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 = VQAFeatureDataset('train', q_dict, c_dict, 'cache/VQAE2',args.att_thr)
#eval_dset = VQAFeatureDataset('val', q_dict, c_dict, 'cache/VQAE2',args.att_thr)
train_dset = VQAEDataset('train', q_dict, c_dict, 'cache/VQAE2')
eval_dset = VQAEDataset('val', q_dict, c_dict, 'cache/VQAE2')
#train_dset = VQAEVQA2Dataset('train', q_dict, c_dict, 'cache')
#eval_dset = VQAEVQA2Dataset('val', q_dict, c_dict, 'cache')
batch_size = args.batch_size
constructor = 'build_%s' % args.model
model = utils.model_factory(constructor, train_dset, args.num_hid,
args.att_dim, args.decode_dim).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)
vqa_score, results = evaluate(model, eval_loader, q_dict, c_dict)
save_obj = {'vqa_score': vqa_score, 'results': results}
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)
vqa_score, results = evaluate(model, eval_loader, q_dict, c_dict)
print(vqa_score)
save_obj = {'vqa_score': vqa_score, 'results': results}
import pdb
pdb.set_trace()
