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Fix GAN optimizer order 

commit 212d330929
Author: Edresson Casanova <edresson1@gmail.com>
Date:   Fri Apr 29 16:29:44 2022 -0300

    Fix unit test

commit 44456b0483
Author: Edresson Casanova <edresson1@gmail.com>
Date:   Fri Apr 29 07:28:39 2022 -0300

    Fix style

commit d545beadb9
Author: Edresson Casanova <edresson1@gmail.com>
Date:   Thu Apr 28 17:08:04 2022 -0300

    Change order of HIFI-GAN optimizers to be equal than the original repository

commit 657c5442e5
Author: Edresson Casanova <edresson1@gmail.com>
Date:   Thu Apr 28 15:40:16 2022 -0300

    Remove audio padding before mel spec extraction

commit 76b274e690
Merge: 379ccd7b 6233f4fc
Author: Edresson Casanova <edresson1@gmail.com>
Date:   Wed Apr 27 07:28:48 2022 -0300

    Merge pull request #1541 from coqui-ai/comp_emb_fix

    Bug fix in compute embedding without eval partition

commit 379ccd7ba6
Author: WeberJulian <julian.weber@hotmail.fr>
Date:   Wed Apr 27 10:42:26 2022 +0200

    returns y_mask in VITS inference (#1540)

    * returns y_mask

    * make style
This commit is contained in:
Eren Gölge 2022-05-07 13:28:22 +02:00
parent 60034674f9
commit a0a9279e4b
1 changed files with 46 additions and 39 deletions
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85
TTS/vocoder/models/gan.py
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@ -90,50 +90,26 @@ class GAN(BaseVocoder):
raise ValueError(" [!] Unexpected `optimizer_idx`.") raise ValueError(" [!] Unexpected `optimizer_idx`.")
if optimizer_idx == 0: if optimizer_idx == 0:
# GENERATOR # DISCRIMINATOR optimization
# generator pass # generator pass
y_hat = self.model_g(x)[:, :, : y.size(2)] y_hat = self.model_g(x)[:, :, : y.size(2)]
self.y_hat_g = y_hat # save for discriminator
y_hat_sub = None # cache for generator loss
y_sub = None # pylint: disable=W0201
self.y_hat_g = y_hat
self.y_hat_sub = None
self.y_sub_g = None
# PQMF formatting # PQMF formatting
if y_hat.shape[1] > 1: if y_hat.shape[1] > 1:
y_hat_sub = y_hat self.y_hat_sub = y_hat
y_hat = self.model_g.pqmf_synthesis(y_hat) y_hat = self.model_g.pqmf_synthesis(y_hat)
self.y_hat_g = y_hat # save for discriminator self.y_hat_g = y_hat # save for generator loss
y_sub = self.model_g.pqmf_analysis(y) self.y_sub_g = self.model_g.pqmf_analysis(y)
scores_fake, feats_fake, feats_real = None, None, None scores_fake, feats_fake, feats_real = None, None, None
if self.train_disc:
if len(signature(self.model_d.forward).parameters) == 2:
D_out_fake = self.model_d(y_hat, x)
else:
D_out_fake = self.model_d(y_hat)
D_out_real = None
if self.config.use_feat_match_loss:
with torch.no_grad():
D_out_real = self.model_d(y)
# format D outputs
if isinstance(D_out_fake, tuple):
scores_fake, feats_fake = D_out_fake
if D_out_real is None:
feats_real = None
else:
_, feats_real = D_out_real
else:
scores_fake = D_out_fake
feats_fake, feats_real = None, None
# compute losses
loss_dict = criterion[optimizer_idx](y_hat, y, scores_fake, feats_fake, feats_real, y_hat_sub, y_sub)
outputs = {"model_outputs": y_hat}
if optimizer_idx == 1:
# DISCRIMINATOR
if self.train_disc: if self.train_disc:
# use different samples for G and D trainings # use different samples for G and D trainings
if self.config.diff_samples_for_G_and_D: if self.config.diff_samples_for_G_and_D:
@ -177,6 +153,36 @@ class GAN(BaseVocoder):
loss_dict = criterion[optimizer_idx](scores_fake, scores_real) loss_dict = criterion[optimizer_idx](scores_fake, scores_real)
outputs = {"model_outputs": y_hat} outputs = {"model_outputs": y_hat}
if optimizer_idx == 1:
# GENERATOR loss
scores_fake, feats_fake, feats_real = None, None, None
if self.train_disc:
if len(signature(self.model_d.forward).parameters) == 2:
D_out_fake = self.model_d(self.y_hat_g, x)
else:
D_out_fake = self.model_d(self.y_hat_g)
D_out_real = None
if self.config.use_feat_match_loss:
with torch.no_grad():
D_out_real = self.model_d(y)
# format D outputs
if isinstance(D_out_fake, tuple):
scores_fake, feats_fake = D_out_fake
if D_out_real is None:
feats_real = None
else:
_, feats_real = D_out_real
else:
scores_fake = D_out_fake
feats_fake, feats_real = None, None
# compute losses
loss_dict = criterion[optimizer_idx](
self.y_hat_g, y, scores_fake, feats_fake, feats_real, self.y_hat_sub, self.y_sub_g
)
outputs = {"model_outputs": self.y_hat_g}
return outputs, loss_dict return outputs, loss_dict
@staticmethod @staticmethod
@ -210,6 +216,7 @@ class GAN(BaseVocoder):
@torch.no_grad() @torch.no_grad()
def eval_step(self, batch: Dict, criterion: nn.Module, optimizer_idx: int) -> Tuple[Dict, Dict]: def eval_step(self, batch: Dict, criterion: nn.Module, optimizer_idx: int) -> Tuple[Dict, Dict]:
"""Call `train_step()` with `no_grad()`""" """Call `train_step()` with `no_grad()`"""
self.train_disc = True # Avoid a bug in the Training with the missing discriminator loss
return self.train_step(batch, criterion, optimizer_idx) return self.train_step(batch, criterion, optimizer_idx)
def eval_log( def eval_log(
@ -266,7 +273,7 @@ class GAN(BaseVocoder):
optimizer2 = get_optimizer( optimizer2 = get_optimizer(
self.config.optimizer, self.config.optimizer_params, self.config.lr_disc, self.model_d self.config.optimizer, self.config.optimizer_params, self.config.lr_disc, self.model_d
) )
return [optimizer1, optimizer2] return [optimizer2, optimizer1]
def get_lr(self) -> List: def get_lr(self) -> List:
"""Set the initial learning rates for each optimizer. """Set the initial learning rates for each optimizer.
@ -274,7 +281,7 @@ class GAN(BaseVocoder):
Returns: Returns:
List: learning rates for each optimizer. List: learning rates for each optimizer.
""" """
return [self.config.lr_gen, self.config.lr_disc] return [self.config.lr_disc, self.config.lr_gen]
def get_scheduler(self, optimizer) -> List: def get_scheduler(self, optimizer) -> List:
"""Set the schedulers for each optimizer. """Set the schedulers for each optimizer.
@ -287,7 +294,7 @@ class GAN(BaseVocoder):
""" """
scheduler1 = get_scheduler(self.config.lr_scheduler_gen, self.config.lr_scheduler_gen_params, optimizer[0]) scheduler1 = get_scheduler(self.config.lr_scheduler_gen, self.config.lr_scheduler_gen_params, optimizer[0])
scheduler2 = get_scheduler(self.config.lr_scheduler_disc, self.config.lr_scheduler_disc_params, optimizer[1]) scheduler2 = get_scheduler(self.config.lr_scheduler_disc, self.config.lr_scheduler_disc_params, optimizer[1])
return [scheduler1, scheduler2] return [scheduler2, scheduler1]
@staticmethod @staticmethod
def format_batch(batch: List) -> Dict: def format_batch(batch: List) -> Dict:
@ -359,7 +366,7 @@ class GAN(BaseVocoder):
def get_criterion(self): def get_criterion(self):
"""Return criterions for the optimizers""" """Return criterions for the optimizers"""
return [GeneratorLoss(self.config), DiscriminatorLoss(self.config)] return [DiscriminatorLoss(self.config), GeneratorLoss(self.config)]
@staticmethod @staticmethod
def init_from_config(config: Coqpit, verbose=True) -> "GAN": def init_from_config(config: Coqpit, verbose=True) -> "GAN":