def main(_):
args = flags.FLAGS
print("\nParameters:")
for attr, value in sorted(args.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
m(args)
def main(_):
if config.debug:
#config.mode = "check"
config.num_batches = 100
config.log_period = 1
config.save_period = 1
config.eval_period = 1
config.batch_size = 2
config.val_num_batches = 3
config.out_dir = "debug"
#print(config.test_batch_size)
if config.model_name.endswith("flat"):
if config.data_from=="reuters": config.n_classes = 18
if config.data_from=="20newsgroup": config.n_classes = 20
config.multilabel_threshold = 0.053
else:
if config.data_from=="reuters": config.n_classes = 21
if config.data_from=="20newsgroup": config.n_classes = 29
if config.data_from == "reuters":
config.max_docs_length = 818
config.tree1 = np.array([2,3,4,5,6,7,8])
config.tree2 = np.array([9,10,11,12,13,14,15])
config.tree3 = np.array([16,17,18,19])
config.layer1 = np.array([2, 9, 16])
config.layer2 = np.array([3, 4, 10, 11, 17, 19])
config.layer3 = np.array([5, 6, 7, 8, 12, 13, 14, 15, 18])
if config.data_from == "20newsgroup":
config.test_batch_size = 26
config.max_docs_length = 1000
config.max_seq_length = 3
config.tree1 = np.array([22,3,4,5,6,7])
config.tree2 = np.array([23,9,10,11,12])
config.tree3 = np.array([24,13,14,15,16])
config.tree4 = np.array([25,8])
config.tree5 = np.array([26,10,18,19])
config.tree6 = np.array([27,21,2,17])
config.layer1 = np.array([22,23,24,25,26,27])
config.layer2 = np.array([3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21])
config.save_dir = os.path.join(config.out_dir, "save")
config.log_dir = os.path.join(config.out_dir, "log")
if not os.path.exists(config.out_dir): # or os.path.isfile(config.out_dir):
os.makedirs(config.out_dir)
if not os.path.exists(config.save_dir):
os.mkdir(config.save_dir)
if not os.path.exists(config.log_dir):
os.mkdir(config.log_dir)
os.environ["CUDA_VISIBLE_DEVICES"]=config.gpu_ids
m(config)
def main():
key_args = key_parser.parse_known_args()[0]
print(key_args)
args = all_parser.parse_args()
args.name = args.name.replace("-", "_")
if args.mode == "train_eval":
assert args.data_name is not None
assert args.hparams_path is not None
run_id, save_dir = prepare_env(args)
# set distribution env
if torch.cuda.is_available():
if args.is_dist:
hvd.init()
args.local_rank = hvd.local_rank()
args.world_size = hvd.size()
print('local rank:', args.local_rank, 'world size',
args.world_size)
if args.local_rank != 0:
args.main_process = False
torch.cuda.set_device(args.local_rank)
seed = args.local_rank
else:
torch.cuda.set_device('cuda:%d' % int(args.gpu_id))
seed = args.seed
# seed = args.gpu_id
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed) # numpy pseudo-random generator
random.seed(seed) # `python` built-in pseudo-random generator
else:
raise RuntimeError('gpu is not available')
# os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
if args.run_id is None:
# hparams = edict(json.loads(open(args.hparams_path, "r").read()))
hparams = json.loads(open(args.hparams_path, "r").read())
if args.main_process:
dump_src(save_dir)
dump_env(vars(args), save_dir, "args.json")
dump_env(hparams, save_dir, "hparams.json")
else:
args, hparams = load_saved_env(
args, save_dir) # TODO some process may not have $savedir
m(args, str(save_dir), hparams=hparams)
def main(job_id, params):
from main import main as m
with file("config.json") as fin:
params_ = json.load(fin)
params['experiment_name'] = params_['experiment-name']
params['dataset'] = params_['dataset']
return m(job_id, params)
def main(_):
config = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpu
m(config)
def main(_):
config = flags.FLAGS
config.out_dir = os.path.join(config.out_base_dir, config.model_name,
str(config.run_id).zfill(2))
m(config)
def main(_):
config = flags.FLAGS
os.environ["CUDA_VISIBLE_DEVICES"] = str(config.gpu)
m(config)
def main(_):
# assert (config.clftype=="") & (not config.model_name.endswith("flat"))
if config.debug:
#config.mode = "check"
config.num_batches = 100
config.log_period = 1
config.save_period = 1
config.eval_period = 1
config.batch_size = 2
config.val_num_batches = 3
#print(config.test_batch_size)
if config.model_name.endswith("flat"):
if config.data_from == "reuters": config.clftype = "flat"
config.eval_layers = False
config.eval_trees = False
if config.data_from == "reuters": config.fn_classes = 18
if config.data_from == "20newsgroup": config.fn_classes = 20
if config.data_from == "ice":
config.fn_classes = 645
config.learning_rate = 0.001
config.thred = 0.053
else:
if config.data_from == "reuters": config.hn_classes = 21
if config.data_from == "ice": config.hn_classes = 648
if config.data_from == "20newsgroup": config.hn_classes = 29
if config.data_from == "reuters":
config.max_docs_length = 818
config.tree1 = np.array([2, 3, 4, 5, 6, 7, 8])
config.tree2 = np.array([9, 10, 11, 12, 13, 14, 15])
config.tree3 = np.array([16, 17, 18, 19])
config.layer1 = np.array([2, 9, 16])
config.layer2 = np.array([3, 4, 10, 11, 17, 19])
config.layer3 = np.array([5, 6, 7, 8, 12, 13, 14, 15, 18])
if config.data_from == "20newsgroup":
if config.mode == "train": config.val_num_batches = 3
config.EOS = 28
config.test_batch_size = 100
config.max_docs_length = 1000
config.max_seq_length = 2
config.tree1 = np.array([22, 3, 4, 5, 6, 7])
config.tree2 = np.array([23, 9, 10, 11, 12])
config.tree3 = np.array([24, 13, 14, 15, 16])
config.tree4 = np.array([25, 8])
config.tree5 = np.array([26, 10, 18, 19])
config.tree6 = np.array([27, 21, 2, 17])
config.layer1 = np.array([22, 23, 24, 25, 26, 27])
config.layer2 = np.array([
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
])
if config.data_from == "ice":
if config.mode == "train": config.val_num_batches = 3
config.EOS = 647
config.batch_size = 600
config.test_batch_size = 600
config.max_docs_length = 48
config.max_seq_length = 8
config.eval_trees = False
config.eval_layers = False
config.num_batches = 80000
# define tree1/tree2/layer1/layer2
config.out_dir = os.path.join("../data/out", config.out_dir)
config.save_dir = os.path.join(config.out_dir, "save")
config.log_dir = os.path.join(config.out_dir, "log")
if not os.path.exists(
config.out_dir): # or os.path.isfile(config.out_dir):
os.makedirs(config.out_dir)
if not os.path.exists(config.save_dir):
os.mkdir(config.save_dir)
if not os.path.exists(config.log_dir):
os.mkdir(config.log_dir)
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpu_ids
m(config)
def main(_):
config = flags.FLAGS
# 위와 같이 정의된 값들은 flags.FLAGS 를 통해서 어디에서든지 호출하여 사용
os.environ["CUDA_VISIBLE_DEVICES"] = str(config.gpu)
m(config)
def main(_):
print(argv)
config = flags.FLAGS
m(config)