masashitsubaki / quantumdeepfield_molecule Goto Github PK
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License: MIT License
Quantum deep field for molecule
License: MIT License
Hello, the great work. I want to know how to generate the electron density map? Can you give me some help? thanks a lot.
リポをclone後、デフォルト設定のまま $ bash train.sh したところ、
以下のエラーが出ました。
学習は走っているようですが、どうやらDataLoader周りでcrushしてしまっています。
ぜひこちらのコードを研究で活用させていただきたいのですが、手詰まりになってしまいました。
もし回避方法などあれば、ご教示いただけますと幸いです。
当方の実行環境は AWS (p2.xlarge) + Deep Learning AMI (Ubuntu 18.04) + RDKit です。
https://aws.amazon.com/marketplace/pp/B07Y43P7X5#pdp-overview
The code uses a GPU.
--------------------------------------------------
# of training samples: 34
# of validation samples: 4
# of test samples: 5
--------------------------------------------------
Set a QDF model.
# of model parameters: 392562
--------------------------------------------------
Start training of the QDF model with QM9under7atoms_atomizationenergy_eV dataset.
The training result is displayed in this terminal every epoch.
The result, prediction, and trained model are saved in the output directory.
Wait for a while...
The training will finish in about 0 hours 26 minutes.
--------------------------------------------------
Epoch Time(sec) Loss_E Loss_V MAE_val(eV) MAE_test(eV)
0 0.8071885499994096 6209.085266113281 404.9622917175293 22.261384963989258 24.396270751953125
1 1.4956756869996752 5485.979797363281 392.7659606933594 20.992084503173828 23.13214683532715
2 2.175580770999659 4863.696319580078 386.9701862335205 19.829853057861328 21.962018966674805
3 2.8557513050000125 4676.126617431641 394.4252738952637 19.19409942626953 21.30068016052246
4 3.5467280840002786 4598.614196777344 386.27272605895996 20.13705062866211 22.27927589416504
5 4.246754251000311 5112.2559814453125 379.0389823913574 20.89983558654785 23.086027145385742
6 4.942768633000014 5517.936248779297 373.1512336730957 22.083404541015625 24.299663543701172
free(): invalid size
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 986, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/queue.py", line 179, in get
self.not_empty.wait(remaining)
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/threading.py", line 300, in wait
gotit = waiter.acquire(True, timeout)
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 17362) is killed by signal: Aborted.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "train.py", line 389, in <module>
MAE_val = tester.test(dataloader_val)[0]
File "train.py", line 217, in test
N = sum([len(data[0]) for data in dataloader])
File "train.py", line 217, in <listcomp>
N = sum([len(data[0]) for data in dataloader])
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
data = self._next_data()
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1182, in _next_data
idx, data = self._get_data()
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1138, in _get_data
success, data = self._try_get_data()
File "/home/ubuntu/anaconda3/envs/torch_mi_apr21/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 999, in _try_get_data
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
RuntimeError: DataLoader worker (pid(s) 17362) exited unexpectedly
train.sh: line 39: 16905 Segmentation fault (core dumped) python train.py $dataset $basis_set $radius $grid_interval $dim $layer_functional $hidden_HK $layer_HK $operation $batch_size $lr $lr_decay $step_size $iteration $setting $num_workers
Dear authors, I have read your paper "Phys. Rev. Lett. 125, 206401" and enjoy it very much.
However I have a problem now about the choose of the external potential. In your paper your pick a gaussian potential as the target of HKS net. However, if I remember correctly, the true external potential corresponding to the electron density of a molecule is the coulomb potential of the each atom.
I just want to know whether it's true to use a coulomb potential instead of gaussian?
No questions now
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