def test_all(): # Run on test data. for test_subset in data['test']: test_data = Data.BucketIterator(data['test'][test_subset], args.batch_size, args.cuda, args, shuffle=False) print('=' * 89) test_loss, test_acc, total_correctness = evaluate(test_data) print('| subset %s | test loss %5.3f | test acc %8.3f' % (test_subset, test_loss, test_acc)) print('=' * 89)
) else: torch.cuda.manual_seed(args.seed) ############################################################################### # Load data ############################################################################### dataset = torch.load(args.data) vocab = dataset["vocab"] data = dataset["data"] ntokens = vocab.size() if not args.test_only: train_data = Data.BucketIterator(data['train'], args.batch_size, args.cuda, args, shuffle=True, infor_weighting=args.infor_weighting) valid_data = Data.BucketIterator(data['valid'], args.batch_size, args.cuda, args, shuffle=False) test_data = Data.BucketIterator(data['test']["whole"], args.batch_size, args.cuda, args, shuffle=False) if args.pre_trained is not None or args.embedding_file == "None": embeddings = gen_embeddings(vocab, ntokens, args.emsize) else: