]:
+    #     full_path = os.path.join(os.path.join(data_path, data_name), path)
+    #     captions = from_txt(full_path)
+
+    #     for i, caption in enumerate(captions):
+    #         tokens = nltk.tokenize.word_tokenize(
+    #             caption.lower().decode('utf-8'))
+    #         punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']
+    #         tokens = [k for k in tokens if k not in punctuations]
+    #         tokens = [k for k in tokens if k not in stopword_list]
+    #         counter.update(tokens)
+
+    #         if i % 1000 == 0:
+    #             print("[%d/%d] tokenized the captions." % (i, len(captions)))
+
+    # # Discard if the occurrence of the word is less than min_word_cnt.
+    # words = [word for word, cnt in counter.items() if cnt >= threshold]
+
+    # Create a vocab wrapper and add some special tokens.
+    words = np.load('/home/wzm/crossmodal/vocab_npa.npy')
+    vocab = Vocabulary()
+    # vocab.add_word('<pad>')
+    # vocab.add_word('<start>')
+    # vocab.add_word('<end>')
+    # vocab.add_word('<unk>')
+
+    # Add words to the vocabulary.
+    for i, word in enumerate(words):
+        vocab.add_word(word)
+    # vocab.add_word('<unk>')
+    return vocab
+
+
+def main(data_path, data_name):
+    vocab = build_vocab(data_path, data_name, caption_file=annotations, threshold=4)
+    serialize_vocab(vocab, 'vocab/%s_vocab.json' % data_name)
+    print("Saved vocabulary file to ", 'vocab/%s_vocab.json' %(data_name))
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_path', default='data')
+    parser.add_argument('--data_name', default='rsitmd_precomp',
+                        help='{coco,f30k}')
+    opt = parser.parse_args()
+    main(opt.data_path, opt.data_name)