def test_dipole (args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
dp = DeepDipole(args.model)
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test)
data.add('dipole', 3, atomic=True, must=True, high_prec=False, type_sel = dp.get_sel_type())
test_data = data.get_test ()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
dipole = dp.eval(coord, box, atype)
dipole = dipole.reshape([numb_test,-1])
l2f = (l2err (dipole - test_data["dipole"] [:numb_test]))
print ("# number of test data : %d " % numb_test)
print ("Dipole L2err : %e eV/A" % l2f)
detail_file = args.detail_file
if detail_file is not None :
pe = np.concatenate((np.reshape(test_data["dipole"][:numb_test], [-1,3]),
np.reshape(dipole, [-1,3])),
axis = 1)
np.savetxt(detail_file+".out", pe,
header = 'data_x data_y data_z pred_x pred_y pred_z')
def test_wfc (args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
dp = DeepWFC(args.model)
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test)
data.add('wfc', 12, atomic=True, must=True, high_prec=False, type_sel = dp.get_sel_type())
test_data = data.get_test ()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
wfc = dp.eval(coord, box, atype)
wfc = wfc.reshape([numb_test,-1])
l2f = (l2err (wfc - test_data["wfc"] [:numb_test]))
print ("# number of test data : %d " % numb_test)
print ("WFC L2err : %e eV/A" % l2f)
detail_file = args.detail_file
if detail_file is not None :
pe = np.concatenate((np.reshape(test_data["wfc"][:numb_test], [-1,12]),
np.reshape(wfc, [-1,12])),
axis = 1)
np.savetxt(detail_file+".out", pe,
header = 'ref_wfc(12 dofs) predicted_wfc(12 dofs)')
def test_polar (args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
dp = DeepPolar(args.model)
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test)
data.add('polarizability', 9, atomic=True, must=True, high_prec=False, type_sel = dp.get_sel_type())
test_data = data.get_test ()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
polar = dp.eval(coord, box, atype)
polar = polar.reshape([numb_test,-1])
l2f = (l2err (polar - test_data["polarizability"] [:numb_test]))
print ("# number of test data : %d " % numb_test)
print ("Polarizability L2err : %e eV/A" % l2f)
detail_file = args.detail_file
if detail_file is not None :
pe = np.concatenate((np.reshape(test_data["polarizability"][:numb_test], [-1,9]),
np.reshape(polar, [-1,9])),
axis = 1)
np.savetxt(detail_file+".out", pe,
header = 'data_pxx data_pxy data_pxz data_pyx data_pyy data_pyz data_pzx data_pzy data_pzz pred_pxx pred_pxy pred_pxz pred_pyx pred_pyy pred_pyz pred_pzx pred_pzy pred_pzz')
def test_polar(dp, args, global_polar=False):
if args.rand_seed is not None:
np.random.seed(args.rand_seed % (2**32))
data = DeepmdData(args.system,
args.set_prefix,
shuffle_test=args.shuffle_test)
if not global_polar:
data.add('polarizability',
9,
atomic=True,
must=True,
high_prec=False,
type_sel=dp.get_sel_type())
else:
data.add('polarizability',
9,
atomic=False,
must=True,
high_prec=False,
type_sel=dp.get_sel_type())
test_data = data.get_test()
numb_test = args.numb_test
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
polar = dp.eval(coord, box, atype)
sel_type = dp.get_sel_type()
sel_natoms = 0
for ii in sel_type:
sel_natoms += sum(atype == ii)
polar = polar.reshape([numb_test, -1])
l2f = (l2err(polar - test_data["polarizability"][:numb_test]))
l2fs = l2f / np.sqrt(sel_natoms)
l2fa = l2f / sel_natoms
print("# number of test data : %d " % numb_test)
print("Polarizability L2err : %e eV/A" % l2f)
if global_polar:
print("Polarizability L2err/sqrtN : %e eV/A" % l2fs)
print("Polarizability L2err/N : %e eV/A" % l2fa)
detail_file = args.detail_file
if detail_file is not None:
pe = np.concatenate(
(np.reshape(test_data["polarizability"][:numb_test],
[-1, 9]), np.reshape(polar, [-1, 9])),
axis=1)
np.savetxt(
detail_file + ".out",
pe,
header=
'data_pxx data_pxy data_pxz data_pyx data_pyy data_pyz data_pzx data_pzy data_pzz pred_pxx pred_pxy pred_pxz pred_pyx pred_pyy pred_pyz pred_pzx pred_pzy pred_pzz'
)
return [l2f], [polar.size]
def test_ener(args):
if args.rand_seed is not None:
np.random.seed(args.rand_seed % (2**32))
dp = DeepPot(args.model)
data = DeepmdData(args.system,
args.set_prefix,
shuffle_test=args.shuffle_test,
type_map=dp.get_type_map())
data.add('energy', 1, atomic=False, must=False, high_prec=True)
data.add('force', 3, atomic=True, must=False, high_prec=False)
data.add('virial', 9, atomic=False, must=False, high_prec=False)
if dp.get_dim_fparam() > 0:
data.add('fparam',
dp.get_dim_fparam(),
atomic=False,
must=True,
high_prec=False)
if dp.get_dim_aparam() > 0:
data.add('aparam',
dp.get_dim_aparam(),
atomic=True,
must=True,
high_prec=False)
test_data = data.get_test()
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = args.numb_test
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
if dp.get_dim_fparam() > 0:
fparam = test_data["fparam"][:numb_test]
else:
fparam = None
if dp.get_dim_aparam() > 0:
aparam = test_data["aparam"][:numb_test]
else:
aparam = None
energy, force, virial, ae, av = dp.eval(coord,
box,
atype,
fparam=fparam,
aparam=aparam,
atomic=True)
energy = energy.reshape([numb_test, 1])
force = force.reshape([numb_test, -1])
virial = virial.reshape([numb_test, 9])
ae = ae.reshape([numb_test, -1])
av = av.reshape([numb_test, -1])
l2e = (l2err(energy - test_data["energy"][:numb_test].reshape([-1, 1])))
l2f = (l2err(force - test_data["force"][:numb_test]))
l2v = (l2err(virial - test_data["virial"][:numb_test]))
l2ea = l2e / natoms
l2va = l2v / natoms
# print ("# energies: %s" % energy)
print("# number of test data : %d " % numb_test)
print("Energy L2err : %e eV" % l2e)
print("Energy L2err/Natoms : %e eV" % l2ea)
print("Force L2err : %e eV/A" % l2f)
print("Virial L2err : %e eV" % l2v)
print("Virial L2err/Natoms : %e eV" % l2va)
detail_file = args.detail_file
if detail_file is not None:
pe = np.concatenate((np.reshape(test_data["energy"][:numb_test],
[-1, 1]), np.reshape(energy, [-1, 1])),
axis=1)
np.savetxt(detail_file + ".e.out", pe, header='data_e pred_e')
pf = np.concatenate((np.reshape(test_data["force"][:numb_test],
[-1, 3]), np.reshape(force, [-1, 3])),
axis=1)
np.savetxt(detail_file + ".f.out",
pf,
header='data_fx data_fy data_fz pred_fx pred_fy pred_fz')
pv = np.concatenate((np.reshape(test_data["virial"][:numb_test],
[-1, 9]), np.reshape(virial, [-1, 9])),
axis=1)
np.savetxt(
detail_file + ".v.out",
pv,
header=
'data_vxx data_vxy data_vxz data_vyx data_vyy data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz'
)
def test_get_test(self):
dd = DeepmdData(self.data_name)
data = dd.get_test()
self._comp_np_mat2(np.sort(data['coord'], axis=0),
np.sort(self.coord_tar, axis=0))
np.random.seed(args.rand_seed % (2**32))
<<<<<<< HEAD
dp = DeepPot(args.model)
=======
>>>>>>> 93aa90768089e265465f93fb3d9f99b494d56b09
data = DeepmdData(args.system, args.set_prefix, shuffle_test = args.shuffle_test, type_map = dp.get_type_map())
data.add('energy', 1, atomic=False, must=False, high_prec=True)
data.add('force', 3, atomic=True, must=False, high_prec=False)
data.add('virial', 9, atomic=False, must=False, high_prec=False)
if dp.get_dim_fparam() > 0:
data.add('fparam', dp.get_dim_fparam(), atomic=False, must=True, high_prec=False)
if dp.get_dim_aparam() > 0:
data.add('aparam', dp.get_dim_aparam(), atomic=True, must=True, high_prec=False)
test_data = data.get_test ()
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = args.numb_test
numb_test = min(nframes, numb_test)
<<<<<<< HEAD
=======
>>>>>>> 93aa90768089e265465f93fb3d9f99b494d56b09
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
if not data.pbc:
box = None
atype = test_data["type"][0]
if dp.get_dim_fparam() > 0:
fparam = test_data["fparam"][:numb_test]
def test_ener(dp,
system,
set_prefix='set',
numb_test=100000,
shuffle_test=False,
rand_seed=None,
detail_file='detail'):
if rand_seed is not None:
np.random.seed(rand_seed % (2**32))
data = DeepmdData(system,
set_prefix,
shuffle_test=shuffle_test,
type_map=dp.get_type_map())
data.add('energy', 1, atomic=False, must=False, high_prec=True)
data.add('force', 3, atomic=True, must=False, high_prec=False)
data.add('virial', 9, atomic=False, must=False, high_prec=False)
if dp.get_dim_fparam() > 0:
data.add('fparam',
dp.get_dim_fparam(),
atomic=False,
must=True,
high_prec=False)
if dp.get_dim_aparam() > 0:
data.add('aparam',
dp.get_dim_aparam(),
atomic=True,
must=True,
high_prec=False)
test_data = data.get_test()
natoms = len(test_data["type"][0])
nframes = test_data["box"].shape[0]
numb_test = min(nframes, numb_test)
coord = test_data["coord"][:numb_test].reshape([numb_test, -1])
box = test_data["box"][:numb_test]
atype = test_data["type"][0]
if dp.get_dim_fparam() > 0:
fparam = test_data["fparam"][:numb_test]
else:
fparam = None
if dp.get_dim_aparam() > 0:
aparam = test_data["aparam"][:numb_test]
else:
aparam = None
energy, force, virial, ae, av = dp.eval(coord,
box,
atype,
fparam=fparam,
aparam=aparam,
atomic=True)
energy = energy.reshape([numb_test, 1])
force = force.reshape([numb_test, -1])
virial = virial.reshape([numb_test, 9])
ae = ae.reshape([numb_test, -1])
av = av.reshape([numb_test, -1])
l2e = (l2err(energy - test_data["energy"][:numb_test].reshape([-1, 1])))
l2f = (l2err(force - test_data["force"][:numb_test]))
l2v = (l2err(virial - test_data["virial"][:numb_test]))
l2ea = l2e / natoms
l2va = l2v / natoms
if detail_file is not None:
pe = np.concatenate(
(np.reshape(test_data["energy"][:numb_test] / natoms,
[-1, 1]), np.reshape(energy / natoms, [-1, 1])),
axis=1)
np.savetxt(detail_file + ".e.out", pe, header='data_e pred_e')
pf = np.concatenate((np.reshape(test_data["force"][:numb_test],
[-1, 3]), np.reshape(force, [-1, 3])),
axis=1)
np.savetxt(detail_file + ".f.out",
pf,
header='data_fx data_fy data_fz pred_fx pred_fy pred_fz')
pv = np.concatenate((np.reshape(test_data["virial"][:numb_test],
[-1, 9]), np.reshape(virial, [-1, 9])),
axis=1)
np.savetxt(
detail_file + ".v.out",
pv,
header=
'data_vxx data_vxy data_vxz data_vyx data_vyy data_vyz data_vzx data_vzy data_vzz pred_vxx pred_vxy pred_vxz pred_vyx pred_vyy pred_vyz pred_vzx pred_vzy pred_vzz'
)
return numb_test, l2e, l2ea, l2f, l2v, l2va