def comp_ef(self, dcoord, dbox, dtype, tnatoms, name, reuse=None):
descrpt, descrpt_deriv, rij, nlist \
= op_module.prod_env_mat_r(dcoord,
dtype,
tnatoms,
dbox,
tf.constant(self.default_mesh),
self.t_avg,
self.t_std,
rcut = self.rcut,
rcut_smth = self.rcut_smth,
sel = self.sel)
inputs_reshape = tf.reshape(descrpt, [-1, self.ndescrpt])
atom_ener = self._net(inputs_reshape, name, reuse=reuse)
atom_ener_reshape = tf.reshape(atom_ener, [-1, self.natoms[0]])
energy = tf.reduce_sum(atom_ener_reshape, 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_r(net_deriv_reshape, descrpt_deriv,
nlist, tnatoms)
virial, atom_vir = op_module.prod_virial_se_r(net_deriv_reshape,
descrpt_deriv, rij,
nlist, tnatoms)
return energy, force, virial
def __init__(self,
rcut: float,
rcut_smth: float,
sel: List[str],
neuron: List[int] = [24, 48, 96],
resnet_dt: bool = False,
trainable: bool = True,
seed: int = None,
type_one_side: bool = True,
exclude_types: List[List[int]] = [],
set_davg_zero: bool = False,
activation_function: str = 'tanh',
precision: str = 'default',
uniform_seed: bool = False) -> None:
"""
Constructor
"""
# args = ClassArg()\
# .add('sel', list, must = True) \
# .add('rcut', float, default = 6.0) \
# .add('rcut_smth',float, default = 0.5) \
# .add('neuron', list, default = [10, 20, 40]) \
# .add('resnet_dt',bool, default = False) \
# .add('trainable',bool, default = True) \
# .add('seed', int) \
# .add('type_one_side', bool, default = False) \
# .add('exclude_types', list, default = []) \
# .add('set_davg_zero', bool, default = False) \
# .add("activation_function", str, default = "tanh") \
# .add("precision", str, default = "default")
# class_data = args.parse(jdata)
self.sel_r = sel
self.rcut = rcut
self.rcut_smth = rcut_smth
self.filter_neuron = neuron
self.filter_resnet_dt = resnet_dt
self.seed = seed
self.uniform_seed = uniform_seed
self.seed_shift = embedding_net_rand_seed_shift(self.filter_neuron)
self.trainable = trainable
self.filter_activation_fn = get_activation_func(activation_function)
self.filter_precision = get_precision(precision)
exclude_types = exclude_types
self.exclude_types = set()
for tt in exclude_types:
assert (len(tt) == 2)
self.exclude_types.add((tt[0], tt[1]))
self.exclude_types.add((tt[1], tt[0]))
self.set_davg_zero = set_davg_zero
self.type_one_side = type_one_side
# descrpt config
self.sel_a = [0 for ii in range(len(self.sel_r))]
self.ntypes = len(self.sel_r)
# numb of neighbors and numb of descrptors
self.nnei_a = np.cumsum(self.sel_a)[-1]
self.nnei_r = np.cumsum(self.sel_r)[-1]
self.nnei = self.nnei_a + self.nnei_r
self.ndescrpt_a = self.nnei_a * 4
self.ndescrpt_r = self.nnei_r * 1
self.ndescrpt = self.nnei_r
self.useBN = False
self.davg = None
self.dstd = None
self.embedding_net_variables = None
self.place_holders = {}
avg_zero = np.zeros([self.ntypes,
self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION)
std_ones = np.ones([self.ntypes,
self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION)
sub_graph = tf.Graph()
with sub_graph.as_default():
name_pfx = 'd_ser_'
for ii in ['coord', 'box']:
self.place_holders[ii] = tf.placeholder(
GLOBAL_NP_FLOAT_PRECISION, [None, None],
name=name_pfx + 't_' + ii)
self.place_holders['type'] = tf.placeholder(tf.int32, [None, None],
name=name_pfx +
't_type')
self.place_holders['natoms_vec'] = tf.placeholder(
tf.int32, [self.ntypes + 2], name=name_pfx + 't_natoms')
self.place_holders['default_mesh'] = tf.placeholder(
tf.int32, [None], name=name_pfx + 't_mesh')
self.stat_descrpt, descrpt_deriv, rij, nlist \
= op_module.prod_env_mat_r(self.place_holders['coord'],
self.place_holders['type'],
self.place_holders['natoms_vec'],
self.place_holders['box'],
self.place_holders['default_mesh'],
tf.constant(avg_zero),
tf.constant(std_ones),
rcut = self.rcut,
rcut_smth = self.rcut_smth,
sel = self.sel_r)
self.sub_sess = tf.Session(graph=sub_graph,
config=default_tf_session_config)
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
"""
davg = self.davg
dstd = self.dstd
with tf.variable_scope('descrpt_attr' + suffix, reuse=reuse):
if davg is None:
davg = np.zeros([self.ntypes, self.ndescrpt])
if dstd is None:
dstd = np.ones([self.ntypes, self.ndescrpt])
t_rcut = tf.constant(self.rcut,
name='rcut',
dtype=GLOBAL_TF_FLOAT_PRECISION)
t_ntypes = tf.constant(self.ntypes, name='ntypes', dtype=tf.int32)
t_ndescrpt = tf.constant(self.ndescrpt,
name='ndescrpt',
dtype=tf.int32)
t_sel = tf.constant(self.sel_a, name='sel', dtype=tf.int32)
self.t_avg = tf.get_variable(
't_avg',
davg.shape,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(davg))
self.t_std = tf.get_variable(
't_std',
dstd.shape,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(dstd))
coord = tf.reshape(coord_, [-1, natoms[1] * 3])
box = tf.reshape(box_, [-1, 9])
atype = tf.reshape(atype_, [-1, natoms[1]])
self.descrpt, self.descrpt_deriv, self.rij, self.nlist \
= op_module.prod_env_mat_r(coord,
atype,
natoms,
box,
mesh,
self.t_avg,
self.t_std,
rcut = self.rcut,
rcut_smth = self.rcut_smth,
sel = self.sel_r)
self.descrpt_reshape = tf.reshape(self.descrpt, [-1, self.ndescrpt])
self._identity_tensors(suffix=suffix)
# only used when tensorboard was set as true
tf.summary.histogram('descrpt', self.descrpt)
tf.summary.histogram('rij', self.rij)
tf.summary.histogram('nlist', self.nlist)
self.dout = self._pass_filter(self.descrpt_reshape,
natoms,
suffix=suffix,
reuse=reuse,
trainable=self.trainable)
tf.summary.histogram('embedding_net_output', self.dout)
return self.dout