def __init__(self, args, loader):
super(StaticMethod, self).__init__(args, loader)
if args.mode == 'train':
self.criterion = losses.StaticLoss(args)
self.criterion = self.criterion.to(self.device)
self.optimizer = get_optimizer(self.G.parameters(), args)
def __init__(self, args, loader):
super(CoVWeightingMethod, self).__init__(args, loader)
if args.mode == 'train':
self.criterion = losses.CoVWeightingLoss(args)
self.criterion.to(self.device)
self.optimizer = get_optimizer(self.G.parameters(), args)
# Record the mean weights for an epoch.
self.mean_weights = [
0.0 for _ in range(self.criterion.alphas.shape[0])
]
def __init__(self, args, loader):
super(UncertaintyMethod, self).__init__(args, loader)
if args.mode == 'train':
self.criterion = losses.UncertaintyLoss(args)
self.criterion.to(self.device)
# Specifically for this architecture, both model parameters and the estimates
# of the log variances have to be optimized.
params_to_optimize = list(
self.G.parameters()) + [self.criterion.log_vars]
self.optimizer = get_optimizer(params_to_optimize, args)
def __init__(self, args, loader):
super(GradNormMethod, self).__init__(args, loader)
if args.mode == 'train':
self.criterion = losses.BaseLoss(args)
self.criterion = self.criterion.to(self.device)
self.optimizer = get_optimizer(self.G.parameters(), args)
# Parameters specific to GradNorm.
self.params = torch.tensor(
[1.0 / NUM_LOSSES for _ in range(NUM_LOSSES)],
requires_grad=True,
device=self.device)
self.first_iter = True
self.L0 = None
self.gamma = args.init_gamma
self.Gradloss = nn.L1Loss()
# Taking optimizer parameters from the GradNorm paper.
self.optimizer_params = optim.Adam([self.params], lr=0.025)