def __init__(self, __C):
self.__C = __C
# Loading all image paths
# if self.__C.PRELOAD:
self.img_feat_path_list = []
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
for split in split_list:
if split in ['train', 'val', 'test']:
self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH[split] + '*.npz')
# Loading question word list
self.stat_ques_list = \
json.load(open(data_map_vqa_oe['train'], 'r')) + \
json.load(open(data_map_vqa_oe['val'], 'r')) + \
json.load(open(data_map_vqa_oe['test'], 'r')) + \
json.load(open(data_map_vqa_oe['testdev'], 'r'))
# Loading question and answer list
self.ques_list = []
# self.ans_list = []
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
for split in split_list:
self.ques_list += json.load(open(data_map_vqa_oe[split], 'r'))
# if __C.RUN_MODE in ['train']:
# self.ans_list += json.load(open(data_map_vqa_oe[split], 'r'))['annotations']
# Define run data size
# if __C.RUN_MODE in ['train']:
# self.data_size = self.ans_list.__len__()
# else:
self.data_size = self.ques_list.__len__()
print('== Dataset size:', self.data_size)
# {image id} -> {image feature absolutely path}
self.iid_to_img_feat_path = img_feat_path_load(self.img_feat_path_list)
# {question id} -> {question}
self.qid_to_ques = ques_load(self.ques_list, element_name='ques_id')
# Tokenize
self.token_to_ix, self.pretrained_emb = tokenize(self.stat_ques_list, __C.USE_GLOVE, element_name='ques')
self.token_size = self.token_to_ix.__len__()
print('== Question token vocab size:', self.token_size)
self.ans_to_ix, self.ix_to_ans = ans_stat('/ExpData/gwy/vqa/v1/preprocessed/ans_dict.json')
self.ans_size = self.ans_to_ix.__len__()
print('== Answer vocab size (occurr more than {} times):'.format(8), self.ans_size)
print('Finished!')
print('')
def __init__(self, __C, img_id, input_question, input_question_id):
self.__C = __C
self.img_feat_path_list = []
self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH['train'] +
'*.npz')
self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH['val'] +
'*.npz')
# Loading question word list
self.stat_ques_list = \
json.load(open(__C.QUESTION_PATH['train'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['val'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['test'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['vg'], 'r'))['questions']
# question and answer list
self.ques_list = [{
'image_id': img_id,
'question': input_question,
'question_id': input_question_id
}]
self.ans_list = []
# define run data size
self.data_size = self.ques_list.__len__()
# print('========== Dataset Size: ', self.data_size)
# {image_id} -> {image feature absolutely path}
self.iid_to_img_feat_path = img_feat_path_load(self.img_feat_path_list)
# tokenize
self.token_to_ix, self.pretrained_emb = tokenize(
self.stat_ques_list, __C.USE_GLOVE)
self.token_size = self.token_to_ix.__len__()
self.ans_to_ix, self.ix_to_ans = ans_stat('core/data/answer_dict.json')
self.ans_size = self.ans_to_ix.__len__()
print('========== Finished!')
def __init__(self, __C):
self.__C = __C
# --------------------------
# ---- Raw data loading ----
# --------------------------
# Loading all image paths
# if self.__C.PRELOAD:
self.img_feat_path_list = []
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
for split in split_list:
if split in ['train']:
self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH[split] +
'*.npz')
print('Length of image features', len(self.img_feat_path_list))
# if __C.EVAL_EVERY_EPOCH and __C.RUN_MODE in ['train']:
# self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH['val'] + '*.npz')
# else:
# self.img_feat_path_list = \
# glob.glob(__C.IMG_FEAT_PATH['train'] + '*.npz') + \
# glob.glob(__C.IMG_FEAT_PATH['val'] + '*.npz') + \
# glob.glob(__C.IMG_FEAT_PATH['test'] + '*.npz')
# Loading question word list
# self.stat_ques_list = \
# json.load(open(__C.QUESTION_PATH['train'], 'r'))['questions'] + \
# json.load(open(__C.QUESTION_PATH['val'], 'r'))['questions'] + \
# json.load(open(__C.QUESTION_PATH['test'], 'r'))['questions'] + \
# json.load(open(__C.QUESTION_PATH['vg'], 'r'))['questions']
self.stat_ques_list = json.load(open(__C.QUESTION_PATH['train'],
'r'))['questions']
print('Length of questions', len(self.stat_ques_list))
# Loading answer word list
# self.stat_ans_list = \
# json.load(open(__C.ANSWER_PATH['train'], 'r'))['annotations'] + \
# json.load(open(__C.ANSWER_PATH['val'], 'r'))['annotations']
self.stat_ans_list = json.load(open(__C.ANSWER_PATH['train'],
'r'))['annotations']
print('Length of answers', len(self.stat_ans_list))
# Loading question and answer list
self.ques_list = []
self.ans_list = []
print('Loading the annotation files')
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
split_list = ['train']
for split in split_list:
self.ques_list += json.load(open(__C.QUESTION_PATH[split],
'r'))['questions']
if __C.RUN_MODE in ['train']:
self.ans_list += json.load(open(__C.ANSWER_PATH[split],
'r'))['annotations']
print('Finished loading the annotation files!!!')
# Define run data size
if __C.RUN_MODE in ['train']:
self.data_size = self.ans_list.__len__()
else:
self.data_size = self.ques_list.__len__()
print('== Dataset size:', self.data_size)
# ------------------------
# ---- Data statistic ----
# ------------------------
# {image id} -> {image feature absolutely path}
if self.__C.PRELOAD:
print('==== Pre-Loading features ...')
time_start = time.time()
self.iid_to_img_feat = img_feat_load(self.img_feat_path_list)
time_end = time.time()
print('==== Finished in {}s'.format(int(time_end - time_start)))
else:
self.iid_to_img_feat_path = img_feat_path_load(
self.img_feat_path_list)
# self.ques_list = self.ques_list[:7]
# {question id} -> {question}
self.qid_to_ques = ques_load(self.ques_list)
# Tokenize
self.token_to_ix, self.pretrained_emb = tokenize(
self.stat_ques_list, __C.USE_GLOVE)
self.token_size = self.token_to_ix.__len__()
print('== Question token vocab size:', self.token_size)
# Answers statistic
# Make answer dict during training does not guarantee
# the same order of {ans_to_ix}, so we published our
# answer dict to ensure that our pre-trained model
# can be adapted on each machine.
# Thanks to Licheng Yu (https://github.com/lichengunc)
# for finding this bug and providing the solutions.
# self.ans_to_ix, self.ix_to_ans = ans_stat(self.stat_ans_list, __C.ANS_FREQ)
self.ans_to_ix, self.ix_to_ans = ans_stat('core/data/answer_dict.json')
self.ans_size = self.ans_to_ix.__len__()
print('== Answer vocab size (occurr more than {} times):'.format(8),
self.ans_size)
print('Finished!')
print('')
def __init__(self, __C):
self.__C = __C
# --------------------------
# ---- Raw data loading ----
# --------------------------
# Loading all image paths
self.img_feat_path_list = \
glob.glob(__C.IMG_FEAT_PATH['train'] + '*.npz') + \
glob.glob(__C.IMG_FEAT_PATH['val'] + '*.npz') + \
glob.glob(__C.IMG_FEAT_PATH['test'] + '*.npz')
# Loading question word list
self.stat_ques_list = \
json.load(open(__C.QUESTION_PATH['train'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['val'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['test'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['vg'], 'r'))['questions']
# Loading answer word list
self.stat_ans_list = \
json.load(open(__C.ANSWER_PATH['train'], 'r'))['annotations'] + \
json.load(open(__C.ANSWER_PATH['val'], 'r'))['annotations']
# Loading question and answer list
self.ques_list = []
self.ans_list = []
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
for split_ in split_list:
self.ques_list += json.load(open(__C.QUESTION_PATH[split_],
'r'))['questions']
if __C.RUN_MODE in ['train']:
self.ans_list += json.load(open(__C.ANSWER_PATH[split_],
'r'))['annotations']
# Define run data size
if __C.RUN_MODE in ['train']:
self.data_size = self.ans_list.__len__()
else:
self.data_size = self.ques_list.__len__()
print(' ========== run data size:', self.data_size)
# ------------------------
# ---- Data statistic ----
# ------------------------
# {image id} -> {image feature absolutely path}
self.iid_to_img_feat_path = img_feat_path_load(self.img_feat_path_list)
# {question id} -> {question}
self.qid_to_ques = ques_load(self.ques_list)
# Tokenize
self.token_to_ix, self.pretrained_emb = tokenize(
self.stat_ques_list, __C.USE_GLOVE)
self.token_size = self.token_to_ix.__len__()
print(' ========== question token size:', self.token_size)
# Answers statistic
self.ans_to_ix, self.ix_to_ans = ans_stat(self.stat_ans_list,
__C.ANS_FREQ)
self.ans_size = self.ans_to_ix.__len__()
print(
' ========== answer frequency more than {} size:'.format(
__C.ANS_FREQ), self.ans_size)
print('Loading finished !!!')
print('')
def __init__(self, __C):
self.__C = __C
# --------------------------
# ---- Raw data loading ----
# --------------------------
# Loading all image paths
# if self.__C.PRELOAD:
self.img_feat_path_list = []
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
for split in split_list:
if split in ['train', 'val', 'test']:
self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH[split] +
'*.npz')
# if __C.EVAL_EVERY_EPOCH and __C.RUN_MODE in ['train']:
# self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH['val'] + '*.npz')
# else:
# self.img_feat_path_list = \
# glob.glob(__C.IMG_FEAT_PATH['train'] + '*.npz') + \
# glob.glob(__C.IMG_FEAT_PATH['val'] + '*.npz') + \
# glob.glob(__C.IMG_FEAT_PATH['test'] + '*.npz')
# Loading question word list
self.stat_ques_list = \
json.load(open(__C.QUESTION_PATH['train'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['val'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['test'], 'r'))['questions'] + \
json.load(open(__C.QUESTION_PATH['vg'], 'r'))['questions']
# Loading answer word list
# self.stat_ans_list = \
# json.load(open(__C.ANSWER_PATH['train'], 'r'))['annotations'] + \
# json.load(open(__C.ANSWER_PATH['val'], 'r'))['annotations']
# Loading question and answer list
self.ques_list = []
self.ans_list = []
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
for split in split_list:
self.ques_list += json.load(open(__C.QUESTION_PATH[split],
'r'))['questions']
if __C.RUN_MODE in ['train']:
self.ans_list += json.load(open(__C.ANSWER_PATH[split],
'r'))['annotations']
# Define run data size
if __C.RUN_MODE in ['train']:
self.data_size = self.ans_list.__len__()
else:
self.data_size = self.ques_list.__len__()
print('== Dataset size:', self.data_size)
# ------------------------
# ---- Data statistic ----
# ------------------------
# {image id} -> {image feature absolutely path}
if self.__C.PRELOAD:
print('==== Pre-Loading features ...')
time_start = time.time()
self.iid_to_img_feat = img_feat_load(self.img_feat_path_list)
time_end = time.time()
print('==== Finished in {}s'.format(int(time_end - time_start)))
else:
self.iid_to_img_feat_path = img_feat_path_load(
self.img_feat_path_list)
# {question id} -> {question}
self.qid_to_ques = ques_load(self.ques_list)
# Tokenize
self.token_to_ix, self.pretrained_emb = tokenize(
self.stat_ques_list, __C.USE_GLOVE)
#ix_to_token
self.ix_to_token = {v: k for k, v in self.token_to_ix.items()}
self.token_size = self.token_to_ix.__len__()
print('== Question token vocab size:', self.token_size)
# self.ans_to_ix, self.ix_to_ans = ans_stat(self.stat_ans_list, __C.ANS_FREQ)
self.ans_to_ix, self.ix_to_ans = ans_stat('core/data/answer_dict.json')
self.ans_size = self.ans_to_ix.__len__()
print('== Answer vocab size (occurr more than {} times):'.format(8),
self.ans_size)
print('Finished!')
print('')
def __init__(self, __C):
"""
:param __C: 配置信息
"""
self.__C = __C
#加载原始数据
self.img_feat_path_list = []
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
#split_list ={'train','val','vg'}
for split in split_list:
if split in ['train']:
self.img_feat_path_list += glob.glob(__C.IMG_FEAT_PATH[split] +
'*.npz')
self.stat_ques_list = json.load(open(__C.QUESTION_PATH['train'],
'r'))['questions']
# json.load(open(__C.QUESTION_PATH['train'], 'r'))['questions'] + \
#json.load(open(__C.QUESTION_PATH['val'], 'r'))['questions']
# json.load(open(__C.QUESTION_PATH['test'], 'r'))['questions'] + \
# json.load(open(__C.QUESTION_PATH['vg'], 'r'))['questions']
self.ques_list = []
self.ans_list = [] #annotations文件内容读取到ans_list列表中
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
for split in split_list:
self.ques_list += json.load(open(__C.QUESTION_PATH[split],
'r'))['questions']
if __C.RUN_MODE in ['train']:
self.ans_list += json.load(open(__C.ANSWER_PATH[split],
'r'))['annotations']
# 定义运行数据大小 = ans_list_len
if __C.RUN_MODE in ['train']:
self.data_size = self.ans_list.__len__()
else:
self.data_size = self.ques_list.__len__()
print('== Dataset size:', self.data_size)
# {image id} -> {image feature absolutely path}
if self.__C.PRELOAD: #PRELOAD=False 执行else
print('==== Pre-Loading features ...')
time_start = time.time()
self.iid_to_img_feat = img_feat_load(self.img_feat_path_list)
time_end = time.time()
print('==== Finished in {}s'.format(int(time_end - time_start)))
else:
#调用data_utils.py中img_feat_path_load函数加载图像特征文件
#图像特征{'9':'COCO_train2015_00000000009.jpg.npz',....}
self.iid_to_img_feat_path = img_feat_path_load(
self.img_feat_path_list)
#调用data_utils ques_load函数加载问题
# 问题加载：{'458752000':{'image_id':'','question':'','question_id':'458752000'}...}
self.qid_to_ques = ques_load(self.ques_list)
#调用data_utils.py的tokenize的函数
#token_to_ix:把问题出现的词写入，如果重复出现不写，例如每个问题都有what，则只写一次
#pretrained_emb:词嵌入
#token_size : 大小18405
self.token_to_ix, self.pretrained_emb = tokenize(
self.stat_ques_list, __C.USE_GLOVE)
self.token_size = self.token_to_ix.__len__()
print('== Question token vocab size:', self.token_size)
#调用 data_utils.py的ans_stat函数
self.ans_to_ix, self.ix_to_ans = ans_stat('core/data/answer_dict.json')
self.ans_size = self.ans_to_ix.__len__()
print('== Answer vocab size (occurr more than {} times):'.format(8),
self.ans_size)
print('Finished!')
print('')