📝 Prevent irrelevant docstrings

Author: francois-rozet

spiq/gmsd.py

@@ -87,6 +87,7 @@ class GMSD(nn.Module):
     """
 
     def __init__(self, reduction: str = 'mean', **kwargs):
+        r""""""
         super().__init__()
 
         self.reduce = build_reduce(reduction)
@@ -97,6 +98,9 @@ def forward(
         input: torch.Tensor,
         target: torch.Tensor,
     ) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+        """
+
         l = gmsd(input, target, **self.kwargs)
 
         return self.reduce(l)

spiq/lpips.py

@@ -48,6 +48,7 @@ def __init__(
         reduction: str = 'mean',
         trainable: bool = False,
     ):
+        r""""""
         super().__init__()
 
         # ImageNet scaling
@@ -97,6 +98,9 @@ def forward(
         input: torch.Tensor,
         target: torch.Tensor,
     ) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+        """
+
         if self.scaling:
             input = (input - self.shift) / self.scale
             target = (target - self.shift) / self.scale

spiq/mdsi.py

@@ -125,6 +125,7 @@ class MDSI(nn.Module):
     """
 
     def __init__(self, reduction: str = 'mean', **kwargs):
+        r""""""
         super().__init__()
 
         self.reduce = build_reduce(reduction)
@@ -135,6 +136,9 @@ def forward(
         input: torch.Tensor,
         target: torch.Tensor,
     ) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+        """
+
         l = mdsi(input, target, **self.kwargs)
 
         return self.reduce(l)

spiq/psnr.py

@@ -55,6 +55,7 @@ class PSNR(nn.Module):
     """
 
     def __init__(self, reduction: str = 'mean', **kwargs):
+        r""""""
         super().__init__()
 
         self.reduce = build_reduce(reduction)
@@ -68,6 +69,9 @@ def forward(
         input: torch.Tensor,
         target: torch.Tensor,
     ) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+        """
+
         l = psnr(
             input.unsqueeze(1).flatten(1),
             target.unsqueeze(1).flatten(1),

spiq/ssim.py

@@ -198,6 +198,7 @@ def __init__(
         reduction: str = 'mean',
         **kwargs,
     ):
+        r""""""
         super().__init__()
 
         self.register_buffer('window', create_window(window_size, n_channels))
@@ -210,6 +211,9 @@ def forward(
         input: torch.Tensor,
         target: torch.Tensor,
     ) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+        """
+
         l = ssim_per_channel(
             input,
             target,
@@ -240,6 +244,9 @@ def forward(
         input: torch.Tensor,
         target: torch.Tensor,
     ) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+        """
+
         l = msssim_per_channel(
             input,
             target,

spiq/tv.py

@@ -46,12 +46,16 @@ class TV(nn.Module):
     """
 
     def __init__(self, reduction: str = 'mean', **kwargs):
+        r""""""
         super().__init__()
 
         self.reduce = build_reduce(reduction)
         self.kwargs = kwargs
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+        """
+
         l = tv(input, **self.kwargs)
 
         return self.reduce(l)

spiq/utils.py

@@ -180,12 +180,16 @@ class Intermediary(nn.Module):
     """
 
     def __init__(self, layers: nn.Sequential, targets: List[int]):
+        r""""""
         super().__init__()
 
         self.layers = layers
         self.targets = set(targets)
 
     def forward(self, input: torch.Tensor) -> List[torch.Tensor]:
+        r"""Defines the computation performed at every call.
+        """
+
         output = []
 
         for i, layer in enumerate(self.layers):