if __name__ == '__main__':
args = parse_args()
vocab_source = 'VQA2' #### 'VQAE' or 'VQAv2'
target_source = vocab_source + '_2_' + args.val_set
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
q_dict = Dictionary.load_from_file(os.path.join('data', vocab_source, 'question_dictionary.pkl'))
c_dict = Dictionary.load_from_file(os.path.join('data', vocab_source, 'explain_dictionary.pkl'))
q_emb_file = os.path.join('data', vocab_source, 'glove6b_init_300d_question.npy')
c_emb_file = os.path.join('data', vocab_source, 'glove6b_init_300d_explain.npy')
train_dset = data_factory(args.train_set, 'train', q_dict, c_dict, os.path.join('cache', target_source), result=True)
eval_dset = data_factory(args.val_set, 'val', q_dict, c_dict, os.path.join('cache', target_source), result=True)
batch_size = args.batch_size
constructor = 'build_%s' % args.model
model = utils.model_factory(constructor, train_dset, args.dim_hid, args.emb_rnn).cuda()
model_path = os.path.join(args.output, 'model.pth')
model_state = torch.load(model_path)
model.load_state_dict(model_state)
print('Model has {} parameters in total'.format(utils.params_count(model)))
#model = nn.DataParallel(model).cuda()
eval_loader = DataLoader(eval_dset, batch_size, shuffle=False, num_workers=1)
model.train(False)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if args.share_qe_dict:
qe_dict = Dictionary.load_from_file(
os.path.join('data', vocab_source,
'question_explain_dictionary.pkl'))
else:
q_dict = Dictionary.load_from_file(
os.path.join('data', vocab_source, 'question_dictionary.pkl'))
c_dict = Dictionary.load_from_file(
os.path.join('data', vocab_source, 'explain_dictionary.pkl'))
train_dset = data_factory(args.dataset, 'train', q_dict, c_dict,
os.path.join('cache', vocab_source))
eval_dset = data_factory(args.dataset, 'val', q_dict, c_dict,
os.path.join('cache', vocab_source))
label2ans = pickle.load(
open(
os.path.join('data', 'cache', vocab_source,
'trainval_label2ans.pkl')))
batch_size = args.batch_size
constructor = 'build_%s' % args.model
model1 = utils.model_factory(constructor, train_dset, args.num_hid,
args.att_dim, args.decode_dim).cuda()
model2 = utils.model_factory(constructor, train_dset, args.num_hid,
args.att_dim, args.decode_dim).cuda()
model1_path = os.path.join(args.source1, 'model.pth')
