【几种数据集采样方式】

在训练神经网络时，如果数据量太大，无法一次性将数据放入到网络中进行训练，所以需要进行分批处理数据读取。这一个问题涉及到如何从数据集中进行读取数据的问题，PyTorch 框架提供了 Sampler 基类与多个子类实现不同方式的数据采样。

基类 Sampler

class Sampler(object):
    r"""Base class for all Samplers.

    Every Sampler subclass has to provide an :meth:__iter__ method, providing a
    way to iterate over indices of dataset elements, and a :meth:__len__ method
    that returns the length of the returned iterators.

    .. note:: The :meth:__len__ method isn't strictly required by
              :class:~torch.utils.data.DataLoader, but is expected in any
              calculation involving the length of a :class:~torch.utils.data.DataLoader.

"""

    def __init__(self, data_source):
        pass

    def __iter__(self):
        raise NotImplementedError

顺序采样 Sequential Sampler

class SequentialSampler(Sampler):
    r"""Samples elements sequentially, always in the same order.

    Arguments:
        data_source (Dataset): dataset to sample from
"""

    def __init__(self, data_source):
        self.data_source = data_source

    def __iter__(self):
        return iter(range(len(self.data_source)))

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

顺序采样类并没有定义过多的方法，其中初始化方法仅仅需要一个 Dataset 类作为参数。
对于 len() 只负责返回数据源包含的数据个数， iter() 方法返回可迭代对象，这个可迭代对象是一个由 range 方法产生的顺序数值序列，也就是说迭代是按照顺序进行的。
每个 Epoch 包含很多 iteration，每个 Epoch 执行一次 iter() 函数，每个 iteration 执行一次可迭代对象的 next() 函数。

//测试

data = list([1, 2, 3, 4, 5])
seq_sampler = sampler.SequentialSampler(data_source=data)

for index in seq_sampler:
    print("index: {}, data: {}".format(str(index), str(data[index])))
//输出
index: 0, data: 1
index: 1, data: 2
index: 2, data: 3
index: 3, data: 4
index: 4, data: 5

随机采样 Random Sampler

class RandomSampler(Sampler):
    r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.

    If with replacement, then user can specify :attr:num_samples to draw.

    Arguments:
        data_source (Dataset): dataset to sample from
        replacement (bool): samples are drawn with replacement if , default=
        num_samples (int): number of samples to draw, default=len(dataset). This argument
            is supposed to be specified only when replacement is .

        generator (Generator): Generator used in sampling.

"""

    def __init__(self, data_source, replacement=False, num_samples=None, generator=None):
        self.data_source = data_source

        self.replacement = replacement
        self._num_samples = num_samples
        self.generator = generator

        if not isinstance(self.replacement, bool):
            raise TypeError("replacement should be a boolean value, but got "
                            "replacement={}".format(self.replacement))

        if self._num_samples is not None and not replacement:
            raise ValueError("With replacement=False, num_samples should not be specified, "
                             "since a random permute will be performed.")

        if not isinstance(self.num_samples, int) or self.num_samples  0:
            raise ValueError("num_samples should be a positive integer "
                             "value, but got num_samples={}".format(self.num_samples))

    @property
    def num_samples(self):

        if self._num_samples is None:
            return len(self.data_source)
        return self._num_samples

    def __iter__(self):
        n = len(self.data_source)
        if self.replacement:
            rand_tensor = torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64, generator=self.generator)
            return iter(rand_tensor.tolist())
        return iter(torch.randperm(n, generator=self.generator).tolist())

    def __len__(self):
        return self.num_samples

最重要的是 iter() 方法，定义了核心的索引生成行为。其中 if 判断处返回了2种随机值，根据是否在初始化参数中给出 replacement 决定是否重复采样。区别核心在于 randint() 函数生成的随机数序列是包含重复数值的，而 randperm() 函数生成的随机数序列是不包含重复数值的。
下面分别测试 replacement 为 False 和 True 两种情况的示例：

ran_sampler = sampler.RandomSampler(data_source=data)
for index in ran_sampler:
    print("index: {}, data: {}".format(str(index), str(data[index])))

index: 3, data: 4
index: 4, data: 5
index: 2, data: 3
index: 1, data: 2
index: 0, data: 1

ran_sampler = sampler.RandomSampler(data_source=data, replacement=True)
for index in ran_sampler:
    print("index: {}, data: {}".format(str(index), str(data[index])))

index: 1, data: 2
index: 2, data: 3
index: 4, data: 5
index: 3, data: 4
index: 1, data: 2

子集随机采样 Subset Random Sampler

class SubsetRandomSampler(Sampler):
    r"""Samples elements randomly from a given list of indices, without replacement.

    Arguments:
        indices (sequence): a sequence of indices
        generator (Generator): Generator used in sampling.

"""

    def __init__(self, indices, generator=None):

        self.indices = indices
        self.generator = generator

    def __iter__(self):

        return (self.indices[i] for i in torch.randperm(len(self.indices), generator=self.generator))

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

上述代码中 len() 的作用是返回随机数序列作为 indice 的索引。需要注意的是采样仍然是不重复的，也是通过 randperm 函数实现的。下面这个例子把用于训练集，验证集和测试集的划分：

sub_sampler_train = sampler.SubsetRandomSampler(indices=data[0:2])
for index in sub_sampler_train:
    print("index: {}".format(str(index)))
print('------------')
sub_sampler_val = sampler.SubsetRandomSampler(indices=data[2:])
for index in sub_sampler_val:
    print("index: {}".format(str(index)))

index: 2
index: 1

index: 3
index: 4
index: 5

加权随机采样 WeightedRandomSampler

class WeightedRandomSampler(Sampler):
    r"""Samples elements from [0,..,len(weights)-1] with given probabilities (weights).

    Args:
        weights (sequence)   : a sequence of weights, not necessary summing up to one
        num_samples (int): number of samples to draw
        replacement (bool): if , samples are drawn with replacement.

            If not, they are drawn without replacement, which means that when a
            sample index is drawn for a row, it cannot be drawn again for that row.

        generator (Generator): Generator used in sampling.

    Example:
        >>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True))
        [4, 4, 1, 4, 5]
        >>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False))
        [0, 1, 4, 3, 2]
"""

    def __init__(self, weights, num_samples, replacement=True, generator=None):

        if not isinstance(num_samples, _int_classes) or isinstance(num_samples, bool) or \
                num_samples  0:
            raise ValueError("num_samples should be a positive integer "
                             "value, but got num_samples={}".format(num_samples))
        if not isinstance(replacement, bool):
            raise ValueError("replacement should be a boolean value, but got "
                             "replacement={}".format(replacement))

        self.weights = torch.as_tensor(weights, dtype=torch.double)
        self.num_samples = num_samples

        self.replacement = replacement
        self.generator = generator

    def __iter__(self):

        rand_tensor = torch.multinomial(self.weights, self.num_samples, self.replacement, generator=self.generator)
        return iter(rand_tensor.tolist())

    def __len__(self):
        return self.num_samples

replacement 参数依旧是控制采样有没有放回的。num_samples 用于控制生成的个数，weights 参数对应的是样本的权重而不是类别的权重。最重要的是 iter() 方法，返回随机数序列，只是这个随机数序列是按照 weights 指定的权重确定的。

data=[1,2,5,78,6,56]

weights=[0.1,0.2,0.3,0.4,0.8,0.3,5]
rsampler=sampler.WeightedRandomSampler(weights=weights,num_samples=10,replacement=True)

for index in rsampler:
    print("index: {}".format(str(index)))

index: 5
index: 4
index: 6
index: 6
index: 6

批采样 BatchSampler

class BatchSampler(Sampler):
    r"""Wraps another sampler to yield a mini-batch of indices.

    Args:
        sampler (Sampler or Iterable): Base sampler. Can be any iterable object
            with __len__ implemented.

        batch_size (int): Size of mini-batch.

        drop_last (bool): If , the sampler will drop the last batch if
            its size would be less than batch_size

    Example:
        >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False))
        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
        >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True))
        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
"""

    def __init__(self, sampler, batch_size, drop_last):

        if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
                batch_size  0:
            raise ValueError("batch_size should be a positive integer value, "
                             "but got batch_size={}".format(batch_size))
        if not isinstance(drop_last, bool):
            raise ValueError("drop_last should be a boolean value, but got "
                             "drop_last={}".format(drop_last))

        self.sampler = sampler
        self.batch_size = batch_size

        self.drop_last = drop_last

    def __iter__(self):
        batch = []
        for idx in self.sampler:
            batch.append(idx)

            if len(batch) == self.batch_size:
                yield batch
                batch = []

        if len(batch) > 0 and not self.drop_last:
            yield batch

    def __len__(self):

        if self.drop_last:
            return len(self.sampler) // self.batch_size
        else:
            return (len(self.sampler) + self.batch_size - 1) // self.batch_size

在定义好各种采样器以后，需要进行批采样。当 drop_last 为 True 时，如果采样的到的数据小于 batch size，则抛弃这个 batch 的数据。下面的例子中 BatchSampler 使用的采样器为顺序采样器。

seq_sampler = sampler.SequentialSampler(data_source=data)
batch_sampler = sampler.BatchSampler(seq_sampler, 4, False)
print(list(batch_sampler))

[[0, 1, 2, 3], [4, 5]]

Original: https://blog.csdn.net/zhaogang12138/article/details/123237984
Author: @zhg12138
Title: 【几种数据集采样方式】