I have the same problem as groupby aggregate mean in pytorch. However, I want to create the product of my tensors inside each group (or labels). Unfortunately, I couldn't find a native PyTorch function that could solve my problem, like a hypothetical scatter_prod_ for products (equivalent to scatter_add_ for sums), which was the function used in one of the answers.
Recycling the example code from @elyase's question, consider the 2D tensor:
samples = torch.Tensor([
[0.1, 0.1], #-> group / class 1
[0.2, 0.2], #-> group / class 2
[0.4, 0.4], #-> group / class 2
[0.0, 0.0] #-> group / class 0
])
with labels where it is true that len(samples) == len(labels)
labels = torch.LongTensor([1, 2, 2, 0])
So my expected output is:
res == torch.Tensor([
[0.0, 0.0],
[0.1, 0.1],
[0.08, 0.08] # -> PRODUCT of [0.2, 0.2] and [0.4, 0.4]
])
Here the question is, again, following @elyase's question, how can this be done in pure PyTorch (i.e. no numpy so that I can autograd) and ideally without for loops?
Crossposted in PyTorch forums.
You can use the scatter_ function to calculate the product of the tensors in each group.
samples = torch.Tensor([
[0.1, 0.1], #-> group / class 1
[0.2, 0.2], #-> group / class 2
[0.4, 0.4], #-> group / class 2
[0.0, 0.0] #-> group / class 0
])
labels = torch.LongTensor([1,2,2,0])
label_size = 3
sample_dim = samples.size(1)
index = labels.unsqueeze(1).repeat((1, sample_dim))
res = torch.ones(label_size, sample_dim, dtype=samples.dtype)
res.scatter_(0, index, samples, reduce='multiply')
res:
tensor([[0.0000, 0.0000],
[0.1000, 0.1000],
[0.0800, 0.0800]])