def __setup_model_hparams(self):
# 1. define losses
self.loss = nn.BCELoss()
# 2. define model metric
self.metric = Dice_score(self.hparams['model']['n_classes'])
# 3. define optimizer
self.optimizer = eval(f"torch.optim.{self.hparams['optimizer_name']}")(
params=self.model.parameters(),
**self.hparams['optimizer_hparams'])
# 4. define scheduler
self.scheduler = eval(
f"torch.optim.lr_scheduler.{self.hparams['scheduler_name']}")(
optimizer=self.optimizer, **self.hparams['scheduler_hparams'])
# 5. define early stopping
self.early_stopping = EarlyStopping(
checkpoint_path=self.hparams['checkpoint_path'] +
f'/checkpoint_{self.start_training}' + '.pt',
patience=self.hparams['patience'],
delta=self.hparams['min_delta'],
is_maximize=False,
)
# 6. set gradient clipping
self.apply_clipping = self.hparams['clipping'] # clipping of gradients
# 7. Set scaler for optimizer
self.scaler = torch.cuda.amp.GradScaler()
return True
def __init__(self, hparams, gpu, model, Dataset_train, eval):
# load the model
self.hparams = hparams
self.gpu = gpu
self.Dataset_train = Dataset_train
self.eval = eval
print('\n')
print('Selected Learning rate:', self.hparams['optimizer_hparams']['lr'])
print('\n')
self.exclusions = []
self.splits, self.splits_test = self.load_split_table()
self.metric = Dice_score(self.hparams['model']['n_classes'])
self.model = model