def build(self,
coord_,
atype_,
natoms,
box,
mesh,
input_dict,
frz_model=None,
suffix='',
reuse=None):
with tf.variable_scope('model_attr' + suffix, reuse=reuse):
t_tmap = tf.constant(' '.join(self.type_map),
name='tmap',
dtype=tf.string)
t_mt = tf.constant(self.model_type,
name='model_type',
dtype=tf.string)
t_ver = tf.constant(MODEL_VERSION,
name='model_version',
dtype=tf.string)
if self.srtab is not None:
tab_info, tab_data = self.srtab.get()
self.tab_info = tf.get_variable(
't_tab_info',
tab_info.shape,
dtype=tf.float64,
trainable=False,
initializer=tf.constant_initializer(tab_info,
dtype=tf.float64))
self.tab_data = tf.get_variable(
't_tab_data',
tab_data.shape,
dtype=tf.float64,
trainable=False,
initializer=tf.constant_initializer(tab_data,
dtype=tf.float64))
coord = tf.reshape(coord_, [-1, natoms[1] * 3])
atype = tf.reshape(atype_, [-1, natoms[1]])
# type embedding if any
if self.typeebd is not None:
type_embedding = self.typeebd.build(
self.ntypes,
reuse=reuse,
suffix=suffix,
)
input_dict['type_embedding'] = type_embedding
if frz_model == None:
dout \
= self.descrpt.build(coord_,
atype_,
natoms,
box,
mesh,
input_dict,
suffix = suffix,
reuse = reuse)
dout = tf.identity(dout, name='o_descriptor')
else:
tf.constant(self.rcut,
name='descrpt_attr/rcut',
dtype=GLOBAL_TF_FLOAT_PRECISION)
tf.constant(self.ntypes,
name='descrpt_attr/ntypes',
dtype=tf.int32)
feed_dict = self.descrpt.get_feed_dict(coord_, atype_, natoms, box,
mesh)
return_elements = [
*self.descrpt.get_tensor_names(), 'o_descriptor:0'
]
imported_tensors \
= self._import_graph_def_from_frz_model(frz_model, feed_dict, return_elements)
dout = imported_tensors[-1]
self.descrpt.pass_tensors_from_frz_model(*imported_tensors[:-1])
if self.srtab is not None:
nlist, rij, sel_a, sel_r = self.descrpt.get_nlist()
nnei_a = np.cumsum(sel_a)[-1]
nnei_r = np.cumsum(sel_r)[-1]
atom_ener = self.fitting.build(dout,
natoms,
input_dict,
reuse=reuse,
suffix=suffix)
if self.srtab is not None:
sw_lambda, sw_deriv \
= op_module.soft_min_switch(atype,
rij,
nlist,
natoms,
sel_a = sel_a,
sel_r = sel_r,
alpha = self.smin_alpha,
rmin = self.sw_rmin,
rmax = self.sw_rmax)
inv_sw_lambda = 1.0 - sw_lambda
# NOTICE:
# atom energy is not scaled,
# force and virial are scaled
tab_atom_ener, tab_force, tab_atom_virial \
= op_module.pair_tab(self.tab_info,
self.tab_data,
atype,
rij,
nlist,
natoms,
sw_lambda,
sel_a = sel_a,
sel_r = sel_r)
energy_diff = tab_atom_ener - tf.reshape(atom_ener,
[-1, natoms[0]])
tab_atom_ener = tf.reshape(sw_lambda, [-1]) * tf.reshape(
tab_atom_ener, [-1])
atom_ener = tf.reshape(inv_sw_lambda, [-1]) * atom_ener
energy_raw = tab_atom_ener + atom_ener
else:
energy_raw = atom_ener
energy_raw = tf.reshape(energy_raw, [-1, natoms[0]],
name='o_atom_energy' + suffix)
energy = tf.reduce_sum(global_cvt_2_ener_float(energy_raw),
axis=1,
name='o_energy' + suffix)
force, virial, atom_virial \
= self.descrpt.prod_force_virial (atom_ener, natoms)
if self.srtab is not None:
sw_force \
= op_module.soft_min_force(energy_diff,
sw_deriv,
nlist,
natoms,
n_a_sel = nnei_a,
n_r_sel = nnei_r)
force = force + sw_force + tab_force
force = tf.reshape(force, [-1, 3 * natoms[1]], name="o_force" + suffix)
if self.srtab is not None:
sw_virial, sw_atom_virial \
= op_module.soft_min_virial (energy_diff,
sw_deriv,
rij,
nlist,
natoms,
n_a_sel = nnei_a,
n_r_sel = nnei_r)
atom_virial = atom_virial + sw_atom_virial + tab_atom_virial
virial = virial + sw_virial \
+ tf.reduce_sum(tf.reshape(tab_atom_virial, [-1, natoms[1], 9]), axis = 1)
virial = tf.reshape(virial, [-1, 9], name="o_virial" + suffix)
atom_virial = tf.reshape(atom_virial, [-1, 9 * natoms[1]],
name="o_atom_virial" + suffix)
model_dict = {}
model_dict['energy'] = energy
model_dict['force'] = force
model_dict['virial'] = virial
model_dict['atom_ener'] = energy_raw
model_dict['atom_virial'] = atom_virial
model_dict['coord'] = coord
model_dict['atype'] = atype
return model_dict
def comp_ef(self, dcoord, dbox, dtype, tnatoms, name, reuse=None):
descrpt, descrpt_deriv, rij, nlist \
= op_module.prod_env_mat_a (dcoord,
dtype,
tnatoms,
dbox,
tf.constant(self.default_mesh),
self.t_avg,
self.t_std,
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
rcut_r_smth = self.rcut_r_smth,
sel_a = self.sel_a,
sel_r = self.sel_r)
inputs_reshape = tf.reshape(descrpt, [-1, self.ndescrpt])
atom_ener = self._net(inputs_reshape, name, reuse=reuse)
sw_lambda, sw_deriv \
= op_module.soft_min_switch(dtype,
rij,
nlist,
tnatoms,
sel_a = self.sel_a,
sel_r = self.sel_r,
alpha = self.smin_alpha,
rmin = self.sw_rmin,
rmax = self.sw_rmax)
inv_sw_lambda = 1.0 - sw_lambda
tab_atom_ener, tab_force, tab_atom_virial \
= op_module.pair_tab(
self.tab_info,
self.tab_data,
dtype,
rij,
nlist,
tnatoms,
sw_lambda,
sel_a = self.sel_a,
sel_r = self.sel_r)
energy_diff = tab_atom_ener - tf.reshape(atom_ener,
[-1, self.natoms[0]])
tab_atom_ener = tf.reshape(sw_lambda, [-1]) * tf.reshape(
tab_atom_ener, [-1])
atom_ener = tf.reshape(inv_sw_lambda, [-1]) * atom_ener
energy_raw = tab_atom_ener + atom_ener
energy_raw = tf.reshape(energy_raw, [-1, self.natoms[0]])
energy = tf.reduce_sum(energy_raw, axis=1)
net_deriv_ = tf.gradients(atom_ener, inputs_reshape)
net_deriv = net_deriv_[0]
net_deriv_reshape = tf.reshape(net_deriv,
[-1, self.natoms[0] * self.ndescrpt])
force = op_module.prod_force_se_a(net_deriv_reshape,
descrpt_deriv,
nlist,
tnatoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
sw_force \
= op_module.soft_min_force(energy_diff,
sw_deriv,
nlist,
tnatoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
force = force + sw_force + tab_force
virial, atom_vir = op_module.prod_virial_se_a(net_deriv_reshape,
descrpt_deriv,
rij,
nlist,
tnatoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
sw_virial, sw_atom_virial \
= op_module.soft_min_virial (energy_diff,
sw_deriv,
rij,
nlist,
tnatoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
# atom_virial = atom_virial + sw_atom_virial + tab_atom_virial
virial = virial + sw_virial \
+ tf.reduce_sum(tf.reshape(tab_atom_virial, [-1, self.natoms[1], 9]), axis = 1)
return energy, force, virial