From c9b6a9695ecc063421c0acbebfaf50f1c0cc492d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stardust=C2=B7=E5=87=8F?= <2225664821@qq.com> Date: Wed, 23 Aug 2023 18:38:51 +0800 Subject: [PATCH] Update train_ms.py --- train_ms.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/train_ms.py b/train_ms.py index d792833..bcecf70 100644 --- a/train_ms.py +++ b/train_ms.py @@ -7,9 +7,8 @@ import torch from torch import nn, optim from torch.nn import functional as F from torch.utils.data import DataLoader -#from torch.utils.tensorboard import SummaryWriter -import wandb -from wandb.tensorboard import WandbSummaryWriter as SummaryWriter +from torch.utils.tensorboard import SummaryWriter +#import wandb import torch.multiprocessing as mp import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP @@ -64,8 +63,9 @@ def run(rank, n_gpus, hps): logger = utils.get_logger(hps.model_dir) logger.info(hps) utils.check_git_hash(hps.model_dir) - wandb.init(project="Bert-VITS2", config=hps, sync_tensorboard=True) + #wandb.init(project="Bert-VITS2", config=hps, sync_tensorboard=True) writer = SummaryWriter(log_dir=hps.model_dir) + #writer = wandb.summary.create_file_writer() writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank) @@ -305,15 +305,15 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} scalar_dict.update( {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl}) - if net_dur_disc is not None: - scalar_dict.update({"loss/dur_disc/total": loss_dur_disc_all, "grad_norm_dur_disc": grad_norm_dur_disc}) + #if net_dur_disc is not None: + #scalar_dict.update({"loss/dur_disc/total": loss_dur_disc_all, "grad_norm_dur_disc": grad_norm_dur_disc}) scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) - if net_dur_disc is not None: - scalar_dict.update({"loss/dur_disc_r" : f"{losses_dur_disc_r}"}) - scalar_dict.update({"loss/dur_disc_g" : f"{losses_dur_disc_g}"}) - scalar_dict.update({"loss/dur_gen" : f"{loss_dur_gen}"}) + #if net_dur_disc is not None: + #scalar_dict.update({"loss/dur_disc_r" : f"{losses_dur_disc_r}"}) + #scalar_dict.update({"loss/dur_disc_g" : f"{losses_dur_disc_g}"}) + #scalar_dict.update({"loss/dur_gen" : f"{loss_dur_gen}"}) image_dict = { "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),