Bug in voxel grid generation

Hi,

I have one doubt regarding the voxel grid. In some of my point clouds this error is triggered:

File "/home/dhorka/phd/code/test_pool.py", line 251, in forward                                                                                   
    data = avg_pool(cluster, data)
  File "/home/dhorka/venv/pytorch11_geometric/lib/python3.6/site-packages/torch_geometric/nn/pool/avg_pool.py", line 61, in avg_pool                                                       
    index, attr = pool_edge(cluster, data.edge_index, data.edge_attr)
  File "/home/dhorka/venv/pytorch11_geometric/lib/python3.6/site-packages/torch_geometric/nn/pool/pool.py", line 11, in pool_edge                                                          
    num_nodes)
  File "/home/dhorka/venv/pytorch11_geometric/lib/python3.6/site-packages/torch_sparse/coalesce.py", line 29, in coalesce                                                                  
    _, perm = unique(row * n + col)
  File "/home/dhorka/venv/pytorch11_geometric/lib/python3.6/site-packages/torch_sparse/utils/unique.py", line 12, in unique                                                                
    out, perm = torch_sparse.unique_cuda.unique(src)
RuntimeError: cuda runtime error (9) : invalid configuration argument at /home/dhorka/libs/pytorch/aten/src/THC/generic/THCTensorMathReduce.cu:293                                      

In order to replicate the error in a more controlled environment I found that, this error is triggered when the voxel grid acts as a global pooling , it is only one grid required to aggroup all the nodes. The following code is what I used:

cluster = voxel_grid(data.pos, data.batch, 100, start = -10e7,end = 10e7)
 data.edge_attr = None                                                                                                                                      
 data = avg_pool(cluster, data)

Is there anything that I am doing wrong? Is it intended this behaviour? I know that there is a specific method to go a global pooling, but it is not my intention to do a global pooling, but some times due to the density of my point cloud, only one grid is required.

Thanks

[dhorka]

It is not the case, both implementations have different results. Moreover, I found that the results of the nnconv with the parameters properly set to have the same behaviour as ecc convolution does not have the same results as the original implementation of ecc. In a couple of hours I can share with you the code that I am using to compare the results of both implementations.

[rusty1s]

Well, cluster indices do not mean much and can vary based on implementation (e.g. different order of dimensions), but rather the coarsened graph/pooled node features should be equal. Isn’t this the case?