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| """ | |
| Preprocess a raw json dataset into hdf5/json files for use in data_loader.py | |
| Input: json file that has the form | |
| [{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...] | |
| example element in this list would look like | |
| {'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a motor bike on a dirt road on the countryside.', u'A man riding on the back of a motorcycle.', u'A dirt path with a young person on a motor bike rests to the foreground of a verdant area with a bridge and a background of cloud-wreathed mountains. ', u'A man in a red shirt and a red hat is on a motorcycle on a hill side.'], 'file_path': u'val2014/COCO_val2014_000000391895.jpg', 'id': 391895} | |
| This script reads this json, does some basic preprocessing on the captions | |
| (e.g. lowercase, etc.), creates a special UNK token, and encodes everything to arrays | |
| Output: a json file and an hdf5 file | |
| The hdf5 file contains several fields: | |
| /labels is (M,max_length) uint32 array of encoded labels, zero padded | |
| /label_start_ix and /label_end_ix are (N,) uint32 arrays of pointers to the | |
| first and last indices (in range 1..M) of labels for each image | |
| /label_length stores the length of the sequence for each of the M sequences | |
| The json file has a dict that contains: | |
| - an 'ix_to_word' field storing the vocab in form {ix:'word'}, where ix is 1-indexed | |
| - an 'images' field that is a list holding auxiliary information for each image, | |
| such as in particular the 'split' it was assigned to. | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os | |
| import json | |
| import argparse | |
| from random import shuffle, seed | |
| import string | |
| # non-standard dependencies: | |
| import h5py | |
| import numpy as np | |
| import torch | |
| import torchvision.models as models | |
| import skimage.io | |
| from PIL import Image | |
| def build_vocab(imgs, params): | |
| count_thr = params['word_count_threshold'] | |
| # count up the number of words | |
| counts = {} | |
| for img in imgs: | |
| for sent in img['sentences']: | |
| for w in sent['tokens']: | |
| counts[w] = counts.get(w, 0) + 1 | |
| cw = sorted([(count,w) for w,count in counts.items()], reverse=True) | |
| print('top words and their counts:') | |
| print('\n'.join(map(str,cw[:20]))) | |
| # print some stats | |
| total_words = sum(counts.values()) | |
| print('total words:', total_words) | |
| bad_words = [w for w,n in counts.items() if n <= count_thr] | |
| vocab = [w for w,n in counts.items() 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 look at the distribution of lengths as well | |
| sent_lengths = {} | |
| for img in imgs: | |
| for sent in img['sentences']: | |
| txt = sent['tokens'] | |
| nw = len(txt) | |
| sent_lengths[nw] = sent_lengths.get(nw, 0) + 1 | |
| max_len = max(sent_lengths.keys()) | |
| print('max length sentence in raw data: ', max_len) | |
| print('sentence length distribution (count, number of words):') | |
| sum_len = sum(sent_lengths.values()) | |
| for i in range(max_len+1): | |
| print('%2d: %10d %f%%' % (i, sent_lengths.get(i,0), sent_lengths.get(i,0)*100.0/sum_len)) | |
| # lets now produce the final annotations | |
| if bad_count > 0: | |
| # 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: | |
| img['final_captions'] = [] | |
| for sent in img['sentences']: | |
| txt = sent['tokens'] | |
| caption = [w if counts.get(w,0) > count_thr else 'UNK' for w in txt] | |
| img['final_captions'].append(caption) | |
| return vocab | |
| def encode_captions(imgs, params, wtoi): | |
| """ | |
| encode all captions into one large array, which will be 1-indexed. | |
| also produces label_start_ix and label_end_ix which store 1-indexed | |
| and inclusive (Lua-style) pointers to the first and last caption for | |
| each image in the dataset. | |
| """ | |
| max_length = params['max_length'] | |
| N = len(imgs) | |
| M = sum(len(img['final_captions']) for img in imgs) # total number of captions | |
| label_arrays = [] | |
| label_start_ix = np.zeros(N, dtype='uint32') # note: these will be one-indexed | |
| label_end_ix = np.zeros(N, dtype='uint32') | |
| label_length = np.zeros(M, dtype='uint32') | |
| caption_counter = 0 | |
| counter = 1 | |
| for i,img in enumerate(imgs): | |
| n = len(img['final_captions']) | |
| assert n > 0, 'error: some image has no captions' | |
| Li = np.zeros((n, max_length), dtype='uint32') | |
| for j,s in enumerate(img['final_captions']): | |
| label_length[caption_counter] = min(max_length, len(s)) # record the length of this sequence | |
| caption_counter += 1 | |
| for k,w in enumerate(s): | |
| if k < max_length: | |
| Li[j,k] = wtoi[w] | |
| # note: word indices are 1-indexed, and captions are padded with zeros | |
| label_arrays.append(Li) | |
| label_start_ix[i] = counter | |
| label_end_ix[i] = counter + n - 1 | |
| counter += n | |
| L = np.concatenate(label_arrays, axis=0) # put all the labels together | |
| assert L.shape[0] == M, 'lengths don\'t match? that\'s weird' | |
| assert np.all(label_length > 0), 'error: some caption had no words?' | |
| print('encoded captions to array of size ', L.shape) | |
| return L, label_start_ix, label_end_ix, label_length | |
| def main(params): | |
| imgs = json.load(open(params['input_json'], 'r')) | |
| imgs = imgs['images'] | |
| seed(123) # make reproducible | |
| # # create the vocab | |
| # vocab = build_vocab(imgs, 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 | |
| itow = imgs['ix_to_word'] | |
| wtoi = {w:i for i, w in itow.items()} | |
| # encode captions in large arrays, ready to ship to hdf5 file | |
| L, label_start_ix, label_end_ix, label_length = encode_captions(imgs, params, wtoi) | |
| # create output h5 file | |
| N = len(imgs) | |
| f_lb = h5py.File(params['output_h5']+'_label.h5', "w") | |
| f_lb.create_dataset("labels", dtype='uint32', data=L) | |
| f_lb.create_dataset("label_start_ix", dtype='uint32', data=label_start_ix) | |
| f_lb.create_dataset("label_end_ix", dtype='uint32', data=label_end_ix) | |
| f_lb.create_dataset("label_length", dtype='uint32', data=label_length) | |
| f_lb.close() | |
| # create output json file | |
| out = {} | |
| out['ix_to_word'] = itow # encode the (1-indexed) vocab | |
| out['images'] = [] | |
| for i,img in enumerate(imgs): | |
| jimg = {} | |
| jimg['split'] = img['split'] | |
| if 'filename' in img: jimg['file_path'] = os.path.join(img.get('filepath', ''), img['filename']) # copy it over, might need | |
| if 'cocoid' in img: | |
| jimg['id'] = img['cocoid'] # copy over & mantain an id, if present (e.g. coco ids, useful) | |
| elif 'imgid' in img: | |
| jimg['id'] = img['imgid'] | |
| if params['images_root'] != '': | |
| with Image.open(os.path.join(params['images_root'], img['filepath'], img['filename'])) as _img: | |
| jimg['width'], jimg['height'] = _img.size | |
| out['images'].append(jimg) | |
| 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_json', required=True, help='input json file to process into hdf5') | |
| parser.add_argument('--output_json', default='data.json', help='output json file') | |
| parser.add_argument('--output_h5', default='data', help='output h5 file') | |
| parser.add_argument('--images_root', default='', help='root location in which images are stored, to be prepended to file_path in input json') | |
| # options | |
| parser.add_argument('--max_length', default=16, 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=5, type=int, help='only words that occur more than this number of times will be put in vocab') | |
| args = parser.parse_args() | |
| params = vars(args) # convert to ordinary dict | |
| print('parsed input parameters:') | |
| print(json.dumps(params, indent = 2)) | |
| main(params) | |