Make lint

TTS/tts/layers/losses.py

@@ -133,11 +133,11 @@ class SSIMLoss(torch.nn.Module):
 
         if ssim_loss.item() > 1.0:
             print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0")
-            ssim_loss == 1.0
+            ssim_loss = torch.tensor([1.0])
 
         if ssim_loss.item() < 0.0:
             print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0")
-            ssim_loss == 0.0
+            ssim_loss =  torch.tensor([0.0])
 
         return ssim_loss
 

TTS/tts/utils/ssim.py

@@ -20,8 +20,7 @@ def _reduce(x: torch.Tensor, reduction: str = "mean") -> torch.Tensor:
         return x.mean(dim=0)
     elif reduction == "sum":
         return x.sum(dim=0)
-    else:
-        raise ValueError("Unknown reduction. Expected one of {'none', 'mean', 'sum'}")
+    raise ValueError("Unknown reduction. Expected one of {'none', 'mean', 'sum'}")
 
 
 def _validate_input(
@@ -140,7 +139,7 @@ def ssim(
 
     kernel = gaussian_filter(kernel_size, kernel_sigma).repeat(x.size(1), 1, 1, 1).to(y)
     _compute_ssim_per_channel = _ssim_per_channel_complex if x.dim() == 5 else _ssim_per_channel
-    ssim_map, cs_map = _compute_ssim_per_channel(x=x, y=y, kernel=kernel, data_range=data_range, k1=k1, k2=k2)
+    ssim_map, cs_map = _compute_ssim_per_channel(x=x, y=y, kernel=kernel, k1=k1, k2=k2)
     ssim_val = ssim_map.mean(1)
     cs = cs_map.mean(1)
 
@@ -268,7 +267,6 @@ def _ssim_per_channel(
     x: torch.Tensor,
     y: torch.Tensor,
     kernel: torch.Tensor,
-    data_range: Union[float, int] = 1.0,
     k1: float = 0.01,
     k2: float = 0.03,
 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
@@ -278,7 +276,6 @@ def _ssim_per_channel(
         x: An input tensor. Shape :math:`(N, C, H, W)`.
         y: A target tensor. Shape :math:`(N, C, H, W)`.
         kernel: 2D Gaussian kernel.
-        data_range: Maximum value range of images (usually 1.0 or 255).
         k1: Algorithm parameter, K1 (small constant, see [1]).
         k2: Algorithm parameter, K2 (small constant, see [1]).
             Try a larger K2 constant (e.g. 0.4) if you get a negative or NaN results.
@@ -321,7 +318,6 @@ def _ssim_per_channel_complex(
     x: torch.Tensor,
     y: torch.Tensor,
     kernel: torch.Tensor,
-    data_range: Union[float, int] = 1.0,
     k1: float = 0.01,
     k2: float = 0.03,
 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
@@ -331,7 +327,6 @@ def _ssim_per_channel_complex(
         x: An input tensor. Shape :math:`(N, C, H, W, 2)`.
         y: A target tensor. Shape :math:`(N, C, H, W, 2)`.
         kernel: 2-D gauss kernel.
-        data_range: Maximum value range of images (usually 1.0 or 255).
         k1: Algorithm parameter, K1 (small constant, see [1]).
         k2: Algorithm parameter, K2 (small constant, see [1]).
             Try a larger K2 constant (e.g. 0.4) if you get a negative or NaN results.

tests/tts_tests/test_losses.py

@@ -1,7 +1,6 @@
 import unittest
 
 import torch as T
-from torch.nn import functional
 
 from TTS.tts.layers.losses import BCELossMasked, L1LossMasked, MSELossMasked, SSIMLoss
 from TTS.tts.utils.helpers import sequence_mask
@@ -208,8 +207,6 @@ class BCELossTest(unittest.TestCase):
         layer = BCELossMasked(pos_weight=5.0)
 
         length = T.tensor([95])
-        mask = sequence_mask(length, 100)
-        pos_weight = T.tensor([5.0])
         target = (
             1.0 - sequence_mask(length - 1, 100).float()
         )  # [0, 0, .... 1, 1] where the first 1 is the last mel frame
@@ -236,6 +233,7 @@ class BCELossTest(unittest.TestCase):
         self.assertEqual(loss.item(), 0.0)
 
         # when pos_weight < 1 overweight the early stopping loss
+
         loss_early = layer(early_x, target, length)
         loss_late = layer(late_x, target, length)
         self.assertGreater(loss_early.item(), loss_late.item())