在为数据分类训练分类器的时候，比如猫狗分类时，我们经常会使用pytorch的ImageFolder:
CLASS torchvision.datasets.ImageFolder(root, transform=None, target_transform=None, loader=<function default_loader>, is_valid_file=None)
使用可见pytorch torchvision.ImageFolder的使用
这里想实现的是如果想要覆写该函数，即能使用它的特性，又可以实现自己的功能
首先先分析下其源代码：
IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', 'webp'] class ImageFolder(DatasetFolder): """A generic data loader where the images are arranged in this way: :: root/dog/xxx.png root/dog/xxy.png root/dog/xxz.png root/cat/123.png root/cat/nsdf3.png root/cat/asd932_.png 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. Attributes: classes (list): List of the class names. class_to_idx (dict): Dict with items (class_name, class_index). imgs (list): List of (image path, class_index) tuples """ def __init__(self, root, transform=None, target_transform=None, loader=default_loader): super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS, transform=transform, target_transform=target_transform) self.imgs = self.samples
ImageFolder的代码很简单，主要是继承了DatasetFolder：
def has_file_allowed_extension(filename, extensions): """查看文件是否是支持的可扩展类型 Args: filename (string): 文件路径 extensions (iterable of strings): 可扩展类型列表，即能接受的图像文件类型 Returns: bool: True if the filename ends with one of given extensions """ filename_lower = filename.lower() return any(filename_lower.endswith(ext) for ext in extensions) # 返回True或False列表 def make_dataset(dir, class_to_idx, extensions): """ 返回形如[(图像路径, 该图像对应的类别索引值),(),...] """ images = [] dir = os.path.expanduser(dir) for target in sorted(class_to_idx.keys()): d = os.path.join(dir, target) if not os.path.isdir(d): continue for root, _, fnames in sorted(os.walk(d)): #层层遍历文件夹，返回当前文件夹路径，存在的所有文件夹名，存在的所有文件名 for fname in sorted(fnames): if has_file_allowed_extension(fname, extensions):查看文件是否是支持的可扩展类型，是则继续 path = os.path.join(root, fname) item = (path, class_to_idx[target]) images.append(item) return images class DatasetFolder(data.Dataset): """A generic data loader where the samples are arranged in this way: :: root/class_x/xxx.ext root/class_x/xxy.ext root/class_x/xxz.ext root/class_y/123.ext root/class_y/nsdf3.ext root/class_y/asd932_.ext Args: root (string): 根目录路径 loader (callable): 根据给定的路径来加载样本的可调用函数 extensions (list[string]): 可扩展类型列表，即能接受的图像文件类型. transform (callable, optional): 用于样本的transform函数，然后返回样本transform后的版本 E.g, ``transforms.RandomCrop`` for images. target_transform (callable, optional): 用于样本标签的transform函数 Attributes: classes (list): 类别名列表 class_to_idx (dict): 项目(class_name, class_index)字典,如{'cat': 0, 'dog': 1} samples (list): (sample path, class_index) 元组列表，即(样本路径, 类别索引) targets (list): 在数据集中每张图片的类索引值，为列表 """ def __init__(self, root, loader, extensions, transform=None, target_transform=None): classes, class_to_idx = self._find_classes(root) # 得到类名和类索引，如['cat', 'dog']和{'cat': 0, 'dog': 1} # 返回形如[(图像路径, 该图像对应的类别索引值),(),...]，即对每个图像进行标记 samples = make_dataset(root, class_to_idx, extensions) if len(samples) == 0: raise(RuntimeError("Found 0 files in subfolders of: " + root + " " "Supported extensions are: " + ",".join(extensions))) self.root = root 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] #所有图像的类索引值组成的列表 self.transform = transform self.target_transform = target_transform def _find_classes(self, dir): """ 在数据集中查找类文件夹。 Args: dir (string): 根目录路径 Returns: 返回元组: (classes, class_to_idx)即(类名, 类索引)，其中classes即相应的目录名，如['cat', 'dog'];class_to_idx为形如{类名:类索引}的字典，如{'cat': 0, 'dog': 1}. Ensures: 保证没有类名是另一个类目录的子目录 """ if sys.version_info >= (3, 5): # Faster and available in Python 3.5 and above classes = [d.name for d in os.scandir(dir) if d.is_dir()] #获得根目录dir的所有第一层子目录名 else: classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))] #效果和上面的一样，只是版本不同方法不同 classes.sort() #然后对类名进行排序 class_to_idx = {classes[i]: i for i in range(len(classes))} #然后将类名和索引值一一对应的到相应字典，如{'cat': 0, 'dog': 1} return classes, class_to_idx #然后返回类名和类索引 def __getitem__(self, index): """ 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) # 加载图片 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): return len(self.samples) def __repr__(self): fmt_str = 'Dataset ' + self.__class__.__name__ + ' ' fmt_str += ' Number of datapoints: {} '.format(self.__len__()) fmt_str += ' Root Location: {} '.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1} '.format(tmp, self.transform.__repr__().replace(' ', ' ' + ' ' * len(tmp))) tmp = ' Target Transforms (if any): ' fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace(' ', ' ' + ' ' * len(tmp))) return fmt_str
此时想要覆写ImageFolder,代码为：
class CustomImageFolder(ImageFolder): """ 为了得到两张图(其中一张是随机选取的)的图像和索引值信息 """ def __init__(self, root, transform=None): super(CustomImageFolder, self).__init__(root, transform) self.indices = range(len(self)) #该文件夹中的长度 def __getitem__(self, index1): index2 = random.choice(self.indices) #从[0,indices]中随机抽取一个数字,为了随机选取一张图 path1 = self.imgs[index1][0] #此时的self.imgs等于self.samples，即内容为[(图像路径, 该图像对应的类别索引值),(),...] label1 = self.imgs[index1][1] path2 = self.imgs[index2][0] label2 = self.imgs[index2][1] img1 = self.loader(path1) img2 = self.loader(path2) if self.transform is not None: img1 = self.transform(img1) img2 = self.transform(img2) return img1, img2, label1, label2