// specify the training losses you want to print out. The program will call base_model.get_current_errors
self.loss_names = ["D_A", "G_A", "cycle_A", "idt_A", "D_B", "G_B", "cycle_B", "idt_B"]
// specify the images you want to save/display. The program will call base_model.get_current_visuals
visual_names_A = ["real_A", "fake_B", "rec_A"]visual_names_B = ["real_B", "fake_A", "rec_B"]
if self.isTrain and self.opt.lambda_identity > 0.0:
visual_names_A.append("idt_A")
visual_names_B.append("idt_B")
self.visual_names = visual_names_A + visual_names_B
// specify the models you want to save to the disk. The program will call base_model.save
if self.isTrain:
self.model_names = ["G_A", "G_B", "D_A", "D_B"]
else: // during test time, only load Gs
self.model_names = ["G_A", "G_B"]
// load/define networks
// The naming conversion is different from those used in the paper