logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(os.path.join(log_dir,
'%s-%d.log' % (args.model, time.time()))),
logging.StreamHandler()
]
)
logger = logging.getLogger()
if hp.data.train == '' or hp.data.val == '':
logger.error("hp.data.train, hp.data.val cannot be empty")
raise Exception("Please specify directories of train data.")
if hp.model.graph0 == '' or hp.model.graph1 == '' or hp.model.graph2 == '':
logger.error("hp.model.graph0, graph1, graph2 cannot be empty")
raise Exception("Please specify random DAG architecture.")
graphs = [
read_graph(hp.model.graph0),
read_graph(hp.model.graph1),
read_graph(hp.model.graph2),
]
writer = MyWriter(log_dir)
trainset = KMNIST_dataloader(hp, args, True)
valset = KMNIST_dataloader(hp, args, False)
train(out_dir, chkpt_path, trainset, valset, writer, logger, hp, hp_str, graphs, in_channels=1)
def ga_trainer(args, index_list, f_path, f_name):
# parser = argparse.ArgumentParser()
# parser.add_argument('-c', '--config', type=str, required=True,
# help="yaml file for configuration")
# parser.add_argument('-p', '--checkpoint_path', type=str, default=None, required=False,
# help="path of checkpoint pt file")
# parser.add_argument('-m', '--model', type=str, required=True,
# help="name of the model. used for logging/saving checkpoints")
# args = parser.parse_args()
individual_model_name = args.model + "_{}_{}_{}".format(
index_list[0], index_list[1], index_list[2])
hp = HParam(args.config)
with open(args.config, 'r') as f:
hp_str = ''.join(f.readlines())
## pytoch 모델 저장하는 위치
pt_path = os.path.join('.', hp.log.chkpt_dir)
## 모델 사전에 정의한 모델 이름으로 저장
out_dir = os.path.join(pt_path, individual_model_name)
os.makedirs(out_dir, exist_ok=True)
log_dir = os.path.join('.', hp.log.log_dir)
log_dir = os.path.join(log_dir, individual_model_name)
os.makedirs(log_dir, exist_ok=True)
if args.checkpoint_path is not None:
chkpt_path = args.checkpoint_path
else:
chkpt_path = None
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(
os.path.join(
log_dir,
'%s-%d.log' % (args.model, time.time()))),
logging.StreamHandler()
])
logger = logging.getLogger()
if hp.data.train == '' or hp.data.val == '':
logger.error("hp.data.train, hp.data.val cannot be empty")
raise Exception("Please specify directories of train data.")
if hp.model.graph0 == '' or hp.model.graph1 == '' or hp.model.graph2 == '':
logger.error("hp.model.graph0, graph1, graph2 cannot be empty")
raise Exception("Please specify random DAG architecture.")
# graphs = [
# read_graph(hp.model.graph0),
# read_graph(hp.model.graph1),
# read_graph(hp.model.graph2),
# ]
## 새로 생성한 파일 위치에서 그래프 읽기
#print(os.path.join(f_path, args.model + '_' + str(7) +'.txt'))
graphs = [
read_graph(os.path.join(f_path, args.model + '_' + str(idx) + '.txt'))
for idx in index_list
]
writer = MyWriter(log_dir)
dataset = hp.data.type
switcher = {
'MNIST': MNIST_dataloader,
'CIFAR10': CIFAR10_dataloader,
'ImageNet': create_dataloader,
}
assert dataset in switcher.keys(), 'Dataset type currently not supported'
dl_func = switcher[dataset]
trainset = dl_func(hp, args, True)
valset = dl_func(hp, args, False)
val_acc = ga_train(out_dir, chkpt_path, trainset, valset, writer, logger,
hp, hp_str, graphs)
return val_acc
