def test_ener (args) :
if args.rand_seed is not None :
np.random.seed(args.rand_seed % (2**32))
data = DataSets (args.system, args.set_prefix, shuffle_test = args.shuffle_test)
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)
dp = DeepPot(args.model)
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_ener(args):
"""
modify based on args file
"""
if args['rand_seed'] is not None:
np.random.seed(args['rand_seed'] % (2**32))
data = DataSets(args['system'],
args['set_prefix'],
shuffle_test=args['shuffle_test'])
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)
dp = DeepPot(args['model'])
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
detail_file = args['detail_file']
if detail_file is not None:
atomic = True
else:
atomic = False
ret = dp.eval(coord,
box,
atype,
fparam=fparam,
aparam=aparam,
atomic=atomic)
energy = ret[0]
force = ret[1]
virial = ret[2]
energy = energy.reshape([numb_test, 1])
force = force.reshape([numb_test, -1])
virial = virial.reshape([numb_test, 9])
if atomic:
ae = ret[3]
av = ret[4]
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)
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(os.path.join(args['system'], 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(os.path.join(args['system'], 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(
os.path.join(args['system'], 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, fparam[0][0], natoms, l2e, l2ea, l2f, l2v
def test_ener (dp, args) :
if args.rand_seed is not None :
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
def train_ener(inputs):
"""
deepmd-kit has function test_ener which deal with test_data only
`train_ener` are for train data only
"""
if inputs['rand_seed'] is not None:
np.random.seed(inputs['rand_seed'] % (2**32))
data = DataSets(inputs['system'],
inputs['set_prefix'],
shuffle_test=inputs['shuffle_test'])
train_data = get_train_data(data)
numb_test = data.get_sys_numb_batch(
1) ## use 1 batch, # of batches are the numb of train
natoms = len(train_data["type"][0])
nframes = train_data["box"].shape[0]
#print("xxxxx",nframes, numb_test)
numb_test = nframes #, to be investigated, original dp use min, but here should be nframes directly, I think, Jan 18, 21, min(nfames, numb_test)
dp = DeepPot(inputs['model'])
coord = train_data["coord"].reshape([numb_test, -1])
box = train_data["box"]
atype = train_data["type"][0]
if dp.get_dim_fparam() > 0:
fparam = train_data["fparam"]
else:
fparam = None
if dp.get_dim_aparam() > 0:
aparam = train_data["aparam"]
else:
aparam = None
detail_file = inputs['detail_file']
if detail_file is not None:
atomic = True
else:
atomic = False
ret = dp.eval(coord,
box,
atype,
fparam=fparam,
aparam=aparam,
atomic=atomic)
energy = ret[0]
force = ret[1]
virial = ret[2]
energy = energy.reshape([numb_test, 1])
force = force.reshape([numb_test, -1])
virial = virial.reshape([numb_test, 9])
if atomic:
ae = ret[3]
av = ret[4]
ae = ae.reshape([numb_test, -1])
av = av.reshape([numb_test, -1])
l2e = (l2err(energy - train_data["energy"].reshape([-1, 1])))
l2f = (l2err(force - train_data["force"]))
l2v = (l2err(virial - train_data["virial"]))
l2ea = l2e / natoms
l2va = l2v / natoms
# print ("# energies: %s" % energy)
print("# number of train 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)
if detail_file is not None:
pe = np.concatenate((np.reshape(train_data["energy"],
[-1, 1]), np.reshape(energy, [-1, 1])),
axis=1)
np.savetxt(os.path.join(inputs['system'], detail_file + ".e.tr.out"),
pe,
header='data_e pred_e')
pf = np.concatenate((np.reshape(train_data["force"],
[-1, 3]), np.reshape(force, [-1, 3])),
axis=1)
np.savetxt(os.path.join(inputs['system'], detail_file + ".f.tr.out"),
pf,
header='data_fx data_fy data_fz pred_fx pred_fy pred_fz')
pv = np.concatenate((np.reshape(train_data["virial"],
[-1, 9]), np.reshape(virial, [-1, 9])),
axis=1)
np.savetxt(
os.path.join(inputs['system'], detail_file + ".v.tr.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, fparam[0][0], natoms, l2e, l2ea, l2f, l2v
