cluster.cpp

#include <torch/torch.h>

inline std::tuple<at::Tensor, at::Tensor> remove_self_loops(at::Tensor row,
                                                            at::Tensor col) {
  auto mask = row != col;
  row = row.masked_select(mask);
  col = col.masked_select(mask);
  return {row, col};
}

inline std::tuple<at::Tensor, at::Tensor>
randperm(at::Tensor row, at::Tensor col, int64_t num_nodes) {
  // Randomly reorder row and column indices.
  auto perm = at::randperm(torch::CPU(at::kLong), row.size(0));
  row = row.index_select(0, perm);
  col = col.index_select(0, perm);

  // Randomly swap row values.
  auto node_rid = at::randperm(torch::CPU(at::kLong), num_nodes);
  row = node_rid.index_select(0, row);

  // Sort row and column indices row-wise.
  std::tie(row, perm) = row.sort();
  col = col.index_select(0, perm);

  // Revert row value swaps.
  row = std::get<1>(node_rid.sort()).index_select(0, row);

  return {row, col};
}

inline at::Tensor degree(at::Tensor index, int64_t num_nodes) {
  auto zero = at::zeros(torch::CPU(at::kLong), {num_nodes});
  return zero.scatter_add_(0, index, at::ones_like(index));
}

at::Tensor graclus(at::Tensor row, at::Tensor col, int64_t num_nodes) {
  std::tie(row, col) = remove_self_loops(row, col);
  std::tie(row, col) = randperm(row, col, num_nodes);

  auto deg = degree(row, num_nodes);
  auto cluster = at::empty(torch::CPU(at::kLong), {num_nodes}).fill_(-1);

  auto *row_data = row.data<int64_t>();
  auto *col_data = col.data<int64_t>();
  auto *deg_data = deg.data<int64_t>();
  auto *cluster_data = cluster.data<int64_t>();

  int64_t e_idx = 0, d_idx, r, c;
  while (e_idx < row.size(0)) {
    r = row_data[e_idx];
    if (cluster_data[r] < 0) {
      cluster_data[r] = r;
      for (d_idx = 0; d_idx < deg_data[r]; d_idx++) {
        c = col_data[e_idx + d_idx];
        if (cluster_data[c] < 0) {
          cluster_data[r] = std::min(r, c);
          cluster_data[c] = std::min(r, c);
          break;
        }
      }
    }
    e_idx += deg_data[r];
  }

  return cluster;
}

at::Tensor grid(at::Tensor pos, at::Tensor size, at::Tensor start,
                at::Tensor end) {
  size = size.toType(pos.type());
  start = start.toType(pos.type());
  end = end.toType(pos.type());

  pos = pos - start.view({1, -1});
  auto num_voxels = ((end - start) / size).toType(at::kLong);
  num_voxels = (num_voxels + 1).cumsum(0);
  num_voxels -= num_voxels.data<int64_t>()[0];
  num_voxels.data<int64_t>()[0] = 1;

  auto cluster = pos / size.view({1, -1});
  cluster = cluster.toType(at::kLong);
  cluster *= num_voxels.view({1, -1});
  cluster = cluster.sum(1);

  return cluster;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("graclus", &graclus, "Graclus (CPU)");
  m.def("grid", &grid, "Grid (CPU)");
}