torchvision.datasets.folder — Torchvision main documentation

link管理

链接快照平台

输入网页链接，自动生成快照
标签化管理网页链接

相关文章推荐

要出家的台灯 · [Feature]: Support ...· 23 小时前 ·

坏坏的猴子 · vision/torchvision/dat ...· 2 天前 ·

坚强的玉米 · What is an ...· 2 天前 ·

咆哮的酱肘子 · GitHub - ...· 3 天前 ·

活泼的椅子 · Hexo博客之butterfly主题优雅魔改 ...· 6 天前 ·

瘦瘦的小马驹 · 池袋西口公园第十卷尊严 03 ...· 1 月前 ·

发怒的饭卡 · 谭松韵评分最高6部剧集：《锦衣》排名第3，第 ...· 1 月前 ·

骑白马的菠萝 · 【行业深度】2024年中国共享经济行业竞争格 ...· 1 月前 ·

坚韧的弓箭 · 傅艺伟涉毒被警方带走 ...· 1 月前 ·

帅气的匕首 · 正则过滤Linux命令符号 • ...· 2 月前 ·

Join the PyTorch developer community to contribute, learn, and get your questions answered.

Learn how our community solves real, everyday machine learning problems with PyTorch.

Find resources and get questions answered

Find events, webinars, and podcasts

A place to discuss PyTorch code, issues, install, research

Discover, publish, and reuse pre-trained models

import os import os.path from typing import Any , Callable , cast , Dict , List , Optional , Tuple , Union from PIL import Image from .vision import VisionDataset def has_file_allowed_extension ( filename : str , extensions : Union [ str , Tuple [ str , ... ]]) -> bool : """Checks if a file is an allowed extension. Args: filename (string): path to a file extensions (tuple of strings): extensions to consider (lowercase) Returns: bool: True if the filename ends with one of given extensions return filename . lower () . endswith ( extensions if isinstance ( extensions , str ) else tuple ( extensions )) def is_image_file ( filename : str ) -> bool : """Checks if a file is an allowed image extension. Args: filename (string): path to a file Returns: bool: True if the filename ends with a known image extension return has_file_allowed_extension ( filename , IMG_EXTENSIONS ) def find_classes ( directory : str ) -> Tuple [ List [ str ], Dict [ str , int ]]: """Finds the class folders in a dataset. See :class:`DatasetFolder` for details. classes = sorted ( entry . name for entry in os . scandir ( directory ) if entry . is_dir ()) if not classes : raise FileNotFoundError ( f "Couldn't find any class folder in { directory } ." ) class_to_idx = { cls_name : i for i , cls_name in enumerate ( classes )} return classes , class_to_idx def make_dataset ( directory : str , class_to_idx : Optional [ Dict [ str , int ]] = None , extensions : Optional [ Union [ str , Tuple [ str , ... ]]] = None , is_valid_file : Optional [ Callable [[ str ], bool ]] = None , ) -> List [ Tuple [ str , int ]]: """Generates a list of samples of a form (path_to_sample, class). See :class:`DatasetFolder` for details. Note: The class_to_idx parameter is here optional and will use the logic of the ``find_classes`` function by default. directory = os . path . expanduser ( directory ) if class_to_idx is None : _ , class_to_idx = find_classes ( directory ) elif not class_to_idx : raise ValueError ( "'class_to_index' must have at least one entry to collect any samples." ) both_none = extensions is None and is_valid_file is None both_something = extensions is not None and is_valid_file is not None if both_none or both_something : raise ValueError ( "Both extensions and is_valid_file cannot be None or not None at the same time" ) if extensions is not None : def is_valid_file ( x : str ) -> bool : return has_file_allowed_extension ( x , extensions ) # type: ignore[arg-type] is_valid_file = cast ( Callable [[ str ], bool ], is_valid_file ) instances = [] available_classes = set () for target_class in sorted ( class_to_idx . keys ()): class_index = class_to_idx [ target_class ] target_dir = os . path . join ( directory , target_class ) if not os . path . isdir ( target_dir ): continue for root , _ , fnames in sorted ( os . walk ( target_dir , followlinks = True )): for fname in sorted ( fnames ): path = os . path . join ( root , fname ) if is_valid_file ( path ): item = path , class_index instances . append ( item ) if target_class not in available_classes : available_classes . add ( target_class ) empty_classes = set ( class_to_idx . keys ()) - available_classes if empty_classes : msg = f "Found no valid file for the classes { ', ' . join ( sorted ( empty_classes )) } . " if extensions is not None : msg += f "Supported extensions are: { extensions if isinstance ( extensions , str ) else ', ' . join ( extensions ) } " raise FileNotFoundError ( msg ) return instances

[docs] class DatasetFolder ( VisionDataset ): """A generic data loader. This default directory structure can be customized by overriding the :meth:`find_classes` method. Args: root (string): Root directory path. loader (callable): A function to load a sample given its path. extensions (tuple[string]): A list of allowed extensions. both extensions and is_valid_file should not be passed. transform (callable, optional): A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. target_transform (callable, optional): A function/transform that takes in the target and transforms it. is_valid_file (callable, optional): A function that takes path of a file and check if the file is a valid file (used to check of corrupt files) both extensions and is_valid_file should not be passed. Attributes: classes (list): List of the class names sorted alphabetically. class_to_idx (dict): Dict with items (class_name, class_index). samples (list): List of (sample path, class_index) tuples targets (list): The class_index value for each image in the dataset def __init__ ( self , root : str , loader : Callable [[ str ], Any ], extensions : Optional [ Tuple [ str , ... ]] = None , transform : Optional [ Callable ] = None , target_transform : Optional [ Callable ] = None , is_valid_file : Optional [ Callable [[ str ], bool ]] = None , ) -> None : super () . __init__ ( root , transform = transform , target_transform = target_transform ) classes , class_to_idx = self . find_classes ( self . root ) samples = self . make_dataset ( self . root , class_to_idx , extensions , is_valid_file ) self . loader = loader self . extensions = extensions self . classes = classes self . class_to_idx = class_to_idx self . samples = samples self . targets = [ s [ 1 ] for s in samples ]

[docs] @staticmethod def make_dataset ( directory : str , class_to_idx : Dict [ str , int ], extensions : Optional [ Tuple [ str , ... ]] = None , is_valid_file : Optional [ Callable [[ str ], bool ]] = None , ) -> List [ Tuple [ str , int ]]: """Generates a list of samples of a form (path_to_sample, class). This can be overridden to e.g. read files from a compressed zip file instead of from the disk. Args: directory (str): root dataset directory, corresponding to ``self.root``. class_to_idx (Dict[str, int]): Dictionary mapping class name to class index. extensions (optional): A list of allowed extensions. Either extensions or is_valid_file should be passed. Defaults to None. is_valid_file (optional): A function that takes path of a file and checks if the file is a valid file (used to check of corrupt files) both extensions and is_valid_file should not be passed. Defaults to None. Raises: ValueError: In case ``class_to_idx`` is empty. ValueError: In case ``extensions`` and ``is_valid_file`` are None or both are not None. FileNotFoundError: In case no valid file was found for any class. Returns: List[Tuple[str, int]]: samples of a form (path_to_sample, class) if class_to_idx is None : # prevent potential bug since make_dataset() would use the class_to_idx logic of the # find_classes() function, instead of using that of the find_classes() method, which # is potentially overridden and thus could have a different logic. raise ValueError ( "The class_to_idx parameter cannot be None." ) return make_dataset ( directory , class_to_idx , extensions = extensions , is_valid_file = is_valid_file )

[docs] def find_classes ( self , directory : str ) -> Tuple [ List [ str ], Dict [ str , int ]]: """Find the class folders in a dataset structured as follows:: directory/ ├── class_x │ ├── xxx.ext │ ├── xxy.ext │ └── ... │ └── xxz.ext └── class_y ├── 123.ext ├── nsdf3.ext └── ... └── asd932_.ext This method can be overridden to only consider a subset of classes, or to adapt to a different dataset directory structure. Args: directory(str): Root directory path, corresponding to ``self.root`` Raises: FileNotFoundError: If ``dir`` has no class folders. Returns: (Tuple[List[str], Dict[str, int]]): List of all classes and dictionary mapping each class to an index. return find_classes ( directory )

def __getitem__ ( self , index : int ) -> Tuple [ Any , Any ]: Args: index (int): Index Returns: tuple: (sample, target) where target is class_index of the target class. path , target = self . samples [ index ] sample = self . loader ( path ) if self . transform is not None : sample = self . transform ( sample ) if self . target_transform is not None : target = self . target_transform ( target ) return sample , target def __len__ ( self ) -> int : return len ( self . samples )

IMG_EXTENSIONS = ( ".jpg" , ".jpeg" , ".png" , ".ppm" , ".bmp" , ".pgm" , ".tif" , ".tiff" , ".webp" ) def pil_loader ( path : str ) -> Image . Image : # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) with open ( path , "rb" ) as f : img = Image . open ( f ) return img . convert ( "RGB" ) # TODO: specify the return type def accimage_loader ( path : str ) -> Any : import accimage try : return accimage . Image ( path ) except OSError : # Potentially a decoding problem, fall back to PIL.Image return pil_loader ( path ) def default_loader ( path : str ) -> Any : from torchvision import get_image_backend if get_image_backend () == "accimage" : return accimage_loader ( path ) else : return pil_loader ( path )

[docs] class ImageFolder ( DatasetFolder ): """A generic data loader where the images are arranged in this way by default: :: root/dog/xxx.png root/dog/xxy.png root/dog/[...]/xxz.png root/cat/123.png root/cat/nsdf3.png root/cat/[...]/asd932_.png This class inherits from :class:`~torchvision.datasets.DatasetFolder` so the same methods can be overridden to customize the dataset. Args: root (string): Root directory path. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. loader (callable, optional): A function to load an image given its path. is_valid_file (callable, optional): A function that takes path of an Image file and check if the file is a valid file (used to check of corrupt files) Attributes: classes (list): List of the class names sorted alphabetically. class_to_idx (dict): Dict with items (class_name, class_index). imgs (list): List of (image path, class_index) tuples def __init__ ( self , root : str , transform : Optional [ Callable ] = None , target_transform : Optional [ Callable ] = None , loader : Callable [[ str ], Any ] = default_loader , is_valid_file : Optional [ Callable [[ str ], bool ]] = None , super () . __init__ ( root , loader , IMG_EXTENSIONS if is_valid_file is None else None , transform = transform , target_transform = target_transform , is_valid_file = is_valid_file , self . imgs = self . samples

For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see www.linuxfoundation.org/policies/ . The PyTorch Foundation supports the PyTorch open source project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC, please see www.lfprojects.org/policies/ .