import numpy as np
from typing import Tuple, List
from deepmd.env import tf
from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
from deepmd.env import GLOBAL_NP_FLOAT_PRECISION
from deepmd.env import op_module
from deepmd.env import default_tf_session_config
from deepmd.utils.sess import run_sess
from .descriptor import Descriptor
[docs]class DescrptLocFrame (Descriptor) :
"""Defines a local frame at each atom, and the compute the descriptor as local
coordinates under this frame.
Parameters
----------
rcut
The cut-off radius
sel_a : list[str]
The length of the list should be the same as the number of atom types in the system.
`sel_a[i]` gives the selected number of type-i neighbors.
The full relative coordinates of the neighbors are used by the descriptor.
sel_r : list[str]
The length of the list should be the same as the number of atom types in the system.
`sel_r[i]` gives the selected number of type-i neighbors.
Only relative distance of the neighbors are used by the descriptor.
sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.
axis_rule: list[int]
The length should be 6 times of the number of types.
- axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
- axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.\n\n\
- axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.\n\n\
- axis_rule[i*6+3]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
- axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.\n\n\
- axis_rule[i*6+5]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.
"""
def __init__(self,
rcut: float,
sel_a : List[int],
sel_r : List[int],
axis_rule : List[int]
) -> None:
"""
Constructor
"""
# args = ClassArg()\
# .add('sel_a', list, must = True) \
# .add('sel_r', list, must = True) \
# .add('rcut', float, default = 6.0) \
# .add('axis_rule',list, must = True)
# class_data = args.parse(jdata)
self.sel_a = sel_a
self.sel_r = sel_r
self.axis_rule = axis_rule
self.rcut_r = rcut
# ntypes and rcut_a === -1
self.ntypes = len(self.sel_a)
assert(self.ntypes == len(self.sel_r))
self.rcut_a = -1
# 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.ndescrpt_a + self.ndescrpt_r
self.davg = None
self.dstd = 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_lf_'
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, axis, rot_mat \
= op_module.descrpt (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_a = self.rcut_a,
rcut_r = self.rcut_r,
sel_a = self.sel_a,
sel_r = self.sel_r,
axis_rule = self.axis_rule)
self.sub_sess = tf.Session(graph = sub_graph, config=default_tf_session_config)
[docs] def get_rcut (self) -> float:
"""
Returns the cut-off radisu
"""
return self.rcut_r
[docs] def get_ntypes (self) -> int:
"""
Returns the number of atom types
"""
return self.ntypes
[docs] def get_dim_out (self) -> int:
"""
Returns the output dimension of this descriptor
"""
return self.ndescrpt
[docs] def get_nlist (self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]:
"""
Returns
-------
nlist
Neighbor list
rij
The relative distance between the neighbor and the center atom.
sel_a
The number of neighbors with full information
sel_r
The number of neighbors with only radial information
"""
return self.nlist, self.rij, self.sel_a, self.sel_r
[docs] 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(np.max([self.rcut_r, self.rcut_a]),
name = 'rcut',
dtype = GLOBAL_TF_FLOAT_PRECISION)
t_ntypes = tf.constant(self.ntypes,
name = 'ntypes',
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, self.axis, self.rot_mat \
= op_module.descrpt (coord,
atype,
natoms,
box,
mesh,
self.t_avg,
self.t_std,
rcut_a = self.rcut_a,
rcut_r = self.rcut_r,
sel_a = self.sel_a,
sel_r = self.sel_r,
axis_rule = self.axis_rule)
self.descrpt = tf.reshape(self.descrpt, [-1, self.ndescrpt])
tf.summary.histogram('descrpt', self.descrpt)
tf.summary.histogram('rij', self.rij)
tf.summary.histogram('nlist', self.nlist)
return self.descrpt
[docs] def get_rot_mat(self) -> tf.Tensor:
"""
Get rotational matrix
"""
return self.rot_mat
[docs] def prod_force_virial(self,
atom_ener : tf.Tensor,
natoms : tf.Tensor
) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
"""
Compute force and virial
Parameters
----------
atom_ener
The atomic energy
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
Returns
-------
force
The force on atoms
virial
The total virial
atom_virial
The atomic virial
"""
[net_deriv] = tf.gradients (atom_ener, self.descrpt)
tf.summary.histogram('net_derivative', net_deriv)
net_deriv_reshape = tf.reshape (net_deriv, [-1, natoms[0] * self.ndescrpt])
force = op_module.prod_force (net_deriv_reshape,
self.descrpt_deriv,
self.nlist,
self.axis,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
virial, atom_virial \
= op_module.prod_virial (net_deriv_reshape,
self.descrpt_deriv,
self.rij,
self.nlist,
self.axis,
natoms,
n_a_sel = self.nnei_a,
n_r_sel = self.nnei_r)
tf.summary.histogram('force', force)
tf.summary.histogram('virial', virial)
tf.summary.histogram('atom_virial', atom_virial)
return force, virial, atom_virial
def _compute_dstats_sys_nonsmth (self,
data_coord,
data_box,
data_atype,
natoms_vec,
mesh) :
dd_all \
= run_sess(self.sub_sess, self.stat_descrpt,
feed_dict = {
self.place_holders['coord']: data_coord,
self.place_holders['type']: data_atype,
self.place_holders['natoms_vec']: natoms_vec,
self.place_holders['box']: data_box,
self.place_holders['default_mesh']: mesh,
})
natoms = natoms_vec
dd_all = np.reshape(dd_all, [-1, self.ndescrpt * natoms[0]])
start_index = 0
sysv = []
sysn = []
sysv2 = []
for type_i in range(self.ntypes):
end_index = start_index + self.ndescrpt * natoms[2+type_i]
dd = dd_all[:, start_index:end_index]
dd = np.reshape(dd, [-1, self.ndescrpt])
start_index = end_index
# compute
sumv = np.sum(dd, axis = 0)
sumn = dd.shape[0]
sumv2 = np.sum(np.multiply(dd,dd), axis = 0)
sysv.append(sumv)
sysn.append(sumn)
sysv2.append(sumv2)
return sysv, sysv2, sysn
def _compute_std (self,sumv2, sumv, sumn) :
return np.sqrt(sumv2/sumn - np.multiply(sumv/sumn, sumv/sumn))