使用pytorch自定义DataSet，以加载图像数据集为例，实现一些骚操作
总共分为四步
- 构造一个
my_dataset类，继承自torch.utils.data.Dataset - 重写
__getitem__和__len__类函数 - 建立两个函数
find_classes、has_file_allowed_extension，直接从这copy过去 - 建立
my_make_dataset函数用来构造(path,lable)对
一、构造一个my_dataset类，继承自torch.utils.data.Dataset
二、 重写__getitem__ 和__len__ 类函数
要构造Dataset的子类，就必须要实现两个方法：
- getitem_(self, index)：根据index来返回数据集中标号为index的元素及其标签。
- len_(self)：返回数据集的长度。
class my_dataset(Dataset):def __init__(self,root_original, root_cdtfed, transform=None):super(my_dataset, self).__init__()self.transform = transformself.root_original = root_originalself.root_cdtfed = root_cdtfedself.original_imgs = []self.cdtfed_imgs = []#add (img_path, label) to listsself.original_imgs = my_make_dataset(root_original, class_to_idx=None, extensions=('.jpg', '.png'), is_valid_file=None)self.cdtfed_imgs = my_make_dataset(root_original, class_to_idx=None, extensions=('.jpg', '.png'), is_valid_file=None)# super(my_dataset, self).__init__()def __getitem__(self, index): #这个方法是必须要有的，用于按照索引读取每个元素的具体内容fn1, label1 = self.original_imgs[index] #fn是图片path #fn和label分别获得imgs[index]也即是刚才每行中word[0]和word[1]的信息fn2, label2 = self.cdtfed_imgs[index]img1 = Image.open(fn1).convert('RGB') #按照path读入图片from PIL import Image # 按照路径读取图片img2 = Image.open(fn2).convert('RGB') #按照path读入图片from PIL import Image # 按照路径读取图片if self.transform is not None:img1 = self.transform(img1) #是否进行transformimg2 = self.transform(img2) #是否进行transformimg_list = [img1, img2]label = label1name = fn1return img_list,label,name #return很关键，return回哪些内容，那么我们在训练时循环读取每个batch时，就能获得哪些内容def __len__(self): #这个函数也必须要写，它返回的是数据集的长度，也就是多少张图片，要和loader的长度作区分return len(self.original_imgs)三、建立两个函数find_classes、has_file_allowed_extension，直接从这copy过去
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_idxdef has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:"""Checks if a file is an allowed extension.Args:filename (string): path to a fileextensions (tuple of strings): extensions to consider (lowercase)Returns:bool: True if the filename ends with one of given extensions"""return filename.lower().endswith(extensions)
- 建立
my_make_dataset函数用来构造(path,lable)对
def my_make_dataset(directory: str,class_to_idx: Optional[Dict[str, int]] = None,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).See :class:`DatasetFolder` for details.Note: The class_to_idx parameter is here optional and will use the logic of the ``find_classes`` functionby 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 Noneboth_something = extensions is not None and is_valid_file is not Noneif 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, cast(Tuple[str, ...], extensions))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):continuefor root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):for fname in sorted(fnames):if is_valid_file(fname):path = os.path.join(root, fname)# item = path, [int(cl) for cl in target_class.split('_')]item = path, target_classinstances.append(item)if target_class not in available_classes:available_classes.add(target_class)empty_classes = set(class_to_idx.keys()) - available_classesif 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: {', '.join(extensions)}"raise FileNotFoundError(msg)return instances #instance:[item:(path, int(class_name)), ]
附录：完整代码
我这里传入两个root_dir，因为我要用一个dataset加载两个数据集，分别放在data1和data2里
class my_dataset(Dataset):def __init__(self,root_original, root_cdtfed, transform=None):super(my_dataset, self).__init__()self.transform = transformself.root_original = root_originalself.root_cdtfed = root_cdtfedself.original_imgs = []self.cdtfed_imgs = []#add (img_path, label) to listsself.original_imgs = my_make_dataset(root_original, class_to_idx=None, extensions=('.jpg', '.png'), is_valid_file=None)self.cdtfed_imgs = my_make_dataset(root_original, class_to_idx=None, extensions=('.jpg', '.png'), is_valid_file=None)# super(my_dataset, self).__init__()def __getitem__(self, index): #这个方法是必须要有的，用于按照索引读取每个元素的具体内容fn1, label1 = self.original_imgs[index] #fn是图片path #fn和label分别获得imgs[index]也即是刚才每行中word[0]和word[1]的信息fn2, label2 = self.cdtfed_imgs[index]img1 = Image.open(fn1).convert('RGB') #按照path读入图片from PIL import Image # 按照路径读取图片img2 = Image.open(fn2).convert('RGB') #按照path读入图片from PIL import Image # 按照路径读取图片if self.transform is not None:img1 = self.transform(img1) #是否进行transformimg2 = self.transform(img2) #是否进行transformimg_list = [img1, img2]label = label1name = fn1return img_list,label,name #return很关键，return回哪些内容，那么我们在训练时循环读取每个batch时，就能获得哪些内容def __len__(self): #这个函数也必须要写，它返回的是数据集的长度，也就是多少张图片，要和loader的长度作区分return len(self.original_imgs)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_idxdef has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:"""Checks if a file is an allowed extension.Args:filename (string): path to a fileextensions (tuple of strings): extensions to consider (lowercase)Returns:bool: True if the filename ends with one of given extensions"""return filename.lower().endswith(extensions)def my_make_dataset(directory: str,class_to_idx: Optional[Dict[str, int]] = None,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).See :class:`DatasetFolder` for details.Note: The class_to_idx parameter is here optional and will use the logic of the ``find_classes`` functionby 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 Noneboth_something = extensions is not None and is_valid_file is not Noneif 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, cast(Tuple[str, ...], extensions))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):continuefor root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):for fname in sorted(fnames):if is_valid_file(fname):path = os.path.join(root, fname)# item = path, [int(cl) for cl in target_class.split('_')]item = path, target_classinstances.append(item)if target_class not in available_classes:available_classes.add(target_class)empty_classes = set(class_to_idx.keys()) - available_classesif 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: {', '.join(extensions)}"raise FileNotFoundError(msg)return instances #instance:[item:(path, int(class_name)), ]
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 和model.eval() 对 BN 层的影响)
