Pytorch 数据加载与数据预处理方式

class torchvision.datasets.CIFAR10(root, train=True, transform=None, target_transform=None, download=False)

root (string) – Root directory of dataset where directory cifar-10-batches-py exists or will be saved to if download is set to True.
train (bool, optional) – If True, creates dataset from training set, otherwise creates from test set.
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.
download (bool, optional) – If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.

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

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

class torchvision.datasets.ImageFolder(root, transform=None, target_transform=None, loader=<function default_loader>)

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

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))

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))

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

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

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()