Minor refactoring.

trainer/common.py

@@ -72,15 +72,6 @@ def forward(self, x, target, lens):
         return loss / mask.sum()
 
 
-class LogL1(torch.nn.Module):
-
-    def forward(self, x, target):
-        target.requires_grad = False
-        target = torch.log(target + 1.0)
-        x = torch.log(x + 1.0)
-        return F.l1_loss(x, target)
-
-
 # Adapted from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
 def pad_mask(lens, max_len):
     batch_size = lens.size(0)

trainer/forward_trainer.py

@@ -9,7 +9,7 @@
 from torch.utils.tensorboard import SummaryWriter
 
 from models.forward_tacotron import ForwardTacotron
-from trainer.common import Averager, TTSSession, MaskedL1, LogL1
+from trainer.common import Averager, TTSSession, MaskedL1
 from utils import hparams as hp
 from utils.checkpoints import save_checkpoint
 from utils.dataset import get_tts_datasets
@@ -25,7 +25,6 @@ def __init__(self, paths: Paths) -> None:
         self.paths = paths
         self.writer = SummaryWriter(log_dir=paths.forward_log, comment='v1')
         self.l1_loss = MaskedL1()
-        self.log_l1_loss = LogL1()
 
     def train(self, model: ForwardTacotron, optimizer: Optimizer) -> None:
         for i, session_params in enumerate(hp.forward_schedule, 1):