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move Subset, random_split to data, use sequence at some places. #7816

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Merged
apaszke merged 1 commit into from
May 25, 2018
Merged

move Subset, random_split to data, use sequence at some places. #7816

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3 changes: 2 additions & 1 deletion docs/source/data.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,13 @@ torch.utils.data
.. autoclass:: Dataset
.. autoclass:: TensorDataset
.. autoclass:: ConcatDataset
.. autoclass:: Subset
.. autoclass:: DataLoader
.. autofunction:: torch.utils.data.random_split
.. autoclass:: torch.utils.data.Sampler
.. autoclass:: torch.utils.data.SequentialSampler
.. autoclass:: torch.utils.data.RandomSampler
.. autoclass:: torch.utils.data.SubsetRandomSampler
.. autoclass:: torch.utils.data.WeightedRandomSampler
.. autoclass:: torch.utils.data.distributed.DistributedSampler
.. autoclass:: torch.utils.data.distributed.BatchSampler
.. autofunction:: torch.utils.data.dataset.random_split
2 changes: 1 addition & 1 deletion torch/utils/data/__init__.py
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@@ -1,4 +1,4 @@

from .sampler import Sampler, SequentialSampler, RandomSampler, SubsetRandomSampler, WeightedRandomSampler, BatchSampler
from .dataset import Dataset, TensorDataset, ConcatDataset
from .dataset import Dataset, TensorDataset, ConcatDataset, Subset, random_split
from .dataloader import DataLoader
14 changes: 10 additions & 4 deletions torch/utils/data/dataset.py
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Expand Up @@ -51,7 +51,7 @@ class ConcatDataset(Dataset):
on-the-fly manner.

Arguments:
datasets (iterable): List of datasets to be concatenated
datasets (sequence): List of datasets to be concatenated
"""

@staticmethod
Expand Down Expand Up @@ -88,6 +88,13 @@ def cummulative_sizes(self):


class Subset(Dataset):
"""
Subset of a dataset at specified indices.

Arguments:
dataset (Dataset): The whole Dataset
indices (sequence): Indices in the whole set selected for subset
"""
def __init__(self, dataset, indices):
self.dataset = dataset
self.indices = indices
Expand All @@ -101,12 +108,11 @@ def __len__(self):

def random_split(dataset, lengths):
"""
Randomly split a dataset into non-overlapping new datasets of given lengths
ds
Randomly split a dataset into non-overlapping new datasets of given lengths.

Arguments:
dataset (Dataset): Dataset to be split
lengths (iterable): lengths of splits to be produced
lengths (sequence): lengths of splits to be produced
"""
if sum(lengths) != len(dataset):
raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
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4 changes: 2 additions & 2 deletions torch/utils/data/sampler.py
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Expand Up @@ -58,7 +58,7 @@ class SubsetRandomSampler(Sampler):
r"""Samples elements randomly from a given list of indices, without replacement.

Arguments:
indices (list): a list of indices
indices (sequence): a sequence of indices
"""

def __init__(self, indices):
Expand All @@ -75,7 +75,7 @@ class WeightedRandomSampler(Sampler):
r"""Samples elements from [0,..,len(weights)-1] with given probabilities (weights).

Arguments:
weights (list) : a list of weights, not necessary summing up to one
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
Expand Down