def main(args):
init_logger()
set_seeds()
tokenizer = load_tokenizer(args)
if args.do_train:
if args.model_4:
first =TrainerFirst(args, tokenizer)
first.train()
second = SecondClassifier(args, tokenizer)
second.classifier()
third = Trainermmd(args, tokenizer)
third.train()
elif args.new_model_4:
first =FirstTrainer(args, tokenizer)
first.train()
second = SecondClassifier_n(args, tokenizer)
second.classifier()
third = Trainermmd_n(args, tokenizer)
third.train()
else:
trainer = Trainer(args, tokenizer)
trainer.train()
elif args.do_test:
if args.model_4 or args.new_model_4:
tester = Predictor(args, tokenizer)
tester.predict()
else:
tester = Tester(args, tokenizer)
tester.test()
elif args.do_interactive:
interactive_predict(args)
def train_run(**kwargs):
cost = kwargs['loss_info']
optimizer = kwargs['optimizer_info']
learning_rate = kwargs['learning_rate']
drop_out_rate = kwargs['drop_out_rate']
act_func = kwargs['act_func']
layer_cnt = int(kwargs['layer_cnt'])
model_id = int(kwargs['tr_model_id'])
normal_data = kwargs['normal_data']
abnormal_data = kwargs['abnormal_data']
validation = kwargs['tr_validation']
k_fold_list = []
# validation 수치 변경
if int(validation) == 2:
print(validation, type(validation))
k_fold_list.append(50)
elif int(validation) == 5:
print(validation, type(validation))
k_fold_list.append(20)
elif int(validation) == 10:
print(validation, type(validation))
k_fold_list.append(10)
elif int(validation) == 15:
print(validation, type(validation))
k_fold_list.append(18)
elif int(validation) == 20:
print(validation, type(validation))
k_fold_list.append(5)
# print('dl_option : ', cost, optimizer, learning_rate, drop_out_rate, act_func, layer_cnt, model_id, validation)
k_fold = k_fold_list[0]
def _return_loop_number(string):
if string != '_':
under_Bar = string.find('_')
first_data_n = int(string[:under_Bar])
last_data_n = int(string[under_Bar + 1:])
return first_data_n, last_data_n
# path = '/home/obsk/Javis_dl_system/data/I66'
# /opt/home/data/I66/DICOM/train/abnormal/I66_mri_AB_00000029
# /home/obsk/v_nas2/I66/DICOM/train/abnormal/I66_mri_AB_00000029
# path = 'D:/data/I66'
# normal data get
normal_file_path_list = []
normal_data_chklist = []
if normal_data != '_':
normal_start, normal_fin = _return_loop_number(normal_data)
# normal_length = normal_fin - normal_start + 1
# client = MongoClient('mongodb://172.16.52.79:27017')
# db = client.ohif_deployment
# cursor = db.study_normtraining.find().sort('normtrain_id', pymongo.ASCENDING)
cur = OraDB.prepareCursor()
cur.execute(
"select del_yn, dataid, file_path, normtrain_id from study_normtraining order by normtrain_id asc"
)
for row in cur:
del_yn, dataid, file_path, normtrain_id = list(row)
if 'del_yn' != 'y' and 'I66' in dataid:
normal_data_chklist.append(file_path)
print(normal_data_chklist)
for idx in range(normal_start - 1, normal_fin):
normal_file_path_list.append(normal_data_chklist[idx])
print(normal_file_path_list)
OraDB.releaseConn()
# abnormal data get
abnormal_file_path_list = []
abnormal_data_chklist = []
if abnormal_data != '_':
abnormal_start, abnormal_fin = _return_loop_number(abnormal_data)
# abnormal_length = abnormal_fin - abnormal_start + 1
# client = MongoClient('mongodb://172.16.52.79:27017')
# db = client.ohif_deployment
# cursor = db.study_abnormtraining.find().sort('abnormtrain_id', pymongo.ASCENDING)
cur = OraDB.prepareCursor()
cur.execute(
"select del_yn, dataid, file_path, normtrain_id from study_abnormtraining order by abnormtrain_id asc"
)
for row in cur:
del_yn, dataid, file_path, normtrain_id = list(row)
if del_yn != 'y' and 'I66' in dataid:
abnormal_data_chklist.append(file_path)
print(abnormal_data_chklist)
for idx in range(abnormal_start - 1, abnormal_fin):
abnormal_file_path_list.append(abnormal_data_chklist[idx])
print(abnormal_file_path_list)
OraDB.releaseConn()
# copy nas normal data to local temp directory
local_n_datapath_list = []
for nas_path in normal_file_path_list:
temp_path = nas_path.replace(
'/medimg/', '/home/user01/Javis_dl_system/data_temp/')
if not os.path.exists(temp_path):
os.makedirs(temp_path)
data_mover.nas_to_dlserver(nas_path, temp_path)
local_n_datapath_list.append(temp_path)
# copy nas abnormal data to local temp directory
local_ab_datapath_list = []
for nas_path in abnormal_file_path_list:
temp_path = nas_path.replace(
'/medimg/', '/home/user01/Javis_dl_system/data_temp/')
if not os.path.exists(temp_path):
os.makedirs(temp_path)
data_mover.nas_to_dlserver(nas_path, temp_path)
local_ab_datapath_list.append(temp_path)
tot_datapath_list = local_n_datapath_list + local_ab_datapath_list
# print('tot_data_id_list : ', tot_datapath_list)
if len(tot_datapath_list) <= 1:
print('CANNOT RUN WITH 0, 1 DATA SET')
raise FileNotFoundError
shuffle(tot_datapath_list)
fin_datapath_list = []
# path : /home/bjh/obsk/v_nas2/I66/DICOM/train/abnormal/I66_mri_AB_00000039
for path in tot_datapath_list:
datasaver = LabelDataSaver.DataSaver(path)
datasaver.saveYdata_labeled()
x_path = path + '/img/x'
y_path = path + '/img/y'
if os.path.isdir(y_path) is True:
if len(os.listdir(y_path)) == len(os.listdir(x_path)):
fin_datapath_list.append(path)
dataset_cnt = len(fin_datapath_list) # n
# b_size = 1
# k = k_fold
data_loader = TrainDataLoader.DataLoader(data_path_list=fin_datapath_list,
k_fold=k_fold,
c_size=256,
i_channel=1,
n_class=2)
trainer = Trainer(data_loader=data_loader,
model_id=model_id,
optimizer=optimizer,
learning_rate=learning_rate,
cost_name=cost,
act_func=act_func,
layer_n=layer_cnt)
trainer.train(n_epochs=1,
n_t_iters=(math.ceil(dataset_cnt / k_fold *
(k_fold - 1)) - 1) * 8,
n_v_iters=math.ceil(dataset_cnt / k_fold) * 8,
b_size=1,
keep_prob=drop_out_rate)
