📝 Adopting Google Python style

See https://google.github.io/styleguide/pyguide.html

.style.yapf

@@ -0,0 +1,10 @@
+[style]
+based_on_style = google
+
+spaces_around_power_operator = true
+
+dedent_closing_brackets = true
+coalesce_brackets = true
+split_arguments_when_comma_terminated = true
+split_all_top_level_comma_separated_values = true
+split_complex_comprehension = true

README.md

@@ -47,3 +47,5 @@ The [documentation](https://francois-rozet.github.io/spiq/) of this package is g
 ```bash
 pdoc spiq --html --config "git_link_template='https://github.com/francois-rozet/spiq/blob/{commit}/{path}#L{start_line}-L{end_line}'"
 ```
+
+> The code follows the [Google Python style](https://google.github.io/styleguide/pyguide.html) and is compliant with [YAPF](https://github.com/google/yapf).

spiq/__init__.py

@@ -1,6 +1,8 @@
 r"""Simple PyTorch Image Quality
 
-The spiq package is divided in several submodules, each of which implements the functions and/or classes related to a specific image quality metric.
+The spiq package is divided in several submodules, each of
+which implements the functions and/or classes related to a
+specific image quality metric.
 """
 
 __version__ = '0.0.2'

spiq/lpips.py

@@ -11,10 +11,6 @@
     https://arxiv.org/abs/1801.03924
 """
 
-###########
-# Imports #
-###########
-
 import inspect
 import os
 import torch
@@ -23,32 +19,30 @@
 
 from spiq.utils import normalize_tensor, Intermediary
 
-
-#############
-# Constants #
-#############
-
 _SHIFT = torch.Tensor([0.485, 0.456, 0.406])
 _SCALE = torch.Tensor([0.229, 0.224, 0.225])
 
 
-###########
-# Classes #
-###########
-
 class LPIPS(nn.Module):
-    r"""Creates a criterion that measures the LPIPS between an input and a target.
+    r"""Creates a criterion that measures the LPIPS
+    between an input and a target.
 
     Args:
-        network: perception network name (`'AlexNet'`, `'SqueezeNet'` or `'VGG16'`)
-        scaling: whether the input and target are sclaed w.r.t. ImageNet
-        reduction: reduction type (`'mean'`, `'sum'` or `'none'`)
+        network: A perception network name (`'AlexNet'`,
+            `'SqueezeNet'` or `'VGG16'`).
+        scaling: Whether the input and target are scaled w.r.t. ImageNet.
+        reduction: A reduction type (`'mean'`, `'sum'` or `'none'`).
 
     Call:
         The input and target tensors should be of shape (N, C, H, W).
     """
 
-    def __init__(self, network: str = 'AlexNet', scaling: bool = False, reduction: str = 'mean'):
+    def __init__(
+        self,
+        network: str = 'AlexNet',
+        scaling: bool = False,
+        reduction: str = 'mean',
+    ):
         super().__init__()
 
         # ImageNet scaling
@@ -93,14 +87,18 @@ def __init__(self, network: str = 'AlexNet', scaling: bool = False, reduction: s
 
         self.reduction = reduction
 
-    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    def forward(
+        self,
+        input: torch.Tensor,
+        target: torch.Tensor,
+    ) -> torch.Tensor:
         if self.scaling:
             input = (input - self.shift) / self.scale
             target = (target - self.shift) / self.scale
 
         residuals = []
 
-        for loss, (fx, fy) in zip(self.lin, zip(self.net(input), self.net(target))):
+        for loss, fx, fy in zip(self.lin, self.net(input), self.net(target)):
             fx = normalize_tensor(fx, dim=1, norm='L2')
             fy = normalize_tensor(fy, dim=1, norm='L2')
 

spiq/psnr.py

@@ -6,46 +6,42 @@
     https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
 """
 
-###########
-# Imports #
-###########
-
 import torch
 import torch.nn as nn
 
 from typing import Tuple
 
 
-#############
-# Functions #
-#############
-
-def psnr(x: torch.Tensor, y: torch.Tensor, dim: Tuple[int, ...] = (), keepdim: bool = False, value_range: float = 1., epsilon: float = 1e-8) -> torch.Tensor:
+def psnr(
+    x: torch.Tensor,
+    y: torch.Tensor,
+    dim: Tuple[int, ...] = (),
+    keepdim: bool = False,
+    value_range: float = 1.,
+    epsilon: float = 1e-8,
+) -> torch.Tensor:
     r"""Returns the PSNR between `x` and `y`.
 
     Args:
-        x: input tensor
-        y: target tensor
-        dim: dimension(s) along which to average
-        keepdim: whether the output tensor has `dim` retained or not
-        value_range: value range of the inputs (usually 1. or 255)
-        epsilon: numerical stability term
+        x: An input tensor.
+        y: A target tensor.
+        dim: The dimension(s) along which to average.
+        keepdim: Whether the output tensor has `dim` retained or not.
+        value_range: The value range of the inputs (usually 1. or 255).
+        epsilon: A numerical stability term.
     """
 
     mse = ((x - y) ** 2).mean(dim=dim, keepdim=keepdim) + epsilon
     return 10 * torch.log10(value_range ** 2 / mse)
 
 
-###########
-# Classes #
-###########
-
 class PSNR(nn.Module):
-    r"""Creates a criterion that measures the PSNR between an input and a target.
+    r"""Creates a criterion that measures the PSNR
+    between an input and a target.
 
     Args:
-        value_range: value range of the inputs (usually 1. or 255)
-        reduction: reduction type (`'mean'`, `'sum'` or `'none'`)
+        value_range: The value range of the inputs (usually 1. or 255).
+        reduction: A reduction type (`'mean'`, `'sum'` or `'none'`).
 
     Call:
         The input and target tensors should be of shape (N, ...).
@@ -57,11 +53,16 @@ def __init__(self, value_range: float = 1., reduction: str = 'mean'):
         self.value_range = value_range
         self.reduction = reduction
 
-    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    def forward(
+        self,
+        input: torch.Tensor,
+        target: torch.Tensor,
+    ) -> torch.Tensor:
         l = psnr(
-            input, target,
+            input,
+            target,
             dim=tuple(range(1, input.ndimension())),
-            value_range=self.value_range
+            value_range=self.value_range,
         )
 
         if self.reduction == 'mean':