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. Continue reading...
