question_generator = QuestionGenerationModel(datasets, logger,
hidden_units, dropout)
question_generator.input_shape = image_embedding_dim
# Calculate image features and store them if save is True
obj_dir = os.path.join('data', dataset, 'obj')
if not os.path.exists(obj_dir):
os.makedirs(obj_dir)
train_imagefeat_dict_name = os.path.join(obj_dir,
'train_imagefeat_dict.pkl')
test_imagefeat_dict_name = os.path.join(obj_dir, 'test_imagefeat_dict.pkl')
dev_imagefeat_dict_name = os.path.join(obj_dir, 'dev_imagefeat_dict.pkl')
if os.path.exists(test_imagefeat_dict_name):
datasets.test_image_id_imagefeat_dict = load_obj(
test_imagefeat_dict_name)
datasets.load_test_data()
# Set this variable to True if you want to save the image features
save = False
if save:
save_obj(datasets.test_image_id_imagefeat_dict,
test_imagefeat_dict_name)
if is_training == 'YES':
if os.path.exists(train_imagefeat_dict_name):
datasets.train_image_id_imagefeat_dict = load_obj(
train_imagefeat_dict_name)
# Validation part of pipeline is commented out to speedup training process
