How to evaluate for dataset zinc?

Thanks for the code. Good job.
I train graphormer_slim in zinc dataset by bash examples/property_prediction/zinc.sh.
Then I try to evalute it by:

python graphormer/evaluate/evaluate.py \  
--user-dir graphormer \  
--num-workers 16 \  
--ddp-backend=legacy_ddp \  
--dataset-name zinc \  
--dataset-source pyg \
--task graph_prediction \  
--criterion l1_loss \  
--arch graphormer_slim \  
--num-classes 1 \  
--batch-size 64 \  
--save-dir exp/checkpoints_dir/ckpts_zinc \  
--metric mae \  
--split test      

An error happen:

TypeError: mean() received an invalid combination of arguments - got (out=NoneType, dtype=NoneType, axis=NoneType, ), but expected one of:
 * (*, torch.dtype dtype)
 * (tuple of ints dim, bool keepdim, *, torch.dtype dtype)
 * (tuple of names dim, bool keepdim, *, torch.dtype dtype)

I change the code
mae = np.mean(np.abs(y_true-y_pred))
to
mae = torch.nn.functional.l1_loss(y_true, y_pred)

But got the mae in zinc dataset is :

2022-01-10 14:40:42 | INFO | graphormer.tasks.graph_prediction | Loaded test with #samples: 5000
2022-01-10 14:40:46 | INFO | __main__ | mae: 0.06235151365399361 

Is this result normal? I doubt that I make some mistakes.

Start with graphormer/evaluate/evaluate.py and the evaluation path invoked by examples/property_prediction/zinc.sh. Reproduce the zinc test evaluation using the command and checkpoint details in the issue, then inspect how y_true and y_pred are passed to the MAE calculation. Done means the evaluation runs without the reported TypeError and the expected zinc MAE is clarified or covered by a regression test.