You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
the pretrained function returns ModuleNotFoundError: No module named 'schnetpack.atomistic.model'
Due to the changes in the schnetpack library, the model now belong to schnet.model instead of atomistic.model
Is there anything I can do with it, thank you
`import argparse
import os.path as osp
import torch
from tqdm import tqdm
from torch_geometric.datasets import QM9
from torch_geometric.loader import DataLoader
from torch_geometric.nn import SchNet
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
for target in range(12):
model, datasets = SchNet.from_qm9_pretrained(path, dataset, target)
train_dataset, val_dataset, test_dataset = datasets
model = model.to(device)
loader = DataLoader(test_dataset, batch_size=256)
maes = []
for data in tqdm(loader):
data = data.to(device)
with torch.no_grad():
pred = model(data.z, data.pos, data.batch)
mae = (pred.view(-1) - data.y[:, target]).abs()
maes.append(mae)
mae = torch.cat(maes, dim=0)
# Report meV instead of eV.
mae = 1000 * mae if target in [2, 3, 4, 6, 7, 8, 9, 10] else mae
print(f'Target: {target:02d}, MAE: {mae.mean():.5f} 卤 {mae.std():.5f}')`
Hello, I also encountered some problems while training SchNet. Can I communicate with you? Could you please provide a contact information, such as email?
馃悰 Describe the bug
the pretrained function returns ModuleNotFoundError: No module named 'schnetpack.atomistic.model'
Due to the changes in the schnetpack library, the model now belong to schnet.model instead of atomistic.model
Is there anything I can do with it, thank you
`import argparse
import os.path as osp
import torch
from tqdm import tqdm
from torch_geometric.datasets import QM9
from torch_geometric.loader import DataLoader
from torch_geometric.nn import SchNet
parser = argparse.ArgumentParser()
parser.add_argument('--cutoff', type=float, default=10.0,
help='Cutoff distance for interatomic interactions')
args = parser.parse_args()
path = osp.join(osp.dirname(osp.realpath(file)), '..', 'data', 'QM9')
dataset = QM9(path)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
for target in range(12):
model, datasets = SchNet.from_qm9_pretrained(path, dataset, target)
train_dataset, val_dataset, test_dataset = datasets
Versions
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.2.1
[pip3] torch==2.2.1
[pip3] torch-ema==0.3
[pip3] torch_geometric==2.5.2
[pip3] torchaudio==2.2.1
[pip3] torchmetrics==1.3.2
[pip3] torchvision==0.17.1
[pip3] triton==2.2.0
[conda] blas 2.116 mkl conda-forge
[conda] blas-devel 3.9.0 16_linux64_mkl conda-forge
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] libblas 3.9.0 16_linux64_mkl conda-forge
[conda] libcblas 3.9.0 16_linux64_mkl conda-forge
[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch
[conda] liblapack 3.9.0 16_linux64_mkl conda-forge
[conda] liblapacke 3.9.0 16_linux64_mkl conda-forge
[conda] mkl 2022.1.0 h84fe81f_915 conda-forge
[conda] mkl-devel 2022.1.0 ha770c72_916 conda-forge
[conda] mkl-include 2022.1.0 h84fe81f_915 conda-forge
[conda] numpy 1.26.4 py310hb13e2d6_0 conda-forge
[conda] pyg 2.5.2 py310_torch_2.2.0_cu118 pyg
[conda] pytorch 2.2.1 py3.10_cuda11.8_cudnn8.7.0_0 pytorch
[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
[conda] pytorch-lightning 2.2.1 pyhd8ed1ab_0 conda-forge
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch-ema 0.3 pyhd8ed1ab_0 conda-forge
[conda] torchaudio 2.2.1 py310_cu118 pytorch
[conda] torchmetrics 1.3.2 pyhd8ed1ab_0 conda-forge
[conda] torchtriton 2.2.0 py310 pytorch
[conda] torchvision 0.17.1 py310_cu118 pytorch
The text was updated successfully, but these errors were encountered: