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cb4c2c3 Mar 8, 2016
@jiasenlu @jnhwkim @honnibal
executable file 268 lines (207 sloc) 9.46 KB
"""
Preoricess a raw json dataset into hdf5/json files.
Caption: Use spaCy or NLTK or split function to get tokens.
"""
import copy
from random import shuffle, seed
import sys
import os.path
import argparse
import glob
import numpy as np
from scipy.misc import imread, imresize
import scipy.io
import pdb
import string
import h5py
from nltk.tokenize import word_tokenize
import json
import spacy.en
import re
def tokenize(sentence):
return [i for i in re.split(r"([-.\"',:? !\$#@~()*&\^%;\[\]/\\\+<>\n=])", sentence) if i!='' and i!=' ' and i!='\n'];
def prepro_question(imgs, params):
# preprocess all the question
print 'example processed tokens:'
for i,img in enumerate(imgs):
s = img['question']
if params['token_method'] == 'nltk':
txt = word_tokenize(str(s).lower())
elif params['token_method'] == 'spacy':
txt = [token.norm_ for token in params['spacy'](s)]
else:
txt = tokenize(s)
img['processed_tokens'] = txt
if i < 10: print txt
if i % 1000 == 0:
sys.stdout.write("processing %d/%d (%.2f%% done) \r" % (i, len(imgs), i*100.0/len(imgs)) )
sys.stdout.flush()
return imgs
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.iteritems()], reverse=True)
print 'top words and their counts:'
print '\n'.join(map(str,cw[:20]))
# print some stats
total_words = sum(counts.itervalues())
print 'total words:', total_words
bad_words = [w for w,n in counts.iteritems() if n <= count_thr]
vocab = [w for w,n in counts.iteritems() 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(vocab), )
print 'number of UNKs: %d/%d = %.2f%%' % (bad_count, total_words, bad_count*100.0/total_words)
# lets now produce the final annotation
# additional special UNK token we will use below to map infrequent words to
print 'inserting the special UNK token'
vocab.append('UNK')
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 apply_vocab_question(imgs, wtoi):
# apply the vocab on test.
for img in imgs:
txt = img['processed_tokens']
question = [w if wtoi.get(w,len(wtoi)+1) != (len(wtoi)+1) else 'UNK' for w in txt]
img['final_question'] = question
return imgs
def get_top_answers(imgs, params):
counts = {}
for img in imgs:
ans = img['ans']
counts[ans] = counts.get(ans, 0) + 1
cw = sorted([(count,w) for w,count in counts.iteritems()], reverse=True)
print 'top answer and their counts:'
print '\n'.join(map(str,cw[:20]))
vocab = []
for i in range(params['num_ans']):
vocab.append(cw[i][1])
return vocab[:params['num_ans']]
def encode_question(imgs, params, wtoi):
max_length = params['max_length']
N = len(imgs)
label_arrays = np.zeros((N, max_length), dtype='uint32')
label_length = np.zeros(N, dtype='uint32')
question_id = np.zeros(N, dtype='uint32')
question_counter = 0
for i,img in enumerate(imgs):
question_id[question_counter] = img['ques_id']
label_length[question_counter] = min(max_length, len(img['final_question'])) # record the length of this sequence
question_counter += 1
for k,w in enumerate(img['final_question']):
if k < max_length:
label_arrays[i,k] = wtoi[w]
return label_arrays, label_length, question_id
def encode_answer(imgs, atoi):
N = len(imgs)
ans_arrays = np.zeros(N, dtype='uint32')
for i, img in enumerate(imgs):
ans_arrays[i] = atoi[img['ans']]
return ans_arrays
def encode_mc_answer(imgs, atoi):
N = len(imgs)
mc_ans_arrays = np.zeros((N, 18), dtype='uint32')
for i, img in enumerate(imgs):
for j, ans in enumerate(img['MC_ans']):
mc_ans_arrays[i,j] = atoi.get(ans, 0)
return mc_ans_arrays
def filter_question(imgs, atoi):
new_imgs = []
for i, img in enumerate(imgs):
if atoi.get(img['ans'],len(atoi)+1) != len(atoi)+1:
new_imgs.append(img)
print 'question number reduce from %d to %d '%(len(imgs), len(new_imgs))
return new_imgs
def get_unqiue_img(imgs):
count_img = {}
N = len(imgs)
img_pos = np.zeros(N, dtype='uint32')
for img in imgs:
count_img[img['img_path']] = count_img.get(img['img_path'], 0) + 1
unique_img = [w for w,n in count_img.iteritems()]
imgtoi = {w:i+1 for i,w in enumerate(unique_img)} # add one for torch, since torch start from 1.
for i, img in enumerate(imgs):
img_pos[i] = imgtoi.get(img['img_path'])
return unique_img, img_pos
def main(params):
if params['token_method'] == 'spacy':
print 'loading spaCy tokenizer for NLP'
params['spacy'] = spacy.en.English(data_dir=params['spacy_data'])
imgs_train = json.load(open(params['input_train_json'], 'r'))
imgs_test = json.load(open(params['input_test_json'], 'r'))
# get top answers
top_ans = get_top_answers(imgs_train, params)
atoi = {w:i+1 for i,w in enumerate(top_ans)}
itoa = {i+1:w for i,w in enumerate(top_ans)}
# filter question, which isn't in the top answers.
imgs_train = filter_question(imgs_train, atoi)
seed(123) # make reproducible
shuffle(imgs_train) # shuffle the order
# tokenization and preprocessing training question
imgs_train = prepro_question(imgs_train, params)
# tokenization and preprocessing testing question
imgs_test = prepro_question(imgs_test, params)
# create the vocab for question
imgs_train, vocab = build_vocab_question(imgs_train, params)
itow = {i+1:w for i,w in enumerate(vocab)} # a 1-indexed vocab translation table
wtoi = {w:i+1 for i,w in enumerate(vocab)} # inverse table
ques_train, ques_length_train, question_id_train = encode_question(imgs_train, params, wtoi)
imgs_test = apply_vocab_question(imgs_test, wtoi)
ques_test, ques_length_test, question_id_test = encode_question(imgs_test, params, wtoi)
# get the unique image for train and test
unique_img_train, img_pos_train = get_unqiue_img(imgs_train)
unique_img_test, img_pos_test = get_unqiue_img(imgs_test)
# get the answer encoding.
A = encode_answer(imgs_train, atoi)
MC_ans_test = encode_mc_answer(imgs_test, atoi)
# create output h5 file for training set.
N = len(imgs_train)
f = h5py.File(params['output_h5'], "w")
f.create_dataset("ques_train", dtype='uint32', data=ques_train)
f.create_dataset("ques_length_train", dtype='uint32', data=ques_length_train)
f.create_dataset("answers", dtype='uint32', data=A)
f.create_dataset("question_id_train", dtype='uint32', data=question_id_train)
f.create_dataset("img_pos_train", dtype='uint32', data=img_pos_train)
f.create_dataset("ques_test", dtype='uint32', data=ques_test)
f.create_dataset("ques_length_test", dtype='uint32', data=ques_length_test)
f.create_dataset("question_id_test", dtype='uint32', data=question_id_test)
f.create_dataset("img_pos_test", dtype='uint32', data=img_pos_test)
f.create_dataset("MC_ans_test", dtype='uint32', data=MC_ans_test)
f.close()
print 'wrote ', params['output_h5']
# create output json file
out = {}
out['ix_to_word'] = itow # encode the (1-indexed) vocab
out['ix_to_ans'] = itoa
out['unique_img_train'] = unique_img_train
out['unique_img_test'] = unique_img_test
json.dump(out, open(params['output_json'], 'w'))
print 'wrote ', params['output_json']
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# input json
parser.add_argument('--input_train_json', required=True, help='input json file to process into hdf5')
parser.add_argument('--input_test_json', required=True, help='input json file to process into hdf5')
parser.add_argument('--num_ans', required=True, type=int, help='number of top answers for the final classifications.')
parser.add_argument('--output_json', default='data_prepro.json', help='output json file')
parser.add_argument('--output_h5', default='data_prepro.h5', help='output h5 file')
# options
parser.add_argument('--max_length', default=26, type=int, help='max length of a caption, in number of words. captions longer than this get clipped.')
parser.add_argument('--word_count_threshold', default=0, type=int, help='only words that occur more than this number of times will be put in vocab')
parser.add_argument('--num_test', default=0, type=int, help='number of test images (to withold until very very end)')
parser.add_argument('--token_method', default='nltk', help='token method. set "spacy" for unigram paraphrasing')
parser.add_argument('--spacy_data', default='spacy_data', help='location of spacy NLP model')
parser.add_argument('--batch_size', default=10, type=int)
args = parser.parse_args()
params = vars(args) # convert to ordinary dict
print 'parsed input parameters:'
print json.dumps(params, indent = 2)
main(params)