nodes/SayanoAI-Comfy-RVC
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

refactored training params to node

Browse Source
This commit is contained in:
SayanoAI
2024-08-30 23:04:28 -04:00
parent b52bae0be0
commit 5ecd6a84f2
4 changed files with 74 additions and 70 deletions
Download Patch File Download Diff File Expand all files Collapse all files
training_cli.py
+10 -10
View File
@@ -127,10 +127,10 @@ def run(rank, n_gpus, hps, device):
if os.path.isfile(loss_file):
with open(loss_file,"r") as f:
data: dict = json.load(f)
least_loss = data.get("least_loss",50)
least_loss = data.get("least_loss",hps.best_model_threshold)
best_model_name = data.get("best_model_name","")
else:
least_loss = 50
least_loss = hps.best_model_threshold
best_model_name = ""
if hps.version == "v1":
@@ -481,7 +481,7 @@ def train_and_evaluate(
with autocast(enabled=False):
gradient_penalty = torch.Tensor([0.]).to(wave.device)
if hps.train.get("gradient_lambda",0.)>0:
if hps.train.get("c_gp",0.)>0:
# Compute the gradient penalty
# Randomly interpolate between real and generated data
size = [1]*wave.ndim
@@ -502,10 +502,10 @@ def train_and_evaluate(
)[0]
gradient = gradient.view(gradient.size(0), -1)
gradient_penalty += torch.log(gradient.norm(2,dim=-1)).abs().mean() # force gradnorm=1
gradient_penalty *= hps.train.gradient_lambda/len(disc_interpolated_output)
gradient_penalty *= hps.train.c_gp/len(disc_interpolated_output)
loss_mfcc = torch.Tensor([0.]).to(wave.device)
if hps.train.get("timbre_lambda",0)>0:
if hps.train.get("c_mfcc",0)>0:
loss_mfcc = mfcc_loss(wave, gen_wave, SR_MAP[hps.sample_rate],
n_fft=hps.data.filter_length,
hop_length=hps.data.hop_length,
@@ -514,7 +514,7 @@ def train_and_evaluate(
norm="slaney",
f_min=hps.data.mel_fmin,
f_max=hps.data.mel_fmax
) * hps.train.timbre_lambda
) * hps.train.c_mfcc
loss_mfcc = loss_mfcc.squeeze()
gradient_penalty = gradient_penalty.squeeze()
@@ -531,9 +531,9 @@ def train_and_evaluate(
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
with autocast(enabled=False):
loss_mel = F.smooth_l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
loss_fm = feature_loss(fmap_r, fmap_g)
loss_mel = F.smooth_l1_loss(y_mel, y_hat_mel) * hps.train.get("c_mel",45.)
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.get("c_kl",1.)
loss_fm = feature_loss(fmap_r, fmap_g) * hps.train.get("c_fm",2.)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
@@ -556,7 +556,7 @@ def train_and_evaluate(
else:
ckpt = net_g.state_dict()
best_model_name = f"{hps.name}_loss{least_loss:2.0f}" if hps.if_save_latest else f"{hps.name}_e{epoch}_s{global_step}_loss{least_loss:2.0f}"
best_model_name = f"{hps.name}_e{epoch}_s{global_step}_loss{least_loss:2.0f}" if hps.save_every_weights else f"{hps.name}_loss{least_loss:2.0f}"
status = save_checkpoint(ckpt,best_model_name,epoch,hps)
logger.info(f"=== saving best model {best_model_name}: {status=} ===")