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torch.Tensor.index_reduce_¶

Tensor.index_reduce_(dim, index, source, reduce, *, include_self=True)

Accumulate the elements of source into the self tensor by accumulating to the indices in the order given in index using the reduction given by the reduce argument. For example, if dim == 0, index[i] == j, reduce == prod and include_self == True then the ith row of source is multiplied by the jth row of self. If include_self="True", the values in the self tensor are included in the reduction, otherwise, rows in the self tensor that are accumulated to are treated as if they were filled with the reduction identites.

The dimth dimension of source must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.

For a 3-D tensor with reduce="prod" and include_self=True the output is given as:

self[index[i], :, :] *= src[i, :, :]  # if dim == 0
self[:, index[i], :] *= src[:, i, :]  # if dim == 1
self[:, :, index[i]] *= src[:, :, i]  # if dim == 2


Note

This operation may behave nondeterministically when given tensors on a CUDA device. See Reproducibility for more information.

Note

This function only supports floating point tensors.

Warning

This function is in beta and may change in the near future.

Parameters
• dim (int) – dimension along which to index

• index (Tensor) – indices of source to select from, should have dtype either torch.int64 or torch.int32

• source (FloatTensor) – the tensor containing values to accumulate

• reduce (str) – the reduction operation to apply ("prod", "mean", "amax", "amin")

Keyword Arguments

include_self (bool) – whether the elements from the self tensor are included in the reduction

Example:

>>> x = torch.empty(5, 3).fill_(2)
>>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=torch.float)
>>> index = torch.tensor([0, 4, 2, 0])
>>> x.index_reduce_(0, index, t, 'prod')
tensor([[20., 44., 72.],
[ 2.,  2.,  2.],
[14., 16., 18.],
[ 2.,  2.,  2.],
[ 8., 10., 12.]])
>>> x = torch.empty(5, 3).fill_(2)
>>> x.index_reduce_(0, index, t, 'prod', include_self=False)
tensor([[10., 22., 36.],
[ 2.,  2.,  2.],
[ 7.,  8.,  9.],
[ 2.,  2.,  2.],
[ 4.,  5.,  6.]])


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