def make_schnet_model(args):
label = args.label
negative_dr = args.derivative is not None
atomrefs = None
#if hasattr(args,'atomrefs') and args.atomrefs is not None:
# atomrefs = self.read_atomrefs(args.atomrefs,args.max_z)
reps = rep.SchNet(n_atom_basis=args.num_filters,
n_filters=args.num_filters,
n_interactions=args.num_interactions,
cutoff=args.cutoff,
n_gaussians=args.num_gaussians,
max_z=args.max_z,
cutoff_network=CosineCutoff)
output = spk.atomistic.Atomwise(n_in=reps.n_atom_basis,
aggregation_mode='sum',
property=label,
derivative=args.derivative,
negative_dr=negative_dr,
mean=None,
stddev=None,
atomref=atomrefs)
model = atm.AtomisticModel(reps, output)
total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('Number of trainable parameters {}'.format(total_params))
return model
import torch
import torch.nn.functional as F
from torch.optim import Adam
import schnetpack as spk
import schnetpack.atomistic as atm
import schnetpack.representation as rep
from schnetpack.datasets import *
# load qm9 dataset and download if necessary
data = QM9("qm9.db", properties=[QM9.U0])
# split in train and val
train, val, test = data.create_splits(100000, 10000)
loader = spk.data.AtomsLoader(train, batch_size=100, num_workers=4)
val_loader = spk.data.AtomsLoader(val)
# create model
reps = rep.SchNet()
output = atm.Atomwise()
model = atm.AtomisticModel(reps, output)
# create trainergit add
opt = Adam(model.parameters(), lr=1e-4)
loss = lambda b, p: F.mse_loss(p["y"], b[QM9.U0])
trainer = spk.train.Trainer("output/", model, loss, opt, loader, val_loader)
# start training
trainer.train(torch.device("cpu"))
