-- <optimize_parameters>: calculate losses, gradients, and update network weights.
-- <optimize_parameters>: calculate losses, gradients, and update network weights.
-- <modify_commandline_options>: (optionally) add model-specific options and set default options.
-- <modify_commandline_options>: (optionally) add model-specific options and set default options.
"""
"""
def__init__(self,opt):
def__init__(self,cfg):
"""Initialize the BaseModel class.
"""Initialize the BaseModel class.
Parameters:
Args:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
cfg (Dict)-- configs of Model.
When creating your custom class, you need to implement your own initialization.
When creating your custom class, you need to implement your own initialization.
In this function, you should first call <BaseModel.__init__(self, opt)>
In this function, you should first call <super(YourClass, self).__init__(self, cfg)>
Then, you need to define four lists:
Then, you need to define four lists:
-- self.losses (str list): specify the training losses that you want to plot and save.
-- self.losses (dict): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- self.nets (dict): define networks used in our training.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
-- self.optimizers (dict): define and initialize optimizers. You can define one optimizer for each network.
If two networks are updated at the same time, you can use itertools.chain to group them.
See cycle_gan_model.py for an example.
"""
"""
self.opt=opt
self.cfg=cfg
self.isTrain=opt.isTrain
self.is_train=cfg.is_train
self.save_dir=os.path.join(
self.save_dir=os.path.join(
opt.output_dir,
cfg.output_dir,
opt.model.name)# save all the checkpoints to save_dir
cfg.model.name)# save all the checkpoints to save_dir
self.losses=OrderedDict()
self.losses=OrderedDict()
self.model_names=[]
self.nets=OrderedDict()
self.visual_names=[]
self.visual_items=OrderedDict()
self.optimizers=[]
self.optimizers=OrderedDict()
self.optimizer_names=[]
self.image_paths=[]
self.image_paths=[]
self.metric=0# used for learning rate policy 'plateau'
self.metric=0# used for learning rate policy 'plateau'
@staticmethod
defmodify_commandline_options(parser,is_train):
"""Add new model-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
returnparser
@abstractmethod
@abstractmethod
defset_input(self,input):
defset_input(self,input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
