When creating your custom class, you need to implement your own initialization.
In this function, you should first call <BaseModel.__init__(self, opt)>
Then, you need to define four lists:
-- self.loss (str list): specify the training losses that you want to plot and save.
-- self.losses (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- 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.
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@@ -37,7 +37,7 @@ class BaseModel(ABC):
opt.output_dir,
opt.model.name)# save all the checkpoints to save_dir
self.loss=OrderedDict()
self.losses=OrderedDict()
self.model_names=[]
self.visual_names=[]
self.optimizers=[]
...
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@@ -115,7 +115,7 @@ class BaseModel(ABC):
defget_current_losses(self):
"""Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
