Pytorch 数据加载与数据预处理方式

数据加载分为加载torchvision.datasets中的数据集以及加载自己使用的数据集两种情况。

torchvision.datasets中的数据集

torchvision.datasets中自带MNIST，Imagenet-12，CIFAR等数据集，所有的数据集都是torch.utils.data.Dataset的子类，都包含 _ _ len _ （获取数据集长度）和 _ getItem _ _ （获取数据集中每一项）两个子方法。

Dataset源码如上，可以看到其中包含了两个没有实现的子方法，之后所有的Dataet类都继承该类，并根据数据情况定制这两个子方法的具体实现。

因此当我们需要加载自己的数据集的时候也可以借鉴这种方法，只需要继承torch.utils.data.Dataset类并重写 init ,len,以及getitem这三个方法即可。这样组着的类可以直接作为参数传入到torch.util.data.DataLoader中去。

以CIFAR10为例 源码：

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.

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=<function default_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()

以上这篇Pytorch 数据加载与数据预处理方式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软件开发网。

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