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convert_birds_to_skipthought_hd5_script.py
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convert_birds_to_skipthought_hd5_script.py
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import os
from os.path import join, isfile
import numpy as np
import h5py
from glob import glob
from torch.utils.serialization import load_lua
from PIL import Image
import yaml
import io
import pdb
import nltk
import re
from collections import Counter
import torch
from torch.autograd import Variable
import sys
sys.path.append('skip-thoughts.torch/pytorch')
from skipthoughts import UniSkip
dir_st = 'data/skip-thoughts'
import tqdm
import pickle
with open('config.yaml', 'r') as f:
config = yaml.load(f)
images_path = config['birds_images_path']
embedding_path = config['birds_embedding_path']
text_path = config['birds_text_path']
datasetDir = config['birds_dataset_path']
val_classes = open(config['val_split_path']).read().splitlines()
train_classes = open(config['train_split_path']).read().splitlines()
test_classes = open(config['test_split_path']).read().splitlines()
f = h5py.File(datasetDir, 'w')
train = f.create_group('train')
valid = f.create_group('valid')
test = f.create_group('test')
class Vocabulary(object):
"""Simple vocabulary wrapper."""
def __init__(self):
self.word2idx = {}
self.idx2word = {}
self.idx = 0
def add_word(self, word):
if not word in self.word2idx:
self.word2idx[word] = self.idx
self.idx2word[self.idx] = word
self.idx += 1
def __call__(self, word):
if not word in self.word2idx:
return self.word2idx['<unk>']
return self.word2idx[word]
def __len__(self):
return len(self.word2idx)
def sanitize_string(caption):
caption = caption.strip()
caption = caption.encode('ascii', 'ignore')
caption = caption.decode('ascii')
caption = re.sub(r'[!\"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~]+', '', caption)
tokens = nltk.tokenize.word_tokenize(caption.lower())
return tokens
def build_vocab():
threshold = 4
"""Build a simple vocabulary wrapper."""
# go through all files
counter = Counter()
for _class in sorted(os.listdir(embedding_path)):
txt_path = os.path.join(text_path, _class)
for txt_file in sorted(glob(txt_path + "/*.txt")):
lines = open(txt_file, "r").readlines()
for caption in lines:
tokens = sanitize_string(caption)
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]
# 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
# vocab = build_vocab()
# with open('data/birds_vocab.pkl', 'wb') as f:
# pickle.dump(vocab, f)
with open('data/birds_vocab.pkl', 'rb') as f:
vocab = pickle.load(f)
all_words_in_vocab = vocab.word2idx.keys()
uniskip = UniSkip(dir_st, all_words_in_vocab)
def get_ids(tokens, vocab):
ids = []
# appending start and eos at the beginning and the end respectively for every sequence
ids.append(vocab('<start>'))
for word in tokens:
ids.append(vocab(word))
ids.append(vocab('<end>'))
return ids
for _class in sorted(os.listdir(embedding_path)):
split = ''
if _class in train_classes:
split = train
elif _class in val_classes:
split = valid
elif _class in test_classes:
split = test
data_path = os.path.join(embedding_path, _class)
txt_path = os.path.join(text_path, _class)
for example, txt_file in zip(sorted(glob(data_path + "/*.t7")), sorted(glob(txt_path + "/*.txt"))):
example_data = load_lua(example)
img_path = example_data['img']
embeddings = example_data['txt'].numpy()
example_name = img_path.split('/')[-1][:-4]
f = open(txt_file, "r")
txt = f.readlines()
f.close()
img_path = os.path.join(images_path, img_path)
img = open(img_path, 'rb').read()
txt_choice = np.random.choice(range(10), 5)
embeddings = embeddings[txt_choice]
txt = np.array(txt)
txt = txt[txt_choice]
dt = h5py.special_dtype(vlen=str)
batch_txt_ids = []
max_len = 0
id_lens = []
for t in txt:
skip_thought_txt = sanitize_string(t)
txt_ids = get_ids(skip_thought_txt, vocab)
max_len = len(txt_ids) if max_len < len(txt_ids) else max_len
batch_txt_ids.append(txt_ids)
id_lens.append(len(txt_ids))
# padding with eos
for arr in batch_txt_ids:
n = len(arr)
rem = max_len - n
concat_arr = [vocab('<end>')]*rem
arr+=concat_arr
input = Variable(torch.LongTensor(batch_txt_ids))
output_seq2vec = uniskip(input, lengths=id_lens).data.numpy()
# for c, e in enumerate(embeddings):
for c, e in enumerate(output_seq2vec):
ex = split.create_group(example_name + '_' + str(c))
ex.create_dataset('name', data=example_name)
ex.create_dataset('img', data=np.void(img))
ex.create_dataset('embeddings', data=e)
ex.create_dataset('class', data=_class)
ex.create_dataset('txt', data=txt[c].astype(object), dtype=dt)
print(example_name)