I want to sample a tensor of probability distributions with shape (N, C, H, W), where dimension 1 (size C) contains normalized probability distributions with ‘C’ possibilities. Is there a pytorch function to efficiently sample all the distributions in the tensor in parallel? I just need to sample each distribution once, so the result could either be a one-hot tensor with the same shape or a tensor of indices with shape (N, 1, H, W).
1 Answers
There was no single function to sample that I saw, but I was able to sample the tensor in several steps by computing the cumulative probabilities, sampling each point independently, and then picking the first point that sampled a 1 in the distribution dimension:
reverse_cumulative = torch.flip(torch.cumsum(torch.flip(probabilities, [1]), dim=1), [1])
cumulative = probabilities / reverse_cumulative
sampled = (torch.rand(cumulative.shape, device=device()) <= cumulative)
idxs = sampled * one_hot
idxs[~sampled] = self.tile_count
sampled_idxs = idxs.min(dim=1).indices
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