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)