def configure_optimizers(self):
# optimizer = getattr(optim, self.hparams.get("optimizer", "Adam"))
# lr = self.hparams.get("lr", 1e-4)
# weight_decay = self.hparams.get("weight_decay", 1e-6)
# return optimizer(self.parameters(), lr=lr, weight_decay=weight_decay)
optimizer = LARS(self.parameters(),
lr=(0.2 * (self.batch_size / 256)),
momentum=0.9,
weight_decay=self.hparams.get("weight_decay", 1e-6),
trust_coefficient=0.001)
train_iters_per_epoch = 8000 // self.batch_size
warmup_steps = train_iters_per_epoch * 10
total_steps = train_iters_per_epoch * 100
scheduler = {
"scheduler":
torch.optim.lr_scheduler.LambdaLR(
optimizer,
linear_warmup_decay(warmup_steps, total_steps, cosine=True),
),
"interval":
"step",
"frequency":
1,
}
return [optimizer], [scheduler]
def configure_optimizers(self):
if self.exclude_bn_bias:
params = self.exclude_from_wt_decay(self.named_parameters(), weight_decay=self.weight_decay)
else:
params = self.parameters()
if self.optim == "lars":
optimizer = LARS(
params,
lr=self.learning_rate,
momentum=0.9,
weight_decay=self.weight_decay,
trust_coefficient=0.001,
)
elif self.optim == "adam":
optimizer = torch.optim.Adam(params, lr=self.learning_rate, weight_decay=self.weight_decay)
warmup_steps = self.train_iters_per_epoch * self.warmup_epochs
total_steps = self.train_iters_per_epoch * self.max_epochs
scheduler = {
"scheduler": torch.optim.lr_scheduler.LambdaLR(
optimizer,
linear_warmup_decay(warmup_steps, total_steps, cosine=True),
),
"interval": "step",
"frequency": 1,
}
return [optimizer], [scheduler]