def __init__(self,
gamma: float = 2.,
alpha: float = 0.25,
pad_idx: int = 0,
scales=None,
ratios=None,
reg_loss=F.smooth_l1_loss):
super().__init__()
self.gamma, self.alpha, self.pad_idx, self.reg_loss = gamma, alpha, pad_idx, reg_loss
self.scales = ifnone(scales, [1, 2**(-1 / 3), 2**(-2 / 3)])
self.ratios = ifnone(ratios, [1 / 2, 1, 2])
def before_fit(self):
self.run = (rank_distrib() == 0) and not (hasattr(
self.learn, 'lr_finder') or hasattr(self, "gather_preds"))
if self.run:
self.writer = SummaryWriter(log_dir=self.log_dir)
self.train_metrics = listify(
ifnone(self.train_metrics, self.learn.loss_func))
self.train_metric_names = listify(
ifnone(self.train_metric_names,
[self.get_name(s) for s in self.train_metrics]))
self.train_metric_names = [
'train_' + name for name in self.train_metric_names
]
self.smooth_dict = {
name: (0, 0)
for name in self.train_metric_names
}