📦 Rename package to piqa and deploy on PyPI

Author: francois-rozet

README.md

@@ -1,33 +1,41 @@
-# Simple PyTorch Image Quality
+# PyTorch Image Quality Assessment
 
 This package is a collection of measures and metrics for image quality assessment in various image processing tasks such as denoising, super-resolution, image interpolation, etc. It relies heavily on [PyTorch](https://github.com/pytorch/pytorch) and takes advantage of its efficiency and automatic differentiation.
 
-It should noted that `spiq` is directly inspired from the [`piq`](https://github.com/photosynthesis-team/piq) project. However, it focuses on the conciseness, readability and understandability of its (sub-)modules, such that anyone can freely and easily reuse and/or adapt them to its needs.
+It should noted that `piqa` is directly inspired from the [`piq`](https://github.com/photosynthesis-team/piq) project. However, it focuses on the conciseness, readability and understandability of its (sub-)modules, such that anyone can freely and easily reuse and/or adapt them to its needs.
+
+> `piqa` should be pronounced *pika* (like Pikachu ⚡️)
 
 ## Installation
 
-To install the current version of `spiq`,
+The `piqa` package is available on [PyPI](https://pypi.org/project/piqa/), which means it is installable with `pip`:
+
+```bash
+pip install piqa
+```
+
+Alternatively, if you need the lastest features, you can install it using
 
 ```bash
-git clone https://github.com/francois-rozet/spiq
-cd spiq
+git clone https://github.com/francois-rozet/piqa
+cd piqa
 python setup.py install
 ```
 
-You can also copy the package directly to your project.
+or copy the package directly to your project, with
 
 ```bash
-git clone https://github.com/francois-rozet/spiq
-cd spiq
-cp -R spiq <path/to/project>/spiq
+git clone https://github.com/francois-rozet/piqa
+cd piqa
+cp -R piqa <path/to/project>/piqa
 ```
 
 ## Getting started
 
 ```python
 import torch
-import spiq.psnr as psnr
-import spiq.ssim as ssim
+import piqa.psnr as psnr
+import piqa.ssim as ssim
 
 x = torch.rand(3, 3, 256, 256)
 y = torch.rand(3, 3, 256, 256)
@@ -42,6 +50,6 @@ l = criterion(x, y)
 
 ## Documentation
 
-The [documentation](https://francois-rozet.github.io/spiq/) of this package is generated automatically using [`pdoc`](https://github.com/pdoc3/pdoc).
+The [documentation](https://francois-rozet.github.io/piqa/) of this package is generated automatically using [`pdoc`](https://github.com/pdoc3/pdoc).
 
 > The code follows the [Google Python style](https://google.github.io/styleguide/pyguide.html) and is compliant with [YAPF](https://github.com/google/yapf).

docs.sh

@@ -1,12 +1,12 @@
 # Env
-source ~/spiq/bin/activate
+source ~/piqa/bin/activate
 
 # Master
 git checkout master
 
 # Generate HTML
-pdoc spiq --html --force \
-	--config "git_link_template='https://github.com/francois-rozet/spiq/blob/{commit}/{path}#L{start_line}-L{end_line}'" \
+pdoc piqa --html --force \
+	--config "git_link_template='https://github.com/francois-rozet/piqa/blob/{commit}/{path}#L{start_line}-L{end_line}'" \
 	--config "show_inherited_members=True" \
 	--config "latex_math=True"
 
@@ -15,7 +15,7 @@ git checkout docs
 git reset --hard master
 
 mkdir docs -p
-mv html/spiq/* docs/
+mv html/piqa/* docs/
 rm -rf html
 
 git add docs/*

piqa/__init__.py

@@ -0,0 +1,8 @@
+r"""PyTorch Image Quality Assessement
+
+The piqa package is divided in several submodules, each of
+which implements the functions and/or classes related to a
+specific image quality assessement metric.
+"""
+
+__version__ = '1.0.0'

spiq/gmsd.py piqa/gmsd.pyspiq/gmsd.py renamed to piqa/gmsd.py

@@ -13,7 +13,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from spiq.utils import build_reduce, prewitt_kernel, gradient2d, tensor_norm
+from piqa.utils import build_reduce, prewitt_kernel, gradient2d, tensor_norm
 
 _L_WEIGHTS = torch.FloatTensor([0.2989, 0.587, 0.114])
 

spiq/lpips.py piqa/lpips.pyspiq/lpips.py renamed to piqa/lpips.py

@@ -17,7 +17,7 @@
 import torch.nn as nn
 import torchvision.models as models
 
-from spiq.utils import build_reduce, normalize_tensor, Intermediary
+from piqa.utils import build_reduce, normalize_tensor, Intermediary
 
 _SHIFT = torch.Tensor([0.485, 0.456, 0.406])
 _SCALE = torch.Tensor([0.229, 0.224, 0.225])

spiq/mdsi.py piqa/mdsi.pyspiq/mdsi.py renamed to piqa/mdsi.py

@@ -13,7 +13,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from spiq.utils import build_reduce, prewitt_kernel, gradient2d, tensor_norm
+from piqa.utils import build_reduce, prewitt_kernel, gradient2d, tensor_norm
 
 _LHM_WEIGHTS = torch.FloatTensor([
     [0.2989, 0.587, 0.114],

spiq/psnr.py piqa/psnr.pyspiq/psnr.py renamed to piqa/psnr.py

@@ -9,7 +9,7 @@
 import torch
 import torch.nn as nn
 
-from spiq.utils import build_reduce
+from piqa.utils import build_reduce
 
 from typing import Tuple
 

spiq/ssim.py piqa/ssim.pyspiq/ssim.py renamed to piqa/ssim.py

@@ -22,7 +22,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from spiq.utils import build_reduce, gaussian_kernel
+from piqa.utils import build_reduce, gaussian_kernel
 
 from typing import Tuple
 

spiq/tv.py piqa/tv.pyspiq/tv.py renamed to piqa/tv.py

@@ -9,7 +9,7 @@
 import torch
 import torch.nn as nn
 
-from spiq.utils import build_reduce, tensor_norm
+from piqa.utils import build_reduce, tensor_norm
 
 
 def tv(x: torch.Tensor, norm: str = 'L2') -> torch.Tensor: