def build_model(self):
self.G = Generator()
self.D = Discriminator()
self.C = DomainClassifier()
self.g_optimizer = flow.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
self.d_optimizer = flow.optim.Adam(self.D.parameters(), self.d_lr,
[self.beta1, self.beta2])
self.c_optimizer = flow.optim.Adam(self.C.parameters(), self.c_lr,
[self.beta1, self.beta2])
self.print_network(self.G, "G")
self.print_network(self.D, "D")
self.print_network(self.C, "C")
self.G.to(self.device)
self.D.to(self.device)
self.C.to(self.device)
def test_all_level_no_mask_yes_attr(args):
"""Test model with input image and attributes."""
transform = transforms.Compose(
[Normalize(0.5, 0.5),
CenterSquareMask(),
ScaleNRotate(),
ToTensor()])
batch_size = 1
num_attrs = 40
resolutions_to = [4, 8, 8, 16, 16, 32, 32, 64, 64, 128, 128, 256,
256] # 512, 512]
levels = [1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7] # 7.5, 8]
data_shape = [batch_size, 3, 512, 512]
G = Generator(data_shape, use_mask=False, num_attrs=num_attrs)
D = Discriminator(data_shape, num_attrs=num_attrs)
for res, lev in zip(resolutions_to, levels):
dataset = CelebAHQDataset(args.data_dir, res, transform)
dataloader = DataLoader(dataset, batch_size, True)
sample = iter(dataloader).next() # noqa: B305
image = sample['image']
masked_image = sample['masked_image']
mask = sample['mask']
attr = sample['attr']
print(f"level: {lev}, resolution: {res}, image: {masked_image.shape}, \
mask: {mask.shape}")
# Generator
if isinstance(lev, int):
# training state
fake_image1 = G(masked_image, attr, cur_level=lev)
assert list(fake_image1.shape) == [batch_size, 3, res, res], \
f'{res, lev} test failed'
else:
# transition state
fake_image2 = G(masked_image, attr, cur_level=lev)
assert list(fake_image2.shape) == [batch_size, 3, res, res], \
f'{res, lev} test failed'
# Discriminator
if isinstance(lev, int):
# training state
cls1, attr1 = D(image, lev)
assert list(cls1.shape) == [batch_size, 1], \
f'{res, lev} test failed'
assert list(attr1.shape) == [batch_size, num_attrs], \
f'{res, lev} test failed'
else:
# transition state
cls2, attr2 = D(image, lev)
assert list(cls2.shape) == [batch_size, 1], \
f'{res, lev} test failed'
assert list(attr2.shape) == [batch_size, num_attrs], \
f'{res, lev} test failed'
def test_end_to_end(args):
"""Test end to end data handling process."""
batch_size = 1
resolutions = [256, 256]
levels = [7, 7]
num_classes = 5
num_layers = 1
data_shape = [batch_size, 3, 256, 256]
transform = transforms.Compose([
Normalize(0.5, 0.5),
TargetMask(num_classes),
ScaleNRotate(),
ToTensor()
])
G = Generator(data_shape)
D = Discriminator(data_shape, num_classes, num_layers)
for res, lev in zip(resolutions, levels):
dataset = VGGFace2Dataset(args.data_dir, res, args.landmark_info_path,
args.identity_info_path, transform)
dataloader = DataLoader(dataset, batch_size, True)
sample = iter(dataloader).next() # noqa: B305
image = sample['image']
real_mask = sample['real_mask']
obs_mask = sample['obs_mask']
target_id = sample['target_id']
print(f"lev: {lev}, res: {res}, image: {image.shape}, \
mask: {real_mask.shape}, {obs_mask.shape}, \
target_id: {target_id}")
# Generator
fake_image = G(image, obs_mask, cur_level=lev)
assert list(fake_image.shape) == [batch_size, 3, res, res], \
f'Generator: {res, lev} test failed'
# Discriminator (original)
cls1, pix_cls1 = D(image, lev)
assert list(cls1.shape) == [batch_size, 1], \
f'Discriminator: {res, lev} test failed'
assert list(pix_cls1.shape) == [batch_size, num_classes, res, res], \
f'Pixel-Discriminator: {res, lev} test failed'
cls2, pix_cls2 = D(fake_image, lev)
assert list(cls2.shape) == [batch_size, 1], \
f'Discriminator: {res, lev} test failed'
assert list(pix_cls2.shape) == [batch_size, num_classes, res, res], \
f'Pixel-Discriminator: {res, lev} test failed'
def run(args):
# create output path
Path(hp.output_path).mkdir(parents=True, exist_ok=True)
# setup nnabla context
ctx = get_extension_context(args.context, device_id='0')
nn.set_default_context(ctx)
hp.comm = CommunicatorWrapper(ctx)
hp.event = StreamEventHandler(int(hp.comm.ctx.device_id))
with open(hp.speaker_dir) as f:
hp.n_speakers = len(f.read().split('\n'))
logger.info(f'Training data with {hp.n_speakers} speakers.')
if hp.comm.n_procs > 1 and hp.comm.rank == 0:
n_procs = hp.comm.n_procs
logger.info(f'Distributed training with {n_procs} processes.')
rng = np.random.RandomState(hp.seed)
train_loader = data_iterator(VCTKDataSource('metadata_train.csv',
hp,
shuffle=True,
rng=rng),
batch_size=hp.batch_size,
with_memory_cache=False,
rng=rng)
dataloader = dict(train=train_loader, valid=None)
gen = NVCNet(hp)
gen_optim = Optimizer(weight_decay=hp.weight_decay,
name='Adam',
alpha=hp.g_lr,
beta1=hp.beta1,
beta2=hp.beta2)
dis = Discriminator(hp)
dis_optim = Optimizer(weight_decay=hp.weight_decay,
name='Adam',
alpha=hp.d_lr,
beta1=hp.beta1,
beta2=hp.beta2)
Trainer(gen, gen_optim, dis, dis_optim, dataloader, rng, hp).run()
def save(ckpt_dir_path, global_step, global_init_step, generator: Generator,
discriminator: Discriminator, gen_optimizer, disc_optimizer):
if not os.path.exists(ckpt_dir_path):
os.makedirs(ckpt_dir_path)
torch.save(generator.state_dict(),
os.path.join(ckpt_dir_path, "generator.pt"))
torch.save(discriminator.state_dict(),
os.path.join(ckpt_dir_path, "discriminator.pt"))
torch.save(gen_optimizer.state_dict(),
os.path.join(ckpt_dir_path, "generator_optimizer.pt"))
torch.save(disc_optimizer.state_dict(),
os.path.join(ckpt_dir_path, "discriminator_optimizer.pt"))
with open(os.path.join(ckpt_dir_path, "learning_state.json"), 'w') as f:
json.dump(
{
'global_step': global_step,
'global_init_step': global_init_step,
},
f,
indent='\t')
def __init__(self, config):
"""Class initializer.
1. Read self.configurations from self.config.py
2. Check gpu availability
3. Create a model and training related objects
- Model (Generator, Discriminator)
- Optimizer
- Loss and loss histories
- Replay memory
- Snapshot
"""
self.config = config
self.D_repeats = self.config.train.D_repeats
self.total_size = int(self.config.train.total_size *
self.config.train.dataset_unit)
self.train_size = int(self.config.train.train_size *
self.config.train.dataset_unit)
self.transition_size = int(self.config.train.transition_size *
self.config.train.dataset_unit)
assert (self.total_size == (self.train_size + self.transition_size)) \
and self.train_size > 0 and self.transition_size > 0
# GPU
self.check_gpu()
self.mode = self.config.train.mode
self.use_mask = self.config.train.use_mask
# Data Shape
dataset_shape = [
1, self.config.dataset.num_channels,
self.config.train.net.max_resolution,
self.config.train.net.max_resolution
]
# Generator & Discriminator Creation
self.G = Generator(dataset_shape,
fmap_base=self.config.train.net.fmap_base,
fmap_min=self.config.train.net.min_resolution,
fmap_max=self.config.train.net.max_resolution,
latent_size=self.config.train.net.latent_size,
use_mask=self.use_mask,
leaky_relu=True,
instancenorm=True)
spectralnorm = True if self.config.loss.gan == Gan.sngan else False
self.D = Discriminator(dataset_shape,
num_classes=self.config.dataset.num_classes,
num_layers=self.config.train.net.num_layers,
fmap_base=self.config.train.net.fmap_base,
fmap_min=self.config.train.net.min_resolution,
fmap_max=self.config.train.net.max_resolution,
latent_size=self.config.train.net.latent_size,
leaky_relu=True,
instancenorm=True,
spectralnorm=spectralnorm)
self.register_on_gpu()
self.create_optimizer()
# Loss
self.loss = FaceGenLoss(self.config, self.use_cuda,
self.config.env.num_gpus)
# Replay Memory
self.replay_memory = ReplayMemory(self.config, self.use_cuda,
self.config.replay.enabled)
self.global_it = 1
self.global_cur_nimg = 1
# restore
self.snapshot = Snapshot(self.config, self.use_cuda)
self.snapshot.prepare_logging()
self.snapshot.restore_model(self.G, self.D, self.optim_G, self.optim_D)
class FaceGen():
"""FaceGen Classes.
Attributes:
D_repeats : How many times the discriminator is trained per G iteration
total_size : Total # of real images in the training
train_size : # of real images to show before doubling the resolution
transition_size : # of real images to show when fading in new layers
mode : running mode {inpainting , generation}
use_mask : flag for mask use in the model
dataset_shape : input data shape
use_cuda : flag for cuda use
G : generator
D : discriminator
optim_G : optimizer for generator
optim_D : optimizer for discriminator
loss : losses of generator and discriminator
replay_memory : replay memory
global_it : global # of iterations through training
global_cur_nimg : global # of current images through training
snapshot : snapshot intermediate images, checkpoints, tensorboard logs
real : real images
obs: observed images
mask : binary mask
syn : synthesized images
cls_real : classes for real images
cls_syn : classes for synthesized images
pix_cls_real : pixelwise classes for real images
pix_cls_syn : pixelwise classes for synthesized images
"""
def __init__(self, config):
"""Class initializer.
1. Read self.configurations from self.config.py
2. Check gpu availability
3. Create a model and training related objects
- Model (Generator, Discriminator)
- Optimizer
- Loss and loss histories
- Replay memory
- Snapshot
"""
self.config = config
self.D_repeats = self.config.train.D_repeats
self.total_size = int(self.config.train.total_size *
self.config.train.dataset_unit)
self.train_size = int(self.config.train.train_size *
self.config.train.dataset_unit)
self.transition_size = int(self.config.train.transition_size *
self.config.train.dataset_unit)
assert (self.total_size == (self.train_size + self.transition_size)) \
and self.train_size > 0 and self.transition_size > 0
# GPU
self.check_gpu()
self.mode = self.config.train.mode
self.use_mask = self.config.train.use_mask
# Data Shape
dataset_shape = [
1, self.config.dataset.num_channels,
self.config.train.net.max_resolution,
self.config.train.net.max_resolution
]
# Generator & Discriminator Creation
self.G = Generator(dataset_shape,
fmap_base=self.config.train.net.fmap_base,
fmap_min=self.config.train.net.min_resolution,
fmap_max=self.config.train.net.max_resolution,
latent_size=self.config.train.net.latent_size,
use_mask=self.use_mask,
leaky_relu=True,
instancenorm=True)
spectralnorm = True if self.config.loss.gan == Gan.sngan else False
self.D = Discriminator(dataset_shape,
num_classes=self.config.dataset.num_classes,
num_layers=self.config.train.net.num_layers,
fmap_base=self.config.train.net.fmap_base,
fmap_min=self.config.train.net.min_resolution,
fmap_max=self.config.train.net.max_resolution,
latent_size=self.config.train.net.latent_size,
leaky_relu=True,
instancenorm=True,
spectralnorm=spectralnorm)
self.register_on_gpu()
self.create_optimizer()
# Loss
self.loss = FaceGenLoss(self.config, self.use_cuda,
self.config.env.num_gpus)
# Replay Memory
self.replay_memory = ReplayMemory(self.config, self.use_cuda,
self.config.replay.enabled)
self.global_it = 1
self.global_cur_nimg = 1
# restore
self.snapshot = Snapshot(self.config, self.use_cuda)
self.snapshot.prepare_logging()
self.snapshot.restore_model(self.G, self.D, self.optim_G, self.optim_D)
def train(self):
"""Training for progressive growing model.
1. Calculate min/max resolution for a model
2. for each layer
2-1. for each phases
1) first layer : {training}
2) remainder layers : {transition, traning}
3) optional : {replaying}
do train one step
"""
min_resol = int(np.log2(self.config.train.net.min_resolution))
max_resol = int(np.log2(self.config.train.net.max_resolution))
assert 2**max_resol == self.config.train.net.max_resolution \
and 2**min_resol == self.config.train.net.min_resolution \
and max_resol >= min_resol >= 2
from_resol = min_resol
if self.snapshot.is_restored:
from_resol = int(np.log2(self.snapshot._resolution))
self.global_it = self.snapshot._global_it
assert from_resol <= max_resol
prev_time = datetime.datetime.now()
# layer iteration
for R in range(from_resol, max_resol + 1):
# Resolution & batch size
cur_resol = 2**R
if self.config.train.forced_stop \
and cur_resol > self.config.train.forced_stop_resolution:
break
batch_size = self.config.sched.batch_dict[cur_resol]
assert batch_size >= 1
train_iter = self.train_size // batch_size
transition_iter = self.transition_size // batch_size
assert (train_iter != 0) and (transition_iter != 0)
cur_time = datetime.datetime.now()
print("Layer Training Time : ", cur_time - prev_time)
prev_time = cur_time
print("********** New Layer [%d x %d] : batch_size %d **********" %
(cur_resol, cur_resol, batch_size))
# Phase
if R == min_resol:
phases = {Phase.training: [1, train_iter]}
phase = Phase.training
total_it = train_iter
else:
phases = {
Phase.transition: [1, transition_iter],
Phase.training:
[train_iter + 1, train_iter + transition_iter]
}
phase = Phase.transition
total_it = train_iter + transition_iter
if self.snapshot.is_restored:
phase = self.snapshot._phase
# Iteration
from_it = phases[phase][0]
to_it = phases[phase][1]
if self.snapshot.is_restored:
from_it = self.snapshot._it + 1
self.snapshot.is_restored = False
cur_nimg = from_it * batch_size
cur_it = from_it
# load traninig set
self.training_set = self.load_train_set(cur_resol, batch_size)
if len(self.training_set) == 0:
print("DataLoding is failed")
return
if self.config.replay.enabled:
self.replay_memory.reset(cur_resol)
# Learningn Rate
lrate = self.config.optimizer.lrate
self.G_lrate = lrate.G_dict.get(cur_resol,
self.config.optimizer.lrate.G_base)
self.D_lrate = lrate.D_dict.get(cur_resol,
self.config.optimizer.lrate.D_base)
# Training Set
replay_mode = False
while cur_it <= total_it:
for _, sample_batched in enumerate(self.training_set):
if sample_batched['image'].shape[0] < batch_size:
break
if cur_it > total_it:
break
# trasnfer tansition to training
if cur_it == to_it and cur_it < total_it:
phase = Phase.training
# calculate current level (from 1)
if phase == Phase.transition:
# transition [pref level, current level]
cur_level = float(R - min_resol +
float(cur_it / to_it))
else:
# training
cur_level = float(R - min_resol + 1)
self.real = sample_batched['image']
self.real_mask = sample_batched['real_mask']
self.obs = sample_batched['image']
self.obs_mask = sample_batched['obs_mask']
self.source_domain = sample_batched['gender']
self.target_domain = sample_batched['fake_gender']
cur_nimg = self.train_step(batch_size, cur_it, total_it,
phase, cur_resol, cur_level,
cur_nimg)
cur_it += 1
self.global_it += 1
self.global_cur_nimg += 1
# Replay Mode
if self.config.replay.enabled:
replay_mode = True
phase = Phase.replaying
total_it = self.config.replay.replay_count
for i_batch in range(self.config.replay.replay_count):
cur_it = i_batch + 1
self.real, self.real_mask, \
self.obs, self.obs_mask, self.syn \
= self.replay_memory.get_batch(cur_resol, batch_size)
if self.real is None:
break
self.syn = util.tofloat(self.use_cuda, self.syn)
cur_nimg = self.train_step(batch_size, cur_it, total_it,
phase, cur_resol, cur_level,
cur_nimg, replay_mode)
def train_step(self,
batch_size,
cur_it,
total_it,
phase,
cur_resol,
cur_level,
cur_nimg,
replay_mode=False):
"""Training one step.
1. Train discrmininator for [D_repeats]
2. Train generator
3. Snapshot
Args:
batch_size: batch size
cur_it: current # of iterations in the phases of the layer
total_it: total # of iterations in the phases of the layer
phase: training, transition, replaying
cur_resol: image resolution of current layer
cur_level: progress indicator of progressive growing network
cur_nimg: current # of images in the phase
replay_mode: Memory replay mode
Returns:
cur_nimg: updated # of images in the phase
"""
self.preprocess()
# Training discriminator
d_cnt = 0
if d_cnt < self.D_repeats:
self.update_lr(cur_it, total_it, replay_mode)
self.optim_D.zero_grad()
self.forward_D(cur_level, detach=True, replay_mode=replay_mode)
self.backward_D(cur_level)
if self.config.replay.enabled and replay_mode is False:
self.replay_memory.append(cur_resol, self.real, self.real_mask,
self.obs, self.obs_mask,
self.syn.detach())
d_cnt += 1
# Training generator
if d_cnt == self.D_repeats:
# Training generator
self.optim_G.zero_grad()
self.forward_G(cur_level)
self.backward_G(cur_level)
d_cnt = 0
# model intermediate results
self.snapshot.snapshot(self.global_it, cur_it, total_it, phase,
cur_resol, cur_level, batch_size, self.real,
self.syn, self.G, self.D, self.optim_G,
self.optim_D, self.loss.g_losses,
self.loss.d_losses)
cur_nimg += batch_size
return cur_nimg
def forward_G(self, cur_level):
"""Forward generator.
Args:
cur_level: progress indicator of progressive growing network
"""
self.cls_syn, self.pixel_cls_syn = self.D(self.syn,
cur_level=cur_level)
def forward_D(self, cur_level, detach=True, replay_mode=False):
"""Forward discriminator.
Args:
cur_level: progress indicator of progressive growing network
detach: flag whether to detach graph from generator or not
replay_mode: memory replay mode
"""
if replay_mode is False:
self.syn = self.G(self.obs,
mask=self.obs_mask,
cur_level=cur_level)
# self.syn = util.normalize_min_max(self.syn)
self.cls_real, self.pixel_cls_real = self.D(self.real,
cur_level=cur_level)
self.cls_syn, self.pixel_cls_syn = self.D(
self.syn.detach() if detach else self.syn, cur_level=cur_level)
def backward_G(self, cur_level):
"""Backward generator."""
self.loss.calc_G_loss(self.G, cur_level, self.real, self.real_mask,
self.obs, self.obs_mask, self.syn, self.cls_real,
self.cls_syn, self.pixel_cls_real,
self.pixel_cls_syn)
self.loss.g_losses.g_loss.backward()
self.optim_G.step()
def backward_D(self, cur_level, retain_graph=True):
"""Backward discriminator.
Args:
cur_level: progress indicator of progressive growing network
retain_graph: flag whether to retain graph of discriminator or not
"""
self.loss.calc_D_loss(self.D, cur_level, self.real, self.real_mask,
self.obs, self.obs_mask, self.source_domain,
self.target_domain, self.syn, self.cls_real,
self.cls_syn, self.pixel_cls_real,
self.pixel_cls_syn)
self.loss.d_losses.d_loss.backward(retain_graph=retain_graph)
self.optim_D.step()
def preprocess(self):
"""Set input type to cuda or cpu according to gpu availability."""
self.real = util.tofloat(self.use_cuda, self.real)
self.real_mask = util.tofloat(self.use_cuda, self.real_mask)
self.obs = util.tofloat(self.use_cuda, self.obs)
self.obs_mask = util.tofloat(self.use_cuda, self.obs_mask)
self.source_domain = util.tofloat(self.use_cuda, self.source_domain)
self.target_domain = util.tofloat(self.use_cuda, self.target_domain)
def check_gpu(self):
"""Check gpu availability."""
self.use_cuda = torch.cuda.is_available() \
and self.config.env.num_gpus > 0
if self.use_cuda:
gpus = str(list(range(self.config.env.num_gpus)))
os.environ['CUDA_VISIBLE_DEVICES'] = gpus
def register_on_gpu(self):
"""Set model to cuda according to gpu availability."""
if self.use_cuda:
self.G.cuda()
self.D.cuda()
def load_train_set(self, resol, batch_size):
"""Load train set.
Args:
resol: progress indicator of progressive growing network
batch_size: flag for detaching syn image from generator graph
"""
num_classes = self.config.dataset.num_classes
transform_options = transforms.Compose([
dt.Normalize(0.5, 0.5),
dt.PolygonMask(num_classes),
dt.ToTensor()
])
dataset_func = self.config.dataset.func
ds = self.config.dataset
datasets = util.call_func_by_name(data_dir=ds.data_dir,
resolution=resol,
landmark_info_path=ds.landmark_path,
identity_info_path=ds.identity_path,
filtered_list=ds.filtering_path,
transform=transform_options,
func=dataset_func)
# train_dataset & data loader
return DataLoader(datasets, batch_size, True)
def create_optimizer(self):
"""Create optimizers of generator and discriminator."""
self.optim_G = optim.Adam(self.G.parameters(),
lr=self.config.optimizer.lrate.G_base,
betas=(self.config.optimizer.G_opt.beta1,
self.config.optimizer.G_opt.beta2))
self.optim_D = optim.Adam(self.D.parameters(),
lr=self.config.optimizer.lrate.D_base,
betas=(self.config.optimizer.D_opt.beta1,
self.config.optimizer.D_opt.beta2))
def rampup(self, cur_it, rampup_it):
"""Ramp up learning rate.
Args:
cur_it: current # of iterations in the phase
rampup_it: # of iterations for ramp up
"""
if cur_it < rampup_it:
p = max(0.0, float(cur_it)) / float(rampup_it)
p = 1.0 - p
return np.exp(-p * p * 5.0)
else:
return 1.0
def rampdown_linear(self, cur_it, total_it, rampdown_it):
"""Ramp down learning rate.
Args:
cur_it: current # of iterations in the phasek
total_it: total # of iterations in the phase
rampdown_it: # of iterations for ramp down
"""
if cur_it >= total_it - rampdown_it:
return float(total_it - cur_it) / rampdown_it
return 1.0
def update_lr(self, cur_it, total_it, replay_mode=False):
"""Update learning rate.
Args:
cur_it: current # of iterations in the phasek
total_it: total # of iterations in the phase
replay_mode: memory replay mode
"""
if replay_mode:
return
rampup_it = total_it * self.config.optimizer.lrate.rampup_rate
rampdown_it = total_it * self.config.optimizer.lrate.rampdown_rate
# learning rate rampup & down
for param_group in self.optim_G.param_groups:
lrate_coef = self.rampup(cur_it, rampup_it)
lrate_coef *= self.rampdown_linear(cur_it, total_it, rampdown_it)
param_group['lr'] = lrate_coef * self.G_lrate
print("learning rate %f" % (param_group['lr']))
for param_group in self.optim_D.param_groups:
lrate_coef = self.rampup(cur_it, rampup_it)
lrate_coef *= self.rampdown_linear(cur_it, self.total_size,
rampdown_it)
param_group['lr'] = lrate_coef * self.D_lrate
def __init__(self, args):
"""
Args:
args (Namespace): Program arguments from argparser
"""
# Store args
self.num_epochs = args.num_epochs
self.start_epoch = args.start_epoch
self.generator_lr = args.generator_lr
self.discriminator_lr = args.discriminator_lr
self.decay_after = args.decay_after
self.mini_batch_size = args.batch_size
self.cycle_loss_lambda = args.cycle_loss_lambda
self.identity_loss_lambda = args.identity_loss_lambda
self.device = args.device
self.epochs_per_save = args.epochs_per_save
self.sample_rate = args.sample_rate
self.validation_A_dir = os.path.join(args.origin_data_dir,
args.speaker_A_id)
self.output_A_dir = os.path.join(args.output_data_dir,
args.speaker_A_id)
self.validation_B_dir = os.path.join(args.origin_data_dir,
args.speaker_B_id)
self.output_B_dir = os.path.join(args.output_data_dir,
args.speaker_B_id)
self.infer_data_dir = args.infer_data_dir
self.pretrain_models = args.pretrain_models
# Initialize speakerA's dataset
self.dataset_A = self.loadPickleFile(
os.path.join(
args.preprocessed_data_dir,
args.speaker_A_id,
f"{args.speaker_A_id}_normalized.pickle",
))
dataset_A_norm_stats = np.load(
os.path.join(
args.preprocessed_data_dir,
args.speaker_A_id,
f"{args.speaker_A_id}_norm_stat.npz",
))
self.dataset_A_mean = dataset_A_norm_stats["mean"]
self.dataset_A_std = dataset_A_norm_stats["std"]
# Initialize speakerB's dataset
self.dataset_B = self.loadPickleFile(
os.path.join(
args.preprocessed_data_dir,
args.speaker_B_id,
f"{args.speaker_B_id}_normalized.pickle",
))
dataset_B_norm_stats = np.load(
os.path.join(
args.preprocessed_data_dir,
args.speaker_B_id,
f"{args.speaker_B_id}_norm_stat.npz",
))
self.dataset_B_mean = dataset_B_norm_stats["mean"]
self.dataset_B_std = dataset_B_norm_stats["std"]
# Compute lr decay rate
self.n_samples = len(self.dataset_A)
print(f"n_samples = {self.n_samples}")
self.generator_lr_decay = self.generator_lr / float(
self.num_epochs * (self.n_samples // self.mini_batch_size))
self.discriminator_lr_decay = self.discriminator_lr / float(
self.num_epochs * (self.n_samples // self.mini_batch_size))
print(f"generator_lr_decay = {self.generator_lr_decay}")
print(f"discriminator_lr_decay = {self.discriminator_lr_decay}")
# Initialize Train Dataloader
self.num_frames = args.num_frames
self.dataset = VCDataset(
datasetA=self.dataset_A,
datasetB=self.dataset_B,
n_frames=args.num_frames,
max_mask_len=args.max_mask_len,
)
self.train_dataloader = flow.utils.data.DataLoader(
dataset=self.dataset,
batch_size=self.mini_batch_size,
shuffle=True,
drop_last=False,
)
# Initialize Generators and Discriminators
self.generator_A2B = Generator().to(self.device)
self.generator_B2A = Generator().to(self.device)
self.discriminator_A = Discriminator().to(self.device)
self.discriminator_B = Discriminator().to(self.device)
# Discriminator to compute 2 step adversarial loss
self.discriminator_A2 = Discriminator().to(self.device)
# Discriminator to compute 2 step adversarial loss
self.discriminator_B2 = Discriminator().to(self.device)
# Initialize Optimizers
g_params = list(self.generator_A2B.parameters()) + list(
self.generator_B2A.parameters())
d_params = (list(self.discriminator_A.parameters()) +
list(self.discriminator_B.parameters()) +
list(self.discriminator_A2.parameters()) +
list(self.discriminator_B2.parameters()))
self.generator_optimizer = flow.optim.Adam(g_params,
lr=self.generator_lr,
betas=(0.5, 0.999))
self.discriminator_optimizer = flow.optim.Adam(
d_params, lr=self.discriminator_lr, betas=(0.5, 0.999))
class MaskCycleGANVCTrainer(object):
"""Trainer for MaskCycleGAN-VC
"""
def __init__(self, args):
"""
Args:
args (Namespace): Program arguments from argparser
"""
# Store args
self.num_epochs = args.num_epochs
self.start_epoch = args.start_epoch
self.generator_lr = args.generator_lr
self.discriminator_lr = args.discriminator_lr
self.decay_after = args.decay_after
self.mini_batch_size = args.batch_size
self.cycle_loss_lambda = args.cycle_loss_lambda
self.identity_loss_lambda = args.identity_loss_lambda
self.device = args.device
self.epochs_per_save = args.epochs_per_save
self.sample_rate = args.sample_rate
self.validation_A_dir = os.path.join(args.origin_data_dir,
args.speaker_A_id)
self.output_A_dir = os.path.join(args.output_data_dir,
args.speaker_A_id)
self.validation_B_dir = os.path.join(args.origin_data_dir,
args.speaker_B_id)
self.output_B_dir = os.path.join(args.output_data_dir,
args.speaker_B_id)
self.infer_data_dir = args.infer_data_dir
self.pretrain_models = args.pretrain_models
# Initialize speakerA's dataset
self.dataset_A = self.loadPickleFile(
os.path.join(
args.preprocessed_data_dir,
args.speaker_A_id,
f"{args.speaker_A_id}_normalized.pickle",
))
dataset_A_norm_stats = np.load(
os.path.join(
args.preprocessed_data_dir,
args.speaker_A_id,
f"{args.speaker_A_id}_norm_stat.npz",
))
self.dataset_A_mean = dataset_A_norm_stats["mean"]
self.dataset_A_std = dataset_A_norm_stats["std"]
# Initialize speakerB's dataset
self.dataset_B = self.loadPickleFile(
os.path.join(
args.preprocessed_data_dir,
args.speaker_B_id,
f"{args.speaker_B_id}_normalized.pickle",
))
dataset_B_norm_stats = np.load(
os.path.join(
args.preprocessed_data_dir,
args.speaker_B_id,
f"{args.speaker_B_id}_norm_stat.npz",
))
self.dataset_B_mean = dataset_B_norm_stats["mean"]
self.dataset_B_std = dataset_B_norm_stats["std"]
# Compute lr decay rate
self.n_samples = len(self.dataset_A)
print(f"n_samples = {self.n_samples}")
self.generator_lr_decay = self.generator_lr / float(
self.num_epochs * (self.n_samples // self.mini_batch_size))
self.discriminator_lr_decay = self.discriminator_lr / float(
self.num_epochs * (self.n_samples // self.mini_batch_size))
print(f"generator_lr_decay = {self.generator_lr_decay}")
print(f"discriminator_lr_decay = {self.discriminator_lr_decay}")
# Initialize Train Dataloader
self.num_frames = args.num_frames
self.dataset = VCDataset(
datasetA=self.dataset_A,
datasetB=self.dataset_B,
n_frames=args.num_frames,
max_mask_len=args.max_mask_len,
)
self.train_dataloader = flow.utils.data.DataLoader(
dataset=self.dataset,
batch_size=self.mini_batch_size,
shuffle=True,
drop_last=False,
)
# Initialize Generators and Discriminators
self.generator_A2B = Generator().to(self.device)
self.generator_B2A = Generator().to(self.device)
self.discriminator_A = Discriminator().to(self.device)
self.discriminator_B = Discriminator().to(self.device)
# Discriminator to compute 2 step adversarial loss
self.discriminator_A2 = Discriminator().to(self.device)
# Discriminator to compute 2 step adversarial loss
self.discriminator_B2 = Discriminator().to(self.device)
# Initialize Optimizers
g_params = list(self.generator_A2B.parameters()) + list(
self.generator_B2A.parameters())
d_params = (list(self.discriminator_A.parameters()) +
list(self.discriminator_B.parameters()) +
list(self.discriminator_A2.parameters()) +
list(self.discriminator_B2.parameters()))
self.generator_optimizer = flow.optim.Adam(g_params,
lr=self.generator_lr,
betas=(0.5, 0.999))
self.discriminator_optimizer = flow.optim.Adam(
d_params, lr=self.discriminator_lr, betas=(0.5, 0.999))
def adjust_lr_rate(self, optimizer, generator):
"""Decays learning rate.
Args:
optimizer (torch.optim): torch optimizer
generator (bool): Whether to adjust generator lr.
"""
if generator:
self.generator_lr = max(
0.0, self.generator_lr - self.generator_lr_decay)
for param_groups in optimizer.param_groups:
param_groups["lr"] = self.generator_lr
else:
self.discriminator_lr = max(
0.0, self.discriminator_lr - self.discriminator_lr_decay)
for param_groups in optimizer.param_groups:
param_groups["lr"] = self.discriminator_lr
def reset_grad(self):
"""Sets gradients of the generators and discriminators to zero before backpropagation.
"""
self.generator_optimizer.zero_grad()
self.discriminator_optimizer.zero_grad()
def loadPickleFile(self, fileName):
"""Loads a Pickle file.
Args:
fileName (str): pickle file path
Returns:
file object: The loaded pickle file object
"""
with open(fileName, "rb") as f:
return pickle.load(f)
def train(self):
"""Implements the training loop for MaskCycleGAN-VC
"""
for epoch in range(self.start_epoch, self.num_epochs + 1):
for i, (real_A, mask_A, real_B,
mask_B) in enumerate(self.train_dataloader):
num_iterations = (self.n_samples //
self.mini_batch_size) * epoch + i
if num_iterations > 10000:
self.identity_loss_lambda = 0
if num_iterations > self.decay_after:
self.adjust_lr_rate(self.generator_optimizer,
generator=True)
self.adjust_lr_rate(self.generator_optimizer,
generator=False)
real_A = real_A.to(self.device, dtype=flow.float)
mask_A = mask_A.to(self.device, dtype=flow.float)
real_B = real_B.to(self.device, dtype=flow.float)
mask_B = mask_B.to(self.device, dtype=flow.float)
# Train Generator
self.generator_A2B.train()
self.generator_B2A.train()
self.discriminator_A.eval()
self.discriminator_B.eval()
self.discriminator_A2.eval()
self.discriminator_B2.eval()
# Generator Feed Forward
fake_B = self.generator_A2B(real_A, mask_A)
cycle_A = self.generator_B2A(fake_B, flow.ones_like(fake_B))
fake_A = self.generator_B2A(real_B, mask_B)
cycle_B = self.generator_A2B(fake_A, flow.ones_like(fake_A))
identity_A = self.generator_B2A(real_A, flow.ones_like(real_A))
identity_B = self.generator_A2B(real_B, flow.ones_like(real_B))
d_fake_A = self.discriminator_A(fake_A)
d_fake_B = self.discriminator_B(fake_B)
# For Two Step Adverserial Loss
d_fake_cycle_A = self.discriminator_A2(cycle_A)
d_fake_cycle_B = self.discriminator_B2(cycle_B)
# Generator Cycle Loss
cycleLoss = flow.mean(flow.abs(real_A - cycle_A)) + flow.mean(
flow.abs(real_B - cycle_B))
# Generator Identity Loss
identityLoss = flow.mean(
flow.abs(real_A - identity_A)) + flow.mean(
flow.abs(real_B - identity_B))
# Generator Loss
g_loss_A2B = flow.mean((1 - d_fake_B)**2)
g_loss_B2A = flow.mean((1 - d_fake_A)**2)
# Generator Two Step Adverserial Loss
generator_loss_A2B_2nd = flow.mean((1 - d_fake_cycle_B)**2)
generator_loss_B2A_2nd = flow.mean((1 - d_fake_cycle_A)**2)
# Total Generator Loss
g_loss = (g_loss_A2B + g_loss_B2A + generator_loss_A2B_2nd +
generator_loss_B2A_2nd +
self.cycle_loss_lambda * cycleLoss +
self.identity_loss_lambda * identityLoss)
# Backprop for Generator
self.reset_grad()
g_loss.backward()
self.generator_optimizer.step()
# Train Discriminator
self.generator_A2B.eval()
self.generator_B2A.eval()
self.discriminator_A.train()
self.discriminator_B.train()
self.discriminator_A2.train()
self.discriminator_B2.train()
# Discriminator Feed Forward
d_real_A = self.discriminator_A(real_A)
d_real_B = self.discriminator_B(real_B)
d_real_A2 = self.discriminator_A2(real_A)
d_real_B2 = self.discriminator_B2(real_B)
generated_A = self.generator_B2A(real_B, mask_B)
d_fake_A = self.discriminator_A(generated_A)
# For Two Step Adverserial Loss A->B
cycled_B = self.generator_A2B(generated_A,
flow.ones_like(generated_A))
d_cycled_B = self.discriminator_B2(cycled_B)
generated_B = self.generator_A2B(real_A, mask_A)
d_fake_B = self.discriminator_B(generated_B)
# For Two Step Adverserial Loss B->A
cycled_A = self.generator_B2A(generated_B,
flow.ones_like(generated_B))
d_cycled_A = self.discriminator_A2(cycled_A)
# Loss Functions
d_loss_A_real = flow.mean((1 - d_real_A)**2)
d_loss_A_fake = flow.mean((0 - d_fake_A)**2)
d_loss_A = (d_loss_A_real + d_loss_A_fake) / 2.0
d_loss_B_real = flow.mean((1 - d_real_B)**2)
d_loss_B_fake = flow.mean((0 - d_fake_B)**2)
d_loss_B = (d_loss_B_real + d_loss_B_fake) / 2.0
# Two Step Adverserial Loss
d_loss_A_cycled = flow.mean((0 - d_cycled_A)**2)
d_loss_B_cycled = flow.mean((0 - d_cycled_B)**2)
d_loss_A2_real = flow.mean((1 - d_real_A2)**2)
d_loss_B2_real = flow.mean((1 - d_real_B2)**2)
d_loss_A_2nd = (d_loss_A2_real + d_loss_A_cycled) / 2.0
d_loss_B_2nd = (d_loss_B2_real + d_loss_B_cycled) / 2.0
# Final Loss for discriminator with the Two Step Adverserial Loss
d_loss = (d_loss_A + d_loss_B) / 2.0 + (d_loss_A_2nd +
d_loss_B_2nd) / 2.0
# Backprop for Discriminator
self.reset_grad()
d_loss.backward()
self.discriminator_optimizer.step()
if (i + 1) % 2 == 0:
print(
"Iter:{} Generator Loss:{:.4f} Discrimator Loss:{:.4f} GA2B:{:.4f} GB2A:{:.4f} G_id:{:.4f} G_cyc:{:.4f} D_A:{:.4f} D_B:{:.4f}"
.format(
num_iterations,
g_loss.item(),
d_loss.item(),
g_loss_A2B,
g_loss_B2A,
identityLoss,
cycleLoss,
d_loss_A,
d_loss_B,
))
# Save each model checkpoint and validation
if epoch % self.epochs_per_save == 0 and epoch != 0:
self.saveModelCheckPoint(epoch, PATH="model_checkpoint")
self.validation_for_A_dir()
self.validation_for_B_dir()
def infer(self):
"""Implements the infering loop for MaskCycleGAN-VC
"""
# load pretrain models
self.loadModel(self.pretrain_models)
num_mcep = 80
sampling_rate = self.sample_rate
frame_period = 5.0
infer_A_dir = self.infer_data_dir
print("Generating Validation Data B from A...")
for file in os.listdir(infer_A_dir):
filePath = os.path.join(infer_A_dir, file)
wav, _ = librosa.load(filePath, sr=sampling_rate, mono=True)
wav = preprocess.wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = preprocess.world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = preprocess.pitch_conversion(
f0=f0,
mean_log_src=self.dataset_A_mean,
std_log_src=self.dataset_A_std,
mean_log_target=self.dataset_B_mean,
std_log_target=self.dataset_B_std,
)
coded_sp = preprocess.world_encode_spectral_envelop(
sp=sp, fs=sampling_rate, dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
self.dataset_A_mean) / self.dataset_A_std
coded_sp_norm = np.array([coded_sp_norm])
if flow.cuda.is_available():
coded_sp_norm = flow.tensor(coded_sp_norm).cuda().float()
else:
coded_sp_norm = flow.tensor(coded_sp_norm).float()
coded_sp_converted_norm = self.generator_A2B(
coded_sp_norm, flow.ones_like(coded_sp_norm))
coded_sp_converted_norm = coded_sp_converted_norm.cpu().detach(
).numpy()
coded_sp_converted_norm = np.squeeze(coded_sp_converted_norm)
coded_sp_converted = (
coded_sp_converted_norm * self.dataset_B_std +
self.dataset_B_mean)
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(
coded_sp_converted).astype(np.double)
decoded_sp_converted = preprocess.world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = preprocess.world_speech_synthesis(
f0=f0_converted[0],
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period,
)
sf.write(
os.path.join(infer_A_dir, "convert_" + os.path.basename(file)),
wav_transformed,
sampling_rate,
)
def validation_for_A_dir(self):
num_mcep = 80
sampling_rate = 22050
frame_period = 5.0
validation_A_dir = self.validation_A_dir
output_A_dir = self.output_A_dir
os.makedirs(output_A_dir, exist_ok=True)
print("Generating Validation Data B from A...")
for file in os.listdir(validation_A_dir):
filePath = os.path.join(validation_A_dir, file)
wav, _ = librosa.load(filePath, sr=sampling_rate, mono=True)
wav = preprocess.wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = preprocess.world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = preprocess.pitch_conversion(
f0=f0,
mean_log_src=self.dataset_A_mean,
std_log_src=self.dataset_A_std,
mean_log_target=self.dataset_B_mean,
std_log_target=self.dataset_B_std,
)
coded_sp = preprocess.world_encode_spectral_envelop(
sp=sp, fs=sampling_rate, dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
self.dataset_A_mean) / self.dataset_A_std
coded_sp_norm = np.array([coded_sp_norm])
if flow.cuda.is_available():
coded_sp_norm = flow.tensor(coded_sp_norm).cuda().float()
else:
coded_sp_norm = flow.tensor(coded_sp_norm).float()
coded_sp_converted_norm = self.generator_A2B(
coded_sp_norm, flow.ones_like(coded_sp_norm))
coded_sp_converted_norm = coded_sp_converted_norm.cpu().detach(
).numpy()
coded_sp_converted_norm = np.squeeze(coded_sp_converted_norm)
coded_sp_converted = (
coded_sp_converted_norm * self.dataset_B_std +
self.dataset_B_mean)
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(
coded_sp_converted).astype(np.double)
decoded_sp_converted = preprocess.world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = preprocess.world_speech_synthesis(
f0=f0_converted[0],
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period,
)
sf.write(
os.path.join(output_A_dir,
"convert_" + os.path.basename(file)),
wav_transformed,
sampling_rate,
)
def validation_for_B_dir(self):
num_mcep = 80
sampling_rate = 22050
frame_period = 5.0
validation_B_dir = self.validation_B_dir
output_B_dir = self.output_B_dir
os.makedirs(output_B_dir, exist_ok=True)
print("Generating Validation Data A from B...")
for file in os.listdir(validation_B_dir):
filePath = os.path.join(validation_B_dir, file)
wav, _ = librosa.load(filePath, sr=sampling_rate, mono=True)
wav = preprocess.wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = preprocess.world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = preprocess.pitch_conversion(
f0=f0,
mean_log_src=self.dataset_B_mean,
std_log_src=self.dataset_B_std,
mean_log_target=self.dataset_A_mean,
std_log_target=self.dataset_A_std,
)
coded_sp = preprocess.world_encode_spectral_envelop(
sp=sp, fs=sampling_rate, dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
self.dataset_B_mean) / self.dataset_B_std
coded_sp_norm = np.array([coded_sp_norm])
if flow.cuda.is_available():
coded_sp_norm = flow.tensor(coded_sp_norm).cuda().float()
else:
coded_sp_norm = flow.tensor(coded_sp_norm).float()
coded_sp_converted_norm = self.generator_B2A(
coded_sp_norm, flow.ones_like(coded_sp_norm))
coded_sp_converted_norm = coded_sp_converted_norm.cpu().detach(
).numpy()
coded_sp_converted_norm = np.squeeze(coded_sp_converted_norm)
coded_sp_converted = (
coded_sp_converted_norm * self.dataset_A_std +
self.dataset_A_mean)
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(
coded_sp_converted).astype(np.double)
decoded_sp_converted = preprocess.world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = preprocess.world_speech_synthesis(
f0=f0_converted[0],
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period,
)
sf.write(
os.path.join(output_B_dir,
"convert_" + os.path.basename(file)),
wav_transformed,
sampling_rate,
)
def saveModelCheckPoint(self, epoch, PATH):
flow.save(
self.generator_A2B.state_dict(),
os.path.join(PATH, "generator_A2B_%d" % epoch),
)
flow.save(
self.generator_B2A.state_dict(),
os.path.join(PATH, "generator_B2A_%d" % epoch),
)
flow.save(
self.discriminator_A.state_dict(),
os.path.join(PATH, "discriminator_A_%d" % epoch),
)
flow.save(
self.discriminator_B.state_dict(),
os.path.join(PATH, "discriminator_B_%d" % epoch),
)
def loadModel(self, PATH):
self.generator_A2B.load_state_dict(
flow.load(os.path.join(PATH, "generator_A2B")))
self.generator_B2A.load_state_dict(
flow.load(os.path.join(PATH, "generator_B2A")))
self.discriminator_A.load_state_dict(
flow.load(os.path.join(PATH, "discriminator_A")))
self.discriminator_B.load_state_dict(
flow.load(os.path.join(PATH, "discriminator_B")))
transform = transforms.Compose([Normalize(0.5, 0.5), ToTensor()])
batch_size = 2
num_classes = 2
num_attrs = 1
resolutions_to = [4, 8, 8, 16, 16, 32, 32]
levels = [1, 1.125, 2, 2.5, 3, 3.5, 4]
data_shape = [batch_size, 3, 32, 32]
G = Generator(data_shape,
use_mask=False,
use_attrs=True,
num_attrs=num_attrs,
latent_size=256)
D = Discriminator(data_shape,
use_attrs=True,
num_attrs=num_attrs,
latent_size=256)
for res, lev in zip(resolutions_to, levels):
dataset = VGGFace2Dataset('./dataset/VGGFACE2/train',
res,
landmark_info_path,
identity_info_path,
filtered_list,
transform=transform)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
sample = iter(dataloader).next() # noqa: B305
print(f"resolution: {res}, image: {sample['image'].shape}, \
attr: {sample['attr'].shape}")
# Setup Metrics
cty_running_metrics = runningScore(num_classes)
model_dict = {}
# Setup Model
print('building models ...')
enc_shared = SharedEncoder().cuda()
dclf1 = DomainClassifier().cuda()
dclf2 = DomainClassifier().cuda()
enc_s = PrivateEncoder(64, private_code_size).cuda()
enc_t = PrivateEncoder(64, private_code_size).cuda()
dec_s = PrivateDecoder(shared_code_channels, private_code_size).cuda()
dec_t = dec_s
dis_s2t = Discriminator().cuda()
dis_t2s = Discriminator().cuda()
model_dict['enc_shared'] = enc_shared
model_dict['dclf1'] = dclf1
model_dict['dclf2'] = dclf2
model_dict['enc_s'] = enc_s
model_dict['enc_t'] = enc_t
model_dict['dec_s'] = dec_s
model_dict['dec_t'] = dec_t
model_dict['dis_s2t'] = dis_s2t
model_dict['dis_t2s'] = dis_t2s
enc_shared_opt = optim.SGD(enc_shared.optim_parameters(learning_rate_seg),
lr=learning_rate_seg,
momentum=0.9,
num_workers=opt.workers)
print("Train set size: " + str(len(trainset)))
# Whether training form checkpoint
if opt.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('./checkpoint/ckpt.t7')
netD = checkpoint['netD']
netG = checkpoint['netG']
start_epoch = checkpoint['epoch']
else:
print('==> Building model..')
# Create an instance of the nn.module class defined above:
netD = Discriminator(trainset, opt.batchSize, reuse=isTrain)
netG = Generator(randomInput, opt.batchSize, reuse=True)
start_epoch = 0
# For training on GPU, we need to transfer net and data onto the GPU
# http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#training-on-gpu
if opt.ngpu >= 1:
netD = netD.cuda()
netD = torch.nn.DataParallel(netD,
device_ids=range(torch.cuda.device_count()))
netG = netG.cuda()
netG = torch.nn.DataParallel(netG,
device_ids=range(torch.cuda.device_count()))
cudnn.benchmark = True
# Optimizers
def train(args,
generator: Generator,
discriminator: Discriminator,
feature_extractor: FeatureExtractor,
photo_dataloader,
edge_smooth_dataloader,
animation_dataloader,
checkpoint_dir=None):
tb_writter = SummaryWriter()
gen_criterion = nn.BCELoss().to(args.device)
disc_criterion = nn.BCELoss().to(args.device)
content_criterion = nn.L1Loss().to(args.device)
gen_optimizer = torch.optim.Adam(generator.parameters(),
lr=args.lr,
betas=(args.adam_beta, 0.999))
disc_optimizer = torch.optim.Adam(discriminator.parameters(),
lr=args.lr,
betas=(args.adam_beta, 0.999))
global_step = 0
global_init_step = 0
# The number of steps to skip when loading a checkpoint
skipped_step = 0
skipped_init_step = 0
cur_epoch = 0
cur_init_epoch = 0
data_len = min(len(photo_dataloader), len(edge_smooth_dataloader),
len(animation_dataloader))
if checkpoint_dir:
try:
checkpoint_dict = load(checkpoint_dir)
generator.load_state_dict(checkpoint_dict['generator'])
discriminator.load_state_dict(checkpoint_dict['discriminator'])
gen_optimizer.load_state_dict(checkpoint_dict['gen_optimizer'])
disc_optimizer.load_state_dict(checkpoint_dict['disc_optimizer'])
global_step = checkpoint_dict['global_step']
global_init_step = checkpoint_dict['global_init_step']
cur_epoch = global_step // data_len
cur_init_epoch = global_init_step // len(photo_dataloader)
skipped_step = global_step % data_len
skipped_init_step = global_init_step % len(photo_dataloader)
logger.info("Start training with,")
logger.info("In initialization step, epoch: %d, step: %d",
cur_init_epoch, skipped_init_step)
logger.info("In main train step, epoch: %d, step: %d", cur_epoch,
skipped_step)
except:
logger.info("Wrong checkpoint path")
t_total = data_len * args.n_epochs
t_init_total = len(photo_dataloader) * args.n_init_epoch
# Train!
logger.info("***** Running training *****")
logger.info(" Num photo examples = %d", len(photo_dataloader))
logger.info(" Num edge_smooth examples = %d", len(edge_smooth_dataloader))
logger.info(" Num animation examples = %d", len(animation_dataloader))
logger.info(" Num Epochs = %d", args.n_epochs)
logger.info(" Total train batch size = %d", args.batch_size)
logger.info(" Total optimization steps = %d", t_total)
logger.info(" Num Init Epochs = %d", args.n_init_epoch)
logger.info(" Total Init optimization steps = %d", t_init_total)
logger.info(" Logging steps = %d", args.logging_steps)
logger.info(" Save steps = %d", args.save_steps)
init_phase = True
try:
generator.train()
discriminator.train()
gloabl_init_loss = 0
# --- Initialization Content loss
mb = master_bar(range(cur_init_epoch, args.n_init_epoch))
for init_epoch in mb:
epoch_iter = progress_bar(photo_dataloader, parent=mb)
for step, (photo, _) in enumerate(epoch_iter):
if skipped_init_step > 0:
skipped_init_step = -1
continue
photo = photo.to(args.device)
gen_optimizer.zero_grad()
x_features = feature_extractor((photo + 1) / 2).detach()
Gx = generator(photo)
Gx_features = feature_extractor((Gx + 1) / 2)
content_loss = args.content_loss_weight * content_criterion(
Gx_features, x_features)
content_loss.backward()
gen_optimizer.step()
gloabl_init_loss += content_loss.item()
global_init_step += 1
if args.save_steps > 0 and global_init_step % args.save_steps == 0:
logger.info(
"Save Initialization Phase, init_epoch: %d, init_step: %d",
init_epoch, global_init_step)
save(checkpoint_dir, global_step, global_init_step,
generator, discriminator, gen_optimizer,
disc_optimizer)
if args.logging_steps > 0 and global_init_step % args.logging_steps == 0:
tb_writter.add_scalar('Initialization Phase/Content Loss',
content_loss.item(),
global_init_step)
tb_writter.add_scalar(
'Initialization Phase/Global Generator Loss',
gloabl_init_loss / global_init_step, global_init_step)
logger.info(
"Initialization Phase, Epoch: %d, Global Step: %d, Content Loss: %.4f",
init_epoch, global_init_step,
gloabl_init_loss / (global_init_step))
# -----------------------------------------------------
logger.info("Finish Initialization Phase, save model...")
save(checkpoint_dir, global_step, global_init_step, generator,
discriminator, gen_optimizer, disc_optimizer)
init_phase = False
global_loss_D = 0
global_loss_G = 0
global_loss_content = 0
mb = master_bar(range(cur_epoch, args.n_epochs))
for epoch in mb:
epoch_iter = progress_bar(list(
zip(animation_dataloader, edge_smooth_dataloader,
photo_dataloader)),
parent=mb)
for step, ((animation, _), (edge_smoothed, _),
(photo, _)) in enumerate(epoch_iter):
if skipped_step > 0:
skipped_step = -1
continue
animation = animation.to(args.device)
edge_smoothed = edge_smoothed.to(args.device)
photo = photo.to(args.device)
disc_optimizer.zero_grad()
# --- Train discriminator
# ------ Train Discriminator with animation image
animation_disc = discriminator(animation)
animation_target = torch.ones_like(animation_disc)
loss_animation_disc = disc_criterion(animation_disc,
animation_target)
# ------ Train Discriminator with edge image
edge_smoothed_disc = discriminator(edge_smoothed)
edge_smoothed_target = torch.zeros_like(edge_smoothed_disc)
loss_edge_disc = disc_criterion(edge_smoothed_disc,
edge_smoothed_target)
# ------ Train Discriminator with generated image
generated_image = generator(photo).detach()
generated_image_disc = discriminator(generated_image)
generated_image_target = torch.zeros_like(generated_image_disc)
loss_generated_disc = disc_criterion(generated_image_disc,
generated_image_target)
loss_disc = loss_animation_disc + loss_edge_disc + loss_generated_disc
loss_disc.backward()
disc_optimizer.step()
global_loss_D += loss_disc.item()
# --- Train Generator
gen_optimizer.zero_grad()
generated_image = generator(photo)
generated_image_disc = discriminator(generated_image)
generated_image_target = torch.ones_like(generated_image_disc)
loss_adv = gen_criterion(generated_image_disc,
generated_image_target)
# ------ Train Generator with content loss
x_features = feature_extractor((photo + 1) / 2).detach()
Gx_features = feature_extractor((generated_image + 1) / 2)
loss_content = args.content_loss_weight * content_criterion(
Gx_features, x_features)
loss_gen = loss_adv + loss_content
loss_gen.backward()
gen_optimizer.step()
global_loss_G += loss_adv.item()
global_loss_content += loss_content.item()
global_step += 1
if args.save_steps > 0 and global_step % args.save_steps == 0:
logger.info("Save Training Phase, epoch: %d, step: %d",
epoch, global_step)
save(checkpoint_dir, global_step, global_init_step,
generator, discriminator, gen_optimizer,
disc_optimizer)
if args.logging_steps > 0 and global_init_step % args.logging_steps == 0:
tb_writter.add_scalar('Train Phase/Generator Loss',
loss_adv.item(), global_step)
tb_writter.add_scalar('Train Phase/Discriminator Loss',
loss_disc.item(), global_step)
tb_writter.add_scalar('Train Phase/Content Loss',
loss_content.item(), global_step)
tb_writter.add_scalar('Train Phase/Global Generator Loss',
global_loss_G / global_step,
global_step)
tb_writter.add_scalar(
'Train Phase/Global Discriminator Loss',
global_loss_D / global_step, global_step)
tb_writter.add_scalar('Train Phase/Global Content Loss',
global_loss_content / global_step,
global_step)
logger.info(
"Training Phase, Epoch: %d, Global Step: %d, Disc Loss %.4f, Gen Loss %.4f, Content Loss: %.4f",
epoch, global_step, global_loss_D / global_step,
global_loss_G / global_step,
global_loss_content / global_step)
except KeyboardInterrupt:
if init_phase:
logger.info("KeyboardInterrupt in Initialization Phase!")
logger.info("Save models, init_epoch: %d, init_step: %d",
init_epoch, global_init_step)
else:
logger.info("KeyboardInterrupt in Training Phase!")
logger.info("Save models, epoch: %d, step: %d", epoch, global_step)
save(checkpoint_dir, global_step, global_init_step, generator,
discriminator, gen_optimizer, disc_optimizer)
class Solver(object):
def __init__(self, data_loader, config):
self.config = config
self.data_loader = data_loader
# Model configurations.
self.lambda_cycle = config.lambda_cycle
self.lambda_cls = config.lambda_cls
self.lambda_identity = config.lambda_identity
# Training configurations.
self.data_dir = config.data_dir
self.test_dir = config.test_dir
self.batch_size = config.batch_size
self.num_iters = config.num_iters
self.num_iters_decay = config.num_iters_decay
self.g_lr = config.g_lr
self.d_lr = config.d_lr
self.c_lr = config.c_lr
self.n_critic = config.n_critic
self.beta1 = config.beta1
self.beta2 = config.beta2
self.resume_iters = config.resume_iters
# Test configurations.
self.pretrain_models = config.pretrain_models
self.sample_dir = config.sample_dir
self.trg_speaker = ast.literal_eval(config.trg_speaker)
self.src_speaker = config.src_speaker
# Miscellaneous.
self.device = flow.device(
"cuda:0" if flow.cuda.is_available() else "cpu")
self.spk_enc = LabelBinarizer().fit(speakers)
# Directories.
self.model_save_dir = config.model_save_dir
self.result_dir = config.result_dir
self.use_gradient_penalty = config.use_gradient_penalty
# Step size.
self.log_step = config.log_step
self.sample_step = config.sample_step
self.model_save_step = config.model_save_step
self.lr_update_step = config.lr_update_step
# Build the model.
self.build_model()
def build_model(self):
self.G = Generator()
self.D = Discriminator()
self.C = DomainClassifier()
self.g_optimizer = flow.optim.Adam(self.G.parameters(), self.g_lr,
[self.beta1, self.beta2])
self.d_optimizer = flow.optim.Adam(self.D.parameters(), self.d_lr,
[self.beta1, self.beta2])
self.c_optimizer = flow.optim.Adam(self.C.parameters(), self.c_lr,
[self.beta1, self.beta2])
self.print_network(self.G, "G")
self.print_network(self.D, "D")
self.print_network(self.C, "C")
self.G.to(self.device)
self.D.to(self.device)
self.C.to(self.device)
def print_network(self, model, name):
"""Print out the network information."""
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(model)
print(name)
print("The number of parameters: {}".format(num_params))
def update_lr(self, g_lr, d_lr, c_lr):
"""Decay learning rates of the generator and discriminator and classifier."""
for param_group in self.g_optimizer.param_groups:
param_group["lr"] = g_lr
for param_group in self.d_optimizer.param_groups:
param_group["lr"] = d_lr
for param_group in self.c_optimizer.param_groups:
param_group["lr"] = c_lr
def train(self):
# Learning rate cache for decaying.
g_lr = self.g_lr
d_lr = self.d_lr
c_lr = self.c_lr
start_iters = 0
if self.resume_iters:
pass
norm = Normalizer()
data_iter = iter(self.data_loader)
print("Start training......")
start_time = datetime.now()
for i in range(start_iters, self.num_iters):
# Preprocess input data
# Fetch real images and labels.
try:
x_real, speaker_idx_org, label_org = next(data_iter)
except:
data_iter = iter(self.data_loader)
x_real, speaker_idx_org, label_org = next(data_iter)
# Generate target domain labels randomly.
rand_idx = flow.randperm(label_org.size(0))
label_trg = label_org[rand_idx]
speaker_idx_trg = speaker_idx_org[rand_idx]
x_real = x_real.to(self.device)
# Original domain one-hot labels.
label_org = label_org.to(self.device)
# Target domain one-hot labels.
label_trg = label_trg.to(self.device)
speaker_idx_org = speaker_idx_org.to(self.device)
speaker_idx_trg = speaker_idx_trg.to(self.device)
# Train the discriminator
# Compute loss with real audio frame.
CELoss = nn.CrossEntropyLoss()
cls_real = self.C(x_real)
cls_loss_real = CELoss(input=cls_real, target=speaker_idx_org)
self.reset_grad()
cls_loss_real.backward()
self.c_optimizer.step()
# Logging.
loss = {}
loss["C/C_loss"] = cls_loss_real.item()
out_r = self.D(x_real, label_org)
# Compute loss with fake audio frame.
x_fake = self.G(x_real, label_trg)
out_f = self.D(x_fake.detach(), label_trg)
d_loss_t = nn.BCEWithLogitsLoss()(
input=out_f, target=flow.zeros_like(
out_f).float()) + nn.BCEWithLogitsLoss()(
input=out_r, target=flow.ones_like(out_r).float())
out_cls = self.C(x_fake)
d_loss_cls = CELoss(input=out_cls, target=speaker_idx_trg)
# Compute loss for gradient penalty.
alpha = flow.rand(x_real.size(0), 1, 1, 1).to(self.device)
x_hat = ((alpha * x_real +
(1 - alpha) * x_fake).detach().requires_grad_(True))
out_src = self.D(x_hat, label_trg)
# TODO: Second-order derivation is not currently supported in oneflow, so gradient penalty cannot be used temporarily.
if self.use_gradient_penalty:
d_loss_gp = self.gradient_penalty(out_src, x_hat)
d_loss = d_loss_t + self.lambda_cls * d_loss_cls + 5 * d_loss_gp
else:
d_loss = d_loss_t + self.lambda_cls * d_loss_cls
self.reset_grad()
d_loss.backward()
self.d_optimizer.step()
loss["D/D_loss"] = d_loss.item()
# Train the generator
if (i + 1) % self.n_critic == 0:
# Original-to-target domain.
x_fake = self.G(x_real, label_trg)
g_out_src = self.D(x_fake, label_trg)
g_loss_fake = nn.BCEWithLogitsLoss()(
input=g_out_src, target=flow.ones_like(g_out_src).float())
out_cls = self.C(x_real)
g_loss_cls = CELoss(input=out_cls, target=speaker_idx_org)
# Target-to-original domain.
x_reconst = self.G(x_fake, label_org)
g_loss_rec = nn.L1Loss()(x_reconst, x_real)
# Original-to-Original domain(identity).
x_fake_iden = self.G(x_real, label_org)
id_loss = nn.L1Loss()(x_fake_iden, x_real)
# Backward and optimize.
g_loss = (g_loss_fake + self.lambda_cycle * g_loss_rec +
self.lambda_cls * g_loss_cls +
self.lambda_identity * id_loss)
self.reset_grad()
g_loss.backward()
self.g_optimizer.step()
# Logging.
loss["G/loss_fake"] = g_loss_fake.item()
loss["G/loss_rec"] = g_loss_rec.item()
loss["G/loss_cls"] = g_loss_cls.item()
loss["G/loss_id"] = id_loss.item()
loss["G/g_loss"] = g_loss.item()
# Miscellaneous
# Print out training information.
if (i + 1) % self.log_step == 0:
et = datetime.now() - start_time
et = str(et)[:-7]
log = "Elapsed [{}], Iteration [{}/{}]".format(
et, i + 1, self.num_iters)
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
# Translate fixed images for debugging.
if (i + 1) % self.sample_step == 0:
with flow.no_grad():
d, speaker = TestSet(self.test_dir).test_data()
target = random.choice(
[x for x in speakers if x != speaker])
label_t = self.spk_enc.transform([target])[0]
label_t = np.asarray([label_t])
for filename, content in d.items():
f0 = content["f0"]
ap = content["ap"]
sp_norm_pad = self.pad_coded_sp(
content["coded_sp_norm"])
convert_result = []
for start_idx in range(
0, sp_norm_pad.shape[1] - FRAMES + 1, FRAMES):
one_seg = sp_norm_pad[:,
start_idx:start_idx + FRAMES]
one_seg = flow.Tensor(one_seg).to(self.device)
one_seg = one_seg.view(1, 1, one_seg.size(0),
one_seg.size(1))
l = flow.Tensor(label_t)
one_seg = one_seg.to(self.device)
l = l.to(self.device)
one_set_return = self.G(one_seg,
l).detach().cpu().numpy()
one_set_return = np.squeeze(one_set_return)
one_set_return = norm.backward_process(
one_set_return, target)
convert_result.append(one_set_return)
convert_con = np.concatenate(convert_result, axis=1)
convert_con = convert_con[:,
0:content["coded_sp_norm"].
shape[1]]
contigu = np.ascontiguousarray(convert_con.T,
dtype=np.float64)
decoded_sp = decode_spectral_envelope(contigu,
SAMPLE_RATE,
fft_size=FFTSIZE)
f0_converted = norm.pitch_conversion(
f0, speaker, target)
wav = synthesize(f0_converted, decoded_sp, ap,
SAMPLE_RATE)
name = f"{speaker}-{target}_iter{i+1}_{filename}"
path = os.path.join(self.sample_dir, name)
print(f"[save]:{path}")
sf.write(path, wav, SAMPLE_RATE)
# Save model checkpoints.
if (i + 1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir,
"{}-G".format(i + 1))
D_path = os.path.join(self.model_save_dir,
"{}-D".format(i + 1))
C_path = os.path.join(self.model_save_dir,
"{}-C".format(i + 1))
flow.save(self.G.state_dict(), G_path)
flow.save(self.D.state_dict(), D_path)
flow.save(self.C.state_dict(), C_path)
print("Saved model checkpoints into {}...".format(
self.model_save_dir))
# Decay learning rates.
if (i + 1) % self.lr_update_step == 0 and (i + 1) > (
self.num_iters - self.num_iters_decay):
g_lr -= self.g_lr / float(self.num_iters_decay)
d_lr -= self.d_lr / float(self.num_iters_decay)
c_lr -= self.c_lr / float(self.num_iters_decay)
self.update_lr(g_lr, d_lr, c_lr)
print("Decayed learning rates, g_lr: {}, d_lr: {}.".format(
g_lr, d_lr))
def gradient_penalty(self, y, x):
"""Compute gradient penalty: (L2_norm(dy/dx) - 1)**2."""
weight = flow.ones(y.size()).to(self.device)
dydx = flow.autograd.grad(outputs=y,
inputs=x,
out_grads=weight,
retain_graph=True,
create_graph=True)[0]
dydx = dydx.view(dydx.size(0), -1)
dydx_l2norm = flow.sqrt(flow.sum(dydx**2, dim=1))
return flow.mean((dydx_l2norm - 1)**2)
def reset_grad(self):
"""Reset the gradient buffers."""
self.g_optimizer.zero_grad()
self.d_optimizer.zero_grad()
self.c_optimizer.zero_grad()
def restore_model(self, model_save_dir):
"""Restore the tra,zined generator and discriminator."""
print("Loading the pretrain models...")
G_path = os.path.join(model_save_dir, "200000-G")
D_path = os.path.join(model_save_dir, "200000-D")
C_path = os.path.join(model_save_dir, "200000-C")
self.G.load_state_dict(flow.load(G_path))
self.D.load_state_dict(flow.load(D_path))
self.C.load_state_dict(flow.load(C_path))
@staticmethod
def pad_coded_sp(coded_sp_norm):
f_len = coded_sp_norm.shape[1]
if f_len >= FRAMES:
pad_length = FRAMES - (f_len - (f_len // FRAMES) * FRAMES)
elif f_len < FRAMES:
pad_length = FRAMES - f_len
sp_norm_pad = np.hstack(
(coded_sp_norm, np.zeros((coded_sp_norm.shape[0], pad_length))))
return sp_norm_pad
def test(self):
"""Translate speech using StarGAN ."""
# Load the trained generator.
self.restore_model(self.pretrain_models)
norm = Normalizer()
# Set data loader.
d, speaker = TestSet(self.test_dir).test_data(self.src_speaker)
targets = self.trg_speaker
for target in targets:
print(target)
assert target in speakers
label_t = self.spk_enc.transform([target])[0]
label_t = np.asarray([label_t])
with flow.no_grad():
for filename, content in d.items():
f0 = content["f0"]
ap = content["ap"]
sp_norm_pad = self.pad_coded_sp(content["coded_sp_norm"])
convert_result = []
for start_idx in range(0,
sp_norm_pad.shape[1] - FRAMES + 1,
FRAMES):
one_seg = sp_norm_pad[:, start_idx:start_idx + FRAMES]
one_seg = flow.Tensor(one_seg).to(self.device)
one_seg = one_seg.view(1, 1, one_seg.size(0),
one_seg.size(1))
l = flow.Tensor(label_t)
one_seg = one_seg.to(self.device)
l = l.to(self.device)
one_set_return = self.G(one_seg,
l).detach().cpu().numpy()
one_set_return = np.squeeze(one_set_return)
one_set_return = norm.backward_process(
one_set_return, target)
convert_result.append(one_set_return)
convert_con = np.concatenate(convert_result, axis=1)
convert_con = convert_con[:, 0:content["coded_sp_norm"].
shape[1]]
contigu = np.ascontiguousarray(convert_con.T,
dtype=np.float64)
decoded_sp = decode_spectral_envelope(contigu,
SAMPLE_RATE,
fft_size=FFTSIZE)
f0_converted = norm.pitch_conversion(f0, speaker, target)
wav = synthesize(f0_converted, decoded_sp, ap, SAMPLE_RATE)
name = f"{speaker}-{target}_{filename}"
path = os.path.join(self.result_dir, name)
print(f"[save]:{path}")
sf.write(path, wav, SAMPLE_RATE)
NUM_EPOCHS = opt.num_epochs
decay_interval = NUM_EPOCHS // 8
decay_indices = [decay_interval, decay_interval*2, decay_interval*4, decay_interval*6]
discriminator_cycle = opt.discriminator_cycle
generator_cycle = opt.generator_cycle
train_set = TrainDatasetFromFolder('data/DIV2K_train_HR', crop_size=CROP_SIZE, upscale_factor=UPSCALE_FACTOR)
val_set = ValDatasetFromFolder('data/DIV2K_valid_HR', crop_size=CROP_SIZE, upscale_factor=UPSCALE_FACTOR)
train_loader = DataLoader(dataset=train_set, num_workers=4, batch_size=8, shuffle=True)
val_loader = DataLoader(dataset=val_set, num_workers=4, batch_size=1, shuffle=False)
netG = Generator(num_rrdb_blocks=16, scaling_factor=UPSCALE_FACTOR)
print('# generator parameters:', sum(param.numel() for param in netG.parameters()))
netD = Discriminator(opt.crop_size)
print('# discriminator parameters:', sum(param.numel() for param in netD.parameters()))
generator_criterion = EGeneratorLoss()
discriminator_criterion = torch.nn.BCEWithLogitsLoss()
if torch.cuda.is_available():
netG.cuda()
netD.cuda()
generator_criterion.cuda()
discriminator_criterion.cuda()
optimizerG = optim.Adam(netG.parameters(), lr=0.0001)
optimizerD = optim.Adam(netD.parameters(), lr=0.0001)
optimizerG_decay = optim.lr_scheduler.MultiStepLR(optimizerG, decay_indices, gamma=0.5)
optimizerD_decay = optim.lr_scheduler.MultiStepLR(optimizerD, decay_indices, gamma=0.5)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
netE = Encoder(ngpu, ndf, nc).cuda()
if (device.type == 'cuda') and (ngpu > 1):
netE = nn.DataParallel(netE, list(range(ngpu)))
netE.apply(weights_init)
netG = Decoder(ngpu, num, ngf).cuda()
if (device.type == 'cuda') and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))
netG.apply(weights_init)
netD = Discriminator(ngpu, ndf, nc).cuda()
if (device.type == 'cuda') and (ngpu > 1):
netD = nn.DataParallel(netD, list(range(ngpu)))
netD.apply(weights_init)
# 损失函数和优化器
criterionE = Encoder_loss
criterionG = Decoder_loss
criterionD = nn.BCELoss()
# 优化器
optimizerE = optim.Adam(netE.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
real_label = 1
fake_label = 0
def build_model(config, from_style, to_style):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
generator_ab = ResidualGenerator(config.image_size,
config.num_residual_blocks).to(device)
generator_ba = ResidualGenerator(config.image_size,
config.num_residual_blocks).to(device)
discriminator_a = Discriminator(config.image_size).to(device)
discriminator_b = Discriminator(config.image_size).to(device)
generator_ab_param = glob(
os.path.join(config.checkpoint_dir, f"{from_style}2{to_style}",
f"generator_ab_{config.epoch-1}.pth"))
generator_ba_param = glob(
os.path.join(config.checkpoint_dir, f"{from_style}2{to_style}",
f"generator_ba_{config.epoch-1}.pth"))
discriminator_a_param = glob(
os.path.join(config.checkpoint_dir, f"{from_style}2{to_style}",
f"discriminator_a_{config.epoch-1}.pth"))
discriminator_b_param = glob(
os.path.join(config.checkpoint_dir, f"{from_style}2{to_style}",
f"discriminator_b_{config.epoch-1}.pth"))
print(f"[*] Load checkpoint in {config.checkpoint_dir}")
if not os.path.exists(
os.path.join(config.checkpoint_dir, f"{from_style}2{to_style}")):
os.makedirs(
os.path.join(config.checkpoint_dir, f"{from_style}2{to_style}"))
if len(
os.listdir(
os.path.join(config.checkpoint_dir,
f"{from_style}2{to_style}"))) == 0:
print(f"[!] No checkpoint in {config.checkpoint_dir}")
generator_ab.apply(weights_init)
generator_ba.apply(weights_init)
discriminator_a.apply(weights_init)
discriminator_b.apply(weights_init)
else:
generator_ab.load_state_dict(
torch.load(generator_ab_param[-1], map_location=device))
generator_ba.load_state_dict(
torch.load(generator_ba_param[-1], map_location=device))
discriminator_a.load_state_dict(
torch.load(discriminator_a_param[-1], map_location=device))
discriminator_b.load_state_dict(
torch.load(discriminator_b_param[-1], map_location=device))
return generator_ab, generator_ba, discriminator_a, discriminator_b
def main(writer):
dataset = AnimeDataset(avatar_tag_dat_path,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))
]))
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
drop_last=True)
G = Generator(noise_size, len(utils.hair) + len(utils.eyes)).to(device)
D = Discriminator(len(utils.hair), len(utils.eyes)).to(device)
G_optim = torch.optim.Adam(G.parameters(),
lr=learning_rate_g,
betas=(beta_1, 0.999))
D_optim = torch.optim.Adam(D.parameters(),
lr=learning_rate_d,
betas=(beta_1, 0.999))
criterion = nn.BCELoss()
# training
iteration = 0
real_label = torch.ones(batch_size).to(device)
# real_label = torch.Tensor(batch_size).uniform_(0.9, 1).to(device) # soft labeling
fake_label = torch.zeros(batch_size).to(device)
for epoch in range(max_epoch + 1):
for i, (real_tag, real_img) in enumerate(data_loader):
real_img = real_img.to(device)
real_tag = real_tag.to(device)
# train D with real images
D.zero_grad()
real_score, real_predict = D(real_img)
real_discrim_loss = criterion(real_score, real_label)
real_classifier_loss = criterion(real_predict, real_tag)
# train D with fake images
z, fake_tag = utils.fake_generator(batch_size, noise_size, device)
fake_img = G(z, fake_tag).to(device)
fake_score, fake_predict = D(fake_img)
fake_discrim_loss = criterion(fake_score, fake_label)
discrim_loss = (real_discrim_loss + fake_discrim_loss) * 0.5
classifier_loss = real_classifier_loss * lambda_cls
# gradient penalty
alpha_size = [1] * real_img.dim()
alpha_size[0] = real_img.size(0)
alpha = torch.rand(alpha_size).to(device)
x_hat = Variable(alpha * real_img.data + (1 - alpha) *
(real_img.data + 0.5 * real_img.data.std() *
torch.rand(real_img.size()).to(device)),
requires_grad=True).to(device)
fake_score, fake_tag = D(x_hat)
gradients = grad(outputs=fake_score,
inputs=x_hat,
grad_outputs=torch.ones(
fake_score.size()).to(device),
create_graph=True,
retain_graph=True,
only_inputs=True)[0].view(x_hat.size(0), -1)
gradient_penalty = lambda_gp * (
(gradients.norm(2, dim=1) - 1)**2).mean()
D_loss = discrim_loss + classifier_loss + gradient_penalty
D_loss.backward()
D_optim.step()
# train G
G.zero_grad()
z, fake_tag = utils.fake_generator(batch_size, noise_size, device)
fake_img = G(z, fake_tag).to(device)
fake_score, fake_predict = D(fake_img)
discrim_loss = criterion(fake_score, real_label)
classifier_loss = criterion(fake_predict, fake_tag) * lambda_cls
G_loss = discrim_loss + classifier_loss
G_loss.backward()
G_optim.step()
# plot loss curve
writer.add_scalar('Loss_D', D_loss.item(), iteration)
writer.add_scalar('Loss_G', G_loss.item(), iteration)
print('[{}/{}][{}/{}] Iteration: {}'.format(
epoch, max_epoch, i, len(data_loader), iteration))
if iteration % interval == interval - 1:
fake_img = G(fix_noise, fix_tag)
vutils.save_image(utils.denorm(fake_img[:64, :, :, :]),
os.path.join(
image_path,
'fake_image_{}.png'.format(iteration)),
padding=0)
vutils.save_image(utils.denorm(real_img[:64, :, :, :]),
os.path.join(
image_path,
'real_image_{}.png'.format(iteration)),
padding=0)
grid = vutils.make_grid(utils.denorm(fake_img[:64, :, :, :]),
padding=0)
writer.add_image('generation results', grid, iteration)
iteration += 1
# checkpoint
torch.save(G.state_dict(),
os.path.join(model_path, 'netG_epoch_{}.pth'.format(epoch)))
torch.save(D.state_dict(),
os.path.join(model_path, 'netD_epoch_{}.pth'.format(epoch)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_dir", type=str, default="output/ckpt")
parser.add_argument("--model_config",
type=str,
default="model_config.json")
parser.add_argument("--no_cuda",
action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--photo_dir',
type=str,
default="data/photo",
help='path to photo datasets.')
parser.add_argument('--edge_smooth_dir',
type=str,
default="data/edge_smooth",
help='path to edge_smooth datasets.')
parser.add_argument('--target_dir',
type=str,
default="data/target",
help='path to target datasets.')
parser.add_argument('--content_loss_weight',
type=float,
default=10,
help='content loss weight')
parser.add_argument('--seed', type=int, default=42, help='seed')
parser.add_argument('--adam_beta',
type=float,
default=0.5,
help='adam_beta')
parser.add_argument('--n_epochs',
type=int,
default=100,
help='number of epochs of training')
parser.add_argument('--n_init_epoch',
type=int,
default=15,
help='number of epochs of initializing')
parser.add_argument('--batch_size',
type=int,
default=8,
help='size of the batches')
parser.add_argument('--lr',
type=float,
default=0.0002,
help='initial learning rate')
parser.add_argument(
'--n_cpu',
type=int,
default=0,
help='number of cpu threads to use during batch generation')
parser.add_argument('--logging_steps',
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps',
type=int,
default=3000,
help='Save checkpoint every X updates steps.')
args = parser.parse_args()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
model_config = Config.load(args.model_config)
args.device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
set_seed(args)
logger.warning("device: %s, n_gpu: %s", args.device, args.n_gpu)
generator = Generator(model_config).to(args.device)
discriminator = Discriminator(model_config).to(args.device)
feature_extractor = FeatureExtractor(model_config).to(args.device)
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
photo_dataloader, _ = load_image_dataloader(args.photo_dir, transform,
args.batch_size, args.n_cpu)
edge_smooth_dataloader, _ = load_image_dataloader(args.edge_smooth_dir,
transform,
args.batch_size,
args.n_cpu)
animation_dataloader, _ = load_image_dataloader(args.target_dir, transform,
args.batch_size,
args.n_cpu)
train(args, generator, discriminator, feature_extractor, photo_dataloader,
edge_smooth_dataloader, animation_dataloader, args.checkpoint_dir)
val_set = ValDatasetFromFolder('../../../CelebA-HQ-img/',
crop_size=CROP_SIZE,
upscale_factor=UPSCALE_FACTOR)
# train_set = TrainDatasetFromFolder('../../../CelebA-HQ-img/', crop_size=CROP_SIZE, upscale_factor=UPSCALE_FACTOR)
# val_set = ValDatasetFromFolder('../../../CelebA-HQ-img/', crop_size=CROP_SIZE, upscale_factor=UPSCALE_FACTOR)
train_loader = DataLoader(dataset=train_set,
num_workers=4,
batch_size=opt.batchSize,
shuffle=True)
val_loader = DataLoader(dataset=val_set,
num_workers=4,
batch_size=opt.testBatchSize,
shuffle=False)
netG = Generator(UPSCALE_FACTOR).to(device)
netD = Discriminator().to(device)
# pretrain the Generator and load it
# netG.load_state_dict(torch.load('epochs/' + opt.pretrain_path))
netG.load_state_dict(torch.load("./300000_G.pth"))
optimizerG = optim.Adam(netG.parameters(), lr=opt.lr, betas=(0.5, 0.9))
optimizerD = optim.Adam(netD.parameters(), lr=opt.lr, betas=(0.5, 0.9))
criterionG = Generator_loss().to(device)
# loop over the dataset multiple times
for epoch in range(opt.start, NUM_EPOCHS + 1):
d_total_loss = 0.0
g_total_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
crop_size=CROP_SIZE,
upscale_factor=UPSCALE_FACTOR)
val_set = ValDatasetFromFolder('../../../CelebA-HQ-img/',
crop_size=CROP_SIZE,
upscale_factor=UPSCALE_FACTOR)
train_loader = DataLoader(dataset=train_set,
num_workers=4,
batch_size=opt.batchSize,
shuffle=True)
val_loader = DataLoader(dataset=val_set,
num_workers=4,
batch_size=opt.testBatchSize,
shuffle=False)
netG = Generator(UPSCALE_FACTOR).to(device)
netD = Discriminator().to(device)
optimizerG = optim.RMSprop(netG.parameters(), lr=opt.lr)
criterion = nn.MSELoss()
# loop over the dataset multiple times
for epoch in range(opt.start, NUM_EPOCHS + 1):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizerG.zero_grad()
# forward + backward + optimize
class CycleGANTrainr(object):
def __init__(
self,
logf0s_normalization,
mcep_normalization,
coded_sps_A_norm,
coded_sps_B_norm,
model_checkpoint,
validation_A_dir,
output_A_dir,
validation_B_dir,
output_B_dir,
restart_training_at=None,
):
self.start_epoch = 0
self.num_epochs = 200000
self.mini_batch_size = 10
self.dataset_A = self.loadPickleFile(coded_sps_A_norm)
self.dataset_B = self.loadPickleFile(coded_sps_B_norm)
self.device = flow.device(
"cuda" if flow.cuda.is_available() else "cpu")
# Speech Parameters
logf0s_normalization = np.load(logf0s_normalization)
self.log_f0s_mean_A = logf0s_normalization["mean_A"]
self.log_f0s_std_A = logf0s_normalization["std_A"]
self.log_f0s_mean_B = logf0s_normalization["mean_B"]
self.log_f0s_std_B = logf0s_normalization["std_B"]
mcep_normalization = np.load(mcep_normalization)
self.coded_sps_A_mean = mcep_normalization["mean_A"]
self.coded_sps_A_std = mcep_normalization["std_A"]
self.coded_sps_B_mean = mcep_normalization["mean_B"]
self.coded_sps_B_std = mcep_normalization["std_B"]
# Generator and Discriminator
self.generator_A2B = Generator().to(self.device)
self.generator_B2A = Generator().to(self.device)
self.discriminator_A = Discriminator().to(self.device)
self.discriminator_B = Discriminator().to(self.device)
# Loss Functions
criterion_mse = flow.nn.MSELoss()
# Optimizer
g_params = list(self.generator_A2B.parameters()) + list(
self.generator_B2A.parameters())
d_params = list(self.discriminator_A.parameters()) + list(
self.discriminator_B.parameters())
# Initial learning rates
self.generator_lr = 2e-4
self.discriminator_lr = 1e-4
# Learning rate decay
self.generator_lr_decay = self.generator_lr / 200000
self.discriminator_lr_decay = self.discriminator_lr / 200000
# Starts learning rate decay from after this many iterations have passed
self.start_decay = 10000
self.generator_optimizer = flow.optim.Adam(g_params,
lr=self.generator_lr,
betas=(0.5, 0.999))
self.discriminator_optimizer = flow.optim.Adam(
d_params, lr=self.discriminator_lr, betas=(0.5, 0.999))
# To Load save previously saved models
self.modelCheckpoint = model_checkpoint
os.makedirs(self.modelCheckpoint, exist_ok=True)
# Validation set Parameters
self.validation_A_dir = validation_A_dir
self.output_A_dir = output_A_dir
os.makedirs(self.output_A_dir, exist_ok=True)
self.validation_B_dir = validation_B_dir
self.output_B_dir = output_B_dir
os.makedirs(self.output_B_dir, exist_ok=True)
# Storing Discriminatior and Generator Loss
self.generator_loss_store = []
self.discriminator_loss_store = []
self.file_name = "log_store_non_sigmoid.txt"
def adjust_lr_rate(self, optimizer, name="generator"):
if name == "generator":
self.generator_lr = max(
0.0, self.generator_lr - self.generator_lr_decay)
for param_groups in optimizer.param_groups:
param_groups["lr"] = self.generator_lr
else:
self.discriminator_lr = max(
0.0, self.discriminator_lr - self.discriminator_lr_decay)
for param_groups in optimizer.param_groups:
param_groups["lr"] = self.discriminator_lr
def reset_grad(self):
self.generator_optimizer.zero_grad()
self.discriminator_optimizer.zero_grad()
def train(self):
# Training Begins
for epoch in range(self.start_epoch, self.num_epochs):
start_time_epoch = time.time()
# Constants
cycle_loss_lambda = 10
identity_loss_lambda = 5
# Preparing Dataset
n_samples = len(self.dataset_A)
dataset = trainingDataset(datasetA=self.dataset_A,
datasetB=self.dataset_B,
n_frames=128)
train_loader = flow.utils.data.DataLoader(
dataset=dataset,
batch_size=self.mini_batch_size,
shuffle=True,
drop_last=False,
)
pbar = tqdm(enumerate(train_loader))
for i, (real_A, real_B) in enumerate(train_loader):
num_iterations = (n_samples //
self.mini_batch_size) * epoch + i
if num_iterations > 10000:
identity_loss_lambda = 0
if num_iterations > self.start_decay:
self.adjust_lr_rate(self.generator_optimizer,
name="generator")
self.adjust_lr_rate(self.generator_optimizer,
name="discriminator")
real_A = real_A.to(self.device).float()
real_B = real_B.to(self.device).float()
# Generator Loss function
fake_B = self.generator_A2B(real_A)
cycle_A = self.generator_B2A(fake_B)
fake_A = self.generator_B2A(real_B)
cycle_B = self.generator_A2B(fake_A)
identity_A = self.generator_B2A(real_A)
identity_B = self.generator_A2B(real_B)
d_fake_A = self.discriminator_A(fake_A)
d_fake_B = self.discriminator_B(fake_B)
# for the second step adverserial loss
d_fake_cycle_A = self.discriminator_A(cycle_A)
d_fake_cycle_B = self.discriminator_B(cycle_B)
# Generator Cycle loss
cycleLoss = flow.mean(flow.abs(real_A - cycle_A)) + flow.mean(
flow.abs(real_B - cycle_B))
# Generator Identity Loss
identiyLoss = flow.mean(
flow.abs(real_A - identity_A)) + flow.mean(
flow.abs(real_B - identity_B))
# Generator Loss
generator_loss_A2B = flow.mean((1 - d_fake_B)**2)
generator_loss_B2A = flow.mean((1 - d_fake_A)**2)
# Total Generator Loss
generator_loss = (generator_loss_A2B + generator_loss_B2A +
cycle_loss_lambda * cycleLoss +
identity_loss_lambda * identiyLoss)
self.generator_loss_store.append(generator_loss.item())
# Backprop for Generator
self.reset_grad()
generator_loss.backward()
self.generator_optimizer.step()
# Discriminator Feed Forward
d_real_A = self.discriminator_A(real_A)
d_real_B = self.discriminator_B(real_B)
generated_A = self.generator_B2A(real_B)
d_fake_A = self.discriminator_A(generated_A)
# for the second step adverserial loss
cycled_B = self.generator_A2B(generated_A)
d_cycled_B = self.discriminator_B(cycled_B)
generated_B = self.generator_A2B(real_A)
d_fake_B = self.discriminator_B(generated_B)
# for the second step adverserial loss
cycled_A = self.generator_B2A(generated_B)
d_cycled_A = self.discriminator_A(cycled_A)
# Loss Functions
d_loss_A_real = flow.mean((1 - d_real_A)**2)
d_loss_A_fake = flow.mean((0 - d_fake_A)**2)
d_loss_A = (d_loss_A_real + d_loss_A_fake) / 2.0
d_loss_B_real = flow.mean((1 - d_real_B)**2)
d_loss_B_fake = flow.mean((0 - d_fake_B)**2)
d_loss_B = (d_loss_B_real + d_loss_B_fake) / 2.0
# the second step adverserial loss
d_loss_A_cycled = flow.mean((0 - d_cycled_A)**2)
d_loss_B_cycled = flow.mean((0 - d_cycled_B)**2)
d_loss_A_2nd = (d_loss_A_real + d_loss_A_cycled) / 2.0
d_loss_B_2nd = (d_loss_B_real + d_loss_B_cycled) / 2.0
# Final Loss for discriminator with the second step adverserial loss
d_loss = (d_loss_A + d_loss_B) / 2.0 + (d_loss_A_2nd +
d_loss_B_2nd) / 2.0
self.discriminator_loss_store.append(d_loss.item())
# Backprop for Discriminator
self.reset_grad()
d_loss.backward()
self.discriminator_optimizer.step()
if (i + 1) % 2 == 0:
pbar.set_description(
"Iter:{} Generator Loss:{:.4f} Discrimator Loss:{:.4f} GA2B:{:.4f} GB2A:{:.4f} G_id:{:.4f} G_cyc:{:.4f} D_A:{:.4f} D_B:{:.4f}"
.format(
num_iterations,
generator_loss.item(),
d_loss.item(),
generator_loss_A2B,
generator_loss_B2A,
identiyLoss,
cycleLoss,
d_loss_A,
d_loss_B,
))
if epoch % 2000 == 0 and epoch != 0:
end_time = time.time()
store_to_file = "Epoch: {} Generator Loss: {:.4f} Discriminator Loss: {}, Time: {:.2f}\n\n".format(
epoch,
generator_loss.item(),
d_loss.item(),
end_time - start_time_epoch,
)
self.store_to_file(store_to_file)
print(
"Epoch: {} Generator Loss: {:.4f} Discriminator Loss: {}, Time: {:.2f}\n\n"
.format(
epoch,
generator_loss.item(),
d_loss.item(),
end_time - start_time_epoch,
))
# Save the Entire model
print("Saving model Checkpoint ......")
store_to_file = "Saving model Checkpoint ......"
self.store_to_file(store_to_file)
self.saveModelCheckPoint(epoch, self.modelCheckpoint)
print("Model Saved!")
if epoch % 2000 == 0 and epoch != 0:
# Validation Set
validation_start_time = time.time()
self.validation_for_A_dir()
self.validation_for_B_dir()
validation_end_time = time.time()
store_to_file = "Time taken for validation Set: {}".format(
validation_end_time - validation_start_time)
self.store_to_file(store_to_file)
print("Time taken for validation Set: {}".format(
validation_end_time - validation_start_time))
def infer(self, PATH="sample"):
num_mcep = 36
sampling_rate = 16000
frame_period = 5.0
n_frames = 128
infer_A_dir = PATH
output_A_dir = PATH
for file in os.listdir(infer_A_dir):
filePath = os.path.join(infer_A_dir, file)
wav, _ = librosa.load(filePath, sr=sampling_rate, mono=True)
wav = preprocess.wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = preprocess.world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = preprocess.pitch_conversion(
f0=f0,
mean_log_src=self.log_f0s_mean_A,
std_log_src=self.log_f0s_std_A,
mean_log_target=self.log_f0s_mean_B,
std_log_target=self.log_f0s_std_B,
)
coded_sp = preprocess.world_encode_spectral_envelop(
sp=sp, fs=sampling_rate, dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
self.coded_sps_A_mean) / self.coded_sps_A_std
coded_sp_norm = np.array([coded_sp_norm])
if flow.cuda.is_available():
coded_sp_norm = flow.tensor(coded_sp_norm).cuda().float()
else:
coded_sp_norm = flow.tensor(coded_sp_norm).float()
coded_sp_converted_norm = self.generator_A2B(coded_sp_norm)
coded_sp_converted_norm = coded_sp_converted_norm.cpu().detach(
).numpy()
coded_sp_converted_norm = np.squeeze(coded_sp_converted_norm)
coded_sp_converted = (
coded_sp_converted_norm * self.coded_sps_B_std +
self.coded_sps_B_mean)
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
decoded_sp_converted = preprocess.world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = preprocess.world_speech_synthesis(
f0=f0_converted,
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period,
)
sf.write(
os.path.join(output_A_dir,
"convert_" + os.path.basename(file)),
wav_transformed,
sampling_rate,
)
def validation_for_A_dir(self):
num_mcep = 36
sampling_rate = 16000
frame_period = 5.0
n_frames = 128
validation_A_dir = self.validation_A_dir
output_A_dir = self.output_A_dir
print("Generating Validation Data B from A...")
for file in os.listdir(validation_A_dir):
filePath = os.path.join(validation_A_dir, file)
wav, _ = librosa.load(filePath, sr=sampling_rate, mono=True)
wav = preprocess.wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = preprocess.world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = preprocess.pitch_conversion(
f0=f0,
mean_log_src=self.log_f0s_mean_A,
std_log_src=self.log_f0s_std_A,
mean_log_target=self.log_f0s_mean_B,
std_log_target=self.log_f0s_std_B,
)
coded_sp = preprocess.world_encode_spectral_envelop(
sp=sp, fs=sampling_rate, dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
self.coded_sps_A_mean) / self.coded_sps_A_std
coded_sp_norm = np.array([coded_sp_norm])
if flow.cuda.is_available():
coded_sp_norm = flow.tensor(coded_sp_norm).cuda().float()
else:
coded_sp_norm = flow.tensor(coded_sp_norm).float()
coded_sp_converted_norm = self.generator_A2B(coded_sp_norm)
coded_sp_converted_norm = coded_sp_converted_norm.cpu().detach(
).numpy()
coded_sp_converted_norm = np.squeeze(coded_sp_converted_norm)
coded_sp_converted = (
coded_sp_converted_norm * self.coded_sps_B_std +
self.coded_sps_B_mean)
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
decoded_sp_converted = preprocess.world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = preprocess.world_speech_synthesis(
f0=f0_converted,
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period,
)
sf.write(
os.path.join(output_A_dir, os.path.basename(file)),
wav_transformed,
sampling_rate,
)
def validation_for_B_dir(self):
num_mcep = 36
sampling_rate = 16000
frame_period = 5.0
n_frames = 128
validation_B_dir = self.validation_B_dir
output_B_dir = self.output_B_dir
print("Generating Validation Data A from B...")
for file in os.listdir(validation_B_dir):
filePath = os.path.join(validation_B_dir, file)
wav, _ = librosa.load(filePath, sr=sampling_rate, mono=True)
wav = preprocess.wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = preprocess.world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = preprocess.pitch_conversion(
f0=f0,
mean_log_src=self.log_f0s_mean_B,
std_log_src=self.log_f0s_std_B,
mean_log_target=self.log_f0s_mean_A,
std_log_target=self.log_f0s_std_A,
)
coded_sp = preprocess.world_encode_spectral_envelop(
sp=sp, fs=sampling_rate, dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
self.coded_sps_B_mean) / self.coded_sps_B_std
coded_sp_norm = np.array([coded_sp_norm])
if flow.cuda.is_available():
coded_sp_norm = flow.tensor(coded_sp_norm).cuda().float()
else:
coded_sp_norm = flow.tensor(coded_sp_norm).float()
coded_sp_converted_norm = self.generator_B2A(coded_sp_norm)
coded_sp_converted_norm = coded_sp_converted_norm.cpu().detach(
).numpy()
coded_sp_converted_norm = np.squeeze(coded_sp_converted_norm)
coded_sp_converted = (
coded_sp_converted_norm * self.coded_sps_A_std +
self.coded_sps_A_mean)
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
decoded_sp_converted = preprocess.world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = preprocess.world_speech_synthesis(
f0=f0_converted,
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period,
)
sf.write(
os.path.join(output_B_dir, os.path.basename(file)),
wav_transformed,
sampling_rate,
)
def savePickle(self, variable, fileName):
with open(fileName, "wb") as f:
pickle.dump(variable, f)
def loadPickleFile(self, fileName):
with open(fileName, "rb") as f:
return pickle.load(f)
def store_to_file(self, doc):
doc = doc + "\n"
with open(self.file_name, "a") as myfile:
myfile.write(doc)
def saveModelCheckPoint(self, epoch, PATH):
flow.save(
self.generator_A2B.state_dict(),
os.path.join(PATH, "generator_A2B_%d" % epoch),
)
flow.save(
self.generator_B2A.state_dict(),
os.path.join(PATH, "generator_B2A_%d" % epoch),
)
flow.save(
self.discriminator_A.state_dict(),
os.path.join(PATH, "discriminator_A_%d" % epoch),
)
flow.save(
self.discriminator_B.state_dict(),
os.path.join(PATH, "discriminator_B_%d" % epoch),
)
def loadModel(self, PATH):
self.generator_A2B.load_state_dict(
flow.load(os.path.join(PATH, "generator_A2B")))
self.generator_B2A.load_state_dict(
flow.load(os.path.join(PATH, "generator_B2A")))
self.discriminator_A.load_state_dict(
flow.load(os.path.join(PATH, "discriminator_A")))
self.discriminator_B.load_state_dict(
flow.load(os.path.join(PATH, "discriminator_B")))
val_set = ValDatasetFromFolder('data/DIV2K_valid_HR',
crop_size=CROP_SIZE,
upscale_factor=UPSCALE_FACTOR)
train_loader = DataLoader(dataset=train_set,
num_workers=4,
batch_size=4,
shuffle=True)
val_loader = DataLoader(dataset=val_set,
num_workers=4,
batch_size=1,
shuffle=False)
netG = Generator(num_rrdb_blocks=16, scaling_factor=UPSCALE_FACTOR)
print('# generator parameters:',
sum(param.numel() for param in netG.parameters()))
netD = Discriminator(opt.crop_size)
print('# discriminator parameters:',
sum(param.numel() for param in netD.parameters()))
netD_HF = Discriminator(image_size=dwt_size)
generator_criterion = EGeneratorLoss()
discriminator_criterion = torch.nn.BCEWithLogitsLoss()
if torch.cuda.is_available():
netG.cuda()
netD.cuda()
netD_HF.cuda()
generator_criterion.cuda()
discriminator_criterion.cuda()
optimizerG = optim.Adam(netG.parameters(), lr=0.0001)
def __init__(
self,
logf0s_normalization,
mcep_normalization,
coded_sps_A_norm,
coded_sps_B_norm,
model_checkpoint,
validation_A_dir,
output_A_dir,
validation_B_dir,
output_B_dir,
restart_training_at=None,
):
self.start_epoch = 0
self.num_epochs = 200000
self.mini_batch_size = 10
self.dataset_A = self.loadPickleFile(coded_sps_A_norm)
self.dataset_B = self.loadPickleFile(coded_sps_B_norm)
self.device = flow.device(
"cuda" if flow.cuda.is_available() else "cpu")
# Speech Parameters
logf0s_normalization = np.load(logf0s_normalization)
self.log_f0s_mean_A = logf0s_normalization["mean_A"]
self.log_f0s_std_A = logf0s_normalization["std_A"]
self.log_f0s_mean_B = logf0s_normalization["mean_B"]
self.log_f0s_std_B = logf0s_normalization["std_B"]
mcep_normalization = np.load(mcep_normalization)
self.coded_sps_A_mean = mcep_normalization["mean_A"]
self.coded_sps_A_std = mcep_normalization["std_A"]
self.coded_sps_B_mean = mcep_normalization["mean_B"]
self.coded_sps_B_std = mcep_normalization["std_B"]
# Generator and Discriminator
self.generator_A2B = Generator().to(self.device)
self.generator_B2A = Generator().to(self.device)
self.discriminator_A = Discriminator().to(self.device)
self.discriminator_B = Discriminator().to(self.device)
# Loss Functions
criterion_mse = flow.nn.MSELoss()
# Optimizer
g_params = list(self.generator_A2B.parameters()) + list(
self.generator_B2A.parameters())
d_params = list(self.discriminator_A.parameters()) + list(
self.discriminator_B.parameters())
# Initial learning rates
self.generator_lr = 2e-4
self.discriminator_lr = 1e-4
# Learning rate decay
self.generator_lr_decay = self.generator_lr / 200000
self.discriminator_lr_decay = self.discriminator_lr / 200000
# Starts learning rate decay from after this many iterations have passed
self.start_decay = 10000
self.generator_optimizer = flow.optim.Adam(g_params,
lr=self.generator_lr,
betas=(0.5, 0.999))
self.discriminator_optimizer = flow.optim.Adam(
d_params, lr=self.discriminator_lr, betas=(0.5, 0.999))
# To Load save previously saved models
self.modelCheckpoint = model_checkpoint
os.makedirs(self.modelCheckpoint, exist_ok=True)
# Validation set Parameters
self.validation_A_dir = validation_A_dir
self.output_A_dir = output_A_dir
os.makedirs(self.output_A_dir, exist_ok=True)
self.validation_B_dir = validation_B_dir
self.output_B_dir = output_B_dir
os.makedirs(self.output_B_dir, exist_ok=True)
# Storing Discriminatior and Generator Loss
self.generator_loss_store = []
self.discriminator_loss_store = []
self.file_name = "log_store_non_sigmoid.txt"
