def setup(self, config):
model = model_creator(config)
optimizer = optimizer_creator(model, config)
train_loader, val_loader = data_creator(config)
self.model, self.optimizer, = \
self.register(
models=model, optimizers=optimizer, ddp_args={
"find_unused_parameters": True})
assert self.model.find_unused_parameters
def __init__(self, config):
super().__init__()
self.config = config
if "layer" in config:
self.layer = copy.deepcopy(config["layer"])
else:
self.layer = model_creator(self.config)
self.rand_int = np.random.randint(10)
def setup(self, config):
model = model_creator(config)
optimizer = optimizer_creator(model, config)
train_loader, val_loader = data_creator(config)
scheduler = scheduler_creator(optimizer, config)
loss = nn.MSELoss()
self.model, self.optimizer, self.criterion, self.scheduler = self.register(
models=model, optimizers=optimizer, criterion=loss, schedulers=scheduler
)
self.register_data(train_loader=train_loader, validation_loader=val_loader)