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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
from collections import Counter
import numpy as np
import utils
import h5py
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
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_idx, question, answer):
answer.pop('image_id')
answer.pop('question_id')
entry = {
'question_id' : question['question_id'],
'image_id' : question['image_id'],
'image_idx' : img_idx,
'question' : question['question'],
'answer' : answer
}
return entry
def _load_dataset(dataroot, name, img_id2val, cp=False):
"""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'
"""
if cp:
answer_path = os.path.join(dataroot, 'cp-cache', '%s_target.pkl' % name)
name = "train" if name == "train" else "test"
question_path = os.path.join(dataroot, 'vqacp_v2_%s_questions.json' % name)
with open(question_path) as f:
questions = json.load(f)
else:
question_path = os.path.join(
dataroot, 'v2_OpenEnded_mscoco_%s2014_questions.json' % name)
with open(question_path) as f:
questions = json.load(f)["questions"]
answer_path = os.path.join(dataroot, 'cache', '%s_target.pkl' % name)
questions.sort(key=lambda x: x['question_id'])
with open(answer_path, 'rb') as f:
answers = cPickle.load(f)
answers.sort(key=lambda x: x['question_id'])
utils.assert_eq(len(questions), len(answers))
entries = []
for question, answer in zip(questions, answers):
if answer["labels"] is None:
raise ValueError()
utils.assert_eq(question['question_id'], answer['question_id'])
utils.assert_eq(question['image_id'], answer['image_id'])
img_id = question['image_id']
img_idx = None
if img_id2val:
img_idx = img_id2val[img_id]
entries.append(_create_entry(img_idx, question, answer))
return entries
class VQAFeatureDataset(Dataset):
def __init__(self, name, dictionary, dataroot='data', cp=False,
use_hdf5=False, cache_image_features=False):
super(VQAFeatureDataset, self).__init__()
assert name in ['train', 'val']
if cp:
ans2label_path = os.path.join(dataroot, 'cp-cache', 'trainval_ans2label.pkl')
label2ans_path = os.path.join(dataroot, 'cp-cache', 'trainval_label2ans.pkl')
else:
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.use_hdf5 = use_hdf5
if use_hdf5:
h5_path = os.path.join(dataroot, 'trainval36.hdf5')
self.hf = h5py.File(h5_path, 'r')
self.features = self.hf.get('image_features')
with open("data/trainval36_imgid2idx.pkl", "rb") as f:
imgid2idx = cPickle.load(f)
else:
imgid2idx = None
self.entries = _load_dataset(dataroot, name, imgid2idx, cp=cp)
if cache_image_features:
image_to_fe = {}
for entry in tqdm(self.entries, ncols=100, desc="caching-features"):
img_id = entry["image_id"]
if img_id not in image_to_fe:
if use_hdf5:
fe = np.array(self.features[imgid2idx[img_id]])
else:
fe = np.fromfile("data/trainval_features/" + str(img_id) + ".bin", np.float32)
image_to_fe[img_id] = torch.from_numpy(fe).view(36, 2048)
self.image_to_fe = image_to_fe
if use_hdf5:
self.hf.close()
else:
self.image_to_fe = None
self.tokenize()
self.tensorize()
self.v_dim = 2048
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 tqdm(self.entries, ncols=100, desc="tokenize"):
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):
for entry in tqdm(self.entries, ncols=100, desc="tensorize"):
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]
if self.image_to_fe is not None:
features = self.image_to_fe[entry["image_id"]]
elif self.use_hdf5:
features = np.array(self.features[entry['image_idx']])
features = torch.from_numpy(features).view(36, 2048)
else:
features = np.fromfile("data/trainval_features/" + str(entry["image_id"]) + ".data", np.float32)
features = torch.from_numpy(features).view(36, 2048)
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)
if "bias" in entry:
return features, question, target, entry["bias"]
else:
return features, question, target, 0
def __len__(self):
return len(self.entries)