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@ -251,8 +251,8 @@ def run(rank, n_gpus, hps):
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scaler = GradScaler(enabled=hps.train.fp16_run)
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print("start training from epoch %s" % epoch_str)
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for epoch in range(epoch_str, hps.train.epochs + 1):
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print("start training from epoch %s"%epoch)
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if rank == 0:
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train_and_evaluate(
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rank,
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@ -282,7 +282,7 @@ def run(rank, n_gpus, hps):
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)
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scheduler_g.step()
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scheduler_d.step()
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print("training done")
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print("training done")
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def train_and_evaluate(
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@ -225,8 +225,8 @@ def run(rank, n_gpus, hps):
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scaler = GradScaler(enabled=hps.train.fp16_run)
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net_d=optim_d=scheduler_d=None
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print("start training from epoch %s" % epoch_str)
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for epoch in range(epoch_str, hps.train.epochs + 1):
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print("start training from epoch %s"%epoch)
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if rank == 0:
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train_and_evaluate(
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rank,
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@ -256,7 +256,7 @@ def run(rank, n_gpus, hps):
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)
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scheduler_g.step()
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# scheduler_d.step()
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print("training done")
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print("training done")
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def train_and_evaluate(
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@ -198,8 +198,8 @@ def run(rank, n_gpus, hps):
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scaler = GradScaler(enabled=hps.train.fp16_run)
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net_d=optim_d=scheduler_d=None
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print("start training from epoch %s"%epoch_str)
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for epoch in range(epoch_str, hps.train.epochs + 1):
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print("start training from epoch %s"%epoch)
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if rank == 0:
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train_and_evaluate(
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rank,
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@ -228,7 +228,7 @@ def run(rank, n_gpus, hps):
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None,
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)
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scheduler_g.step()
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print("training done")
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print("training done")
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def train_and_evaluate(
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rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers
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