510 lines
22 KiB
Python
510 lines
22 KiB
Python
import sys,os
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now_dir=os.getcwd()
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sys.path.append(os.path.join(now_dir,"train"))
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import utils
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hps = utils.get_hparams()
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os.environ["CUDA_VISIBLE_DEVICES"]=hps.gpus.replace("-",",")
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n_gpus=len(hps.gpus.split("-"))
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from random import shuffle
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import traceback,json,argparse,itertools,math,torch,pdb
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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from torch import nn, optim
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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import torch.multiprocessing as mp
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.cuda.amp import autocast, GradScaler
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from infer_pack import commons
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from time import time as ttime
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from data_utils import TextAudioLoaderMultiNSFsid,TextAudioLoader, TextAudioCollateMultiNSFsid,TextAudioCollate, DistributedBucketSampler
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from infer_pack.models import (
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SynthesizerTrnMs256NSFsid,SynthesizerTrnMs256NSFsid_nono,
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MultiPeriodDiscriminator,
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)
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from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
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from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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global_step = 0
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def main():
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"""Assume Single Node Multi GPUs Training Only"""
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assert torch.cuda.is_available(), "CPU training is not allowed."
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# n_gpus = torch.cuda.device_count()
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = "5555"
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mp.spawn(
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run,
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nprocs=n_gpus,
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args=(
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n_gpus,
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hps,
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),
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)
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def run(rank, n_gpus, hps):
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global global_step
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if rank == 0:
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logger = utils.get_logger(hps.model_dir)
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logger.info(hps)
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utils.check_git_hash(hps.model_dir)
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writer = SummaryWriter(log_dir=hps.model_dir)
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writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
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dist.init_process_group(
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backend="gloo", init_method="env://", world_size=n_gpus, rank=rank
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)
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torch.manual_seed(hps.train.seed)
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torch.cuda.set_device(rank)
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if (hps.if_f0 == 1):train_dataset = TextAudioLoaderMultiNSFsid(hps.data.training_files, hps.data)
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else:train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
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train_sampler = DistributedBucketSampler(
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train_dataset,
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hps.train.batch_size,
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# [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200,1400], # 16s
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[100, 200, 300, 400, 500, 600, 700, 800, 900], # 16s
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num_replicas=n_gpus,
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rank=rank,
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shuffle=True,
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)
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# It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
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# num_workers=8 -> num_workers=4
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if (hps.if_f0 == 1):collate_fn = TextAudioCollateMultiNSFsid()
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else:collate_fn = TextAudioCollate()
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train_loader = DataLoader(
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train_dataset,
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num_workers=4,
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shuffle=False,
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pin_memory=True,
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collate_fn=collate_fn,
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batch_sampler=train_sampler,
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persistent_workers=True,
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prefetch_factor=8,
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)
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if(hps.if_f0==1):net_g = SynthesizerTrnMs256NSFsid(hps.data.filter_length // 2 + 1,hps.train.segment_size // hps.data.hop_length,**hps.model,is_half=hps.train.fp16_run,sr=hps.sample_rate).cuda(rank)
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else:net_g = SynthesizerTrnMs256NSFsid_nono(hps.data.filter_length // 2 + 1,hps.train.segment_size // hps.data.hop_length,**hps.model,is_half=hps.train.fp16_run).cuda(rank)
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
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optim_g = torch.optim.AdamW(
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net_g.parameters(),
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps,
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)
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optim_d = torch.optim.AdamW(
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net_d.parameters(),
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps,
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)
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# net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
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# net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
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net_g = DDP(net_g, device_ids=[rank])
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net_d = DDP(net_d, device_ids=[rank])
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try:#如果能加载自动resume
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d) # D多半加载没事
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if rank == 0:
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logger.info("loaded D")
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# _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
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global_step = (epoch_str - 1) * len(train_loader)
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# epoch_str = 1
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# global_step = 0
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except:#如果首次不能加载，加载pretrain
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traceback.print_exc()
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epoch_str = 1
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global_step = 0
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if rank == 0:
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logger.info("loaded pretrained %s %s"%(hps.pretrainG,hps.pretrainD))
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print(net_g.module.load_state_dict(torch.load(hps.pretrainG,map_location="cpu")["model"]))##测试不加载优化器
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print(net_d.module.load_state_dict(torch.load(hps.pretrainD,map_location="cpu")["model"]))
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scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
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optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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)
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scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
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optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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)
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scaler = GradScaler(enabled=hps.train.fp16_run)
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cache=[]
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for epoch in range(epoch_str, hps.train.epochs + 1):
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if rank == 0:
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train_and_evaluate(
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rank,
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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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[scheduler_g, scheduler_d],
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scaler,
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[train_loader, None],
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logger,
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[writer, writer_eval],cache
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)
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else:
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train_and_evaluate(
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rank,
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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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[scheduler_g, scheduler_d],
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scaler,
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[train_loader, None],
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None,
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None,cache
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)
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scheduler_g.step()
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scheduler_d.step()
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def train_and_evaluate(
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rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers,cache
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):
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net_g, net_d = nets
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optim_g, optim_d = optims
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train_loader, eval_loader = loaders
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if writers is not None:
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writer, writer_eval = writers
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train_loader.batch_sampler.set_epoch(epoch)
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global global_step
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net_g.train()
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net_d.train()
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if(cache==[]or hps.if_cache_data_in_gpu==False):#第一个epoch把cache全部填满训练集
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# print("caching")
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for batch_idx, info in enumerate(train_loader):
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if (hps.if_f0 == 1):phone,phone_lengths,pitch,pitchf,spec,spec_lengths,wave,wave_lengths,sid=info
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else:phone,phone_lengths,spec,spec_lengths,wave,wave_lengths,sid=info
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phone, phone_lengths = phone.cuda(rank, non_blocking=True),phone_lengths.cuda(rank, non_blocking=True )
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if (hps.if_f0 == 1):pitch,pitchf = pitch.cuda(rank, non_blocking=True),pitchf.cuda(rank, non_blocking=True)
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sid = sid.cuda(rank, non_blocking=True)
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spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
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wave, wave_lengths = wave.cuda(rank, non_blocking=True), wave_lengths.cuda(rank, non_blocking=True)
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if(hps.if_cache_data_in_gpu==True):
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if (hps.if_f0 == 1):cache.append((batch_idx, (phone,phone_lengths,pitch,pitchf,spec,spec_lengths,wave,wave_lengths ,sid)))
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else:cache.append((batch_idx, (phone,phone_lengths,spec,spec_lengths,wave,wave_lengths ,sid)))
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with autocast(enabled=hps.train.fp16_run):
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if (hps.if_f0 == 1):y_hat,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(phone, phone_lengths, pitch,pitchf, spec, spec_lengths,sid)
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else:y_hat,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(phone, phone_lengths, spec, spec_lengths,sid)
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mel = spec_to_mel_torch(spec,hps.data.filter_length,hps.data.n_mel_channels,hps.data.sampling_rate,hps.data.mel_fmin,hps.data.mel_fmax,)
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y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
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with autocast(enabled=False):
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y_hat_mel = mel_spectrogram_torch(
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y_hat.float().squeeze(1),
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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hps.data.mel_fmin,
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hps.data.mel_fmax,
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)
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if(hps.train.fp16_run==True):
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y_hat_mel=y_hat_mel.half()
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wave = commons.slice_segments(
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wave, ids_slice * hps.data.hop_length, hps.train.segment_size
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) # slice
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# Discriminator
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y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
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with autocast(enabled=False):
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loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
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y_d_hat_r, y_d_hat_g
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)
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optim_d.zero_grad()
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scaler.scale(loss_disc).backward()
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scaler.unscale_(optim_d)
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grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
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scaler.step(optim_d)
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with autocast(enabled=hps.train.fp16_run):
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# Generator
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y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
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with autocast(enabled=False):
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loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
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loss_fm = feature_loss(fmap_r, fmap_g)
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loss_gen, losses_gen = generator_loss(y_d_hat_g)
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
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optim_g.zero_grad()
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scaler.scale(loss_gen_all).backward()
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scaler.unscale_(optim_g)
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grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
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scaler.step(optim_g)
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scaler.update()
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if rank == 0:
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if global_step % hps.train.log_interval == 0:
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lr = optim_g.param_groups[0]["lr"]
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logger.info(
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"Train Epoch: {} [{:.0f}%]".format(
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epoch, 100.0 * batch_idx / len(train_loader)
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)
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)
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# Amor For Tensorboard display
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if loss_mel > 50:
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loss_mel = 50
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if loss_kl > 5:
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loss_kl = 5
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logger.info([global_step, lr])
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logger.info(
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f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
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)
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scalar_dict = {
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"loss/g/total": loss_gen_all,
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"loss/d/total": loss_disc,
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"learning_rate": lr,
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"grad_norm_d": grad_norm_d,
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"grad_norm_g": grad_norm_g,
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}
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scalar_dict.update(
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{"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl}
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)
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scalar_dict.update(
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{"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
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)
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scalar_dict.update(
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{"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}
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)
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scalar_dict.update(
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{"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}
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)
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image_dict = {
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"slice/mel_org": utils.plot_spectrogram_to_numpy(
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y_mel[0].data.cpu().numpy()
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),
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"slice/mel_gen": utils.plot_spectrogram_to_numpy(
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y_hat_mel[0].data.cpu().numpy()
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),
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"all/mel": utils.plot_spectrogram_to_numpy(
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mel[0].data.cpu().numpy()
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),
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}
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utils.summarize(
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writer=writer,
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global_step=global_step,
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images=image_dict,
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scalars=scalar_dict,
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)
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global_step += 1
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# if global_step % hps.train.eval_interval == 0:
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if epoch % hps.save_every_epoch == 0:
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if(hps.if_latest==0):
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utils.save_checkpoint(
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net_g,
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optim_g,
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hps.train.learning_rate,
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epoch,
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os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
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)
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utils.save_checkpoint(
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net_d,
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optim_d,
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hps.train.learning_rate,
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epoch,
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os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
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)
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else:
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utils.save_checkpoint(
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net_g,
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optim_g,
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hps.train.learning_rate,
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epoch,
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os.path.join(hps.model_dir, "G_{}.pth".format(2333333)),
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)
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utils.save_checkpoint(
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net_d,
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optim_d,
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hps.train.learning_rate,
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epoch,
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os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
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)
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else:#后续的epoch直接使用打乱的cache
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shuffle(cache)
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# print("using cache")
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for batch_idx, info in cache:
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if (hps.if_f0 == 1):phone,phone_lengths,pitch,pitchf,spec,spec_lengths,wave,wave_lengths,sid=info
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else:phone,phone_lengths,spec,spec_lengths,wave,wave_lengths,sid=info
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with autocast(enabled=hps.train.fp16_run):
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if (hps.if_f0 == 1):y_hat,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(phone, phone_lengths, pitch,pitchf, spec, spec_lengths,sid)
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else:y_hat,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(phone, phone_lengths, spec, spec_lengths,sid)
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mel = spec_to_mel_torch(
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spec,
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.mel_fmin,
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hps.data.mel_fmax,
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)
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y_mel = commons.slice_segments(
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mel, ids_slice, hps.train.segment_size // hps.data.hop_length
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)
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with autocast(enabled=False):
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y_hat_mel = mel_spectrogram_torch(
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y_hat.float().squeeze(1),
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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hps.data.mel_fmin,
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hps.data.mel_fmax,
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)
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if(hps.train.fp16_run==True):
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y_hat_mel=y_hat_mel.half()
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wave = commons.slice_segments(
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wave, ids_slice * hps.data.hop_length, hps.train.segment_size
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) # slice
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# Discriminator
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y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
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with autocast(enabled=False):
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loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
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y_d_hat_r, y_d_hat_g
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)
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optim_d.zero_grad()
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scaler.scale(loss_disc).backward()
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scaler.unscale_(optim_d)
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grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
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scaler.step(optim_d)
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with autocast(enabled=hps.train.fp16_run):
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# Generator
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y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
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with autocast(enabled=False):
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loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
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loss_fm = feature_loss(fmap_r, fmap_g)
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loss_gen, losses_gen = generator_loss(y_d_hat_g)
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
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optim_g.zero_grad()
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scaler.scale(loss_gen_all).backward()
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scaler.unscale_(optim_g)
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grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
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scaler.step(optim_g)
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scaler.update()
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if rank == 0:
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if global_step % hps.train.log_interval == 0:
|
||
lr = optim_g.param_groups[0]["lr"]
|
||
logger.info(
|
||
"Train Epoch: {} [{:.0f}%]".format(
|
||
epoch, 100.0 * batch_idx / len(train_loader)
|
||
)
|
||
)
|
||
# Amor For Tensorboard display
|
||
if loss_mel > 50:
|
||
loss_mel = 50
|
||
if loss_kl > 5:
|
||
loss_kl = 5
|
||
|
||
logger.info([global_step, lr])
|
||
logger.info(
|
||
f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
|
||
)
|
||
scalar_dict = {
|
||
"loss/g/total": loss_gen_all,
|
||
"loss/d/total": loss_disc,
|
||
"learning_rate": lr,
|
||
"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/kl": loss_kl}
|
||
)
|
||
|
||
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)}
|
||
)
|
||
image_dict = {
|
||
"slice/mel_org": utils.plot_spectrogram_to_numpy(
|
||
y_mel[0].data.cpu().numpy()
|
||
),
|
||
"slice/mel_gen": utils.plot_spectrogram_to_numpy(
|
||
y_hat_mel[0].data.cpu().numpy()
|
||
),
|
||
"all/mel": utils.plot_spectrogram_to_numpy(
|
||
mel[0].data.cpu().numpy()
|
||
),
|
||
}
|
||
utils.summarize(
|
||
writer=writer,
|
||
global_step=global_step,
|
||
images=image_dict,
|
||
scalars=scalar_dict,
|
||
)
|
||
global_step += 1
|
||
# if global_step % hps.train.eval_interval == 0:
|
||
if epoch % hps.save_every_epoch == 0:
|
||
if(hps.if_latest==0):
|
||
utils.save_checkpoint(
|
||
net_g,
|
||
optim_g,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
|
||
)
|
||
utils.save_checkpoint(
|
||
net_d,
|
||
optim_d,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
|
||
)
|
||
else:
|
||
utils.save_checkpoint(
|
||
net_g,
|
||
optim_g,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "G_{}.pth".format(2333333)),
|
||
)
|
||
utils.save_checkpoint(
|
||
net_d,
|
||
optim_d,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
|
||
)
|
||
|
||
|
||
if rank == 0:
|
||
logger.info("====> Epoch: {}".format(epoch))
|
||
if(epoch>=hps.total_epoch):
|
||
if rank == 0:
|
||
logger.info("Training is done. The program is closed.")
|
||
from process_ckpt import savee#def savee(ckpt,sr,if_f0,name,epoch):
|
||
if hasattr(net_g, 'module'):ckpt = net_g.module.state_dict()
|
||
else:ckpt = net_g.state_dict()
|
||
print("saving final ckpt:",savee(ckpt,hps.sample_rate,hps.if_f0,hps.name,epoch))
|
||
os._exit(2333333)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|