def save(self, filename: Path):
"""
Save a model to disk.
Args:
filename: Path to store the model.
"""
filename = to_path(filename)
create_directory(filename)
torch.save(self.state_dict(), str(filename))
# save descriptor mean and stdev
fname = filename.parent.joinpath("mean_and_stdev.pkl")
self.descriptor.dump_mean_stdev(fname)
def save(self, filename: Path):
"""
Save a model to disk.
Args:
filename: Path to store the model.
"""
state_dict = {
"model_state_dict": self.state_dict(),
"descriptor_state_dict": self.descriptor.state_dict(),
}
filename = to_path(filename)
create_directory(filename)
torch.save(state_dict, str(filename))
def _compute_single_config(self, ca, normalize, verbose, common_path,
prefix):
self.calculator.compute(ca)
conf = _get_config(ca)
conf_path = os.path.abspath(conf.path)
natoms = conf.get_num_atoms()
if self.compute_energy:
pred_e = self.calculator.get_energy(ca)
pred_e = _to_numpy(pred_e, ca)
ref_e = conf.energy
ediff = pred_e - ref_e
enorm = abs(ediff)
if normalize:
enorm /= natoms
else:
ediff = None
enorm = None
if self.compute_forces:
pred_f = self.calculator.get_forces(ca)
pred_f = _to_numpy(pred_f, ca).reshape(-1, 3)
ref_f = conf.forces.reshape(-1, 3)
fdiff = pred_f - ref_f
fnorm = np.linalg.norm(fdiff)
if normalize:
fnorm /= natoms
else:
fdiff = None
fnorm = None
# write the difference to extxyz files
if verbose >= 2:
if conf_path.startswith(common_path):
base = conf_path[len(common_path):]
else:
raise AnalyzerError(
'identifier "{}" not start with common_path "{}".'.format(
conf_path, common_path))
path = os.path.join(prefix, base)
create_directory(path)
conf.to_file(path)
return enorm, fnorm
def _dump_fingerprints(
self,
configs,
fname,
all_zeta,
all_dzetadr_forces,
all_dzetadr_stress,
fit_forces,
fit_stress,
):
"""
Dump fingerprints to a pickle file.
"""
logger.info(f"Pickling fingerprints to `{fname}`")
create_directory(fname, is_directory=False)
# remove it, because we use append mode for the file below
fname = to_path(fname)
if fname.exists():
fname.unlink()
with open(fname, "ab") as f:
for i, conf in enumerate(configs):
if i % 100 == 0:
logger.info(f"Processing configuration: {i}.")
if all_zeta is None:
zeta, dzetadr_f, dzetadr_s = self.transform(
conf, fit_forces, fit_stress
)
else:
zeta = all_zeta[i]
dzetadr_f = all_dzetadr_forces[i]
dzetadr_s = all_dzetadr_stress[i]
# centering and normalization
if self.normalize:
zeta = (zeta - self.mean) / self.stdev
if fit_forces or fit_stress:
stdev_3d = np.atleast_3d(self.stdev)
if fit_forces:
dzetadr_f = dzetadr_f / stdev_3d
if fit_stress:
dzetadr_s = dzetadr_s / stdev_3d
# pickling data
zeta = np.asarray(zeta, self.dtype)
energy = np.asarray(conf.energy, self.dtype)
if fit_forces:
dzetadr_f = np.asarray(dzetadr_f, self.dtype)
forces = np.asarray(conf.forces, self.dtype)
if fit_stress:
dzetadr_s = np.asarray(dzetadr_s, self.dtype)
stress = np.asarray(conf.stress, self.dtype)
volume = np.asarray(conf.get_volume(), self.dtype)
example = {"configuration": conf, "zeta": zeta, "energy": energy}
if fit_forces:
example["dzetadr_forces"] = dzetadr_f
example["forces"] = forces
if fit_stress:
example["dzetadr_stress"] = dzetadr_s
example["stress"] = stress
example["volume"] = volume
pickle.dump(example, f)
logger.info(f"Pickle {len(configs)} configurations finished.")