/usr/local/lib64/python3.6/site-packages/torch/utils/data/__pycache__
Edit: /usr/local/lib64/python3.6/site-packages/torch/utils/data/__pycache__/sampler.cpython-36.pyc (9012B)
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d de e ZG dd de e ZG dd de ee ZdS ) N)Tensor)IteratorOptionalSequenceListTypeVarGenericSizedT_coT) covariantc @ s6 e Zd ZdZee ddddZee dddZ dS ) Samplera 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`.
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__name__
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dS ) SequentialSamplerz~Samples elements sequentially, always in the same order.
Args:
data_source (Dataset): dataset to sample from
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ZdS )
RandomSamplera5 Samples elements randomly. If without replacement, then sample from a shuffled dataset.
If with replacement, then user can specify :attr:`num_samples` to draw.
Args:
data_source (Dataset): dataset to sample from
replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False``
num_samples (int): number of samples to draw, default=`len(dataset)`. This argument
is supposed to be specified only when `replacement` is ``True``.
generator (Generator): Generator used in sampling.
FN)r
replacement num_samplesr c C sx || _ || _|| _|| _t| jts4tdj| j| jd k rL| rLtdt| j t
sd| j dkrttdj| j d S )Nz=replacement should be a boolean value, but got replacement={}zfWith replacement=False, num_samples should not be specified, since a random permute will be performed.r zFnum_samples should be a positive integer value, but got num_samples={}) r
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"(zRandomSampler.__iter__c C s | j S )N)r! )r r r r r s zRandomSampler.__len__)FNN)r r r r r r
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dS ) SubsetRandomSamplerzSamples elements randomly from a given list of indices, without replacement.
Args:
indices (sequence): a sequence of indices
generator (Generator): Generator used in sampling.
N)indicesr c C s || _ || _d S )N)r9 r# )r r9 r# r r r r s z SubsetRandomSampler.__init__)r c c s0 x*t jt| j| jdD ]}| j| V qW d S )N)r# )r- r3 r r9 r# )r ir r r r s z SubsetRandomSampler.__iter__c C s
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WeightedRandomSamplera 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 ``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.
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]
TN)weightsr! r r c C sl t |t s t |ts |dkr,tdj|t |tsDtdj|tj|tjd| _|| _ || _
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Z dS ) BatchSamplerai Wraps another sampler to yield a mini-batch of indices.
Args:
sampler (Sampler or Iterable): Base sampler. Can be any iterable object
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]]
N)sampler
batch_size drop_lastr c C sZ t |t s t |ts |dkr,tdj|t |tsDtdj||| _|| _|| _d S )Nr zDbatch_size should be a positive integer value, but got batch_size={}z9drop_last should be a boolean value, but got drop_last={})r$ r r% r( r' r@ rA rB )r r@ rA rB r r r r s
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