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dataset.py
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dataset.py
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# from __future__ import print_function
# import os
# import json
# import cPickle
# import numpy as np
# import utils
# import h5py
# import torch
# from torch.utils.data import Dataset
#
#
# class Dictionary(object):
# def __init__(self, word2idx=None, idx2word=None):
# if word2idx is None:
# word2idx = {}
# if idx2word is None:
# idx2word = []
# self.word2idx = word2idx
# self.idx2word = idx2word
#
# @property
# def ntoken(self):
# return len(self.word2idx)
#
# @property
# def padding_idx(self):
# return len(self.word2idx)
#
# def tokenize(self, sentence, add_word):
# sentence = sentence.lower()
# sentence = sentence.replace(',', '').replace('?', '').replace('\'s', ' \'s')
# words = sentence.split()
# tokens = []
# if add_word:
# for w in words:
# tokens.append(self.add_word(w))
# else:
# for w in words:
# tokens.append(self.word2idx[w])
# return tokens
#
# def dump_to_file(self, path):
# cPickle.dump([self.word2idx, self.idx2word], open(path, 'wb'))
# print('dictionary dumped to %s' % path)
#
# @classmethod
# def load_from_file(cls, path):
# print('loading dictionary from %s' % path)
# word2idx, idx2word = cPickle.load(open(path, 'rb'))
# d = cls(word2idx, idx2word)
# return d
#
# def add_word(self, word):
# if word not in self.word2idx:
# self.idx2word.append(word)
# self.word2idx[word] = len(self.idx2word) - 1
# return self.word2idx[word]
#
# def __len__(self):
# return len(self.idx2word)
#
#
# def _create_entry(img, question, answer):
# answer.pop('image_id')
# answer.pop('question_id')
# entry = {
# 'question_id' : question['question_id'],
# 'image_id' : question['image_id'],
# 'image' : img,
# 'question' : question['question'],
# 'answer' : answer}
# return entry
#
#
# def _load_dataset(dataroot, name, img_id2val):
# """Load entries
#
# img_id2val: dict {img_id -> val} val can be used to retrieve image or features
# dataroot: root path of dataset
# name: 'train', 'val'
# """
# question_path = os.path.join(
# dataroot, 'v2_OpenEnded_mscoco_%s2014_questions.json' % name)
# questions = sorted(json.load(open(question_path))['questions'],
# key=lambda x: x['question_id'])
# answer_path = os.path.join(dataroot, 'cache', '%s_target.pkl' % name)
# answers = cPickle.load(open(answer_path, 'rb'))
# answers = sorted(answers, key=lambda x: x['question_id'])
#
# utils.assert_eq(len(questions), len(answers))
# entries = []
# for question, answer in zip(questions, answers):
# utils.assert_eq(question['question_id'], answer['question_id'])
# utils.assert_eq(question['image_id'], answer['image_id'])
# img_id = question['image_id']
# entries.append(_create_entry(img_id2val[img_id], question, answer))
#
# return entries
#
#
# class VQAFeatureDataset(Dataset):
# def __init__(self, name, dictionary, dataroot='data'):
# super(VQAFeatureDataset, self).__init__()
# assert name in ['train', 'val']
#
# ans2label_path = os.path.join(dataroot, 'cache', 'trainval_ans2label.pkl')
# label2ans_path = os.path.join(dataroot, 'cache', 'trainval_label2ans.pkl')
# self.ans2label = cPickle.load(open(ans2label_path, 'rb'))
# self.label2ans = cPickle.load(open(label2ans_path, 'rb'))
# self.num_ans_candidates = len(self.ans2label)
#
# self.dictionary = dictionary
#
# self.img_id2idx = cPickle.load(
# open(os.path.join(dataroot, '%s36_imgid2idx.pkl' % name)))
# print('loading features from h5 file')
# h5_path = os.path.join(dataroot, '%s36.hdf5' % name)
# with h5py.File(h5_path, 'r') as hf:
# self.features = np.array(hf.get('image_features'))
# self.spatials = np.array(hf.get('spatial_features'))
#
# self.entries = _load_dataset(dataroot, name, self.img_id2idx)
#
# self.tokenize()
# self.tensorize()
# self.v_dim = self.features.size(2)
# self.s_dim = self.spatials.size(2)
#
# def tokenize(self, max_length=14):
# """Tokenizes the questions.
#
# This will add q_token in each entry of the dataset.
# -1 represent nil, and should be treated as padding_idx in embedding
# """
# for entry in self.entries:
# tokens = self.dictionary.tokenize(entry['question'], False)
# tokens = tokens[:max_length]
# if len(tokens) < max_length:
# # Note here we pad in front of the sentence
# padding = [self.dictionary.padding_idx] * (max_length - len(tokens))
# tokens = padding + tokens
# utils.assert_eq(len(tokens), max_length)
# entry['q_token'] = tokens
#
# def tensorize(self):
# self.features = torch.from_numpy(self.features)
# self.spatials = torch.from_numpy(self.spatials)
#
# for entry in self.entries:
# question = torch.from_numpy(np.array(entry['q_token']))
# entry['q_token'] = question
#
# answer = entry['answer']
# labels = np.array(answer['labels'])
# scores = np.array(answer['scores'], dtype=np.float32)
# if len(labels):
# labels = torch.from_numpy(labels)
# scores = torch.from_numpy(scores)
# entry['answer']['labels'] = labels
# entry['answer']['scores'] = scores
# else:
# entry['answer']['labels'] = None
# entry['answer']['scores'] = None
#
# def __getitem__(self, index):
# entry = self.entries[index]
# features = self.features[entry['image']]
# spatials = self.spatials[entry['image']]
#
# question = entry['q_token']
# answer = entry['answer']
# labels = answer['labels']
# scores = answer['scores']
# target = torch.zeros(self.num_ans_candidates)
# if labels is not None:
# target.scatter_(0, labels, scores)
#
# return features, spatials, question, target
#
# def __len__(self):
# return len(self.entries)