From 6f4c1108f07ead33bf6da21675fdb2765e0fc133 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 3 Jun 2020 12:33:53 +0200
Subject: [PATCH] suppress pylint no-self-use for loss layers
---
vocoder/layers/losses.py | 6 ++++++
1 file changed, 6 insertions(+)
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index fb4e85d4..22e2fb54 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -77,6 +77,7 @@ class MultiScaleSTFTLoss(torch.nn.Module):
class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
""" Multiscale STFT loss for multi band model outputs """
+ # pylint: disable=no-self-use
def forward(self, y_hat, y):
y_hat = y_hat.view(-1, 1, y_hat.shape[2])
y = y.view(-1, 1, y.shape[2])
@@ -85,6 +86,7 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
class MSEGLoss(nn.Module):
""" Mean Squared Generator Loss """
+ # pylint: disable=no-self-use
def forward(self, score_fake):
loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
return loss_fake
@@ -92,6 +94,7 @@ class MSEGLoss(nn.Module):
class HingeGLoss(nn.Module):
""" Hinge Discriminator Loss """
+ # pylint: disable=no-self-use
def forward(self, score_fake):
loss_fake = torch.mean(F.relu(1. + score_fake))
return loss_fake
@@ -104,6 +107,7 @@ class HingeGLoss(nn.Module):
class MSEDLoss(nn.Module):
""" Mean Squared Discriminator Loss """
+ # pylint: disable=no-self-use
def forward(self, score_fake, score_real):
loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
@@ -113,6 +117,7 @@ class MSEDLoss(nn.Module):
class HingeDLoss(nn.Module):
""" Hinge Discriminator Loss """
+ # pylint: disable=no-self-use
def forward(self, score_fake, score_real):
loss_real = torch.mean(F.relu(1. - score_real))
loss_fake = torch.mean(F.relu(1. + score_fake))
@@ -121,6 +126,7 @@ class HingeDLoss(nn.Module):
class MelganFeatureLoss(nn.Module):
+ # pylint: disable=no-self-use
def forward(self, fake_feats, real_feats):
loss_feats = 0
for fake_feat, real_feat in zip(fake_feats, real_feats):