graclus cpu implementation

aten/cpu/cluster.cpp

 #include <torch/torch.h>
 
 
-at::Tensor graclus(at::Tensor row, at::Tensor col, at::Tensor weight) {
-  return row;
+at::Tensor degree(at::Tensor index, int64_t num_nodes) {
+  auto one = at::ones_like(index);
+  auto zero = at::zeros(torch::CPU(at::kLong), { num_nodes });
+  return zero.scatter_add_(0, index, one);
+}
+
+
+at::Tensor graclus(at::Tensor row, at::Tensor col, int64_t num_nodes) {
+  auto cluster = at::empty(torch::CPU(at::kLong), { num_nodes }).fill_(-1);
+  auto deg = degree(row, num_nodes);
+
+  /* at::Tensor perm; */
+  /* std::tie(row, perm) = row.sort(); */
+  /* col = col.index_select(0, perm); */
+
+  /* TODO: randperm */
+  /* TODO: randperm_sort_row */
+
+  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 n_idx = 0, 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[c] = r;
+          break;
+        }
+      }
+    }
+    e_idx += deg_data[n_idx];
+    n_idx++;
+  }
+
+  return cluster;
 }
 
 
@@ -14,8 +53,8 @@ at::Tensor grid(at::Tensor pos, at::Tensor size, at::Tensor start, at::Tensor en
   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 - num_voxels[0];
-  num_voxels[0] = 1;
+  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);

aten/cpu/cluster.py

@@ -9,11 +9,21 @@ def grid_cluster(pos, size, start=None, end=None):
     return cluster_cpu.grid(pos, size, start, end)
 
 
-pos = torch.tensor([[1, 1], [3, 3], [5, 5], [7, 7]])
-size = torch.tensor([2, 2])
-start = torch.tensor([0, 0])
-end = torch.tensor([7, 7])
-print('pos', pos.tolist())
-print('size', size.tolist())
-cluster = grid_cluster(pos, size)
-print('result', cluster.tolist(), cluster.dtype)
+def graclus_cluster(row, col, num_nodes):
+    return cluster_cpu.graclus(row, col, num_nodes)
+
+
+# pos = torch.tensor([[1, 1], [3, 3], [5, 5], [7, 7]])
+# size = torch.tensor([2, 2])
+# start = torch.tensor([0, 0])
+# end = torch.tensor([7, 7])
+# print('pos', pos.tolist())
+# print('size', size.tolist())
+# cluster = grid_cluster(pos, size)
+# print('result', cluster.tolist(), cluster.dtype)
+
+row = torch.tensor([0, 0, 1, 1, 1, 2, 2, 2, 3, 3])
+col = torch.tensor([1, 2, 0, 2, 3, 0, 1, 3, 1, 2])
+print(row)
+
+print(graclus_cluster(row, col, 4))