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train_nsf_sim_cache_sid_load_pretrain_embed_IN_dec_rand_energy_zx_diff_fi.py
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import os, sys
ddpm_dp = ddpm_para()
now_dir = os.getcwd()
sys.path.append(os.path.join(now_dir))
from lib.train import utils
import datetime
hps = utils.get_hparams()
print("hps.gpus: ",hps.gpus.replace("-", ","))
os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
n_gpus = len(hps.gpus.split("-"))
from random import shuffle, randint
import torch
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from lib.infer_pack import commons
from time import sleep
from time import time as ttime
#from lib.train.data_utils import (
#from lib.train.data_utils_embed import (
from lib.train.data_utils_embed_random075 import (
TextAudioLoaderMultiNSFsid,
TextAudioLoader,
TextAudioCollateMultiNSFsid,
TextAudioCollate,
DistributedBucketSampler,
)
if hps.version == "v1":
from lib.infer_pack.models_embed_in_dec_diff_fi import (
SynthesizerTrnMs256NSFsid as RVC_Model_f0,
SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
MultiPeriodDiscriminator,
)
else:
from lib.infer_pack.models_embed_in_dec_diff_fi import (
SynthesizerTrnMs768NSFsid as RVC_Model_f0,
SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
)
from lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
#from lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from lib.train.mel_processing_energy1_zx import mel_spectrogram_torch, spec_to_mel_torch
from lib.train.mel_processing_energy1_zx import ( spec_to_mel_torch_energy_zx as spec_to_mel_torch_energy, mel_spectrogram_torch_energy_zx as mel_spectrogram_torch_energy)
from lib.train.process_ckpt import savee
global_step = 0
class EpochRecorder:
def __init__(self):
self.last_time = ttime()
def record(self):
now_time = ttime()
elapsed_time = now_time - self.last_time
self.last_time = now_time
elapsed_time_str = str(datetime.timedelta(seconds=elapsed_time))
current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
return f"[{current_time}] | ({elapsed_time_str})"
def main():
n_gpus = torch.cuda.device_count()
print('n_gpus num:',n_gpus)
if torch.cuda.is_available() == False and torch.backends.mps.is_available() == True:
n_gpus = 1
if n_gpus < 1:
# patch to unblock people without gpus. there is probably a better way.
print("NO GPU DETECTED: falling back to CPU - this may take a while")
n_gpus = 1
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(randint(20000, 55555))
children = []
for i in range(n_gpus):
subproc = mp.Process(
target=run,
args=(
i,
n_gpus,
hps,
),
)
children.append(subproc)
subproc.start()
for i in range(n_gpus):
children[i].join()
def run(rank, n_gpus, hps):
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
# utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
dist.init_process_group(
backend="gloo", init_method="env://", world_size=n_gpus, rank=rank
)
torch.manual_seed(hps.train.seed)
if torch.cuda.is_available():
torch.cuda.set_device(rank)
if hps.if_f0 == 1:
train_dataset = TextAudioLoaderMultiNSFsid(hps.data.training_files, hps.data)
print("xxxxxxxxxx using embeding xxxxxxxxx: ")
else:
train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size * n_gpus,
# [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200,1400], # 16s
[100, 200, 300, 400, 500, 600, 700, 800, 900], # 16s
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
# It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
# num_workers=8 -> num_workers=4
if hps.if_f0 == 1:
collate_fn = TextAudioCollateMultiNSFsid()
else:
collate_fn = TextAudioCollate()
train_loader = DataLoader(
train_dataset,
num_workers=4,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
persistent_workers=True,
prefetch_factor=8,
)
if hps.if_f0 == 1:
net_g = RVC_Model_f0(
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,
)
else:
net_g = RVC_Model_nof0(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model,
is_half=hps.train.fp16_run,
)
if torch.cuda.is_available():
net_g = net_g.cuda(rank)
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
if torch.cuda.is_available():
net_d = net_d.cuda(rank)
optim_g = torch.optim.AdamW(
net_g.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
optim_d = torch.optim.AdamW(
net_d.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
# net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
# net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
if torch.cuda.is_available():
net_g = DDP(net_g, device_ids=[rank],find_unused_parameters=True)
net_d = DDP(net_d, device_ids=[rank],find_unused_parameters=True)
else:
net_g = DDP(net_g)
net_d = DDP(net_d)
#'''
try: # 如果能加载自动resume
_, _, _, 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
if rank == 0:
logger.info("loaded D")
# _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
optim_g
)
global_step = (epoch_str - 1) * len(train_loader)
# epoch_str = 1
# global_step = 0
except: # 如果首次不能加载，加载pretrain
# traceback.print_exc()
print("@@@@@@@load pretrianed @@@@@@@")
epoch_str = 1
global_step = 0
if hps.pretrainG != "":
if rank == 0:
logger.info("loaded pretrained %s" % (hps.pretrainG))
print(
net_g.module.load_state_dict(
torch.load(hps.pretrainG, map_location="cpu")["model"],strict=False
)
) ##测试不加载优化器
print("@@@@@@@load pretrianed 1@@@@@@@" )
if hps.pretrainD != "":
if rank == 0:
logger.info("loaded pretrained %s" % (hps.pretrainD))
print(
net_d.module.load_state_dict(
torch.load(hps.pretrainD, map_location="cpu")["model"],strict=False
)
)
print("@@@@@@@load pretrianed 2@@@@@@@")
#'''
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
scaler = GradScaler(enabled=hps.train.fp16_run)
cache = []
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(
rank,
epoch,
hps,
[net_g, net_d],
[optim_g, optim_d],
[scheduler_g, scheduler_d],
scaler,
[train_loader, None],
logger,
[writer, writer_eval],
cache,
)
else:
train_and_evaluate(
rank,
epoch,
hps,
[net_g, net_d],
[optim_g, optim_d],
[scheduler_g, scheduler_d],
scaler,
[train_loader, None],
None,
None,
cache,
)
scheduler_g.step()
scheduler_d.step()
def train_and_evaluate(
rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache
):
net_g, net_d = nets
optim_g, optim_d = optims
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
# Prepare data iterator
if hps.if_cache_data_in_gpu == True:
# Use Cache
data_iterator = cache
if cache == []:
# Make new cache
for batch_idx, info in enumerate(train_loader):
# Unpack
if hps.if_f0 == 1:
(
phone,
phone_lengths,
pitch,
pitchf,
spec,
spec_lengths,
wave,
wave_lengths,
sid,
) = info
else:
(
phone,
phone_lengths,
spec,
spec_lengths,
wave,
wave_lengths,
sid,
) = info
# Load on CUDA
if torch.cuda.is_available():
phone = phone.cuda(rank, non_blocking=True)
phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
if hps.if_f0 == 1:
pitch = pitch.cuda(rank, non_blocking=True)
pitchf = pitchf.cuda(rank, non_blocking=True)
sid = sid.cuda(rank, non_blocking=True)
spec = spec.cuda(rank, non_blocking=True)
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
wave = wave.cuda(rank, non_blocking=True)
wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
# Cache on list
if hps.if_f0 == 1:
cache.append(
(
batch_idx,
(
phone,
phone_lengths,
pitch,
pitchf,
spec,
spec_lengths,
wave,
wave_lengths,
sid,
),
)
)
else:
cache.append(
(
batch_idx,
(
phone,
phone_lengths,
spec,
spec_lengths,
wave,
wave_lengths,
sid,
),
)
)
else:
# Load shuffled cache
shuffle(cache)
else:
# Loader
data_iterator = enumerate(train_loader)
# Run steps
epoch_recorder = EpochRecorder()
for batch_idx, info in data_iterator:
# Data
## Unpack
if hps.if_f0 == 1:
(
phone,
phone_lengths,
pitch,
pitchf,
spec,
spec_lengths,
wave,
wave_lengths,
sid,
) = info
else:
phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
## Load on CUDA
if (hps.if_cache_data_in_gpu == False) and torch.cuda.is_available():
phone = phone.cuda(rank, non_blocking=True)
phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
if hps.if_f0 == 1:
pitch = pitch.cuda(rank, non_blocking=True)
pitchf = pitchf.cuda(rank, non_blocking=True)
sid = sid.cuda(rank, non_blocking=True)
spec = spec.cuda(rank, non_blocking=True)
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
wave = wave.cuda(rank, non_blocking=True)
# wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
# Calculate
with autocast(enabled=hps.train.fp16_run):
if hps.if_f0 == 1:
(
y_hat,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),diff_loss
) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
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)
'''
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,
)
#'''
mel, energy, zx = spec_to_mel_torch_energy(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
#y_mel = commons.slice_segments(
# mel, ids_slice, hps.train.segment_size // hps.data.hop_length
#)
y_mel, y_energy = commons.slice_segments_energy(
mel, energy,ids_slice, hps.train.segment_size // hps.data.hop_length
)
with autocast(enabled=False):
#y_hat_mel = mel_spectrogram_torch(
y_hat_mel , energy_hat , zx_hat = mel_spectrogram_torch_energy(
y_hat.float().squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
if hps.train.fp16_run == True:
y_hat_mel = y_hat_mel.half()
wave = commons.slice_segments(
wave, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
optim_d.zero_grad()
scaler.scale(loss_disc).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
energy_loss = torch.nn.MSELoss()
zx_loss = torch.nn.MSELoss()
with autocast(enabled=hps.train.fp16_run):
# 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.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
#print("@@z_p:",z_p.shape,"m_p:",m_p.shape)
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_energy = energy_loss(y_energy,energy_hat)* hps.train.c_mel
loss_zx = F.l1_loss(zx, zx_hat)#* hps.train.c_mel*0.1
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_energy + loss_zx + loss_kl
#+ diff_loss + loss_kl
optim_g.zero_grad()
scaler.scale(loss_gen_all).backward()
scaler.unscale_(optim_g)
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
scaler.step(optim_g)
scaler.update()
if rank == 0:
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 > 75:
loss_mel = 75
if loss_kl > 9:
loss_kl = 9
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_energy={loss_energy:.3f}, loss_zx={loss_zx:.3f},loss_kl={loss_kl:.3f},"# diff_loss={diff_loss:.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 % 5000 == 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 and hps.save_every_weights == "1":
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
logger.info(
"saving ckpt %s_e%s:%s"
% (
hps.name,
epoch,
savee(
ckpt,
hps.sample_rate,
hps.if_f0,
hps.name + "_e%s_s%s" % (epoch, global_step),
epoch,
hps.version,
hps,
),
)
)
# /Run steps
if epoch % hps.save_every_epoch == 0 and rank == 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 and hps.save_every_weights == "1":
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
logger.info(
"saving ckpt %s_e%s:%s"
% (
hps.name,
epoch,
savee(
ckpt,
hps.sample_rate,
hps.if_f0,
hps.name + "_e%s_s%s" % (epoch, global_step),
epoch,
hps.version,
hps,
),
)
)
if rank == 0:
logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
if epoch >= hps.total_epoch and rank == 0:
logger.info("Training is done. The program is closed.")
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
logger.info(
"saving final ckpt:%s"
% (
savee(
ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps
)
)
)
sleep(1)
os._exit(2333333)
if __name__ == "__main__":
n_gpus = torch.cuda.device_count()
print('n_gpus num:',n_gpus)
torch.multiprocessing.set_start_method("spawn")#由父进程创建子进程。 父进程既可以在产生子进程之后继续异步执行
main()

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