dev(narugo): add corrupt docs

docs/source/api_doc/corrupt/aicorrupt.rst

@@ -0,0 +1,22 @@
+sdeval.corrupt.aicorrupt
+=================================
+
+.. currentmodule:: sdeval.corrupt.aicorrupt
+
+.. automodule:: sdeval.corrupt.aicorrupt
+
+
+get_ai_corrupted
+---------------------------------
+
+.. autofunction:: get_ai_corrupted
+
+
+
+AICorruptMetrics
+--------------------------------
+
+.. autoclass:: AICorruptMetrics
+    :members: __init__, score
+
+

docs/source/api_doc/corrupt/index.rst

@@ -0,0 +1,12 @@
+sdeval.corrupt
+=====================
+
+.. currentmodule:: sdeval.corrupt
+
+.. automodule:: sdeval.corrupt
+
+
+.. toctree::
+    :maxdepth: 3
+
+    aicorrupt

docs/source/index.rst

@@ -29,6 +29,7 @@ configuration file's structure and their versions.
     :caption: API Documentation
 
     api_doc/config/index
+    api_doc/corrupt/index
     api_doc/fidelity/index
     api_doc/utils/index
 

sdeval/corrupt/aicorrupt.py

@@ -1,3 +1,7 @@
+"""
+Overview:
+    AI image corrupt evaluation metrics.
+"""
 import json
 from functools import lru_cache
 from typing import Tuple, Optional, Mapping
@@ -14,15 +18,37 @@
 
 
 @lru_cache()
-def _open_anime_aicop_model(model_name):
+def _open_anime_aicop_model(model_name: str):
+    """
+    Open the AI image corrupted detection model.
+
+    This function downloads and opens the AI image corrupted detection model specified by the given model name using Hugging Face Hub.
+
+    :param model_name: The name of the AI image corrupted detection model.
+    :type model_name: str
+
+    :return: The opened AI image corrupted detection model.
+    :rtype: Model
+    """
     return open_onnx_model(hf_hub_download(
         f'deepghs/ai_image_corrupted',
         f'{model_name}/model.onnx',
     ))
 
 
 @lru_cache()
-def _open_anime_aicop_meta(model_name):
+def _open_anime_aicop_meta(model_name: str):
+    """
+    Open the meta information of the AI image corrupted detection model.
+
+    This function downloads and opens the meta information of the AI image corrupted detection model specified by the given model name using Hugging Face Hub.
+
+    :param model_name: The name of the AI image corrupted detection model.
+    :type model_name: str
+
+    :return: The opened meta information of the AI image corrupted detection model.
+    :rtype: dict
+    """
     with open(hf_hub_download(
             f'deepghs/ai_image_corrupted',
             f'{model_name}/meta.json',
@@ -31,12 +57,38 @@ def _open_anime_aicop_meta(model_name):
 
 
 @lru_cache()
-def _open_anime_aicop_labels(model_name):
+def _open_anime_aicop_labels(model_name: str):
+    """
+    Open the labels of the AI image corrupted detection model.
+
+    This function opens the labels of the AI image corrupted detection model specified by the given model name.
+
+    :param model_name: The name of the AI image corrupted detection model.
+    :type model_name: str
+
+    :return: The labels of the AI image corrupted detection model.
+    :rtype: List[str]
+    """
     return _open_anime_aicop_meta(model_name)['labels']
 
 
 def _img_encode(image: Image.Image, size: Tuple[int, int] = (384, 384),
                 normalize: Optional[Tuple[float, float]] = (0.5, 0.5)):
+    """
+    Encode the image for AI image corrupted detection.
+
+    This function resizes and encodes the image for AI image corrupted detection.
+
+    :param image: The input image.
+    :type image: Image.Image
+    :param size: The target size for encoding. Default is (384, 384).
+    :type size: Tuple[int, int]
+    :param normalize: The normalization parameters. Default is (0.5, 0.5).
+    :type normalize: Optional[Tuple[float, float]]
+
+    :return: The encoded image data.
+    :rtype: np.ndarray
+    """
     image = image.resize(size, Image.BILINEAR)
     data = rgb_encode(image, order_='CHW')
 
@@ -50,20 +102,59 @@ def _img_encode(image: Image.Image, size: Tuple[int, int] = (384, 384),
 
 
 def get_ai_corrupted(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) -> Mapping[str, float]:
+    """
+    Get AI image corrupted detection scores for an image.
+
+    This function calculates AI image corrupted detection scores for a given image using the specified model.
+
+    :param image: The input image.
+    :type image: ImageTyping
+    :param model_name: The name of the AI image corrupted detection model. Default is 'caformer_s36_v0_focal'.
+    :type model_name: str
+
+    :return: A dictionary containing the corrupted score.
+    :rtype: Mapping[str, float]
+    """
     image = load_image(image, force_background='white', mode='RGB')
     input_ = _img_encode(image)[None, ...]
     output, = _open_anime_aicop_model(model_name).run(['output'], {'input': input_})
     return dict(zip(_open_anime_aicop_labels(model_name), output[0].tolist()))
 
 
 class AICorruptMetrics:
+    """
+    Class for calculating an AI image corruptness score.
+
+    The `AICorruptMetrics` class allows you to calculate an AI image corruptness score using the AI image corrupted detection model.
+
+    :param model_name: The name of the AI image corrupted detection model. Default is 'caformer_s36_v0_focal'.
+    :type model_name: str
+    :param silent: If True, suppresses progress bars and additional output during calculation.
+    :type silent: bool
+    :param tqdm_desc: Description for the tqdm progress bar during calculation.
+    :type tqdm_desc: str
+    """
+
     def __init__(self, model_name: str = _DEFAULT_MODEL_NAME,
                  silent: bool = False, tqdm_desc: str = None):
         self._model_name = model_name
         self.silent = silent
         self.tqdm_desc = tqdm_desc or self.__class__.__name__
 
     def score(self, images: ImagesTyping, silent: bool = None):
+        """
+        Calculate the AI image corruptness score for a set of images.
+
+        This method calculates the AI image corruptness score for a set of input images using the AI image corrupted detection model.
+
+        :param images: The set of input images for calculating the AI image corruptness score.
+        :type images: ImagesTyping
+        :param silent: If True, suppresses progress bars and additional output during calculation.
+        :type silent: bool
+
+        :return: The AI image corruptness score.
+        :rtype: float
+        """
         image_list = load_images(images)
         if not image_list:
             raise FileNotFoundError(f'Images for calculating AI corrupt score not provided - {images}.')