def start_func(args):
if args.train_method=="train_by_custom_net":
train_script.train(args)
elif args.train_method=="train_by_transfer_learning_using_resnet":
train_by_transfer_learning_using_resnet.train(args)
else:
pass
def start_func(args):
if args.start_func_mode == "pre_visualize_data":
visualize_data.visualize(args)
elif args.train_method == "train_by_transfer_learning_using_resnet":
train_by_transfer_learning_using_resnet.train(args)
else:
pass
def start_train(args):
if args.train_method == "train_by_custom_net":
train_script.train(args)
elif args.train_method == "train_by_transfer_learning_using_resnet":
train_by_transfer_learning_using_resnet.train(args)
elif args.train_method == "Scikit_Learn_SVM":
train_by_Scikit_Learn_SVM.train(args)
elif args.train_method == "xgboost":
train_by_xgboost.train(args)
elif args.train_method == "xgboost_resample":
train_by_xgboost_resample.train(args)
else:
pass
def test_utils_net_for_cgintrinsic_net(self):
# ================================================================================
# Arrange
args=utils_create_argument.return_argument()
# ================================================================================
# Act
netG=train_by_transfer_learning_using_resnet.train(args)
# print('netG',type(netG))
# <class 'prj_root.utils.utils_net_for_cgintrinsic_net.MultiUnetGenerator'>
# ================================================================================
# Assert
# self.assertEqual(3,netG)
self.assertIsInstance(netG,utils_net_for_cgintrinsic_net.MultiUnetGenerator)