def _pass_filter(self,
inputs,
natoms,
reuse=None,
suffix='',
trainable=True):
start_index = 0
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
shape = inputs.get_shape().as_list()
output = []
output_qmat = []
for type_i in range(self.ntypes):
inputs_i = tf.slice(inputs, [0, start_index * self.ndescrpt],
[-1, natoms[2 + type_i] * self.ndescrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
layer, qmat = self._filter(inputs_i,
name='filter_type_' + str(type_i) +
suffix,
natoms=natoms,
reuse=reuse,
seed=self.seed,
trainable=trainable)
layer = tf.reshape(
layer,
[tf.shape(inputs)[0], natoms[2 + type_i] * self.get_dim_out()])
qmat = tf.reshape(qmat, [
tf.shape(inputs)[0],
natoms[2 + type_i] * self.get_dim_rot_mat_1() * 3
])
output.append(layer)
output_qmat.append(qmat)
start_index += natoms[2 + type_i]
output = tf.concat(output, axis=1)
output_qmat = tf.concat(output_qmat, axis=1)
return output, output_qmat
def _pass_filter(self,
inputs,
natoms,
reuse=None,
suffix='',
trainable=True):
start_index = 0
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
output = []
output_qmat = []
if not self.type_one_side:
for type_i in range(self.ntypes):
inputs_i = tf.slice(inputs, [0, start_index * self.ndescrpt],
[-1, natoms[2 + type_i] * self.ndescrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
layer, qmat = self._filter(
tf.cast(inputs_i, self.filter_precision),
type_i,
name='filter_type_' + str(type_i) + suffix,
natoms=natoms,
reuse=reuse,
seed=self.seed,
trainable=trainable,
activation_fn=self.filter_activation_fn)
layer = tf.reshape(layer, [
tf.shape(inputs)[0],
natoms[2 + type_i] * self.get_dim_out()
])
qmat = tf.reshape(qmat, [
tf.shape(inputs)[0],
natoms[2 + type_i] * self.get_dim_rot_mat_1() * 3
])
output.append(layer)
output_qmat.append(qmat)
start_index += natoms[2 + type_i]
else:
inputs_i = inputs
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
type_i = -1
layer, qmat = self._filter(tf.cast(inputs_i,
self.filter_precision),
type_i,
name='filter_type_all' + suffix,
natoms=natoms,
reuse=reuse,
seed=self.seed,
trainable=trainable,
activation_fn=self.filter_activation_fn)
layer = tf.reshape(
layer, [tf.shape(inputs)[0], natoms[0] * self.get_dim_out()])
qmat = tf.reshape(qmat, [
tf.shape(inputs)[0], natoms[0] * self.get_dim_rot_mat_1() * 3
])
output.append(layer)
output_qmat.append(qmat)
output = tf.concat(output, axis=1)
output_qmat = tf.concat(output_qmat, axis=1)
return output, output_qmat
def _concat_type_embedding(
self,
xyz_scatter,
nframes,
natoms,
type_embedding,
):
'''Concatenate `type_embedding` of neighbors and `xyz_scatter`.
If not self.type_one_side, concatenate `type_embedding` of center atoms as well.
Parameters
----------
xyz_scatter:
shape is [nframes*natoms[0]*self.nnei, 1]
nframes:
shape is []
natoms:
shape is [1+1+self.ntypes]
type_embedding:
shape is [self.ntypes, Y] where Y=jdata['type_embedding']['neuron'][-1]
Returns
-------
embedding:
environment of each atom represented by embedding.
'''
te_out_dim = type_embedding.get_shape().as_list()[-1]
nei_embed = tf.nn.embedding_lookup(
type_embedding,
tf.cast(self.nei_type,
dtype=tf.int32)) # shape is [self.nnei, 1+te_out_dim]
nei_embed = tf.tile(
nei_embed,
(nframes * natoms[0],
1)) # shape is [nframes*natoms[0]*self.nnei, te_out_dim]
nei_embed = tf.reshape(nei_embed, [-1, te_out_dim])
embedding_input = tf.concat(
[xyz_scatter, nei_embed],
1) # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim]
if not self.type_one_side:
atm_embed = embed_atom_type(
self.ntypes, natoms,
type_embedding) # shape is [natoms[0], te_out_dim]
atm_embed = tf.tile(
atm_embed,
(nframes, self.nnei
)) # shape is [nframes*natoms[0], self.nnei*te_out_dim]
atm_embed = tf.reshape(
atm_embed,
[-1, te_out_dim
]) # shape is [nframes*natoms[0]*self.nnei, te_out_dim]
embedding_input = tf.concat(
[embedding_input, atm_embed], 1
) # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim+te_out_dim]
return embedding_input
def build(self, coord_, atype_, natoms, box, mesh, suffix='', reuse=None):
davg = self.davg
dstd = self.dstd
if davg is None:
davg = [
np.zeros([self.descrpt_a.ntypes, self.descrpt_a.ndescrpt]),
np.zeros([self.descrpt_r.ntypes, self.descrpt_r.ndescrpt])
]
if dstd is None:
dstd = [
np.ones([self.descrpt_a.ntypes, self.descrpt_a.ndescrpt]),
np.ones([self.descrpt_r.ntypes, self.descrpt_r.ndescrpt])
]
# dout
self.dout_a = self.descrpt_a.build(coord_,
atype_,
natoms,
box,
mesh,
suffix=suffix + '_a',
reuse=reuse)
self.dout_r = self.descrpt_r.build(coord_,
atype_,
natoms,
box,
mesh,
suffix=suffix,
reuse=reuse)
self.dout_a = tf.reshape(self.dout_a,
[-1, self.descrpt_a.get_dim_out()])
self.dout_r = tf.reshape(self.dout_r,
[-1, self.descrpt_r.get_dim_out()])
self.dout = tf.concat([self.dout_a, self.dout_r], axis=1)
self.dout = tf.reshape(self.dout, [-1, natoms[0] * self.get_dim_out()])
return self.dout
def _pass_filter(self,
inputs,
atype,
natoms,
input_dict,
reuse=None,
suffix='',
trainable=True):
start_index = 0
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
output = []
output_qmat = []
inputs_i = inputs
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
type_i = -1
layer, qmat = self._filter(inputs_i,
type_i,
name='filter_type_all' + suffix,
natoms=natoms,
reuse=reuse,
trainable=trainable,
activation_fn=self.filter_activation_fn)
layer = tf.reshape(
layer, [tf.shape(inputs)[0], natoms[0] * self.get_dim_out()])
# qmat = tf.reshape(qmat, [tf.shape(inputs)[0], natoms[0] * self.get_dim_rot_mat_1() * 3])
output.append(layer)
# output_qmat.append(qmat)
output = tf.concat(output, axis=1)
# output_qmat = tf.concat(output_qmat, axis = 1)
return output, None
def _pass_filter(self,
inputs,
natoms,
reuse=None,
suffix='',
trainable=True):
start_index = 0
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
output = []
for type_i in range(self.ntypes):
inputs_i = tf.slice(inputs, [0, start_index * self.ndescrpt],
[-1, natoms[2 + type_i] * self.ndescrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
layer = self._filter_r(tf.cast(inputs_i, self.filter_precision),
type_i,
name='filter_type_' + str(type_i) + suffix,
natoms=natoms,
reuse=reuse,
seed=self.seed,
trainable=trainable,
activation_fn=self.filter_activation_fn)
layer = tf.reshape(
layer,
[tf.shape(inputs)[0], natoms[2 + type_i] * self.get_dim_out()])
output.append(layer)
start_index += natoms[2 + type_i]
output = tf.concat(output, axis=1)
return output
def _filter_r(self,
inputs,
type_input,
natoms,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
trainable=True):
# natom x nei
outputs_size = [1] + self.filter_neuron
with tf.variable_scope(name, reuse=reuse):
start_index = 0
xyz_scatter_total = []
for type_i in range(self.ntypes):
# cut-out inputs
# with natom x nei_type_i
inputs_i = tf.slice(inputs, [0, start_index],
[-1, self.sel_r[type_i]])
start_index += self.sel_r[type_i]
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei_type_i) x 1
xyz_scatter = tf.reshape(inputs_i, [-1, 1])
if (type_input, type_i) not in self.exclude_types:
xyz_scatter = embedding_net(
xyz_scatter,
self.filter_neuron,
self.filter_precision,
activation_fn=activation_fn,
resnet_dt=self.filter_resnet_dt,
name_suffix="_" + str(type_i),
stddev=stddev,
bavg=bavg,
seed=self.seed,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.embedding_net_variables,
)
if (not self.uniform_seed) and (self.seed is not None):
self.seed += self.seed_shift
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(
xyz_scatter, (-1, shape_i[1], outputs_size[-1]))
else:
natom = tf.shape(inputs)[0]
xyz_scatter = tf.cast(
tf.fill((natom, shape_i[1], outputs_size[-1]), 0.),
GLOBAL_TF_FLOAT_PRECISION)
xyz_scatter_total.append(xyz_scatter)
# natom x nei x outputs_size
xyz_scatter = tf.concat(xyz_scatter_total, axis=1)
# natom x outputs_size
#
res_rescale = 1. / 5.
result = tf.reduce_mean(xyz_scatter, axis=1) * res_rescale
return result
def _slice_descrpt_deriv(self, deriv):
coll = []
start_idx = 0
for type_i in range(self.ntypes):
if type_i in self.sel_type:
di = tf.slice(deriv, [0, start_idx * self.ndescrpt],
[-1, self.t_natoms[2 + type_i] * self.ndescrpt])
coll.append(di)
start_idx += self.t_natoms[2 + type_i]
return tf.concat(coll, axis=1)
def build (self,
coord_ : tf.Tensor,
atype_ : tf.Tensor,
natoms : tf.Tensor,
box_ : tf.Tensor,
mesh : tf.Tensor,
input_dict : dict,
reuse : bool = None,
suffix : str = ''
) -> tf.Tensor:
"""
Build the computational graph for the descriptor
Parameters
----------
coord_
The coordinate of atoms
atype_
The type of atoms
natoms
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh
For historical reasons, only the length of the Tensor matters.
if size of mesh == 6, pbc is assumed.
if size of mesh == 0, no-pbc is assumed.
input_dict
Dictionary for additional inputs
reuse
The weights in the networks should be reused when get the variable.
suffix
Name suffix to identify this descriptor
Returns
-------
descriptor
The output descriptor
"""
with tf.variable_scope('descrpt_attr' + suffix, reuse = reuse) :
t_rcut = tf.constant(self.get_rcut(),
name = 'rcut',
dtype = GLOBAL_TF_FLOAT_PRECISION)
t_ntypes = tf.constant(self.get_ntypes(),
name = 'ntypes',
dtype = tf.int32)
all_dout = []
for idx,ii in enumerate(self.descrpt_list):
dout = ii.build(coord_, atype_, natoms, box_, mesh, input_dict, suffix=suffix+f'_{idx}', reuse=reuse)
dout = tf.reshape(dout, [-1, ii.get_dim_out()])
all_dout.append(dout)
dout = tf.concat(all_dout, axis = 1)
dout = tf.reshape(dout, [-1, natoms[0] * self.get_dim_out()])
return dout
def build (self,
input_d,
rot_mat,
natoms,
reuse = None,
suffix = '') :
start_index = 0
inputs = tf.cast(tf.reshape(input_d, [-1, self.dim_descrpt * natoms[0]]), self.fitting_precision)
rot_mat = tf.reshape(rot_mat, [-1, 9 * natoms[0]])
count = 0
outs_list = []
for type_i in range(self.ntypes):
# cut-out inputs
inputs_i = tf.slice (inputs,
[ 0, start_index* self.dim_descrpt],
[-1, natoms[2+type_i]* self.dim_descrpt] )
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
rot_mat_i = tf.slice (rot_mat,
[ 0, start_index* 9],
[-1, natoms[2+type_i]* 9] )
rot_mat_i = tf.reshape(rot_mat_i, [-1, 3, 3])
start_index += natoms[2+type_i]
if not type_i in self.sel_type :
continue
layer = inputs_i
for ii in range(0,len(self.n_neuron)) :
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii-1] :
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision)
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision)
# (nframes x natoms) x 9
final_layer = one_layer(layer, 9, activation_fn = None, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision, final_layer = True)
# (nframes x natoms) x 3 x 3
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0] * natoms[2+type_i], 3, 3])
# (nframes x natoms) x 3 x 3
final_layer = final_layer + tf.transpose(final_layer, perm = [0,2,1])
# (nframes x natoms) x 3 x 3(coord)
final_layer = tf.matmul(final_layer, rot_mat_i)
# (nframes x natoms) x 3(coord) x 3(coord)
final_layer = tf.matmul(rot_mat_i, final_layer, transpose_a = True)
# nframes x natoms x 3 x 3
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[2+type_i], 3, 3])
# concat the results
outs_list.append(final_layer)
count += 1
outs = tf.concat(outs_list, axis = 1)
tf.summary.histogram('fitting_net_output', outs)
return tf.cast(tf.reshape(outs, [-1]), GLOBAL_TF_FLOAT_PRECISION)
def build (self,
input_d,
rot_mat,
natoms,
reuse = None,
suffix = '') :
start_index = 0
inputs = tf.cast(tf.reshape(input_d, [-1, self.dim_descrpt * natoms[0]]), self.fitting_precision)
rot_mat = tf.reshape(rot_mat, [-1, 9 * natoms[0]])
count = 0
for type_i in range(self.ntypes):
# cut-out inputs
inputs_i = tf.slice (inputs,
[ 0, start_index* self.dim_descrpt],
[-1, natoms[2+type_i]* self.dim_descrpt] )
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
rot_mat_i = tf.slice (rot_mat,
[ 0, start_index* 9],
[-1, natoms[2+type_i]* 9] )
rot_mat_i = tf.reshape(rot_mat_i, [-1, 3, 3])
start_index += natoms[2+type_i]
if not type_i in self.sel_type :
continue
layer = inputs_i
for ii in range(0,len(self.n_neuron)) :
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii-1] :
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x (nwfc x 3)
final_layer = one_layer(layer, self.wfc_numb * 3, activation_fn = None, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x nwfc(wc) x 3(coord_local)
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0] * natoms[2+type_i], self.wfc_numb, 3])
# (nframes x natoms) x nwfc(wc) x 3(coord)
final_layer = tf.matmul(final_layer, rot_mat_i)
# nframes x natoms x nwfc(wc) x 3(coord_local)
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[2+type_i], self.wfc_numb, 3])
# concat the results
if count == 0:
outs = final_layer
else:
outs = tf.concat([outs, final_layer], axis = 1)
count += 1
tf.summary.histogram('fitting_net_output', outs)
return tf.cast(tf.reshape(outs, [-1]), GLOBAL_TF_FLOAT_PRECISION)
def _enrich(self, dipole, dof = 3):
coll = []
sel_start_idx = 0
for type_i in range(self.ntypes):
if type_i in self.sel_type:
di = tf.slice(dipole,
[ 0, sel_start_idx * dof],
[-1, self.t_natoms[2+type_i] * dof])
sel_start_idx += self.t_natoms[2+type_i]
else:
di = tf.zeros([tf.shape(dipole)[0], self.t_natoms[2+type_i] * dof],
dtype = global_tf_float_precision)
coll.append(di)
return tf.concat(coll, axis = 1)
def _embedding_net(self,
inputs,
natoms,
filter_neuron,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
seed=None,
trainable=True):
'''
inputs: nf x na x (nei x 4)
outputs: nf x na x nei x output_size
'''
# natom x (nei x 4)
inputs = tf.reshape(inputs, [-1, self.ndescrpt])
shape = inputs.get_shape().as_list()
outputs_size = [1] + filter_neuron
with tf.variable_scope(name, reuse=reuse):
xyz_scatter_total = []
# with natom x (nei x 4)
inputs_i = inputs
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei) x 4
inputs_reshape = tf.reshape(inputs_i, [-1, 4])
# with (natom x nei) x 1
xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0, 0], [-1, 1]),
[-1, 1])
# with (natom x nei) x out_size
xyz_scatter = embedding_net(xyz_scatter,
self.filter_neuron,
self.filter_precision,
activation_fn=activation_fn,
resnet_dt=self.filter_resnet_dt,
stddev=stddev,
bavg=bavg,
seed=seed,
trainable=trainable)
# natom x nei x out_size
xyz_scatter = tf.reshape(xyz_scatter,
(-1, shape_i[1] // 4, outputs_size[-1]))
xyz_scatter_total.append(xyz_scatter)
# natom x nei x outputs_size
xyz_scatter = tf.concat(xyz_scatter_total, axis=1)
# nf x natom x nei x outputs_size
xyz_scatter = tf.reshape(
xyz_scatter,
[tf.shape(inputs)[0], natoms[0], self.nnei, outputs_size[-1]])
return xyz_scatter
def _enrich(self, dipole, dof=3):
coll = []
sel_start_idx = 0
for type_i in range(self.ntypes):
if type_i in self.sel_type:
di = tf.slice(dipole, [0, sel_start_idx * dof],
[-1, self.t_natoms[2 + type_i] * dof])
sel_start_idx += self.t_natoms[2 + type_i]
else:
di = tf.zeros(
[tf.shape(dipole)[0], self.t_natoms[2 + type_i] * dof],
dtype=GLOBAL_TF_FLOAT_PRECISION)
coll.append(di)
return tf.concat(coll, axis=1)
def _pass_filter(self,
inputs,
natoms,
reuse=None,
suffix='',
trainable=True):
start_index = 0
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
output = []
if not (self.type_one_side and len(self.exclude_types) == 0):
for type_i in range(self.ntypes):
inputs_i = tf.slice(inputs, [0, start_index * self.ndescrpt],
[-1, natoms[2 + type_i] * self.ndescrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
if self.type_one_side:
# reuse NN parameters for all types to support type_one_side along with exclude_types
reuse = tf.AUTO_REUSE
filter_name = 'filter_type_all' + suffix
else:
filter_name = 'filter_type_' + str(type_i) + suffix
layer = self._filter_r(inputs_i,
type_i,
name=filter_name,
natoms=natoms,
reuse=reuse,
trainable=trainable,
activation_fn=self.filter_activation_fn)
layer = tf.reshape(layer, [
tf.shape(inputs)[0],
natoms[2 + type_i] * self.get_dim_out()
])
output.append(layer)
start_index += natoms[2 + type_i]
else:
inputs_i = inputs
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
type_i = -1
layer = self._filter_r(inputs_i,
type_i,
name='filter_type_all' + suffix,
natoms=natoms,
reuse=reuse,
trainable=trainable,
activation_fn=self.filter_activation_fn)
layer = tf.reshape(
layer, [tf.shape(inputs)[0], natoms[0] * self.get_dim_out()])
output.append(layer)
output = tf.concat(output, axis=1)
return output
def build (self,
coord_,
atype_,
natoms,
box,
mesh,
davg,
dstd,
suffix = '',
reuse = None):
# dout
self.dout_a = self.descrpt_a.build(coord_, atype_, natoms, box, mesh, davg[0], dstd[0], suffix=suffix+'_a', reuse=reuse)
self.dout_r = self.descrpt_r.build(coord_, atype_, natoms, box, mesh, davg[1], dstd[1], suffix=suffix+'_r', reuse=reuse)
self.dout_a = tf.reshape(self.dout_a, [-1, self.descrpt_a.get_dim_out()])
self.dout_r = tf.reshape(self.dout_r, [-1, self.descrpt_r.get_dim_out()])
self.dout = tf.concat([self.dout_a, self.dout_r], axis = 1)
self.dout = tf.reshape(self.dout, [-1, natoms[0] * self.get_dim_out()])
return self.dout
def embed_atom_type(
ntypes: int,
natoms: tf.Tensor,
type_embedding: tf.Tensor,
):
"""
Make the embedded type for the atoms in system.
The atoms are assumed to be sorted according to the type,
thus their types are described by a `tf.Tensor` natoms, see explanation below.
Parameters
----------
ntypes:
Number of types.
natoms:
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
type_embedding:
The type embedding.
It has the shape of [ntypes, embedding_dim]
Returns
-------
atom_embedding
The embedded type of each atom.
It has the shape of [numb_atoms, embedding_dim]
"""
te_out_dim = type_embedding.get_shape().as_list()[-1]
atype = []
for ii in range(ntypes):
atype.append(tf.tile([ii], [natoms[2 + ii]]))
atype = tf.concat(atype, axis=0)
atm_embed = tf.nn.embedding_lookup(
type_embedding, tf.cast(atype, dtype=tf.int32)) #(nf*natom)*nchnl
atm_embed = tf.reshape(atm_embed, [-1, te_out_dim])
return atm_embed
def build(self,
coord_: tf.Tensor,
atype_: tf.Tensor,
natoms: tf.Tensor,
box_: tf.Tensor,
mesh: tf.Tensor,
input_dict: dict,
reuse: bool = None,
suffix: str = '') -> tf.Tensor:
"""
Build the computational graph for the descriptor
Parameters
----------
coord_
The coordinate of atoms
atype_
The type of atoms
natoms
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
mesh
For historical reasons, only the length of the Tensor matters.
if size of mesh == 6, pbc is assumed.
if size of mesh == 0, no-pbc is assumed.
input_dict
Dictionary for additional inputs. Should have 'efield'.
reuse
The weights in the networks should be reused when get the variable.
suffix
Name suffix to identify this descriptor
Returns
-------
descriptor
The output descriptor
"""
self.dout_vert = self.descrpt_vert.build(coord_, atype_, natoms, box_,
mesh, input_dict)
self.dout_para = self.descrpt_para.build(coord_,
atype_,
natoms,
box_,
mesh,
input_dict,
reuse=True)
coord = tf.reshape(coord_, [-1, natoms[1] * 3])
nframes = tf.shape(coord)[0]
self.dout_vert = tf.reshape(
self.dout_vert,
[nframes * natoms[0],
self.descrpt_vert.get_dim_out()])
self.dout_para = tf.reshape(
self.dout_para,
[nframes * natoms[0],
self.descrpt_para.get_dim_out()])
self.dout = tf.concat([self.dout_vert, self.dout_para], axis=1)
self.dout = tf.reshape(self.dout,
[nframes, natoms[0] * self.get_dim_out()])
self.qmat = self.descrpt_vert.qmat + self.descrpt_para.qmat
tf.summary.histogram('embedding_net_output', self.dout)
return self.dout
def _build_fv_graph_inner(self):
self.t_ef = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None],
name='t_ef')
nf = 10
nfxnas = 64 * nf
nfxna = 192 * nf
nf = -1
nfxnas = -1
nfxna = -1
self.t_box_reshape = tf.reshape(self.t_box, [-1, 9])
t_nframes = tf.shape(self.t_box_reshape)[0]
# (nframes x natoms_sel) x 1 x 3
self.t_ef_reshape = tf.reshape(self.t_ef, [nfxnas, 1, 3])
# (nframes x natoms) x ndescrpt
self.descrpt = self.graph.get_tensor_by_name(
os.path.join(self.modifier_prefix, 'o_rmat:0'))
self.descrpt_deriv = self.graph.get_tensor_by_name(
os.path.join(self.modifier_prefix, 'o_rmat_deriv:0'))
self.nlist = self.graph.get_tensor_by_name(
os.path.join(self.modifier_prefix, 'o_nlist:0'))
self.rij = self.graph.get_tensor_by_name(
os.path.join(self.modifier_prefix, 'o_rij:0'))
# self.descrpt_reshape = tf.reshape(self.descrpt, [nf, 192 * self.ndescrpt])
# self.descrpt_deriv = tf.reshape(self.descrpt_deriv, [nf, 192 * self.ndescrpt * 3])
# nframes x (natoms_sel x 3)
self.t_tensor_reshpe = tf.reshape(self.t_tensor, [t_nframes, -1])
# nframes x (natoms x 3)
self.t_tensor_reshpe = self._enrich(self.t_tensor_reshpe, dof=3)
# (nframes x natoms) x 3
self.t_tensor_reshpe = tf.reshape(self.t_tensor_reshpe, [nfxna, 3])
# (nframes x natoms) x 1
self.t_dipole_x = tf.slice(self.t_tensor_reshpe, [0, 0], [nfxna, 1])
self.t_dipole_y = tf.slice(self.t_tensor_reshpe, [0, 1], [nfxna, 1])
self.t_dipole_z = tf.slice(self.t_tensor_reshpe, [0, 2], [nfxna, 1])
self.t_dipole_z = tf.reshape(self.t_dipole_z, [nfxna, 1])
# (nframes x natoms) x ndescrpt
[self.t_dipole_x_d] = tf.gradients(self.t_dipole_x, self.descrpt)
[self.t_dipole_y_d] = tf.gradients(self.t_dipole_y, self.descrpt)
[self.t_dipole_z_d] = tf.gradients(self.t_dipole_z, self.descrpt)
# nframes x (natoms x ndescrpt)
self.t_dipole_x_d = tf.reshape(self.t_dipole_x_d,
[-1, self.t_natoms[0] * self.ndescrpt])
self.t_dipole_y_d = tf.reshape(self.t_dipole_y_d,
[-1, self.t_natoms[0] * self.ndescrpt])
self.t_dipole_z_d = tf.reshape(self.t_dipole_z_d,
[-1, self.t_natoms[0] * self.ndescrpt])
# nframes x (natoms_sel x ndescrpt)
self.t_dipole_x_d = self._slice_descrpt_deriv(self.t_dipole_x_d)
self.t_dipole_y_d = self._slice_descrpt_deriv(self.t_dipole_y_d)
self.t_dipole_z_d = self._slice_descrpt_deriv(self.t_dipole_z_d)
# (nframes x natoms_sel) x ndescrpt
self.t_dipole_x_d = tf.reshape(self.t_dipole_x_d,
[nfxnas, self.ndescrpt])
self.t_dipole_y_d = tf.reshape(self.t_dipole_y_d,
[nfxnas, self.ndescrpt])
self.t_dipole_z_d = tf.reshape(self.t_dipole_z_d,
[nfxnas, self.ndescrpt])
# (nframes x natoms_sel) x 3 x ndescrpt
self.t_dipole_d = tf.concat(
[self.t_dipole_x_d, self.t_dipole_y_d, self.t_dipole_z_d], axis=1)
self.t_dipole_d = tf.reshape(self.t_dipole_d,
[nfxnas, 3 * self.ndescrpt])
# (nframes x natoms_sel) x 3 x ndescrpt
self.t_dipole_d = tf.reshape(self.t_dipole_d, [-1, 3, self.ndescrpt])
# (nframes x natoms_sel) x 1 x ndescrpt
self.t_ef_d = tf.matmul(self.t_ef_reshape, self.t_dipole_d)
# nframes x (natoms_sel x ndescrpt)
self.t_ef_d = tf.reshape(self.t_ef_d, [t_nframes, -1])
# nframes x (natoms x ndescrpt)
self.t_ef_d = self._enrich(self.t_ef_d, dof=self.ndescrpt)
self.t_ef_d = tf.reshape(self.t_ef_d,
[nf, self.t_natoms[0] * self.ndescrpt])
# t_ef_d is force (with -1), prod_forc takes deriv, so we need the opposite
self.t_ef_d_oppo = -self.t_ef_d
force = op_module.prod_force_se_a(self.t_ef_d_oppo,
self.descrpt_deriv,
self.nlist,
self.t_natoms,
n_a_sel=self.nnei_a,
n_r_sel=self.nnei_r)
virial, atom_virial \
= op_module.prod_virial_se_a (self.t_ef_d_oppo,
self.descrpt_deriv,
self.rij,
self.nlist,
self.t_natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
force = tf.identity(force, name='o_dm_force')
virial = tf.identity(virial, name='o_dm_virial')
atom_virial = tf.identity(atom_virial, name='o_dm_av')
return force, virial, atom_virial
def build(
self,
inputs: tf.Tensor,
natoms: tf.Tensor,
input_dict: dict = None,
reuse: bool = None,
suffix: str = '',
) -> tf.Tensor:
"""
Build the computational graph for fitting net
Parameters
----------
inputs
The input descriptor
input_dict
Additional dict for inputs.
if numb_fparam > 0, should have input_dict['fparam']
if numb_aparam > 0, should have input_dict['aparam']
natoms
The number of atoms. This tensor has the length of Ntypes + 2
natoms[0]: number of local atoms
natoms[1]: total number of atoms held by this processor
natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
reuse
The weights in the networks should be reused when get the variable.
suffix
Name suffix to identify this descriptor
Returns
-------
ener
The system energy
"""
if input_dict is None:
input_dict = {}
bias_atom_e = self.bias_atom_e
if self.numb_fparam > 0 and (self.fparam_avg is None
or self.fparam_inv_std is None):
raise RuntimeError(
'No data stat result. one should do data statisitic, before build'
)
if self.numb_aparam > 0 and (self.aparam_avg is None
or self.aparam_inv_std is None):
raise RuntimeError(
'No data stat result. one should do data statisitic, before build'
)
with tf.variable_scope('fitting_attr' + suffix, reuse=reuse):
t_dfparam = tf.constant(self.numb_fparam,
name='dfparam',
dtype=tf.int32)
t_daparam = tf.constant(self.numb_aparam,
name='daparam',
dtype=tf.int32)
if self.numb_fparam > 0:
t_fparam_avg = tf.get_variable(
't_fparam_avg',
self.numb_fparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.fparam_avg))
t_fparam_istd = tf.get_variable(
't_fparam_istd',
self.numb_fparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.fparam_inv_std))
if self.numb_aparam > 0:
t_aparam_avg = tf.get_variable(
't_aparam_avg',
self.numb_aparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.aparam_avg))
t_aparam_istd = tf.get_variable(
't_aparam_istd',
self.numb_aparam,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.aparam_inv_std))
inputs = tf.reshape(inputs, [-1, self.dim_descrpt * natoms[0]])
if len(self.atom_ener):
# only for atom_ener
nframes = input_dict.get('nframes')
if nframes is not None:
# like inputs, but we don't want to add a dependency on inputs
inputs_zero = tf.zeros((nframes, self.dim_descrpt * natoms[0]),
dtype=self.fitting_precision)
else:
inputs_zero = tf.zeros_like(inputs,
dtype=self.fitting_precision)
if bias_atom_e is not None:
assert (len(bias_atom_e) == self.ntypes)
fparam = None
aparam = None
if self.numb_fparam > 0:
fparam = input_dict['fparam']
fparam = tf.reshape(fparam, [-1, self.numb_fparam])
fparam = (fparam - t_fparam_avg) * t_fparam_istd
if self.numb_aparam > 0:
aparam = input_dict['aparam']
aparam = tf.reshape(aparam, [-1, self.numb_aparam])
aparam = (aparam - t_aparam_avg) * t_aparam_istd
aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]])
type_embedding = input_dict.get('type_embedding', None)
if type_embedding is not None:
atype_embed = embed_atom_type(self.ntypes, natoms, type_embedding)
atype_embed = tf.tile(atype_embed, [tf.shape(inputs)[0], 1])
else:
atype_embed = None
if atype_embed is None:
start_index = 0
outs_list = []
for type_i in range(self.ntypes):
if bias_atom_e is None:
type_bias_ae = 0.0
else:
type_bias_ae = bias_atom_e[type_i]
final_layer = self._build_lower(start_index,
natoms[2 + type_i],
inputs,
fparam,
aparam,
bias_atom_e=type_bias_ae,
suffix='_type_' + str(type_i) +
suffix,
reuse=reuse)
# concat the results
if type_i < len(
self.atom_ener) and self.atom_ener[type_i] is not None:
zero_layer = self._build_lower(start_index,
natoms[2 + type_i],
inputs_zero,
fparam,
aparam,
bias_atom_e=type_bias_ae,
suffix='_type_' +
str(type_i) + suffix,
reuse=True)
final_layer += self.atom_ener[type_i] - zero_layer
final_layer = tf.reshape(
final_layer, [tf.shape(inputs)[0], natoms[2 + type_i]])
outs_list.append(final_layer)
start_index += natoms[2 + type_i]
# concat the results
# concat once may be faster than multiple concat
outs = tf.concat(outs_list, axis=1)
# with type embedding
else:
if len(self.atom_ener) > 0:
raise RuntimeError(
"setting atom_ener is not supported by type embedding")
atype_embed = tf.cast(atype_embed, self.fitting_precision)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat(
[tf.reshape(inputs, [-1, self.dim_descrpt]), atype_embed],
axis=1)
self.dim_descrpt = self.dim_descrpt + type_shape[1]
inputs = tf.reshape(inputs, [-1, self.dim_descrpt * natoms[0]])
final_layer = self._build_lower(0,
natoms[0],
inputs,
fparam,
aparam,
bias_atom_e=0.0,
suffix=suffix,
reuse=reuse)
outs = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[0]])
# add atom energy bias; TF will broadcast to all batches
# tf.repeat is avaiable in TF>=2.1 or TF 1.15
_TF_VERSION = Version(TF_VERSION)
if (Version('1.15') <= _TF_VERSION < Version('2') or _TF_VERSION >=
Version('2.1')) and self.bias_atom_e is not None:
outs += tf.repeat(
tf.Variable(self.bias_atom_e,
dtype=self.fitting_precision,
trainable=False,
name="bias_atom_ei"), natoms[2:])
if self.tot_ener_zero:
force_tot_ener = 0.0
outs = tf.reshape(outs, [-1, natoms[0]])
outs_mean = tf.reshape(tf.reduce_mean(outs, axis=1), [-1, 1])
outs_mean = outs_mean - tf.ones_like(
outs_mean, dtype=GLOBAL_TF_FLOAT_PRECISION) * (
force_tot_ener / global_cvt_2_tf_float(natoms[0]))
outs = outs - outs_mean
outs = tf.reshape(outs, [-1])
tf.summary.histogram('fitting_net_output', outs)
return tf.reshape(outs, [-1])
def _build_lower(self,
start_index,
natoms,
inputs,
fparam=None,
aparam=None,
bias_atom_e=0.0,
suffix='',
reuse=None):
# cut-out inputs
inputs_i = tf.slice(inputs, [0, start_index * self.dim_descrpt],
[-1, natoms * self.dim_descrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
layer = inputs_i
if fparam is not None:
ext_fparam = tf.tile(fparam, [1, natoms])
ext_fparam = tf.reshape(ext_fparam, [-1, self.numb_fparam])
ext_fparam = tf.cast(ext_fparam, self.fitting_precision)
layer = tf.concat([layer, ext_fparam], axis=1)
if aparam is not None:
ext_aparam = tf.slice(aparam, [0, start_index * self.numb_aparam],
[-1, natoms * self.numb_aparam])
ext_aparam = tf.reshape(ext_aparam, [-1, self.numb_aparam])
ext_aparam = tf.cast(ext_aparam, self.fitting_precision)
layer = tf.concat([layer, ext_aparam], axis=1)
for ii in range(0, len(self.n_neuron)):
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]:
layer += one_layer(
layer,
self.n_neuron[ii],
name='layer_' + str(ii) + suffix,
reuse=reuse,
seed=self.seed,
use_timestep=self.resnet_dt,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii],
uniform_seed=self.uniform_seed,
initial_variables=self.fitting_net_variables,
mixed_prec=self.mixed_prec)
else:
layer = one_layer(layer,
self.n_neuron[ii],
name='layer_' + str(ii) + suffix,
reuse=reuse,
seed=self.seed,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii],
uniform_seed=self.uniform_seed,
initial_variables=self.fitting_net_variables,
mixed_prec=self.mixed_prec)
if (not self.uniform_seed) and (self.seed is not None):
self.seed += self.seed_shift
final_layer = one_layer(layer,
1,
activation_fn=None,
bavg=bias_atom_e,
name='final_layer' + suffix,
reuse=reuse,
seed=self.seed,
precision=self.fitting_precision,
trainable=self.trainable[-1],
uniform_seed=self.uniform_seed,
initial_variables=self.fitting_net_variables,
mixed_prec=self.mixed_prec,
final_layer=True)
if (not self.uniform_seed) and (self.seed is not None):
self.seed += self.seed_shift
return final_layer
def embedding_net(xx,
network_size,
precision,
activation_fn=tf.nn.tanh,
resnet_dt=False,
name_suffix='',
stddev=1.0,
bavg=0.0,
seed=None,
trainable=True,
uniform_seed=False,
initial_variables=None,
mixed_prec=None):
r"""The embedding network.
The embedding network function :math:`\mathcal{N}` is constructed by is the
composition of multiple layers :math:`\mathcal{L}^{(i)}`:
.. math::
\mathcal{N} = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)}
\circ \cdots \circ \mathcal{L}^{(1)}
A layer :math:`\mathcal{L}` is given by one of the following forms,
depending on the number of nodes: [1]_
.. math::
\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})=
\begin{cases}
\boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + \mathbf{x}, & N_2=N_1 \\
\boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) + (\mathbf{x}, \mathbf{x}), & N_2 = 2N_1\\
\boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}), & \text{otherwise} \\
\end{cases}
where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}`
is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and
:math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively,
both of which are trainable if `trainable` is `True`. :math:`\boldsymbol{\phi}`
is the activation function.
Parameters
----------
xx : Tensor
Input tensor :math:`\mathbf{x}` of shape [-1,1]
network_size: list of int
Size of the embedding network. For example [16,32,64]
precision:
Precision of network weights. For example, tf.float64
activation_fn:
Activation function :math:`\boldsymbol{\phi}`
resnet_dt: boolean
Using time-step in the ResNet construction
name_suffix: str
The name suffix append to each variable.
stddev: float
Standard deviation of initializing network parameters
bavg: float
Mean of network intial bias
seed: int
Random seed for initializing network parameters
trainable: boolean
If the network is trainable
uniform_seed : boolean
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
initial_variables : dict
The input dict which stores the embedding net variables
mixed_prec
The input dict which stores the mixed precision setting for the embedding net
References
----------
.. [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identitymappings
in deep residual networks. InComputer Vision – ECCV 2016,pages 630–645. Springer
International Publishing, 2016.
"""
input_shape = xx.get_shape().as_list()
outputs_size = [input_shape[1]] + network_size
for ii in range(1, len(outputs_size)):
w_initializer = tf.random_normal_initializer(
stddev=stddev / np.sqrt(outputs_size[ii] + outputs_size[ii - 1]),
seed=seed if (seed is None or uniform_seed) else seed + ii * 3 + 0)
b_initializer = tf.random_normal_initializer(
stddev=stddev,
mean=bavg,
seed=seed if (seed is None or uniform_seed) else seed + 3 * ii + 1)
if initial_variables is not None:
scope = tf.get_variable_scope().name
w_initializer = tf.constant_initializer(
initial_variables[scope + '/matrix_' + str(ii) + name_suffix])
b_initializer = tf.constant_initializer(
initial_variables[scope + '/bias_' + str(ii) + name_suffix])
w = tf.get_variable('matrix_' + str(ii) + name_suffix,
[outputs_size[ii - 1], outputs_size[ii]],
precision,
w_initializer,
trainable=trainable)
variable_summaries(w, 'matrix_' + str(ii) + name_suffix)
b = tf.get_variable('bias_' + str(ii) + name_suffix,
[outputs_size[ii]],
precision,
b_initializer,
trainable=trainable)
variable_summaries(b, 'bias_' + str(ii) + name_suffix)
if mixed_prec is not None:
xx = tf.cast(xx, get_precision(mixed_prec['compute_prec']))
w = tf.cast(w, get_precision(mixed_prec['compute_prec']))
b = tf.cast(b, get_precision(mixed_prec['compute_prec']))
hidden = tf.reshape(activation_fn(tf.nn.bias_add(tf.matmul(xx, w), b)),
[-1, outputs_size[ii]])
if resnet_dt:
idt_initializer = tf.random_normal_initializer(
stddev=0.001,
mean=1.0,
seed=seed if
(seed is None or uniform_seed) else seed + 3 * ii + 2)
if initial_variables is not None:
scope = tf.get_variable_scope().name
idt_initializer = tf.constant_initializer(
initial_variables[scope + '/idt_' + str(ii) + name_suffix])
idt = tf.get_variable('idt_' + str(ii) + name_suffix,
[1, outputs_size[ii]],
precision,
idt_initializer,
trainable=trainable)
variable_summaries(idt, 'idt_' + str(ii) + name_suffix)
if mixed_prec is not None:
idt = tf.cast(idt, get_precision(mixed_prec['compute_prec']))
if outputs_size[ii] == outputs_size[ii - 1]:
if resnet_dt:
xx += hidden * idt
else:
xx += hidden
elif outputs_size[ii] == outputs_size[ii - 1] * 2:
if resnet_dt:
xx = tf.concat([xx, xx], 1) + hidden * idt
else:
xx = tf.concat([xx, xx], 1) + hidden
else:
xx = hidden
if mixed_prec is not None:
xx = tf.cast(xx, get_precision(mixed_prec['output_prec']))
return xx
def build(self, inputs, input_dict, natoms, reuse=None, suffix=''):
bias_atom_e = self.bias_atom_e
if self.numb_fparam > 0 and (self.fparam_avg is None
or self.fparam_inv_std is None):
raise RuntimeError(
'No data stat result. one should do data statisitic, before build'
)
if self.numb_aparam > 0 and (self.aparam_avg is None
or self.aparam_inv_std is None):
raise RuntimeError(
'No data stat result. one should do data statisitic, before build'
)
with tf.variable_scope('fitting_attr' + suffix, reuse=reuse):
t_dfparam = tf.constant(self.numb_fparam,
name='dfparam',
dtype=tf.int32)
t_daparam = tf.constant(self.numb_aparam,
name='daparam',
dtype=tf.int32)
if self.numb_fparam > 0:
t_fparam_avg = tf.get_variable(
't_fparam_avg',
self.numb_fparam,
dtype=global_tf_float_precision,
trainable=False,
initializer=tf.constant_initializer(self.fparam_avg))
t_fparam_istd = tf.get_variable(
't_fparam_istd',
self.numb_fparam,
dtype=global_tf_float_precision,
trainable=False,
initializer=tf.constant_initializer(self.fparam_inv_std))
if self.numb_aparam > 0:
t_aparam_avg = tf.get_variable(
't_aparam_avg',
self.numb_aparam,
dtype=global_tf_float_precision,
trainable=False,
initializer=tf.constant_initializer(self.aparam_avg))
t_aparam_istd = tf.get_variable(
't_aparam_istd',
self.numb_aparam,
dtype=global_tf_float_precision,
trainable=False,
initializer=tf.constant_initializer(self.aparam_inv_std))
start_index = 0
inputs = tf.cast(
tf.reshape(inputs, [-1, self.dim_descrpt * natoms[0]]),
self.fitting_precision)
if bias_atom_e is not None:
assert (len(bias_atom_e) == self.ntypes)
if self.numb_fparam > 0:
fparam = input_dict['fparam']
fparam = tf.reshape(fparam, [-1, self.numb_fparam])
fparam = (fparam - t_fparam_avg) * t_fparam_istd
if self.numb_aparam > 0:
aparam = input_dict['aparam']
aparam = tf.reshape(aparam, [-1, self.numb_aparam])
aparam = (aparam - t_aparam_avg) * t_aparam_istd
aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]])
for type_i in range(self.ntypes):
# cut-out inputs
inputs_i = tf.slice(inputs, [0, start_index * self.dim_descrpt],
[-1, natoms[2 + type_i] * self.dim_descrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
layer = inputs_i
if self.numb_fparam > 0:
ext_fparam = tf.tile(fparam, [1, natoms[2 + type_i]])
ext_fparam = tf.reshape(ext_fparam, [-1, self.numb_fparam])
layer = tf.concat([layer, ext_fparam], axis=1)
if self.numb_aparam > 0:
ext_aparam = tf.slice(
aparam, [0, start_index * self.numb_aparam],
[-1, natoms[2 + type_i] * self.numb_aparam])
ext_aparam = tf.reshape(ext_aparam, [-1, self.numb_aparam])
layer = tf.concat([layer, ext_aparam], axis=1)
start_index += natoms[2 + type_i]
if bias_atom_e is None:
type_bias_ae = 0.0
else:
type_bias_ae = bias_atom_e[type_i]
for ii in range(0, len(self.n_neuron)):
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]:
layer += one_layer(
layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' + str(type_i) +
suffix,
reuse=reuse,
seed=self.seed,
use_timestep=self.resnet_dt,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii])
else:
layer = one_layer(layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' +
str(type_i) + suffix,
reuse=reuse,
seed=self.seed,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii])
final_layer = one_layer(layer,
1,
activation_fn=None,
bavg=type_bias_ae,
name='final_layer_type_' + str(type_i) +
suffix,
reuse=reuse,
seed=self.seed,
precision=self.fitting_precision,
trainable=self.trainable[-1])
if type_i < len(
self.atom_ener) and self.atom_ener[type_i] is not None:
inputs_zero = tf.zeros_like(inputs_i,
dtype=global_tf_float_precision)
layer = inputs_zero
if self.numb_fparam > 0:
layer = tf.concat([layer, ext_fparam], axis=1)
if self.numb_aparam > 0:
layer = tf.concat([layer, ext_aparam], axis=1)
for ii in range(0, len(self.n_neuron)):
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]:
layer += one_layer(
layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' + str(type_i) +
suffix,
reuse=True,
seed=self.seed,
use_timestep=self.resnet_dt,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii])
else:
layer = one_layer(
layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' + str(type_i) +
suffix,
reuse=True,
seed=self.seed,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision,
trainable=self.trainable[ii])
zero_layer = one_layer(layer,
1,
activation_fn=None,
bavg=type_bias_ae,
name='final_layer_type_' + str(type_i) +
suffix,
reuse=True,
seed=self.seed,
precision=self.fitting_precision,
trainable=self.trainable[-1])
final_layer += self.atom_ener[type_i] - zero_layer
final_layer = tf.reshape(final_layer,
[tf.shape(inputs)[0], natoms[2 + type_i]])
# concat the results
if type_i == 0:
outs = final_layer
else:
outs = tf.concat([outs, final_layer], axis=1)
return tf.cast(tf.reshape(outs, [-1]), global_tf_float_precision)
def _filter(self,
inputs,
type_input,
natoms,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
seed=None,
trainable=True):
# natom x (nei x 4)
shape = inputs.get_shape().as_list()
outputs_size = [1] + self.filter_neuron
outputs_size_2 = self.n_axis_neuron
with tf.variable_scope(name, reuse=reuse):
start_index = 0
xyz_scatter_total = []
for type_i in range(self.ntypes):
# cut-out inputs
# with natom x (nei_type_i x 4)
inputs_i = tf.slice(inputs, [0, start_index * 4],
[-1, self.sel_a[type_i] * 4])
start_index += self.sel_a[type_i]
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei_type_i) x 4
inputs_reshape = tf.reshape(inputs_i, [-1, 4])
xyz_scatter = tf.reshape(
tf.slice(inputs_reshape, [0, 0], [-1, 1]), [-1, 1])
if (type_input, type_i) not in self.exclude_types:
for ii in range(1, len(outputs_size)):
w = tf.get_variable(
'matrix_' + str(ii) + '_' + str(type_i),
[outputs_size[ii - 1], outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(
stddev=stddev / np.sqrt(outputs_size[ii] +
outputs_size[ii - 1]),
seed=seed),
trainable=trainable)
b = tf.get_variable(
'bias_' + str(ii) + '_' + str(type_i),
[1, outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(stddev=stddev,
mean=bavg,
seed=seed),
trainable=trainable)
if self.filter_resnet_dt:
idt = tf.get_variable(
'idt_' + str(ii) + '_' + str(type_i),
[1, outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(stddev=0.001,
mean=1.0,
seed=seed),
trainable=trainable)
if outputs_size[ii] == outputs_size[ii - 1]:
if self.filter_resnet_dt:
xyz_scatter += activation_fn(
tf.matmul(xyz_scatter, w) + b) * idt
else:
xyz_scatter += activation_fn(
tf.matmul(xyz_scatter, w) + b)
elif outputs_size[ii] == outputs_size[ii - 1] * 2:
if self.filter_resnet_dt:
xyz_scatter = tf.concat(
[xyz_scatter, xyz_scatter],
1) + activation_fn(
tf.matmul(xyz_scatter, w) + b) * idt
else:
xyz_scatter = tf.concat(
[xyz_scatter, xyz_scatter],
1) + activation_fn(
tf.matmul(xyz_scatter, w) + b)
else:
xyz_scatter = activation_fn(
tf.matmul(xyz_scatter, w) + b)
else:
w = tf.zeros((outputs_size[0], outputs_size[-1]),
dtype=global_tf_float_precision)
xyz_scatter = tf.matmul(xyz_scatter, w)
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(
xyz_scatter, (-1, shape_i[1] // 4, outputs_size[-1]))
xyz_scatter_total.append(xyz_scatter)
# natom x nei x outputs_size
xyz_scatter = tf.concat(xyz_scatter_total, axis=1)
# natom x nei x 4
inputs_reshape = tf.reshape(inputs, [-1, shape[1] // 4, 4])
# natom x 4 x outputs_size
xyz_scatter_1 = tf.matmul(inputs_reshape,
xyz_scatter,
transpose_a=True)
xyz_scatter_1 = xyz_scatter_1 * (4.0 / shape[1])
# natom x 4 x outputs_size_2
xyz_scatter_2 = tf.slice(xyz_scatter_1, [0, 0, 0],
[-1, -1, outputs_size_2])
# # natom x 3 x outputs_size_2
# qmat = tf.slice(xyz_scatter_2, [0,1,0], [-1, 3, -1])
# natom x 3 x outputs_size_1
qmat = tf.slice(xyz_scatter_1, [0, 1, 0], [-1, 3, -1])
# natom x outputs_size_2 x 3
qmat = tf.transpose(qmat, perm=[0, 2, 1])
# natom x outputs_size x outputs_size_2
result = tf.matmul(xyz_scatter_1, xyz_scatter_2, transpose_a=True)
# natom x (outputs_size x outputs_size_2)
result = tf.reshape(result,
[-1, outputs_size_2 * outputs_size[-1]])
return result, qmat
def build(self, input_d, rot_mat, natoms, reuse=None, suffix=''):
start_index = 0
inputs = tf.cast(
tf.reshape(input_d, [-1, self.dim_descrpt * natoms[0]]),
self.fitting_precision)
rot_mat = tf.reshape(rot_mat, [-1, self.dim_rot_mat * natoms[0]])
count = 0
for type_i in range(self.ntypes):
# cut-out inputs
inputs_i = tf.slice(inputs, [0, start_index * self.dim_descrpt],
[-1, natoms[2 + type_i] * self.dim_descrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
rot_mat_i = tf.slice(rot_mat, [0, start_index * self.dim_rot_mat],
[-1, natoms[2 + type_i] * self.dim_rot_mat])
rot_mat_i = tf.reshape(rot_mat_i, [-1, self.dim_rot_mat_1, 3])
start_index += natoms[2 + type_i]
if not type_i in self.sel_type:
continue
layer = inputs_i
for ii in range(0, len(self.n_neuron)):
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]:
layer += one_layer(
layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' + str(type_i) +
suffix,
reuse=reuse,
seed=self.seed,
use_timestep=self.resnet_dt,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision)
else:
layer = one_layer(layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' +
str(type_i) + suffix,
reuse=reuse,
seed=self.seed,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision)
if self.fit_diag:
bavg = np.zeros(self.dim_rot_mat_1)
# bavg[0] = self.avgeig[0]
# bavg[1] = self.avgeig[1]
# bavg[2] = self.avgeig[2]
# (nframes x natoms) x naxis
final_layer = one_layer(layer,
self.dim_rot_mat_1,
activation_fn=None,
name='final_layer_type_' +
str(type_i) + suffix,
reuse=reuse,
seed=self.seed,
bavg=bavg,
precision=self.fitting_precision)
# (nframes x natoms) x naxis
final_layer = tf.reshape(final_layer, [
tf.shape(inputs)[0] * natoms[2 + type_i],
self.dim_rot_mat_1
])
# (nframes x natoms) x naxis x naxis
final_layer = tf.matrix_diag(final_layer)
else:
bavg = np.zeros(self.dim_rot_mat_1 * self.dim_rot_mat_1)
# bavg[0*self.dim_rot_mat_1+0] = self.avgeig[0]
# bavg[1*self.dim_rot_mat_1+1] = self.avgeig[1]
# bavg[2*self.dim_rot_mat_1+2] = self.avgeig[2]
# (nframes x natoms) x (naxis x naxis)
final_layer = one_layer(
layer,
self.dim_rot_mat_1 * self.dim_rot_mat_1,
activation_fn=None,
name='final_layer_type_' + str(type_i) + suffix,
reuse=reuse,
seed=self.seed,
bavg=bavg,
precision=self.fitting_precision)
# (nframes x natoms) x naxis x naxis
final_layer = tf.reshape(final_layer, [
tf.shape(inputs)[0] * natoms[2 + type_i],
self.dim_rot_mat_1, self.dim_rot_mat_1
])
# (nframes x natoms) x naxis x naxis
final_layer = final_layer + tf.transpose(final_layer,
perm=[0, 2, 1])
# (nframes x natoms) x naxis x 3(coord)
final_layer = tf.matmul(final_layer, rot_mat_i)
# (nframes x natoms) x 3(coord) x 3(coord)
final_layer = tf.matmul(rot_mat_i, final_layer, transpose_a=True)
# nframes x natoms x 3 x 3
final_layer = tf.reshape(
final_layer, [tf.shape(inputs)[0], natoms[2 + type_i], 3, 3])
# shift and scale
sel_type_idx = self.sel_type.index(type_i)
final_layer = final_layer * self.scale[sel_type_idx]
final_layer = final_layer + self.diag_shift[sel_type_idx] * tf.eye(
3,
batch_shape=[tf.shape(inputs)[0], natoms[2 + type_i]],
dtype=global_tf_float_precision)
# concat the results
if count == 0:
outs = final_layer
else:
outs = tf.concat([outs, final_layer], axis=1)
count += 1
return tf.cast(tf.reshape(outs, [-1]), global_tf_float_precision)
def build(self, input_d, rot_mat, natoms, reuse=None, suffix=''):
start_index = 0
inputs = tf.cast(
tf.reshape(input_d, [-1, self.dim_descrpt * natoms[0]]),
self.fitting_precision)
rot_mat = tf.reshape(rot_mat, [-1, self.dim_rot_mat * natoms[0]])
count = 0
for type_i in range(self.ntypes):
# cut-out inputs
inputs_i = tf.slice(inputs, [0, start_index * self.dim_descrpt],
[-1, natoms[2 + type_i] * self.dim_descrpt])
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
rot_mat_i = tf.slice(rot_mat, [0, start_index * self.dim_rot_mat],
[-1, natoms[2 + type_i] * self.dim_rot_mat])
rot_mat_i = tf.reshape(rot_mat_i, [-1, self.dim_rot_mat_1, 3])
start_index += natoms[2 + type_i]
if not type_i in self.sel_type:
continue
layer = inputs_i
for ii in range(0, len(self.n_neuron)):
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii - 1]:
layer += one_layer(
layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' + str(type_i) +
suffix,
reuse=reuse,
seed=self.seed,
use_timestep=self.resnet_dt,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision)
else:
layer = one_layer(layer,
self.n_neuron[ii],
name='layer_' + str(ii) + '_type_' +
str(type_i) + suffix,
reuse=reuse,
seed=self.seed,
activation_fn=self.fitting_activation_fn,
precision=self.fitting_precision)
# (nframes x natoms) x naxis
final_layer = one_layer(layer,
self.dim_rot_mat_1,
activation_fn=None,
name='final_layer_type_' + str(type_i) +
suffix,
reuse=reuse,
seed=self.seed,
precision=self.fitting_precision)
# (nframes x natoms) x 1 * naxis
final_layer = tf.reshape(final_layer, [
tf.shape(inputs)[0] * natoms[2 + type_i], 1, self.dim_rot_mat_1
])
# (nframes x natoms) x 1 x 3(coord)
final_layer = tf.matmul(final_layer, rot_mat_i)
# nframes x natoms x 3
final_layer = tf.reshape(
final_layer, [tf.shape(inputs)[0], natoms[2 + type_i], 3])
# concat the results
if count == 0:
outs = final_layer
else:
outs = tf.concat([outs, final_layer], axis=1)
count += 1
return tf.cast(tf.reshape(outs, [-1]), global_tf_float_precision)
def _filter_r(self,
inputs,
type_input,
natoms,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
seed=None,
trainable=True):
# natom x nei
outputs_size = [1] + self.filter_neuron
with tf.variable_scope(name, reuse=reuse):
start_index = 0
xyz_scatter_total = []
for type_i in range(self.ntypes):
# cut-out inputs
# with natom x nei_type_i
inputs_i = tf.slice(inputs, [0, start_index],
[-1, self.sel_r[type_i]])
start_index += self.sel_r[type_i]
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei_type_i) x 1
xyz_scatter = tf.reshape(inputs_i, [-1, 1])
if (type_input, type_i) not in self.exclude_types:
for ii in range(1, len(outputs_size)):
w = tf.get_variable(
'matrix_' + str(ii) + '_' + str(type_i),
[outputs_size[ii - 1], outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(
stddev=stddev / np.sqrt(outputs_size[ii] +
outputs_size[ii - 1]),
seed=seed),
trainable=trainable)
b = tf.get_variable(
'bias_' + str(ii) + '_' + str(type_i),
[1, outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(stddev=stddev,
mean=bavg,
seed=seed),
trainable=trainable)
if self.filter_resnet_dt:
idt = tf.get_variable(
'idt_' + str(ii) + '_' + str(type_i),
[1, outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(stddev=0.001,
mean=1.0,
seed=seed),
trainable=trainable)
if outputs_size[ii] == outputs_size[ii - 1]:
if self.filter_resnet_dt:
xyz_scatter += activation_fn(
tf.matmul(xyz_scatter, w) + b) * idt
else:
xyz_scatter += activation_fn(
tf.matmul(xyz_scatter, w) + b)
elif outputs_size[ii] == outputs_size[ii - 1] * 2:
if self.filter_resnet_dt:
xyz_scatter = tf.concat(
[xyz_scatter, xyz_scatter],
1) + activation_fn(
tf.matmul(xyz_scatter, w) + b) * idt
else:
xyz_scatter = tf.concat(
[xyz_scatter, xyz_scatter],
1) + activation_fn(
tf.matmul(xyz_scatter, w) + b)
else:
xyz_scatter = activation_fn(
tf.matmul(xyz_scatter, w) + b)
else:
w = tf.zeros((outputs_size[0], outputs_size[-1]),
dtype=global_tf_float_precision)
xyz_scatter = tf.matmul(xyz_scatter, w)
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(xyz_scatter,
(-1, shape_i[1], outputs_size[-1]))
xyz_scatter_total.append(xyz_scatter)
# natom x nei x outputs_size
xyz_scatter = tf.concat(xyz_scatter_total, axis=1)
# natom x outputs_size
#
res_rescale = 1. / 5.
result = tf.reduce_mean(xyz_scatter, axis=1) * res_rescale
return result
def _layer_1(self, x, w, b):
t = tf.concat([x, x], axis=1)
return t, self.activation_fn(tf.matmul(x, w) + b) + t
def _filter_type_ext(self,
inputs,
natoms,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
seed=None,
trainable=True):
# natom x (nei x 4)
outputs_size = [1] + self.filter_neuron
outputs_size_2 = self.n_axis_neuron
with tf.variable_scope(name, reuse=reuse):
start_index = 0
result_all = []
xyz_scatter_1_all = []
xyz_scatter_2_all = []
for type_i in range(self.ntypes):
# cut-out inputs
# with natom x (nei_type_i x 4)
inputs_i = tf.slice(inputs, [0, start_index * 4],
[-1, self.sel_a[type_i] * 4])
start_index += self.sel_a[type_i]
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei_type_i) x 4
inputs_reshape = tf.reshape(inputs_i, [-1, 4])
xyz_scatter = tf.reshape(
tf.slice(inputs_reshape, [0, 0], [-1, 1]), [-1, 1])
for ii in range(1, len(outputs_size)):
w = tf.get_variable(
'matrix_' + str(ii) + '_' + str(type_i),
[outputs_size[ii - 1], outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(
stddev=stddev /
np.sqrt(outputs_size[ii] + outputs_size[ii - 1]),
seed=seed),
trainable=trainable)
b = tf.get_variable('bias_' + str(ii) + '_' + str(type_i),
[1, outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(
stddev=stddev,
mean=bavg,
seed=seed),
trainable=trainable)
if self.filter_resnet_dt:
idt = tf.get_variable(
'idt_' + str(ii) + '_' + str(type_i),
[1, outputs_size[ii]],
self.filter_precision,
tf.random_normal_initializer(stddev=0.001,
mean=1.0,
seed=seed),
trainable=trainable)
if outputs_size[ii] == outputs_size[ii - 1]:
if self.filter_resnet_dt:
xyz_scatter += activation_fn(
tf.matmul(xyz_scatter, w) + b) * idt
else:
xyz_scatter += activation_fn(
tf.matmul(xyz_scatter, w) + b)
elif outputs_size[ii] == outputs_size[ii - 1] * 2:
if self.filter_resnet_dt:
xyz_scatter = tf.concat(
[xyz_scatter, xyz_scatter], 1) + activation_fn(
tf.matmul(xyz_scatter, w) + b) * idt
else:
xyz_scatter = tf.concat(
[xyz_scatter, xyz_scatter], 1) + activation_fn(
tf.matmul(xyz_scatter, w) + b)
else:
xyz_scatter = activation_fn(
tf.matmul(xyz_scatter, w) + b)
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(
xyz_scatter, (-1, shape_i[1] // 4, outputs_size[-1]))
# natom x nei_type_i x 4
inputs_i_reshape = tf.reshape(inputs_i,
[-1, shape_i[1] // 4, 4])
# natom x 4 x outputs_size
xyz_scatter_1 = tf.matmul(inputs_i_reshape,
xyz_scatter,
transpose_a=True)
xyz_scatter_1 = xyz_scatter_1 * (4.0 / shape_i[1])
# natom x 4 x outputs_size_2
xyz_scatter_2 = tf.slice(xyz_scatter_1, [0, 0, 0],
[-1, -1, outputs_size_2])
xyz_scatter_1_all.append(xyz_scatter_1)
xyz_scatter_2_all.append(xyz_scatter_2)
# for type_i in range(self.ntypes):
# for type_j in range(type_i, self.ntypes):
# # natom x outputs_size x outputs_size_2
# result = tf.matmul(xyz_scatter_1_all[type_i], xyz_scatter_2_all[type_j], transpose_a = True)
# # natom x (outputs_size x outputs_size_2)
# result = tf.reshape(result, [-1, outputs_size_2 * outputs_size[-1]])
# result_all.append(tf.identity(result))
xyz_scatter_2_coll = tf.concat(xyz_scatter_2_all, axis=2)
for type_i in range(self.ntypes):
# natom x outputs_size x (outputs_size_2 x ntypes)
result = tf.matmul(xyz_scatter_1_all[type_i],
xyz_scatter_2_coll,
transpose_a=True)
# natom x (outputs_size x outputs_size_2 x ntypes)
result = tf.reshape(
result,
[-1, outputs_size_2 * self.ntypes * outputs_size[-1]])
result_all.append(tf.identity(result))
# natom x (ntypes x outputs_size x outputs_size_2 x ntypes)
result_all = tf.concat(result_all, axis=1)
return result_all
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_st = tf.constant(self.get_sel_type(),
name = 'sel_type',
dtype = tf.int32)
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)
t_od = tf.constant(self.get_out_size(),
name = 'output_dim',
dtype = tf.int32)
natomsel = sum(natoms[2+type_i] for type_i in self.get_sel_type())
nout = self.get_out_size()
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])
rot_mat = self.descrpt.get_rot_mat()
rot_mat = tf.identity(rot_mat, name = 'o_rot_mat'+suffix)
output = self.fitting.build (dout,
rot_mat,
natoms,
reuse = reuse,
suffix = suffix)
framesize = nout if "global" in self.model_type else natomsel * nout
output = tf.reshape(output, [-1, framesize], name = 'o_' + self.model_type + suffix)
model_dict = {self.model_type: output}
if "global" not in self.model_type:
gname = "global_"+self.model_type
atom_out = tf.reshape(output, [-1, natomsel, nout])
global_out = tf.reduce_sum(atom_out, axis=1)
global_out = tf.reshape(global_out, [-1, nout], name="o_" + gname + suffix)
out_cpnts = tf.split(atom_out, nout, axis=-1)
force_cpnts = []
virial_cpnts = []
atom_virial_cpnts = []
for out_i in out_cpnts:
force_i, virial_i, atom_virial_i \
= self.descrpt.prod_force_virial(out_i, natoms)
force_cpnts.append (tf.reshape(force_i, [-1, 3*natoms[1]]))
virial_cpnts.append (tf.reshape(virial_i, [-1, 9]))
atom_virial_cpnts.append(tf.reshape(atom_virial_i, [-1, 9*natoms[1]]))
# [nframe x nout x (natom x 3)]
force = tf.concat(force_cpnts, axis=1, name="o_force" + suffix)
# [nframe x nout x 9]
virial = tf.concat(virial_cpnts, axis=1, name="o_virial" + suffix)
# [nframe x nout x (natom x 9)]
atom_virial = tf.concat(atom_virial_cpnts, axis=1, name="o_atom_virial" + suffix)
model_dict[gname] = global_out
model_dict["force"] = force
model_dict["virial"] = virial
model_dict["atom_virial"] = atom_virial
return model_dict
