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Revise the warm-up setup process

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Jared 2023-04-11 11:49:41 +08:00
parent e0b7261aa0
commit 8cff6c5cb0
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1 changed files with 12 additions and 2 deletions
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train.py
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@ -114,20 +114,30 @@ def run(rank, n_gpus, hps):
epoch_str = 1
global_step = 0
warmup_epoch = hps.train.warmup_epochs
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
scaler = GradScaler(enabled=hps.train.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
# update learning rate
if epoch > 1:
scheduler_g.step()
scheduler_d.step()
# set up warm-up learning rate
if epoch <= warmup_epoch:
for param_group in optim_g.param_groups:
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
for param_group in optim_d.param_groups:
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
# training
if rank == 0:
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
[train_loader, eval_loader], logger, [writer, writer_eval])
else:
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
[train_loader, None], None, None)
scheduler_g.step()
scheduler_d.step()
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):