update back to original version of torch.div and torch.meshgrid to al…

…so support lower version pytorch

pcdet/models/model_utils/centernet_utils.py

@@ -139,11 +139,11 @@ def _topk(scores, K=40):
     topk_scores, topk_inds = torch.topk(scores.flatten(2, 3), K)
 
     topk_inds = topk_inds % (height * width)
-    topk_ys = torch.div(topk_inds, width, rounding_mode='floor').float()
+    topk_ys = (topk_inds // width).float()
     topk_xs = (topk_inds % width).int().float()
 
     topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K)
-    topk_classes = torch.div(topk_ind, K, rounding_mode='floor').int()
+    topk_classes = (topk_ind // K).int()
     topk_inds = _gather_feat(topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)
     topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K)
     topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K)

pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py

@@ -215,7 +215,7 @@ def forward(self, support_xyz, support_features, xyz_batch_cnt, new_xyz, new_xyz
                 self.max_neighbour_distance, self.nsample, self.neighbor_type,
                 self.num_avg_length_of_neighbor_idxs, self.num_total_grids, self.neighbor_distance_multiplier
             )
-        self.num_avg_length_of_neighbor_idxs = max(self.num_avg_length_of_neighbor_idxs, num_avg_length_of_neighbor_idxs)
+        self.num_avg_length_of_neighbor_idxs = max(self.num_avg_length_of_neighbor_idxs, num_avg_length_of_neighbor_idxs.item())
 
         dist_recip = 1.0 / (dist + 1e-8)
         norm = torch.sum(dist_recip, dim=-1, keepdim=True)
@@ -349,7 +349,7 @@ def get_dense_voxels_by_center(point_centers, max_neighbour_distance, num_voxels
         x_grids = torch.arange(-R + R / num_voxels[0], R - R / num_voxels[0] + 1e-5, 2 * R / num_voxels[0], device=device)
         y_grids = torch.arange(-R + R / num_voxels[1], R - R / num_voxels[1] + 1e-5, 2 * R / num_voxels[1], device=device)
         z_grids = torch.arange(-R + R / num_voxels[2], R - R / num_voxels[2] + 1e-5, 2 * R / num_voxels[2], device=device)
-        x_offset, y_offset, z_offset = torch.meshgrid(x_grids, y_grids, z_grids, indexing='ij')  # shape: [num_x, num_y, num_z]
+        x_offset, y_offset, z_offset = torch.meshgrid(x_grids, y_grids, z_grids)  # shape: [num_x, num_y, num_z]
         xyz_offset = torch.cat((
             x_offset.contiguous().view(-1, 1),
             y_offset.contiguous().view(-1, 1),

pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py

@@ -348,7 +348,7 @@ def forward(ctx, support_xyz, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_
             stack_neighbor_idxs, start_len, num_new_xyz, num_total_grids
         )
 
-        return torch.sqrt(new_xyz_grid_dist2), new_xyz_grid_idxs, avg_length_of_neighbor_idxs
+        return torch.sqrt(new_xyz_grid_dist2), new_xyz_grid_idxs, torch.tensor(avg_length_of_neighbor_idxs)
 
 
 three_nn_for_vector_pool_by_two_step = ThreeNNForVectorPoolByTwoStep.apply