Epoch 1
train mode with negative samples 1 ...: 1%|▏ | 1/184 [00:00<02:51, 1.07it/s]Traceback (most recent call last):
File "/home/p/Documents/GraphMixer/train.py", line 221, in <module>
model = link_pred_train(model.to(args.device), args, g, df, node_feats, edge_feats)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home//Documents/GraphMixer/link_pred_train_utils.py", line 157, in link_pred_train
train_ap, train_auc, train_loss = run(model, optimizer, args, train_subgraphs, df,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/p/Documents/GraphMixer/link_pred_train_utils.py", line 76, in run
subgraph_node_feats = compute_sign_feats(node_feats, df, cur_inds, num_of_df_links, subgraph_data['root_nodes'], args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/p/Documents/GraphMixer/link_pred_train_utils.py", line 239, in compute_sign_feats
sign_feats.append(adj_norm@sign_feats[-1])
~~~~~~~~^~~~~~~~~~~~~~~
File "/home/p/anaconda3/envs/ml/lib/python3.11/site-packages/torch_sparse/matmul.py", line 171, in <lambda>
SparseTensor.__matmul__ = lambda self, other: matmul(self, other, 'sum')
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/p/anaconda3/envs/ml/lib/python3.11/site-packages/torch_sparse/matmul.py", line 160, in matmul
return spmm(src, other, reduce)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/p/anaconda3/envs/ml/lib/python3.11/site-packages/torch_sparse/matmul.py", line 83, in spmm
return spmm_sum(src, other)
^^^^^^^^^^^^^^^^^^^^
File "/home/p/anaconda3/envs/ml/lib/python3.11/site-packages/torch_sparse/matmul.py", line 27, in spmm_sum
return torch.ops.torch_sparse.spmm_sum(row, rowptr, col, value, colptr,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/p/anaconda3/envs/ml/lib/python3.11/site-packages/torch/_ops.py", line 502, in __call__
return self._op(*args, **kwargs or {})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Not compiled with CUDA support
train mode with negative samples 1 ...: 1%|▏ | 1/184 [00:01<05:24, 1.77s/it]
