def __init__(self,
imdb_file,
image_feat_directories,
verbose=False,
**data_params):
super(vqa_dataset, self).__init__()
if imdb_file.endswith('.npy'):
imdb = np.load(imdb_file)
else:
raise TypeError('unknown imdb format.')
self.verbose = verbose
self.imdb = imdb
self.image_feat_directories = image_feat_directories
self.data_params = data_params
self.cp = data_params['cp']
self.image_depth_first = data_params['image_depth_first']
self.image_max_loc = (data_params['image_max_loc']
if 'image_max_loc' in data_params else None)
self.vocab_dict = text_processing.VocabDict(
data_params['vocab_question_file'])
self.T_encoder = data_params['T_encoder']
# read the header of imdb file
header_idx = 0
self.first_element_idx = 1
header = self.imdb[header_idx]
self.load_answer = header['has_answer']
self.load_gt_layout = header['has_gt_layout']
self.load_gt_layout = False
data_version = header['version']
if data_version != imdb_version:
print("observed imdb_version is", data_version,
"expected imdb version is", imdb_version)
raise TypeError('imdb version do not match.')
if 'load_gt_layout' in data_params:
self.load_gt_layout = data_params['load_gt_layout']
# the answer dict is always loaded, regardless of self.load_answer
self.answer_dict = text_processing.VocabDict(
data_params['vocab_answer_file'])
if self.load_gt_layout:
self.T_decoder = data_params['T_decoder']
self.assembler = data_params['assembler']
self.prune_filter_module = (data_params['prune_filter_module']
if 'prune_filter_module' in data_params
else False)
else:
print('imdb does not contain ground-truth layout')
print('Loading model and config ...')
# load one feature map to peek its size
self.image_feat_readers = []
for image_dir in self.image_feat_directories:
image_file_name = os.path.basename(
self.imdb[self.first_element_idx]['feature_path'])
if self.cp:
image_feat_path = get_cp_feat_path(self.image_feat_directories,
image_file_name)
else:
image_feat_path = os.path.join(image_dir, image_file_name)
feats = np.load(image_feat_path)
self.image_feat_readers.append(
get_image_feat_reader(feats.ndim, self.image_depth_first,
feats, self.image_max_loc))
self.fastRead = False
self.testMode = False
if data_params['test_mode']:
self.testMode = True
if data_params['fastRead']:
self.fastRead = True
self.featDict = {}
image_count = 0
image_dir0 = self.image_feat_directories[0]
for feat_file in tqdm(os.listdir(image_dir0)):
if feat_file.endswith("npy"):
image_feats = read_in_image_feats(
self.image_feat_directories, self.image_feat_readers,
feat_file, self.cp)
self.featDict[feat_file] = image_feats
image_count += 1
print("load %d images" % image_count)
parser.add_argument("--data_dir",
type=str,
required=True,
help="data directory")
parser.add_argument("--out_dir",
type=str,
required=True,
help="imdb output directory")
args = parser.parse_args()
data_dir = args.data_dir
out_dir = args.out_dir
#vocab_answer_file = os.path.join(out_dir, 'answers_vqa.txt')
vocab_answer_file = '/home1/BTP/pg_aa_1/btp/data/answers_vqa_larger.txt'
answer_dict = text_processing.VocabDict(vocab_answer_file)
valid_answer_set = set(answer_dict.word_list)
# imdb_train2014 = build_imdb('train2014', valid_answer_set)
imdb_val2014 = build_imdb('val2014', valid_answer_set)
# imdb_test2015 = build_imdb('test2015', valid_answer_set)
imdb_dir = os.path.join(out_dir, 'imdb')
os.makedirs(imdb_dir, exist_ok=True)
#np.save(os.path.join(imdb_dir, 'imdb_train2014.npy'),
# np.array(imdb_train2014))
np.save(os.path.join(imdb_dir, 'imdb_val2014.npy'), np.array(imdb_val2014))
# np.save(os.path.join(imdb_dir, 'imdb_test2015.npy'),
# np.array(imdb_test2015))
'''imdb_minival2014 = build_imdb('minival2014',
valid_answer_set,