Method forward

pointops2/functions/pointops_ablation.py:184–200

input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b) output: (n, c)

(ctx, xyz, new_xyz, input, offset, new_offset, k=3)

class Interpolation(Function):
    @staticmethod
    def forward(ctx, xyz, new_xyz, input, offset, new_offset, k=3):
        """
        input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b)
        output: (n, c)
        """
        assert xyz.is_contiguous() and new_xyz.is_contiguous() and input.is_contiguous()
        idx, dist = knnquery(k, xyz, new_xyz, offset, new_offset) # (n, k), (n, k)
        dist_recip = 1.0 / (dist + 1e-8) # (n, k)
        norm = torch.sum(dist_recip, dim=1, keepdim=True)
        weight = dist_recip / norm # (n, k)

        n, c, m = new_xyz.shape[0], input.shape[1], input.shape[0]
        output = torch.cuda.FloatTensor(n, c).zero_()
        pointops_cuda.interpolation_forward_cuda(n, c, k, input, idx, weight, output)
        ctx.m, ctx.k = m, k
        ctx.save_for_backward(idx, weight)
        return output

    @staticmethod
    def backward(ctx, grad_output):