return self.mse(pred, gt) + self.gdl_weight * gdl_loss
img_transform = transforms.Compose(
[
transforms.ToTensor(),
# transforms.Normalize((0.5), (0.5))
]
)
dataset = MNIST(DATA_PATH, transform=img_transform)
dataloader = DataLoader(dataset, batch_size=128, shuffle=True)
model = Model()
model.torchnet = autoencoder()
model.optimizer = torch.optim.Adam(
model.torchnet.parameters(), lr=1e-3, weight_decay=1e-5
)
config = ConfigManger().parsed_args
if config["loss"] == "mse":
criterion = nn.MSELoss()
elif config["loss"] == "gdl":
criterion = gradient_difference_loss(config["weight"])
trainer = MNISTTrainer(
model=model,
train_loader=dataloader,
val_loader=dataloader,
criterion=nn.MSELoss(),
device="cuda",
**config["Trainer"]
)
prior = get_prior_from_dataset(unlabeled_loader.dataset)
print("prior for unlabeled dataset", prior)
# network part
import warnings
fix_all_seed(int(config.get("Seed", 0)))
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
net = SimpleNet(1, len(unlabeled_class_sample_nums))
optim = RAdam(net.parameters(), lr=1e-4, weight_decay=1e-4)
scheduler = MultiStepLR(optim, milestones=[50, 80], gamma=0.2)
model = Model()
model.torchnet = net
model.optimizer = optim
model.scheduler = scheduler
# trainer part
Trainer = {
"SemiTrainer": SemiTrainer,
"SemiEntropyTrainer": SemiEntropyTrainer,
"SemiPrimalDualTrainer": SemiPrimalDualTrainer,
"SemiWeightedIICTrainer": SemiWeightedIICTrainer,
"SemiUDATrainer": SemiUDATrainer,
}.get(config["Trainer"]["name"])
assert Trainer
trainer = Trainer(
model,
labeled_loader,