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train_watermark.py
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train_watermark.py
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import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import itertools
import os
import time
import argparse
import json
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DistributedSampler, DataLoader
import torch.multiprocessing as mp
from torch.distributed import init_process_group
from torch.nn.parallel import DistributedDataParallel
from env import AttrDict, build_env
from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
from models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\
discriminator_loss
from modules.metrics import DiscriminatorMetrics
from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
from modules.watermark import WatermarkModelEnsemble
from modules.augment import get_augmentations
torch.backends.cudnn.benchmark = True
def train(rank, a, h):
print(f"Arguments: {a}")
print(f"Config: {h}")
if h.num_gpus > 1:
init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'],
world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank)
torch.cuda.manual_seed(h.seed)
if torch.cuda.is_available():
device = torch.device('cuda:{:d}'.format(rank))
else:
device = torch.device('cpu')
generator = Generator(h, input_channels=h.num_mels)
generator = generator.to(device)
mpd = MultiPeriodDiscriminator().to(device)
msd = MultiScaleDiscriminator().to(device)
watermark = WatermarkModelEnsemble(
model_type=h.watermark_model,
sample_rate=h.sampling_rate,
config=h
).to(device)
if rank == 0:
print(generator)
os.makedirs(a.checkpoint_path, exist_ok=True)
print("checkpoints directory : ", a.checkpoint_path)
if os.path.isdir(a.checkpoint_path):
cp_g = scan_checkpoint(a.checkpoint_path, 'g_')
cp_do = scan_checkpoint(a.checkpoint_path, 'do_')
cp_wm = scan_checkpoint(a.checkpoint_path, 'wm_')
steps = 0
if cp_g is None or cp_do is None:
state_dict_do = None
last_epoch = -1
else:
state_dict_g = load_checkpoint(cp_g, device)
state_dict_do = load_checkpoint(cp_do, device)
generator.load_state_dict(state_dict_g['generator'])
mpd.load_state_dict(state_dict_do['mpd'])
msd.load_state_dict(state_dict_do['msd'])
steps = state_dict_do['steps'] + 1
last_epoch = state_dict_do['epoch']
if cp_wm is None:
state_dict_wm = None
else:
state_dict_wm = load_checkpoint(cp_wm, device)
watermark.load_state_dict(state_dict_wm['watermark'])
if h.num_gpus > 1:
generator = DistributedDataParallel(generator, device_ids=[rank]).to(device)
mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)
msd = DistributedDataParallel(msd, device_ids=[rank]).to(device)
watermark = DistributedDataParallel(watermark, device_ids=[rank]).to(device)
if a.pretrained_watermark_path is not None:
state_dict = torch.load(a.pretrained_watermark_path, map_location='cpu')
watermark.load_pretrained_state_dict(state_dict)
if a.freeze_watermark_weights:
for param in watermark.parameters():
param.requires_grad_(False)
if h.get('watermark_unfreeze_output_layer', True):
watermark.output_layer_requires_grad_(True)
watermark.eval()
watermark.train_rnns() # enable gradients for RNNs
optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2])
optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()),
h.learning_rate, betas=[h.adam_b1, h.adam_b2])
optim_wm = torch.optim.AdamW(watermark.parameters(),
h.learning_rate, betas=[h.adam_b1, h.adam_b2])
if state_dict_do is not None:
optim_g.load_state_dict(state_dict_do['optim_g'])
optim_d.load_state_dict(state_dict_do['optim_d'])
if state_dict_wm is not None:
optim_wm.load_state_dict(state_dict_wm['optim_wm'])
elif last_epoch > -1:
for pg in optim_wm.param_groups:
pg['initial_lr'] = pg['lr']
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch)
scheduler_wm = torch.optim.lr_scheduler.ExponentialLR(optim_wm, gamma=h.lr_decay, last_epoch=last_epoch)
training_filelist, validation_filelist = get_dataset_filelist(a)
trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels,
h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0,
shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device,
fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir)
train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None
train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False,
sampler=train_sampler,
batch_size=h.batch_size,
pin_memory=True,
drop_last=True)
augmentation_train, augmentation_valid = get_augmentations(h, a, device=device)
if rank == 0:
validset = MelDataset(
validation_filelist,
segment_size=h.segment_size,
n_fft=h.n_fft, num_mels=h.num_mels,
hop_size=h.hop_size, win_size=h.win_size,
sampling_rate=h.sampling_rate,
fmin=h.fmin, fmax=h.fmax,
split=False, shuffle=False,
n_cache_reuse=0, fmax_loss=h.fmax_for_loss,
device=device, fine_tuning=a.fine_tuning,
base_mels_path=a.input_mels_dir)
validation_loader = DataLoader(
validset, num_workers=0, shuffle=False,
sampler=None, batch_size=1, pin_memory=True,
drop_last=True)
validset_batch = MelDataset(
validation_filelist,
segment_size=h.segment_size,
n_fft=h.n_fft, num_mels=h.num_mels,
hop_size=h.hop_size, win_size=h.win_size,
sampling_rate=h.sampling_rate,
fmin=h.fmin, fmax=h.fmax,
split=True, shuffle=False,
n_cache_reuse=0, fmax_loss=h.fmax_for_loss,
device=device, fine_tuning=a.fine_tuning,
base_mels_path=a.input_mels_dir)
validation_loader_batch = DataLoader(
validset_batch, num_workers=h.num_workers, shuffle=False,
sampler=None, batch_size=h.batch_size, pin_memory=True,
drop_last=True)
sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs'))
generator.train()
mpd.train()
msd.train()
if a.freeze_watermark_weights:
watermark.eval()
watermark.train_rnns() # enable gradients for RNNs
else:
watermark.train()
# generator = torch.compile(generator)
# mpd = torch.compile(mpd)
# msd = torch.compile(msd)
for epoch in range(max(0, last_epoch), a.training_epochs):
if rank == 0:
start = time.time()
print("Epoch: {}".format(epoch+1))
if h.num_gpus > 1:
train_sampler.set_epoch(epoch)
for i, batch in enumerate(train_loader):
if rank == 0:
start_b = time.time()
x, y, _, y_mel = batch # mel, audio, filename, mel_loss
x = torch.autograd.Variable(x.to(device, non_blocking=True))
y = torch.autograd.Variable(y.to(device, non_blocking=True))
y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
y = y.unsqueeze(1)
y_g_hat = generator(x)
y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft,
h.num_mels, h.sampling_rate,
h.hop_size, h.win_size,
h.fmin, h.fmax_for_loss)
optim_d.zero_grad()
# MPD
y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())
loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
# MSD
y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach())
loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
loss_disc_all = loss_disc_s + loss_disc_f
loss_disc_all.backward()
optim_d.step()
# Generator
optim_g.zero_grad()
optim_wm.zero_grad()
# L1 Mel-Spectrogram Loss
loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45
# Adversary
y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)
loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
# Watermark
y_r_wm_input = y
wm_role = h.get('watermark_role', 'collaborator')
if wm_role == 'collaborator':
y_g_wm_input = y_g_hat
elif wm_role == 'observer':
y_g_wm_input = y_g_hat.detach()
if augmentation_train is not None:
y_g_wm_input = augmentation_train(y_g_wm_input)
y_r_wm_input = augmentation_train(y)
y_wm_real, y_wm_fake = watermark(y_r_wm_input, y_g_wm_input)
loss_wm_total = 0.0
wm_losses_r = []
wm_losses_f = []
for y_wm_real_i, y_wm_fake_i in zip(y_wm_real, y_wm_fake):
wm_loss, wm_loss_r, wm_loss_f = discriminator_loss(
disc_real_outputs=y_wm_real_i, disc_generated_outputs=y_wm_fake_i)
loss_wm_total += wm_loss
wm_losses_r.append(wm_loss_r)
wm_losses_f.append(wm_loss_f)
# Adversarial (S, F), Feature matching (S, F), Mel, Collaborative
loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel + loss_wm_total
loss_gen_all.backward()
optim_g.step()
optim_wm.step()
if rank == 0:
# STDOUT logging
if steps % a.stdout_interval == 0:
with torch.no_grad():
mel_error = F.l1_loss(y_mel, y_g_hat_mel).item()
print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'.
format(steps, loss_gen_all, mel_error, time.time() - start_b))
# checkpointing
if steps % a.checkpoint_interval == 0 and steps != 0:
checkpoint_path = "{}/g_{:08d}".format(a.checkpoint_path, steps)
save_checkpoint(checkpoint_path,
{'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()})
checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps)
save_checkpoint(checkpoint_path,
{'mpd': (mpd.module if h.num_gpus > 1
else mpd).state_dict(),
'msd': (msd.module if h.num_gpus > 1
else msd).state_dict(),
'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps,
'epoch': epoch})
checkpoint_path = "{}/wm_{:08d}".format(a.checkpoint_path, steps)
save_checkpoint(checkpoint_path,
{'watermark': (watermark.module if h.num_gpus > 1
else watermark).state_dict(),
'optim_wm': optim_wm.state_dict()
})
# Tensorboard summary logging
if steps % a.summary_interval == 0:
sw.add_scalar("training/gen_loss_total", loss_gen_all, steps)
sw.add_scalar("training/mel_spec_error", mel_error, steps)
# Framed Discriminator losses
sw.add_scalar("training_gan/disc_f_r", sum(losses_disc_f_r), steps)
sw.add_scalar("training_gan/disc_f_g", sum(losses_disc_f_g), steps)
# Multiscale Discriminator losses
sw.add_scalar("training_gan/disc_s_r", sum(losses_disc_s_r), steps)
sw.add_scalar("training_gan/disc_s_g", sum(losses_disc_s_g), steps)
# Framed Generator losses
sw.add_scalar("training_gan/gen_f", sum(losses_gen_f), steps)
# Multiscale Generator losses
sw.add_scalar("training_gan/gen_s", sum(losses_gen_s), steps)
# Feature Matching losses
sw.add_scalar("training_gan/loss_fm_f", loss_fm_f, steps)
sw.add_scalar("training_gan/loss_fm_s", loss_fm_s, steps)
# WATERMARK LOSSES
for label, losses in zip(watermark.get_labels(), wm_losses_r):
sw.add_scalar(f"training_watermark/{label}_real", sum(losses), steps)
for label, losses in zip(watermark.get_labels(), wm_losses_f):
sw.add_scalar(f"training_watermark/{label}_fake", sum(losses), steps)
# log minibatch EER
if a.log_training_eer:
watermark_metrics = [DiscriminatorMetrics() for i in range(watermark.get_num_models())]
with torch.no_grad():
for metrics, y_wm_real_i, y_wm_fake_i in zip(watermark_metrics, y_wm_real, y_wm_fake):
metrics.accumulate(
disc_real_outputs = y_wm_real_i,
disc_fake_outputs = y_wm_fake_i
)
for label, metric in zip(watermark.get_labels(), watermark_metrics):
sw.add_scalar(f"training_watermark/{label}_equal_error_rate", metric.eer, steps)
# Validation
if steps % a.validation_interval == 0:
# if steps % a.validation_interval == 0 and steps != 0:
print(f"Validating at step {steps}")
generator.eval()
mpd.eval()
msd.eval()
watermark.eval()
torch.cuda.empty_cache()
val_err_tot = 0
# Validation set metrics
mpd_adversary_metrics = DiscriminatorMetrics()
msd_adversary_metrics = DiscriminatorMetrics()
watermark_metrics = [DiscriminatorMetrics() for i in range(watermark.get_num_models())]
with torch.no_grad():
for j, batch in enumerate(validation_loader_batch):
x, y, _, y_mel = batch
y_g_hat = generator(x.to(device))
# Apply augmentation and batch validation samples
y = torch.autograd.Variable(y.to(device, non_blocking=True))
y = y.unsqueeze(1)
if augmentation_valid is not None:
y_g_wm_input = augmentation_valid(y_g_hat)
y_r_wm_input = augmentation_valid(y)
else:
y_g_wm_input = y_g_hat
y_r_wm_input = y
# Watermark EER
y_wm_real, y_wm_fake = watermark(y_r_wm_input, y_g_wm_input)
for metrics, y_wm_real_i, y_wm_fake_i in zip(watermark_metrics, y_wm_real, y_wm_fake):
metrics.accumulate(
disc_real_outputs = y_wm_real_i,
disc_fake_outputs = y_wm_fake_i
)
if a.log_discriminator_validation_eer:
# Calculate discriminator EER
y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat)
mpd_adversary_metrics.accumulate(
disc_real_outputs = y_df_hat_r,
disc_fake_outputs = y_df_hat_g
)
y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat)
msd_adversary_metrics.accumulate(
disc_real_outputs = y_ds_hat_r,
disc_fake_outputs = y_ds_hat_g
)
for j, batch in enumerate(validation_loader):
x, y, _, y_mel = batch
y_g_hat = generator(x.to(device))
y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate,
h.hop_size, h.win_size,
h.fmin, h.fmax_for_loss)
val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()
if j <= 4:
if steps == 0:
sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate)
sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps)
sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate)
y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels,
h.sampling_rate, h.hop_size, h.win_size,
h.fmin, h.fmax)
sw.add_figure('generated/y_hat_spec_{}'.format(j),
plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps)
val_err = val_err_tot / (j+1)
sw.add_scalar("validation/mel_spec_error", val_err, steps)
for label, metric in zip(watermark.get_labels(), watermark_metrics):
sw.add_scalar(f"validation/watermark_{label}_equal_error_rate", metric.eer, steps)
if a.log_discriminator_validation_eer:
sw.add_scalar("validation/mpd_adversary_equal_error_rate", mpd_adversary_metrics.eer, steps)
sw.add_scalar("validation/msd_adversary_equal_error_rate", msd_adversary_metrics.eer, steps)
generator.train()
mpd.train()
msd.train()
if a.freeze_watermark_weights:
watermark.eval()
watermark.train_rnns() # enable gradients for RNNs
else:
watermark.train()
steps += 1
scheduler_g.step()
scheduler_d.step()
scheduler_wm.step()
if rank == 0:
print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start)))
def main():
print('Initializing Training Process..')
parser = argparse.ArgumentParser()
parser.add_argument('--group_name', default=None)
parser.add_argument('--input_wavs_dir', default='LJSpeech-1.1/wavs')
parser.add_argument('--input_mels_dir', default='ft_dataset')
parser.add_argument('--input_training_file', default='LJSpeech-1.1/training.txt')
parser.add_argument('--input_validation_file', default='LJSpeech-1.1/validation.txt')
parser.add_argument('--checkpoint_path', default='cp_hifigan')
parser.add_argument('--pretrained_watermark_path', default=None)
parser.add_argument('--freeze_watermark_weights', default=False, type=bool)
parser.add_argument('--config', default='')
parser.add_argument('--training_epochs', default=3100, type=int)
parser.add_argument('--stdout_interval', default=5, type=int)
parser.add_argument('--checkpoint_interval', default=5000, type=int)
parser.add_argument('--summary_interval', default=100, type=int)
parser.add_argument('--validation_interval', default=1000, type=int)
parser.add_argument('--log_training_eer', default=False, type=bool)
parser.add_argument('--log_discriminator_validation_eer', default=False)
parser.add_argument('--fine_tuning', default=False, type=bool)
parser.add_argument('--wavefile_ext', default='.wav', type=str)
parser.add_argument('--use_augmentation', default=False, type=bool)
parser.add_argument('--noise_input_training_file', default='experiments/filelists/musan/train_list.txt')
parser.add_argument('--noise_input_validation_file', default='experiments/filelists/musan/valid_list.txt')
parser.add_argument('--noise_input_wavs_dir', default='../../DATA/musan/noise/free-sound')
parser.add_argument('--reverb_input_training_file', default='experiments/filelists/mit-rir/train_list.txt')
parser.add_argument('--reverb_input_validation_file', default='experiments/filelists/mit-rir/valid_list.txt')
parser.add_argument('--reverb_input_wavs_dir', default='../../DATA/MIT-RIR/Audio')
a = parser.parse_args()
with open(a.config) as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
build_env(a.config, 'config.json', a.checkpoint_path)
torch.manual_seed(h.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(h.seed)
h.num_gpus = torch.cuda.device_count()
h.batch_size = int(h.batch_size / h.num_gpus)
print('Batch size per GPU :', h.batch_size)
else:
pass
if h.num_gpus > 1:
mp.spawn(train, nprocs=h.num_gpus, args=(a, h,))
else:
train(0, a, h)
if __name__ == '__main__':
main()