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Stardust·减
2023-09-01 10:34:14 +08:00
committed by GitHub
parent 1db84f5bcf
commit 4c35709d22
1 changed files with 0 additions and 9 deletions
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train_ms.py
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@@ -38,7 +38,6 @@ from text.symbols import symbols
torch.backends.cudnn.benchmark = True torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cuda.matmul.allow_tf32 = True
# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
torch.backends.cudnn.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision('medium') torch.set_float32_matmul_precision('medium')
global_step = 0 global_step = 0
@@ -62,9 +61,7 @@ def run(rank, n_gpus, hps):
logger = utils.get_logger(hps.model_dir) logger = utils.get_logger(hps.model_dir)
logger.info(hps) logger.info(hps)
utils.check_git_hash(hps.model_dir) utils.check_git_hash(hps.model_dir)
#wandb.init(project="Bert-VITS2", config=hps, sync_tensorboard=True)
writer = SummaryWriter(log_dir=hps.model_dir) 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")) 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) dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
@@ -307,15 +304,9 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
"grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
scalar_dict.update( scalar_dict.update(
{"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl}) {"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})
scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) 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_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)}) 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}"})
image_dict = { image_dict = {
"slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),