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Stardust·减
2023-08-30 14:23:45 +08:00
committed by GitHub
parent 20529f6e17
commit df9438dd4a
1 changed files with 2 additions and 3 deletions
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train_ms.py
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@@ -8,7 +8,6 @@ from torch import nn, optim
from torch.nn import functional as F from torch.nn import functional as F
from torch.utils.data import DataLoader from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter from torch.utils.tensorboard import SummaryWriter
#import wandb
import torch.multiprocessing as mp import torch.multiprocessing as mp
import torch.distributed as dist import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.parallel import DistributedDataParallel as DDP
@@ -255,7 +254,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
loss_disc_all = loss_disc loss_disc_all = loss_disc
if net_dur_disc is not None: if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw_.detach(), logw.detach()) y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
with autocast(enabled=False): with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all # TODO: I think need to mean using the mask, but for now, just mean all
loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g) loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
@@ -276,7 +275,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
# Generator # Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None: if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw_, logw) y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
with autocast(enabled=False): with autocast(enabled=False):
loss_dur = torch.sum(l_length.float()) loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel