torch.sort¶
- torch.sort(input, dim=- 1, descending=False, stable=False, *, out=None)¶
Sorts the elements of the
input
tensor along a given dimension in ascending order by value.If
dim
is not given, the last dimension of the input is chosen.If
descending
isTrue
then the elements are sorted in descending order by value.If
stable
isTrue
then the sorting routine becomes stable, preserving the order of equivalent elements.A namedtuple of (values, indices) is returned, where the values are the sorted values and indices are the indices of the elements in the original input tensor.
- Parameters:
- Keyword Arguments:
out (tuple, optional) – the output tuple of (Tensor, LongTensor) that can be optionally given to be used as output buffers
Example:
>>> x = torch.randn(3, 4) >>> sorted, indices = torch.sort(x) >>> sorted tensor([[-0.2162, 0.0608, 0.6719, 2.3332], [-0.5793, 0.0061, 0.6058, 0.9497], [-0.5071, 0.3343, 0.9553, 1.0960]]) >>> indices tensor([[ 1, 0, 2, 3], [ 3, 1, 0, 2], [ 0, 3, 1, 2]]) >>> sorted, indices = torch.sort(x, 0) >>> sorted tensor([[-0.5071, -0.2162, 0.6719, -0.5793], [ 0.0608, 0.0061, 0.9497, 0.3343], [ 0.6058, 0.9553, 1.0960, 2.3332]]) >>> indices tensor([[ 2, 0, 0, 1], [ 0, 1, 1, 2], [ 1, 2, 2, 0]]) >>> x = torch.tensor([0, 1] * 9) >>> x.sort() torch.return_types.sort( values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), indices=tensor([ 2, 16, 4, 6, 14, 8, 0, 10, 12, 9, 17, 15, 13, 11, 7, 5, 3, 1])) >>> x.sort(stable=True) torch.return_types.sort( values=tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]), indices=tensor([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 1, 3, 5, 7, 9, 11, 13, 15, 17]))