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Better documentation for variadic_sum() #215

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19 changes: 15 additions & 4 deletions torchdrug/layers/functional/functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,11 +163,22 @@ def variadic_sum(input, size):
"""
Compute sum over sets with variadic sizes.

Suppose there are :math:`N` sets, and the sizes of all sets are summed to :math:`B`.
Suppose there are :math:`N` sets, and the sizes of all sets :math:`\sum_{i=0}^{N-1} n_i` are summed to :math:`B`.

Parameters:
Input:
input (Tensor): input of shape :math:`(B, ...)`
size (LongTensor): size of sets of shape :math:`(N,)`
size (LongTensor): size of sets of shape :math:`(n_0, n_1, ..., n_{N-1})`

Output:
value (Tensor): output of shape :math:`(N, ...)`

Example:
>>> input = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9,], [4, 5, 6], [7, 8, 9,]])
>>> size = torch.tensor([1,1,3])
>>> print(variadic_sum(input, size))
tensor([[ 1, 2, 3],
[ 4, 5, 6],
[18, 21, 24]])
"""
index2sample = torch.repeat_interleave(size)
index2sample = index2sample.view([-1] + [1] * (input.ndim - 1))
Expand Down Expand Up @@ -358,7 +369,7 @@ def variadic_sort(input, size, descending=False):
input (Tensor): input of shape :math:`(B, ...)`
size (LongTensor): size of sets of shape :math:`(N,)`
descending (bool, optional): return ascending or descending order

Returns
(Tensor, LongTensor): sorted values and indexes
"""
Expand Down