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chore(train): logger
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@HuanLinOTO
HuanLinOTO committed Nov 11, 2023
1 parent 7080166 commit 1c234be
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4 changes: 3 additions & 1 deletion logger/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ def format_level(str, length):
return str

def default_format(record):
# print(record)
print(record)
return f"[green]{record['time'].strftime('%Y-%m-%d %H:%M:%S')}[/green] | [level]{format_level(record['level'].name,7)}[/level] | [cyan]{record['file'].path.replace(os.getcwd()+os.sep,'')}:{record['line']}[/cyan] - [level]{record['message']}[/level]\n"


Expand All @@ -61,6 +61,8 @@ def addLogger(path):
warn = logger.warning
debug = logger.debug

def hps(hps):
console.print(hps)

def Progress():
return _Progress(
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287 changes: 146 additions & 141 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import multiprocessing
import os
import time
from rich import progress

import torch
import torch.distributed as dist
Expand Down Expand Up @@ -48,7 +49,7 @@ def run(rank, n_gpus, hps):
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
logger.hps(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"))
Expand Down Expand Up @@ -112,27 +113,27 @@ def run(rank, n_gpus, hps):
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)

for epoch in range(epoch_str, hps.train.epochs + 1):
# set up warm-up learning rate
if epoch <= warmup_epoch:
for param_group in optim_g.param_groups:
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
for param_group in optim_d.param_groups:
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
# training
if rank == 0:
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
[train_loader, eval_loader], logger, [writer, writer_eval])
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)
# update learning rate
scheduler_g.step()
scheduler_d.step()
with logger.Progress() as progress:
for epoch in range(epoch_str, hps.train.epochs + 1):
# set up warm-up learning rate
if epoch <= warmup_epoch:
for param_group in optim_g.param_groups:
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
for param_group in optim_d.param_groups:
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
# training
if rank == 0:
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
[train_loader, eval_loader], logger, [writer, writer_eval], progress)
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, progress)
# update learning rate
scheduler_g.step()
scheduler_d.step()


def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, progress: progress.Progress):
net_g, net_d = nets
optim_g, optim_d = optims
scheduler_g, scheduler_d = schedulers
Expand All @@ -148,135 +149,139 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
net_g.train()
net_d.train()
enumerated_train_loader = enumerate(train_loader)
with logger.Progress() as progress:
for batch_idx, items in progress.track(enumerated_train_loader, description="Epoch {}".format(epoch)):
c, f0, spec, y, spk, lengths, uv,volume = items
g = spk.cuda(rank, non_blocking=True)
spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
c = c.cuda(rank, non_blocking=True)
f0 = f0.cuda(rank, non_blocking=True)
uv = uv.cuda(rank, non_blocking=True)
lengths = lengths.cuda(rank, non_blocking=True)
mel = spec_to_mel_torch(
spec,
# logger.info(f"enumerated_train_loader len: {len(enumerated_train_loader)}")
# "Epoch {}".format(epoch)
task = progress.add_task(f"Epoch {epoch}", total=len(train_loader))
for batch_idx, items in enumerated_train_loader:
# logger.info(f"finish {progress.} ")
c, f0, spec, y, spk, lengths, uv,volume = items
g = spk.cuda(rank, non_blocking=True)
spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
c = c.cuda(rank, non_blocking=True)
f0 = f0.cuda(rank, non_blocking=True)
uv = uv.cuda(rank, non_blocking=True)
lengths = lengths.cuda(rank, non_blocking=True)
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)

with autocast(enabled=hps.train.fp16_run, dtype=half_type):
y_hat, ids_slice, z_mask, \
(z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
spec_lengths=lengths,vol = volume)

y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
y_hat_mel = mel_spectrogram_torch(
y_hat.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)

with autocast(enabled=hps.train.fp16_run, dtype=half_type):
y_hat, ids_slice, z_mask, \
(z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
spec_lengths=lengths,vol = volume)

y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
y_hat_mel = mel_spectrogram_torch(
y_hat.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
)
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice

# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())

with autocast(enabled=False, dtype=half_type):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
loss_disc_all = loss_disc

optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)


with autocast(enabled=hps.train.fp16_run, dtype=half_type):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
with autocast(enabled=False, dtype=half_type):
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
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_lf0 = F.mse_loss(pred_lf0, lf0) if net_g.module.use_automatic_f0_prediction else 0
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
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']
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
reference_loss=0
for i in losses:
reference_loss += i
logger.info('Train Epoch: {} [{:.0f}%]'.format(
epoch,
100. * batch_idx / len(train_loader)))
logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}")

scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "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,
"loss/g/lf0": loss_lf0})

# 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())
}

if net_g.module.use_automatic_f0_prediction:
image_dict.update({
"all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
pred_lf0[0, 0, :].detach().cpu().numpy()),
"all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
norm_lf0[0, 0, :].detach().cpu().numpy())
})

utils.summarize(
writer=writer,
global_step=global_step,
images=image_dict,
scalars=scalar_dict
)
# 达到保存步数或者 stop 文件存在
if global_step % hps.train.eval_interval == 0 or os.path.exists("stop.txt"):
if os.path.exists("stop.txt"):
logger.info("stop.txt found, stop training")
evaluate(hps, net_g, eval_loader, writer_eval)
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)))
keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
if keep_ckpts > 0:
utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
if os.path.exists("stop.txt"):
logger.info("good bye!")
os.remove("stop.txt")
os._exit(0)
global_step += 1
hps.data.mel_fmax
)
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice

# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())

with autocast(enabled=False, dtype=half_type):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
loss_disc_all = loss_disc

optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)


with autocast(enabled=hps.train.fp16_run, dtype=half_type):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
with autocast(enabled=False, dtype=half_type):
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
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_lf0 = F.mse_loss(pred_lf0, lf0) if net_g.module.use_automatic_f0_prediction else 0
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
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']
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
reference_loss=0
for i in losses:
reference_loss += i
logger.info('Train Epoch: {} [{:.0f}%]'.format(
epoch,
100. * batch_idx / len(train_loader)))
logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}")

scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "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,
"loss/g/lf0": loss_lf0})

# 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())
}

if net_g.module.use_automatic_f0_prediction:
image_dict.update({
"all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
pred_lf0[0, 0, :].detach().cpu().numpy()),
"all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
norm_lf0[0, 0, :].detach().cpu().numpy())
})

utils.summarize(
writer=writer,
global_step=global_step,
images=image_dict,
scalars=scalar_dict
)
# 达到保存步数或者 stop 文件存在
if global_step % hps.train.eval_interval == 0 or os.path.exists("stop.txt"):
if os.path.exists("stop.txt"):
logger.info("stop.txt found, stop training")
evaluate(hps, net_g, eval_loader, writer_eval)
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)))
keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
if keep_ckpts > 0:
utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
if os.path.exists("stop.txt"):
logger.info("good bye!")
os.remove("stop.txt")
os._exit(0)
global_step += 1
progress.advance(task)
progress.remove_task(task)
if rank == 0:
global start_time
now = time.time()
durtaion = format(now - start_time, '.2f')
logger.info(f'====> Epoch: {epoch}, cost {durtaion} s')
duration = format(now - start_time, '.2f') # 这里原本是 durtaion，让我看看是谁拼错了（
logger.info(f'Epoch: {epoch} finished, cost {duration} s, {round(float(duration)/len(train_loader),3)}s per batch')
start_time = now


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