Fix typos discovered by codespell (#95)

* Improve performance

* !fixup Fix typo discovered by codespell

* fixup! Fix typo discovered by codespell

* fixup! Add codespell to lint process

.github/workflows/pylint.yml

@@ -20,11 +20,12 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install flake8 yapf isort
+        pip install codespell flake8 isort yapf
 
     # modify the folders accordingly
     - name: Lint
       run: |
+        codespell
         flake8 .
         isort --check-only --diff realesrgan/ scripts/ inference_realesrgan.py setup.py
         yapf -r -d realesrgan/ scripts/ inference_realesrgan.py setup.py

Training.md

@@ -32,7 +32,7 @@ Here are steps for data preparation.
 #### Step 1: [Optional] Generate multi-scale images
 
 For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales. <br>
-You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to geneate multi-scale images. <br>
+You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to generate multi-scale images. <br>
 Note that this step can be omitted if you just want to have a fast try.
 
 ```bash

docs/anime_model.md

@@ -6,7 +6,7 @@
   - [PyTorch Inference](#PyTorch-Inference)
   - [ncnn Executable File](#ncnn-Executable-File)
 - [Comparisons with waifu2x](#Comparisons-with-waifu2x)
-- [Comparions with Sliding Bars](#Comparions-with-Sliding-Bars)
+- [Comparisons with Sliding Bars](#Comparions-with-Sliding-Bars)
 
 <p align="center">
   <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">

inference_realesrgan.py

@@ -81,10 +81,10 @@ def main():
         h, w = img.shape[0:2]
         if max(h, w) > 1000 and args.netscale == 4:
             import warnings
-            warnings.warn('The input image is large, try X2 model for better performace.')
+            warnings.warn('The input image is large, try X2 model for better performance.')
         if max(h, w) < 500 and args.netscale == 2:
             import warnings
-            warnings.warn('The input image is small, try X4 model for better performace.')
+            warnings.warn('The input image is small, try X4 model for better performance.')
 
         try:
             if args.face_enhance: