加载数据

加载自己的数据集

对于torchvision.datasets中有两个不同的类，分别为DatasetFolder和ImageFolder,ImageFolder是继承自DatasetFolder。 

下面我们通过源码来看一看folder文件中DatasetFolder和ImageFolder分别做了些什么

import torch.utils.data as data
from PIL import Image
import os
import os.path

def has_file_allowed_extension(filename, extensions):  //检查输入是否是规定的扩展名
    """Checks if a file is an allowed extension.

Args:
        filename (string): path to a file

Returns:
        bool: True if the filename ends with a known image extension
    """
    filename_lower = filename.lower()
    return any(filename_lower.endswith(ext) for ext in extensions)

def find_classes(dir):
    classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))] //获取root目录下所有的文件夹名称

classes.sort()
    class_to_idx = {classes[i]: i for i in range(len(classes))} //生成类别名称与类别id的对应Dictionary
    return classes, class_to_idx

def make_dataset(dir, class_to_idx, extensions):
    images = []
    dir = os.path.expanduser(dir)// 将~和~user转化为用户目录，对参数中出现~进行处理
    for target in sorted(os.listdir(dir)):
        d = os.path.join(dir, target)
        if not os.path.isdir(d):
            continue

for root, _, fnames in sorted(os.walk(d)): //os.work包含三个部分，root代表该目录路径 _代表该路径下的文件夹名称集合，fnames代表该路径下的文件名称集合
            for fname in sorted(fnames):
                if has_file_allowed_extension(fname, extensions):
                    path = os.path.join(root, fname)
                    item = (path, class_to_idx[target])
                    images.append(item)    //生成（训练样本图像目录，训练样本所属类别）的元组

return images   //返回上述元组的列表

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): Root directory path.
        loader (callable): A function to load a sample given its path.
        extensions (list[string]): A list of allowed extensions.
        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.

Attributes:
        classes (list): List of the class names.
        class_to_idx (dict): Dict with items (class_name, class_index).
        samples (list): List of (sample path, class_index) tuples
    """

def __init__(self, root, loader, extensions, transform=None, target_transform=None):
        classes, class_to_idx = find_classes(root)
        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.transform = transform
        self.target_transform = target_transform

def __getitem__(self, index):
        """
        根据index获取sample 返回值为（sample，target）元组，同时如果该类输入参数中有transform和target_transform，torchvision.transforms类型的参数时，将获取的元组分别执行transform和target_transform中的数据转换方法。
              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):
        return len(self.samples)

def __repr__(self): //定义输出对象格式 其中和__str__的区别是__repr__无论是print输出还是直接输出对象自身 都是以定义的格式进行输出，而__str__ 只有在print输出的时候会是以定义的格式进行输出
        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

IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']

def pil_loader(path):
    # 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')

def accimage_loader(path):
    import accimage
    try:
        return accimage.Image(path)
    except IOError:
        # Potentially a decoding problem, fall back to PIL.Image
        return pil_loader(path)

def default_loader(path):
    from torchvision import get_image_backend
    if get_image_backend() == 'accimage':
        return accimage_loader(path)
    else:
        return pil_loader(path)

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

如果自己所要加载的数据组织形式如下

root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png
root/cat/123.png
root/cat/nsdf3.png
root/cat/asd932_.png

即不同类别的训练数据分别存储在不同的文件夹中，这些文件夹都在root（即形如 D:/animals 或者 /usr/animals ）路径下

class torchvision.datasets.ImageFolder(root, transform=None, target_transform=None, loader=)

参数如下：

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 – A function to load an image given its path. 就是上述源码中

__getitem__(index)
Parameters: index (int) – Index
Returns:    (sample, target) where target is class_index of the target class.
Return type:    tuple

可以通过torchvision.datasets.ImageFolder进行加载

img_data = torchvision.datasets.ImageFolder('D:/bnu/database/flower',
                                            transform=transforms.Compose([
                                                transforms.Scale(256),
                                                transforms.CenterCrop(224),
                                                transforms.ToTensor()])
                                            )
print(len(img_data))
data_loader = torch.utils.data.DataLoader(img_data, batch_size=20,shuffle=True)
print(len(data_loader))

对于所有的训练样本都在一个文件夹中 同时有一个对应的txt文件每一行分别是对应图像的路径以及其所属的类别，可以参照上述class写出对应的加载类

def default_loader(path):
    return Image.open(path).convert('RGB')

class MyDataset(Dataset):
    def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
        fh = open(txt, 'r')
        imgs = []
        for line in fh:
            line = line.strip('\n')
            line = line.rstrip()
            words = line.split()
            imgs.append((words[0],int(words[1])))
        self.imgs = imgs
        self.transform = transform
        self.target_transform = target_transform
        self.loader = loader

def __getitem__(self, index):
        fn, label = self.imgs[index]
        img = self.loader(fn)
        if self.transform is not None:
            img = self.transform(img)
        return img,label

def __len__(self):
        return len(self.imgs)

train_data=MyDataset(txt='mnist_test.txt', transform=transforms.ToTensor())
data_loader = DataLoader(train_data, batch_size=100,shuffle=True)
print(len(data_loader))

DataLoader解析

位于torch.util.data.DataLoader中 源代码 

该接口的主要目的是将pytorch中已有的数据接口如torchvision.datasets.ImageFolder，或者自定义的数据读取接口转化按照batch_size的大小封装为Tensor,即相当于在内置数据接口或者自定义数据接口的基础上增加一维，大小为batch_size的大小，得到的数据在之后可以通过封装为Variable，作为模型的输出 

_ _ init _ _中所需的参数如下

1. dataset  torch.utils.data.Dataset类的子类，可以是torchvision.datasets.ImageFolder等内置类，也可是继承了torch.utils.data.Dataset的自定义类
2. batch_size 每一个batch中包含的样本个数，默认是1
3. shuffle 一般在训练集中采用，默认是false，设置为true则每一个epoch都会将训练样本打乱
4. sampler 训练样本选取策略，和shuffle是互斥的 如果 shuffle为true，该参数一定要为None
5.  batch_sampler  BatchSampler 一次产生一个 batch 的 indices，和sampler以及shuffle互斥，一般使用默认的即可
    上述Sampler的源代码地址如下[源代码](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/sampler.py)
6. num_workers 用于数据加载的线程数量 默认为0 即只有主线程用来加载数据
7. collate_fn 用来聚合数据生成mini_batch

使用的时候一般为如下使用方法：

train_data=torch.utils.data.DataLoader(...)
for i, (input, target) in enumerate(train_data):
...

循环取DataLoader中的数据会触发类中_ _ iter __方法，查看源代码可知 其中调用的方法为 return _DataLoaderIter(self)，因此需要查看 DataLoaderIter 这一内部类

class DataLoaderIter(object):
    "Iterates once over the DataLoader's dataset, as specified by the sampler"

def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
        self.timeout = loader.timeout
        self.done_event = threading.Event()

self.sample_iter = iter(self.batch_sampler)

if self.num_workers > 0:
            self.worker_init_fn = loader.worker_init_fn
            self.index_queue = multiprocessing.SimpleQueue()
            self.worker_result_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.worker_pids_set = False
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

base_seed = torch.LongTensor(1).random_()[0]
            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,
                          base_seed + i, self.worker_init_fn, i))
                for i in range(self.num_workers)]

if self.pin_memory or self.timeout > 0:
                self.data_queue = queue.Queue()
                self.worker_manager_thread = threading.Thread(
                    target=_worker_manager_loop,
                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
                          torch.cuda.current_device()))
                self.worker_manager_thread.daemon = True
                self.worker_manager_thread.start()
            else:
                self.data_queue = self.worker_result_queue

for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

_update_worker_pids(id(self), tuple(w.pid for w in self.workers))
            _set_SIGCHLD_handler()
            self.worker_pids_set = True

# prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices()