关于为什么要用Sampler可以阅读一文弄懂Pytorch的DataLoader, DataSet, Sampler之间的关系。
本文我们会从源代码的角度了解Sampler。
Sampler首先需要知道的是所有的采样器都继承自Sampler这个类，如下：
可以看到主要有三种方法：分别是：
__init__: 这个很好理解，就是初始化__iter__: 这个是用来产生迭代索引值的，也就是指定每个step需要读取哪些数据__len__: 这个是用来返回每次迭代器的长度
class Sampler(object):
r"""Base class for all Samplers.
Every Sampler subclass has to provide an __iter__ method, providing a way
to iterate over indices of dataset elements, and a __len__ method that
returns the length of the returned iterators.
"""
# 一个 迭代器 基类
def __init__(self, data_source):
pass
def __iter__(self):
raise NotImplementedError
def __len__(self):
raise NotImplementedError介绍完父类后我们看看Pytorch给我们提供了哪些采样器
SequentialSampler
这个看名字就很好理解，其实就是按顺序对数据集采样。
其原理是首先在初始化的时候拿到数据集data_source，之后在__iter__方法中首先得到一个和data_source一样长度的range可迭代器。每次只会返回一个索引值。
class SequentialSampler(Sampler):
r"""Samples elements sequentially, always in the same order.
Arguments:
data_source (Dataset): dataset to sample from
"""
# 产生顺序 迭代器
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)使用示例：
a = [1,5,78,9,68]
b = torch.utils.data.SequentialSampler(a)
for x in b:
print(x)
>>> 0
1
2
3
4RandomSampler
参数作用：
- data_source: 同上
- num_samples: 指定采样的数量，默认是所有。
- replacement: 若为True，则表示可以重复采样，即同一个样本可以重复采样，这样可能导致有的样本采样不到。所以此时我们可以设置num_samples来增加采样数量使得每个样本都可能被采样到。
class RandomSampler(Sampler):
r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.
If with replacement, then user can specify ``num_samples`` to draw.
Arguments:
data_source (Dataset): dataset to sample from
num_samples (int): number of samples to draw, default=len(dataset)
replacement (bool): samples are drawn with replacement if ``True``, default=False
"""
def __init__(self, data_source, replacement=False, num_samples=None):
self.data_source = data_source
self.replacement = replacement
self.num_samples = num_samples
if self.num_samples is not None and replacement is False:
raise ValueError("With replacement=False, num_samples should not be specified, "
"since a random permute will be performed.")
if self.num_samples is None:
self.num_samples = len(self.data_source)
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
raise ValueError("num_samples should be a positive integeral "
"value, but got num_samples={}".format(self.num_samples))
if not isinstance(self.replacement, bool):
raise ValueError("replacement should be a boolean value, but got "
"replacement={}".format(self.replacement))
def __iter__(self):
n = len(self.data_source)
if self.replacement:
return iter(torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64).tolist())
return iter(torch.randperm(n).tolist())
def __len__(self):
return len(self.data_source)SubsetRandomSampler
class SubsetRandomSampler(Sampler):
r"""Samples elements randomly from a given list of indices, without replacement.
Arguments:
indices (sequence): a sequence of indices
"""
def __init__(self, indices):
self.indices = indices
def __iter__(self):
return (self.indices[i] for i in torch.randperm(len(self.indices)))
def __len__(self):
return len(self.indices)这个采样器常见的使用场景是将训练集划分成训练集和验证集，示例如下：
n_train = len(train_dataset)
split = n_train // 3
indices = random.shuffle(list(range(n_train)))
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split:])
valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split])
train_loader = DataLoader(..., sampler=train_sampler, ...)
valid_loader = DataLoader(..., sampler=valid_sampler, ...)WeightedRandomSampler
参数作用同上面的RandomSampler，不再赘述。
class WeightedRandomSampler(Sampler):
r"""Samples elements from [0,..,len(weights)-1] with given probabilities (weights).
Arguments:
weights (sequence) : a sequence of weights, not necessary summing up to one
num_samples (int): number of samples to draw
replacement (bool): if ``True``, 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.
"""
def __init__(self, weights, num_samples, replacement=True):
if not isinstance(num_samples, _int_classes) or isinstance(num_samples, bool) or \
num_samples <= 0:
raise ValueError("num_samples should be a positive integeral "
"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.tensor(weights, dtype=torch.double)
self.num_samples = num_samples
self.replacement = replacement
def __iter__(self):
return iter(torch.multinomial(self.weights, self.num_samples, self.replacement).tolist())
def __len__(self):
return self.num_samples ## 指的是一次一共采样的样本的数量BatchSampler
前面的采样器每次都只返回一个索引，但是我们在训练时是对批量的数据进行训练，而这个工作就需要BatchSampler来做。也就是说BatchSampler的作用就是将前面的Sampler采样得到的索引值进行合并，当数量等于一个batch大小后就将这一批的索引值返回。
class BatchSampler(Sampler):
r"""Wraps another sampler to yield a mini-batch of indices.
Args:
sampler (Sampler): Base sampler.
batch_size (int): Size of mini-batch.
drop_last (bool): If ``True``, 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]]
"""
# 批次采样
def __init__(self, sampler, batch_size, drop_last):
if not isinstance(sampler, Sampler):
raise ValueError("sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}"
.format(sampler))
if not isinstance(batch_size, _int_classes) or isinstance(batch_size, bool) or \
batch_size 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
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2020-01-23 17:45:35
