def __init__(self, opt):
"""Initialize this model class.
Parameters:
opt -- training/test options
A few things can be done here.
- (required) call the initialization function of BaseModel
- define loss function, visualization images, model names, and optimizers
"""
BaseModel.__init__(self,
opt) # call the initialization method of BaseModel
# specify the training losses you want to print out. The program will call base_model.get_current_losses to plot the losses to the console and save them to the disk.
self.loss_names = ['loss_G']
# 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 = ['data_A', 'data_B', 'output']
# 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.
self.model_names = ['G']
# define networks; you can use opt.isTrain to specify different behaviors for training and test.
self.netG = networks.define_G(opt.input_nc,
opt.output_nc,
opt.ngf,
opt.netG,
gpu_ids=self.gpu_ids)
if self.isTrain: # only defined during training time
# define your loss functions. You can use losses provided by torch.nn such as torch.nn.L1Loss.
# We also provide a GANLoss class "networks.GANLoss". self.criterionGAN = networks.GANLoss().to(self.device)
self.criterionLoss = torch.nn.L1Loss()
# 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.optimizer = torch.optim.Adam(self.netG.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
self.optimizers = [self.optimizer]
def __init__(self, opt):
"""Initialize the pix2pixPL class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseModel.__init__(self, opt)
# specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
self.loss_names = ['G_GAN', 'G_Style', 'G_Content', 'D_real', 'D_fake']
# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
self.visual_names = ['real_A', 'fake_B', 'real_B']
# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
if self.isTrain:
self.model_names = ['G', 'D']
else: # during test time, only load G
self.model_names = ['G']
# define networks (both generator and discriminator)
self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf,
opt.netG, opt.norm, not opt.no_dropout,
opt.init_type, opt.init_gain,
self.gpu_ids)
if self.isTrain: # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
self.netD = networks.define_D(opt.input_nc + opt.output_nc,
opt.ndf, opt.netD, opt.n_layers_D,
opt.norm, opt.init_type,
opt.init_gain, self.gpu_ids)
if self.isTrain:
# define loss functions
self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
#self.criterionL1 = torch.nn.L1Loss()
self.criterionPL = PerceptualLoss(self.device).to(self.device)
# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
def __init__(self, opt):
"""Initialize the pix2pix class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
assert (not opt.isTrain)
BaseModel.__init__(self, opt)
# specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
self.loss_names = []
# specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
self.visual_names = ['real', 'fake']
# specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
self.model_names = ['G' + opt.model_suffix
] # only generator is needed.
self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf,
opt.netG, opt.norm, not opt.no_dropout,
opt.init_type, opt.init_gain,
self.gpu_ids)
# assigns the model to self.netG_[suffix] so that it can be loaded
# please see <BaseModel.load_networks>
setattr(self, 'netG' + opt.model_suffix,
self.netG) # store netG in self.