系列文章目录

PyTorch深度学习——Anaconda和PyTorch安装
Pytorch深度学习-----数据模块Dataset类
Pytorch深度学习------TensorBoard的使用
Pytorch深度学习------Torchvision中Transforms的使用（ToTensor，Normalize，Resize ，Compose，RandomCrop）
Pytorch深度学习------torchvision中dataset数据集的使用（CIFAR10）

---

---

一、DataLoader是什么？

DataLoader是Pytorch中用来处理模型输入数据的一个工具类。组合了数据集（dataset） + 采样器(sampler)，如果把Dataset比作一副扑克牌，则DataLoader就是每次手中处理的某一批扑克牌，然后每一批取多少张，总共能取多少批，用不用打乱顺序等，都可以在创建DataLoader时从参数自行设定。

二、使用步骤

1.相关参数

class DataLoader(Generic[T_co]):
    r"""
    Data loader. Combines a dataset and a sampler, and provides an iterable over
    the given dataset.

    The :class:`~torch.utils.data.DataLoader` supports both map-style and
    iterable-style datasets with single- or multi-process loading, customizing
    loading order and optional automatic batching (collation) and memory pinning.

    See :py:mod:`torch.utils.data` documentation page for more details.

    Args:
        dataset (Dataset): dataset from which to load the data.
        batch_size (int, optional): how many samples per batch to load
            (default: ``1``).
        shuffle (bool, optional): set to ``True`` to have the data reshuffled
            at every epoch (default: ``False``).
        sampler (Sampler or Iterable, optional): defines the strategy to draw
            samples from the dataset. Can be any ``Iterable`` with ``__len__``
            implemented. If specified, :attr:`shuffle` must not be specified.
        batch_sampler (Sampler or Iterable, optional): like :attr:`sampler`, but
            returns a batch of indices at a time. Mutually exclusive with
            :attr:`batch_size`, :attr:`shuffle`, :attr:`sampler`,
            and :attr:`drop_last`.
        num_workers (int, optional): how many subprocesses to use for data
            loading. ``0`` means that the data will be loaded in the main process.
            (default: ``0``)
        collate_fn (Callable, optional): merges a list of samples to form a
            mini-batch of Tensor(s).  Used when using batched loading from a
            map-style dataset.
        pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
            into device/CUDA pinned memory before returning them.  If your data elements
            are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
            see the example below.
        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
            if the dataset size is not divisible by the batch size. If ``False`` and
            the size of dataset is not divisible by the batch size, then the last batch
            will be smaller. (default: ``False``)
        timeout (numeric, optional): if positive, the timeout value for collecting a batch
            from workers. Should always be non-negative. (default: ``0``)
        worker_init_fn (Callable, optional): If not ``None``, this will be called on each
            worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
            input, after seeding and before data loading. (default: ``None``)
        generator (torch.Generator, optional): If not ``None``, this RNG will be used
            by RandomSampler to generate random indexes and multiprocessing to generate
            `base_seed` for workers. (default: ``None``)
        prefetch_factor (int, optional, keyword-only arg): Number of batches loaded
            in advance by each worker. ``2`` means there will be a total of
            2 * num_workers batches prefetched across all workers. (default value depends
            on the set value for num_workers. If value of num_workers=0 default is ``None``.
            Otherwise if value of num_workers>0 default is ``2``).
        persistent_workers (bool, optional): If ``True``, the data loader will not shutdown
            the worker processes after a dataset has been consumed once. This allows to
            maintain the workers `Dataset` instances alive. (default: ``False``)
        pin_memory_device (str, optional): the data loader will copy Tensors
            into device pinned memory before returning them if pin_memory is set to true.

在上述中共有15个参数，我们常用的有如下5个参数

dataset (Dataset)– 表示要读取的数据集

batch_size (python:int, optional) – 表示每次从数据集中取多少个数据

shuffle (bool, optional) –表示是否为乱序取出

num_workers (python:int, optional) – 表示是否多进程读取数据（默认为０);

drop_last (bool, optional) – 表示当样本数不能被batchsize整除时（即总数据集/batch_size 不能除尽，有余数时），最后一批数据（余数）是否舍弃（default:
False)

pin_memory（bool, optional) - 如果为True会将数据放置到GPU上去（默认为false）

2.引入库

from torch.utils.data import DataLoader

3.创建数据（使用CIFAR10为例）

创建CIFAR10的测试集

test_set = torchvision.datasets.CIFAR10("dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)

4.创建DataLoader实例

# 创建DataLoader实例
test_loader = DataLoader(
    dataset=test_set, # 引入数据集
    batch_size=4, # 每次取4个数据
    shuffle=True, # 打乱顺序
    num_workers=0, # 非多进程
    drop_last=False # 最后数据（余数）不舍弃
)

几点解释
以此次一批数据为4为例
一个批次dataloader[0]就是
img0,target0 = dateset[0] . . . img3,target3 = dateset[3]
总共4个数据
故，
dataloader会将上面的img0……img3进行打包成imgs
target0……target3进行打包成target
如下小土堆的图所示

5.在Tensorboard中显示即完整代码如下

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

# 准备测试集
test_set = torchvision.datasets.CIFAR10("dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)

# 创建test_loader实例
test_loader = DataLoader(
    dataset=test_set, # 引入数据集
    batch_size=4, # 每次取4个数据
    shuffle=True, # 打乱顺序
    num_workers=0, # 非多进程
    drop_last=False # 最后数据（余数）不舍弃
)

img,index = test_set[0]
print(img.shape) # 查看图片大小 torch.Size([3, 32, 32]) C h w，即三通道 32*32
print(index) # 查看图片标签
# 遍历test_loader
for data in test_loader:
    img,target = data
    print(img.shape) # 查看图片信息torch.Size([4, 3, 32, 32])表示一次4张图片，图片为3通道RGB,大小为32*32
    print(target)  # tensor([4, 9, 8, 8])表示4张图片的target
# 在tensorboard 中显示
writer = SummaryWriter("logs")
step = 0
for data in test_loader:
    img, target = data
    writer.add_images("test_loader",img,step)
    step = step+1
writer.close()