def test_eval(self, qm9_train_loader, qm9_val_loader, qm9_test_loader,
modeldir):
args = Namespace(
model="schnet",
cutoff_function="hard",
features=100,
n_filters=5,
interactions=2,
cutoff=4.0,
num_gaussians=30,
modelpath=modeldir,
split="test",
)
repr = get_representation(args)
output_module = spk.atomistic.Atomwise(args.features,
property="energy_U0")
model = get_model(repr, output_module)
evaluate(
args,
model,
qm9_train_loader,
qm9_val_loader,
qm9_test_loader,
"cpu",
metrics=[
schnetpack.train.metrics.MeanAbsoluteError(
"energy_U0", model_output="energy_U0")
],
)
assert os.path.exists(os.path.join(modeldir, "evaluation.txt"))
args.split = "train"
evaluate(
args,
model,
qm9_train_loader,
qm9_val_loader,
qm9_test_loader,
"cpu",
metrics=[
schnetpack.train.metrics.MeanAbsoluteError(
"energy_U0", model_output="energy_U0")
],
)
args.split = "val"
evaluate(
args,
model,
qm9_train_loader,
qm9_val_loader,
qm9_test_loader,
"cpu",
metrics=[
schnetpack.train.metrics.MeanAbsoluteError(
"energy_U0", model_output="energy_U0")
],
)
def test_schnet(self):
args = Namespace(
model="schnet",
cutoff_function="hard",
features=100,
n_filters=5,
interactions=2,
cutoff=4.0,
num_gaussians=30,
normalize_filter=False,
)
repr = get_representation(args)
assert type(repr) == spk.SchNet
assert len(repr.interactions) == 2
assert type(repr) != spk.representation.BehlerSFBlock
args = Namespace(
model="schnet",
cutoff_function="cosine",
features=100,
n_filters=5,
interactions=2,
cutoff=4.0,
num_gaussians=30,
normalize_filter=False,
)
repr = get_representation(args)
assert type(repr) == spk.SchNet
assert len(repr.interactions) == 2
assert type(repr) != spk.representation.BehlerSFBlock
args = Namespace(
model="schnet",
cutoff_function="mollifier",
features=100,
n_filters=5,
interactions=2,
cutoff=4.0,
num_gaussians=30,
normalize_filter=False,
)
repr = get_representation(args)
assert type(repr) == spk.SchNet
assert len(repr.interactions) == 2
assert type(repr) != spk.representation.BehlerSFBlock
def get_model(args, train_loader, mean, stddev, atomref, logging=None):
if args.mode == "train":
if logging:
logging.info("building model...")
if args.dropout == 0 and args.n_layers == 2:
from schnetpack.utils import get_representation, get_output_module
representation = get_representation(args, train_loader)
output_module = get_output_module(
args,
representation=representation,
mean=mean,
stddev=stddev,
atomref=atomref,
)
else:
from schnetpack.utils import get_representation #get_output_module# get_representation
representation = get_representation(args, train_loader)
# representation = get_rep_with_dropout(args, train_loader)
# output_module = get_output_module(
output_module = get_outmod_with_dropout(
args,
representation=representation,
mean=mean,
stddev=stddev,
atomref=atomref,
)
model = spk.AtomisticModel(representation, [output_module])
if args.parallel:
model = nn.DataParallel(model)
if logging:
logging.info("The model you built has: %d parameters" %
spk.utils.count_params(model))
return model
else:
raise spk.utils.ScriptError("Invalid mode selected: {}".format(
args.mode))
def test_wacsf(self, qm9_train_loader):
args = Namespace(
model="wacsf",
cutoff_function="cosine",
features=100,
n_filters=5,
interactions=3,
cutoff=4.0,
num_gaussians=30,
behler=False,
elements=["C"],
radial=22,
angular=5,
zetas=[1],
centered=True,
crossterms=True,
standardize=False,
cuda=False,
)
repr = get_representation(args, qm9_train_loader)
assert type(repr) != spk.SchNet
assert type(repr) == spk.representation.BehlerSFBlock