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sigmoid value scaler

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SayanoAI
2024-10-09 00:07:23 -04:00
parent 8d4808f5a1
commit d57f222fa2
2 changed files with 35 additions and 6 deletions
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training_cli.py
+8 -5
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@@ -122,10 +122,9 @@ def train_model(hps: "utils.HParams"):
def run(rank, n_gpus, hps, device):
print(f"{__name__=}")
global global_step, least_loss, loss_file, best_model_name, gradient_clip_value, MultiscaleMelLoss
global global_step, least_loss, loss_file, best_model_name, MultiscaleMelLoss
global_step = 0
loss_file = os.path.join(hps.model_dir,"losses.json")
gradient_clip_value = np.log(hps.train.c_gp/hps.train.learning_rate) if hps.train.get("c_gp",0.)>0 else None
if os.path.isfile(loss_file):
with open(loss_file,"r") as f:
@@ -325,7 +324,7 @@ def run(rank, n_gpus, hps, device):
logger.error(f"Failed to load balancer state: {e}")
balancer_d = LossBalancer(
net_d,
weights_decay=.5 / (1 + np.exp(-10 * (epoch_str / hps.total_epoch - 0.16)))+.5, #sigmoid scaled ema .8 at 20% epoch
weights_decay=commons.sigmoid_value(global_step,total_steps=10000,start_value=.5, end_value=.999, midpoint=.2),
loss_decay=.8,
epsilon=hps.train.eps,
active=hps.train.get("use_balancer",False),
@@ -386,6 +385,7 @@ def train_and_evaluate(
net_g.train()
net_d.train()
balancer_g, balancer_d = balancer
gradient_clip_value = commons.sigmoid_value(global_step, total_steps=10000, start_value=1, end_value=500, midpoint=.2)
# Prepare data iterator
if hps.if_cache_data_in_gpu:
@@ -696,8 +696,11 @@ def train_and_evaluate(
logger.info(f"|| {loss_disc_all=:.3f}: {loss_disc=:.3f}, {gradient_penalty=:.3f}")
logger.info(f"|| {loss_gen_all=:.3f}: {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
logger.info(f"|| {aux_loss=:.3f}: {harmonic_loss=:.3f}, {tefs_loss=:.3f}, {tsi_loss=:.3f}")
balancer_g.on_epoch_end(.5 / (1 + np.exp(-10 * (epoch / hps.total_epoch - 0.16)))+.5) #sigmoid scaling of ema
balancer_d.on_epoch_end(.5 / (1 + np.exp(-10 * (epoch / hps.total_epoch - 0.16)))+.5) #sigmoid scaling of ema
#sigmoid scaling of ema
weights_decay = commons.sigmoid_value(global_step,total_steps=10000,start_value=.5, end_value=.999, midpoint=.2)
balancer_g.on_epoch_end(weights_decay)
balancer_d.on_epoch_end(weights_decay)
if loss_gen_all<least_loss:
least_loss = loss_gen_all