def __init__(self, opt):
BaseModel.__init__(self, opt) # call the initialization method of BaseModel
# specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images.
self.visual_names = []
# specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks.
# you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them.
# define networks; you can use opt.isTrain to specify different behaviors for training and test.
self.net_names = ["main"]
self.net_main = create_net(opt)
self.loss_criterion = create_loss(opt)
self.init_optimizers(opt)
self.loss_names = getattr(self.loss_criterion, "loss_names")
"""
unfreeze shared_cnn_layers, shared_fc_layers and other_layers,
calculate other loss
not backward
"""
self.plus_other_loss = True
self.need_backward = False
self.max_step = 1
class JointTrainModel(BaseModel):
@staticmethod
def modify_commandline_options(parser):
"""Add new dataset-specific options, and rewrite default values for existing options.
num_classes is the number of classes per task
for example, num_classes = [10,10,10], means the number of classes on taks1 is 10, and then so on.
Parameters:
parser -- original option parser
Returns:
the modified parser.
"""
parser.add_argument('--net_name', type=str, default="alexnet", choices=["alexnet", "imagenet"],
help='network select from alexnet|imagenet', )
parser.add_argument('--loss_name', type=str, default="total", choices=["total"],
help='loss select from total', )
parser.add_argument('--taskdataset_name', type=str, default="total", choices=["total"],
help='loss from total', )
return parser
@staticmethod
def default_value(opt):
return opt
def __init__(self, opt):
BaseModel.__init__(self, opt) # call the initialization method of BaseModel
# specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images.
self.visual_names = []
# specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks.
# you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them.
# define networks; you can use opt.isTrain to specify different behaviors for training and test.
self.net_names = ["main"]
self.net_main = create_net(opt)
self.loss_criterion = create_loss(opt)
self.init_optimizers(opt)
self.loss_names = getattr(self.loss_criterion, "loss_names")
"""
unfreeze shared_cnn_layers, shared_fc_layers and other_layers,
calculate other loss
not backward
"""
self.plus_other_loss = False
self.need_backward = False
self.max_step = self.nb_tasks