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

zasdfgbnm · apaszke · commit d7c32df67fda · 2018-05-25T12:50:50.000+02:00
diff --git a/docs/source/data.rst b/docs/source/data.rst
@@ -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
diff --git a/torch/utils/data/__init__.py b/torch/utils/data/__init__.py
@@ -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
diff --git a/torch/utils/data/dataset.py b/torch/utils/data/dataset.py
@@ -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
@@ -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
@@ -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!")
diff --git a/torch/utils/data/sampler.py b/torch/utils/data/sampler.py
@@ -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):
@@ -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
