def main():
args = process_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
if args.seed == -1:
RANDOMSEED = None
else:
RANDOMSEED = args.seed
torch.manual_seed(RANDOMSEED)
torch.cuda.manual_seed(RANDOMSEED)
IMP_WEIGHT = args.imp_weight
if not IMP_WEIGHT:
imp = torch.ones(args.class_num - 1, dtype=torch.float)
elif IMP_WEIGHT == 1:
pass
else:
raise ValueError('Incorrect importance weight parameter.')
imp = imp.cuda()
embedding = 'random'
torch.backends.cudnn.deterministic = True
config = Config(args, embedding, 'OR')
start_time = time.time()
print("Loading data...")
vocab, train_data, test_data = build_dataset(config, args.word)
train_iter = build_iterator(train_data, config, doubly_flag=False)
# train_iter = buil_random_iterator(train_data,config)
test_iter = build_test_iterator(test_data, config)
print("Time used: ", get_time_dif(start_time))
config.n_vocab = len(vocab)
model = OR(config).cuda()
init_model(model)
print("start training...")
train_or(config, model, train_iter, test_iter, imp)
def main():
args = process_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
if args.seed == -1:
RANDOMSEED = None
else:
RANDOMSEED = args.seed
torch.manual_seed(RANDOMSEED)
torch.cuda.manual_seed(RANDOMSEED)
embedding = 'random'
torch.backends.cudnn.deterministic = True
config = Config(args, embedding, 'POR')
start_time = time.time()
print("Loading data...")
vocab, train_data, test_data = build_adaptive_dataset(config, args.word)
train_iter2 = build_iterator(train_data, config, doubly_flag=False)
test_iter = build_test_iterator(test_data, config)
print("Time used: ", get_time_dif(start_time))
config.n_vocab = len(vocab)
model = POR(config).cuda()
init_model(model)
print("start training...")
train_por(config, model, train_iter, train_iter2, test_iter)