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的用法介绍
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 + "\n"
"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__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * 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
以上这篇pytorch ImageFolder的覆写实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持呐喊教程。
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